feat: updated go migration data to have metadata to make it easier to get all of the information from one file

db/go/util.go

@@ -3,7 +3,9 @@ package migration
 import (
 	"fmt"
 	v1 "github.com/onepanelio/core/pkg"
+	"github.com/onepanelio/core/pkg/util/data"
 	uid2 "github.com/onepanelio/core/pkg/util/uid"
+	"path/filepath"
 )
 
 // createWorkspaceTemplate will create the workspace template given by {{templateName}} with the contents
@@ -21,7 +23,13 @@ func createWorkspaceTemplate(filename, templateName, description string) error {
 		return err
 	}
 
-	newManifest, err := readDataFile(filename)
+	filename = filepath.Join("db", "yaml", filename)
+	manifestFile, err := data.ManifestFileFromFile(filename)
+	if err != nil {
+		return err
+	}
+
+	newManifest, err := manifestFile.SpecString()
 	if err != nil {
 		return err
 	}
@@ -97,12 +105,19 @@ func updateWorkspaceTemplateManifest(filename, templateName string) error {
 	}
 	defer client.DB.Close()
 
+	filename = filepath.Join("db", "yaml", filename)
+
 	namespaces, err := client.ListOnepanelEnabledNamespaces()
 	if err != nil {
 		return err
 	}
 
-	newManifest, err := readDataFile(filename)
+	manifest, err := data.ManifestFileFromFile(filename)
+	if err != nil {
+		return err
+	}
+
+	newManifest, err := manifest.SpecString()
 	if err != nil {
 		return err
 	}
@@ -145,7 +160,14 @@ func createWorkflowTemplate(filename, templateName string, labels map[string]str
 		return err
 	}
 
-	manifest, err := readDataFile(filename)
+	filename = filepath.Join("db", "yaml", filename)
+
+	manifestFile, err := data.ManifestFileFromFile(filename)
+	if err != nil {
+		return err
+	}
+
+	manifest, err := manifestFile.SpecString()
 	if err != nil {
 		return err
 	}
@@ -190,7 +212,14 @@ func updateWorkflowTemplateManifest(filename, templateName string, labels map[st
 		return err
 	}
 
-	newManifest, err := readDataFile(filename)
+	filename = filepath.Join("db", "yaml", filename)
+
+	manifestFile, err := data.ManifestFileFromFile(filename)
+	if err != nil {
+		return err
+	}
+
+	newManifest, err := manifestFile.SpecString()
 	if err != nil {
 		return err
 	}

db/yaml/workflows/hyperparameter-tuning/20201225172926.yaml

@@ -1,183 +1,194 @@
-# source: https://github.com/onepanelio/templates/blob/master/workflows/nni-hyperparameter-tuning/mnist/
-entrypoint: main
-arguments:
-  parameters:
-    - name: source
-      value: https://github.com/onepanelio/templates
-    - name: revision
-      value: master
-    - name: config
-      displayName: Configuration
-      required: true
-      hint: NNI configuration
-      type: textarea.textarea
-      value: |-
-        authorName: Onepanel, Inc.
-        experimentName: MNIST TF v2.x
-        trialConcurrency: 1
-        maxExecDuration: 1h
-        maxTrialNum: 10
-        trainingServicePlatform: local
-        searchSpacePath: search_space.json
-        useAnnotation: false
-        tuner:
-          # gpuIndices: '0'           # uncomment and update to the GPU indices to assign this tuner
-          builtinTunerName: TPE       # choices: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
-          classArgs:
-            optimize_mode: maximize   # choices: maximize, minimize
-        trial:
-          command: python main.py --output /mnt/output
-          codeDir: .
-          # gpuNum: 1                 # uncomment and update to number of GPUs
-    - name: search-space
-      displayName: Search space configuration
-      required: true
-      type: textarea.textarea
-      value: |-
-        {
-          "dropout_rate": { "_type": "uniform", "_value": [0.5, 0.9] },
-          "conv_size": { "_type": "choice", "_value": [2, 3, 5, 7] },
-          "hidden_size": { "_type": "choice", "_value": [124, 512, 1024] },
-          "batch_size": { "_type": "choice", "_value": [16, 32] },
-          "learning_rate": { "_type": "choice", "_value": [0.0001, 0.001, 0.01, 0.1] },
-          "epochs": { "_type": "choice", "_value": [10] }
-        }
-    - displayName: Node pool
-      hint: Name of node pool or group to run this workflow task
-      type: select.nodepool
-      name: sys-node-pool
-      value: {{.DefaultNodePoolOption}}
-      required: true
+metadata:
+  name: "Hyperparameter Tuning Example"
+  kind: Workflow
+  version: 20201225172926
+  action: create
+  source: "https://github.com/onepanelio/templates/blob/master/workflows/nni-hyperparameter-tuning/mnist/"
+  deprecated: true
+  labels:
+    framework: tensorflow
+    tuner: TPE
+    "created-by": system
+spec:
+  entrypoint: main
+  arguments:
+    parameters:
+      - name: source
+        value: https://github.com/onepanelio/templates
+      - name: revision
+        value: master
+      - name: config
+        displayName: Configuration
+        required: true
+        hint: NNI configuration
+        type: textarea.textarea
+        value: |-
+          authorName: Onepanel, Inc.
+          experimentName: MNIST TF v2.x
+          trialConcurrency: 1
+          maxExecDuration: 1h
+          maxTrialNum: 10
+          trainingServicePlatform: local
+          searchSpacePath: search_space.json
+          useAnnotation: false
+          tuner:
+            # gpuIndices: '0'           # uncomment and update to the GPU indices to assign this tuner
+            builtinTunerName: TPE       # choices: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
+            classArgs:
+              optimize_mode: maximize   # choices: maximize, minimize
+          trial:
+            command: python main.py --output /mnt/output
+            codeDir: .
+            # gpuNum: 1                 # uncomment and update to number of GPUs
+      - name: search-space
+        displayName: Search space configuration
+        required: true
+        type: textarea.textarea
+        value: |-
+          {
+            "dropout_rate": { "_type": "uniform", "_value": [0.5, 0.9] },
+            "conv_size": { "_type": "choice", "_value": [2, 3, 5, 7] },
+            "hidden_size": { "_type": "choice", "_value": [124, 512, 1024] },
+            "batch_size": { "_type": "choice", "_value": [16, 32] },
+            "learning_rate": { "_type": "choice", "_value": [0.0001, 0.001, 0.01, 0.1] },
+            "epochs": { "_type": "choice", "_value": [10] }
+          }
+      - displayName: Node pool
+        hint: Name of node pool or group to run this workflow task
+        type: select.nodepool
+        name: sys-node-pool
+        value: "{{.DefaultNodePoolOption}}"
+        required: true
 
-volumeClaimTemplates:
-  - metadata:
-      name: hyperparamtuning-data
-    spec:
-      accessModes: [ "ReadWriteOnce" ]
-      resources:
-        requests:
-          storage: 20Gi
-  - metadata:
-      name: hyperparamtuning-output
-    spec:
-      accessModes: [ "ReadWriteOnce" ]
-      resources:
-        requests:
-          storage: 20Gi
+  volumeClaimTemplates:
+    - metadata:
+        name: hyperparamtuning-data
+      spec:
+        accessModes: [ "ReadWriteOnce" ]
+        resources:
+          requests:
+            storage: 20Gi
+    - metadata:
+        name: hyperparamtuning-output
+      spec:
+        accessModes: [ "ReadWriteOnce" ]
+        resources:
+          requests:
+            storage: 20Gi
 
-templates:
-  - name: main
-    dag:
-      tasks:
-        - name: hyperparameter-tuning
-          template: hyperparameter-tuning
-        - name: workflow-metrics-writer
-          template: workflow-metrics-writer
-          dependencies: [hyperparameter-tuning]
-          arguments:
-            # Use sys-metrics artifact output from hyperparameter-tuning Task
-            artifacts:
-              - name: best-metrics
-                from: "{{tasks.hyperparameter-tuning.outputs.artifacts.sys-metrics}}"
-  - name: hyperparameter-tuning
-    inputs:
-      artifacts:
-        - name: src
-          git:
-            repo: '{{workflow.parameters.source}}'
-            revision: '{{workflow.parameters.revision}}'
-          path: /mnt/data/src
-        - name: config
-          path: /mnt/data/src/workflows/hyperparameter-tuning/mnist/config.yaml
-          raw:
-            data: '{{workflow.parameters.config}}'
-        - name: search-space
-          path: /mnt/data/src/workflows/hyperparameter-tuning/mnist/search_space.json
-          raw:
-            data: '{{workflow.parameters.search-space}}'
-    outputs:
-      artifacts:
-        - name: output
-          path: /mnt/output
-          optional: true
-    container:
-      image: onepanel/dl:0.17.0
-      args:
-        - --config
-        - /mnt/data/src/workflows/hyperparameter-tuning/mnist/config.yaml
-      workingDir: /mnt
-      volumeMounts:
-        - name: hyperparamtuning-data
-          mountPath: /mnt/data
-        - name: hyperparamtuning-output
-          mountPath: /mnt/output
-    nodeSelector:
-      beta.kubernetes.io/instance-type: '{{workflow.parameters.sys-node-pool}}'
-    sidecars:
-      - name: nni-web-ui
-        image: 'onepanel/nni-web-ui:0.17.0'
-        env:
-          - name: ONEPANEL_INTERACTIVE_SIDECAR
-            value: 'true'
-        ports:
-          - containerPort: 9000
-            name: nni
-      - name: tensorboard
-        image: 'tensorflow/tensorflow:2.3.0'
-        command:
-          - sh
-          - '-c'
-        env:
-          - name: ONEPANEL_INTERACTIVE_SIDECAR
-            value: 'true'
+  templates:
+    - name: main
+      dag:
+        tasks:
+          - name: hyperparameter-tuning
+            template: hyperparameter-tuning
+          - name: workflow-metrics-writer
+            template: workflow-metrics-writer
+            dependencies: [hyperparameter-tuning]
+            arguments:
+              # Use sys-metrics artifact output from hyperparameter-tuning Task
+              artifacts:
+                - name: best-metrics
+                  from: "{{tasks.hyperparameter-tuning.outputs.artifacts.sys-metrics}}"
+    - name: hyperparameter-tuning
+      inputs:
+        artifacts:
+          - name: src
+            git:
+              repo: '{{workflow.parameters.source}}'
+              revision: '{{workflow.parameters.revision}}'
+            path: /mnt/data/src
+          - name: config
+            path: /mnt/data/src/workflows/hyperparameter-tuning/mnist/config.yaml
+            raw:
+              data: '{{workflow.parameters.config}}'
+          - name: search-space
+            path: /mnt/data/src/workflows/hyperparameter-tuning/mnist/search_space.json
+            raw:
+              data: '{{workflow.parameters.search-space}}'
+      outputs:
+        artifacts:
+          - name: output
+            path: /mnt/output
+            optional: true
+      container:
+        image: onepanel/dl:0.17.0
         args:
-          # Read logs from /mnt/output/tensorboard - /mnt/output is auto-mounted from volumeMounts
-          - tensorboard --logdir /mnt/output/tensorboard
-        ports:
-          - containerPort: 6006
-            name: tensorboard
-  - name: workflow-metrics-writer
-    inputs:
-      artifacts:
-        - name: best-metrics
-          path: /tmp/sys-metrics.json
-    script:
-      image: onepanel/python-sdk:v0.16.0
-      command: [python, '-u']
-      source: |
-        import os
-        import json
+          - --config
+          - /mnt/data/src/workflows/hyperparameter-tuning/mnist/config.yaml
+        workingDir: /mnt
+        volumeMounts:
+          - name: hyperparamtuning-data
+            mountPath: /mnt/data
+          - name: hyperparamtuning-output
+            mountPath: /mnt/output
+      nodeSelector:
+        beta.kubernetes.io/instance-type: '{{workflow.parameters.sys-node-pool}}'
+      sidecars:
+        - name: nni-web-ui
+          image: 'onepanel/nni-web-ui:0.17.0'
+          env:
+            - name: ONEPANEL_INTERACTIVE_SIDECAR
+              value: 'true'
+          ports:
+            - containerPort: 9000
+              name: nni
+        - name: tensorboard
+          image: 'tensorflow/tensorflow:2.3.0'
+          command:
+            - sh
+            - '-c'
+          env:
+            - name: ONEPANEL_INTERACTIVE_SIDECAR
+              value: 'true'
+          args:
+            # Read logs from /mnt/output/tensorboard - /mnt/output is auto-mounted from volumeMounts
+            - tensorboard --logdir /mnt/output/tensorboard
+          ports:
+            - containerPort: 6006
+              name: tensorboard
+    - name: workflow-metrics-writer
+      inputs:
+        artifacts:
+          - name: best-metrics
+            path: /tmp/sys-metrics.json
+      script:
+        image: onepanel/python-sdk:v0.16.0
+        command: [python, '-u']
+        source: |
+          import os
+          import json
 
-        import onepanel.core.api
-        from onepanel.core.api.models.metric import Metric
-        from onepanel.core.api.rest import ApiException
-        from onepanel.core.api.models import Parameter
+          import onepanel.core.api
+          from onepanel.core.api.models.metric import Metric
+          from onepanel.core.api.rest import ApiException
+          from onepanel.core.api.models import Parameter
 
-        # Load Task A metrics
-        with open('/tmp/sys-metrics.json') as f:
-            metrics = json.load(f)
+          # Load Task A metrics
+          with open('/tmp/sys-metrics.json') as f:
+              metrics = json.load(f)
 
-        with open('/var/run/secrets/kubernetes.io/serviceaccount/token') as f:
-            token = f.read()
+          with open('/var/run/secrets/kubernetes.io/serviceaccount/token') as f:
+              token = f.read()
 
-        # Configure API authorization
-        configuration = onepanel.core.api.Configuration(
-            host = os.getenv('ONEPANEL_API_URL'),
-            api_key = {
-                'authorization': token
-            }
-        )
-        configuration.api_key_prefix['authorization'] = 'Bearer'
+          # Configure API authorization
+          configuration = onepanel.core.api.Configuration(
+              host = os.getenv('ONEPANEL_API_URL'),
+              api_key = {
+                  'authorization': token
+              }
+          )
+          configuration.api_key_prefix['authorization'] = 'Bearer'
 
-        # Call SDK method to save metrics
-        with onepanel.core.api.ApiClient(configuration) as api_client:
-            api_instance = onepanel.core.api.WorkflowServiceApi(api_client)
-            namespace = '{{workflow.namespace}}'
-            uid = '{{workflow.name}}'
-            body = onepanel.core.api.AddWorkflowExecutionsMetricsRequest()
-            body.metrics = metrics
-            try:
-                api_response = api_instance.add_workflow_execution_metrics(namespace, uid, body)
-                print('Metrics added.')
-            except ApiException as e:
-                print("Exception when calling WorkflowServiceApi->add_workflow_execution_metrics: %s\n" % e)
+          # Call SDK method to save metrics
+          with onepanel.core.api.ApiClient(configuration) as api_client:
+              api_instance = onepanel.core.api.WorkflowServiceApi(api_client)
+              namespace = '{{workflow.namespace}}'
+              uid = '{{workflow.name}}'
+              body = onepanel.core.api.AddWorkflowExecutionsMetricsRequest()
+              body.metrics = metrics
+              try:
+                  api_response = api_instance.add_workflow_execution_metrics(namespace, uid, body)
+                  print('Metrics added.')
+              except ApiException as e:
+                  print("Exception when calling WorkflowServiceApi->add_workflow_execution_metrics: %s\n" % e)

db/yaml/workflows/hyperparameter-tuning/20210118175809.yaml

@@ -1,194 +1,205 @@
-# source: https://github.com/onepanelio/templates/blob/master/workflows/nni-hyperparameter-tuning/mnist/
-# Workflow Template example for hyperparameter tuning
-# Documentation: https://docs.onepanel.ai/docs/reference/workflows/hyperparameter-tuning
-#
-# Only change the fields marked with [CHANGE]
-entrypoint: main
-arguments:
-  parameters:
+metadata:
+  name: "Hyperparameter Tuning Example"
+  kind: Workflow
+  version: 20210118175809
+  action: update
+  source: "https://github.com/onepanelio/templates/blob/master/workflows/nni-hyperparameter-tuning/mnist/"
+  deprecated: true
+  labels:
+    framework: tensorflow
+    tuner: TPE
+    "created-by": system
+spec:
+  # Workflow Template example for hyperparameter tuning
+  # Documentation: https://docs.onepanel.ai/docs/reference/workflows/hyperparameter-tuning
+  #
+  # Only change the fields marked with [CHANGE]
+  entrypoint: main
+  arguments:
+    parameters:
 
-    # [CHANGE] Path to your training/model architecture code repository
-    # Change this value and revision value to your code repository and branch respectively
-    - name: source
-      value: https://github.com/onepanelio/templates
+      # [CHANGE] Path to your training/model architecture code repository
+      # Change this value and revision value to your code repository and branch respectively
+      - name: source
+        value: https://github.com/onepanelio/templates
 
-    # [CHANGE] Revision is the branch or tag that you want to use
-    # You can change this to any tag or branch name in your repository
-    - name: revision
-      value: v0.18.0
+      # [CHANGE] Revision is the branch or tag that you want to use
+      # You can change this to any tag or branch name in your repository
+      - name: revision
+        value: v0.18.0
 
-    # [CHANGE] Default configuration for the NNI tuner
-    # See https://docs.onepanel.ai/docs/reference/workflows/hyperparameter-tuning#understanding-the-configurations
-    - name: config
-      displayName: Configuration
-      required: true
-      hint: NNI configuration
-      type: textarea.textarea
-      value: |-
-        authorName: Onepanel, Inc.
-        experimentName: MNIST TF v2.x
-        trialConcurrency: 1
-        maxExecDuration: 1h
-        maxTrialNum: 10
-        trainingServicePlatform: local
-        searchSpacePath: search_space.json
-        useAnnotation: false
-        tuner:
-          # gpuIndices: '0'           # uncomment and update to the GPU indices to assign this tuner
-          builtinTunerName: TPE       # choices: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
-          classArgs:
-            optimize_mode: maximize   # choices: maximize, minimize
-        trial:
-          command: python main.py --output /mnt/output
-          codeDir: .
-          # gpuNum: 1                 # uncomment and update to number of GPUs
+      # [CHANGE] Default configuration for the NNI tuner
+      # See https://docs.onepanel.ai/docs/reference/workflows/hyperparameter-tuning#understanding-the-configurations
+      - name: config
+        displayName: Configuration
+        required: true
+        hint: NNI configuration
+        type: textarea.textarea
+        value: |-
+          authorName: Onepanel, Inc.
+          experimentName: MNIST TF v2.x
+          trialConcurrency: 1
+          maxExecDuration: 1h
+          maxTrialNum: 10
+          trainingServicePlatform: local
+          searchSpacePath: search_space.json
+          useAnnotation: false
+          tuner:
+            # gpuIndices: '0'           # uncomment and update to the GPU indices to assign this tuner
+            builtinTunerName: TPE       # choices: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
+            classArgs:
+              optimize_mode: maximize   # choices: maximize, minimize
+          trial:
+            command: python main.py --output /mnt/output
+            codeDir: .
+            # gpuNum: 1                 # uncomment and update to number of GPUs
 
-    # [CHANGE] Search space configuration
-    # Change according to your hyperparameters and ranges
-    - name: search-space
-      displayName: Search space configuration
-      required: true
-      type: textarea.textarea
-      value: |-
-        {
-          "dropout_rate": { "_type": "uniform", "_value": [0.5, 0.9] },
-          "conv_size": { "_type": "choice", "_value": [2, 3, 5, 7] },
-          "hidden_size": { "_type": "choice", "_value": [124, 512, 1024] },
-          "batch_size": { "_type": "choice", "_value": [16, 32] },
-          "learning_rate": { "_type": "choice", "_value": [0.0001, 0.001, 0.01, 0.1] },
-          "epochs": { "_type": "choice", "_value": [10] }
-        }
+      # [CHANGE] Search space configuration
+      # Change according to your hyperparameters and ranges
+      - name: search-space
+        displayName: Search space configuration
+        required: true
+        type: textarea.textarea
+        value: |-
+          {
+            "dropout_rate": { "_type": "uniform", "_value": [0.5, 0.9] },
+            "conv_size": { "_type": "choice", "_value": [2, 3, 5, 7] },
+            "hidden_size": { "_type": "choice", "_value": [124, 512, 1024] },
+            "batch_size": { "_type": "choice", "_value": [16, 32] },
+            "learning_rate": { "_type": "choice", "_value": [0.0001, 0.001, 0.01, 0.1] },
+            "epochs": { "_type": "choice", "_value": [10] }
+          }
 
-    # Node pool dropdown (Node group in EKS)
-    # You can add more of these if you have additional tasks that can run on different node pools
-    - displayName: Node pool
-      hint: Name of node pool or group to run this workflow task
-      type: select.nodepool
-      name: sys-node-pool
-      value: {{.DefaultNodePoolOption}}
-      required: true
+      # Node pool dropdown (Node group in EKS)
+      # You can add more of these if you have additional tasks that can run on different node pools
+      - displayName: Node pool
+        hint: Name of node pool or group to run this workflow task
+        type: select.nodepool
+        name: sys-node-pool
+        value: "{{.DefaultNodePoolOption}}"
+        required: true
 
-templates:
-  - name: main
-    dag:
-      tasks:
-        - name: hyperparameter-tuning
-          template: hyperparameter-tuning
-        - name: metrics-writer
-          template: metrics-writer
-          dependencies: [hyperparameter-tuning]
-          arguments:
-            # Use sys-metrics artifact output from hyperparameter-tuning Task
-            # This writes the best metrics to the Workflow
-            artifacts:
-              - name: sys-metrics
-                from: "{{tasks.hyperparameter-tuning.outputs.artifacts.sys-metrics}}"
-  - name: hyperparameter-tuning
-    inputs:
-      artifacts:
-        - name: src
-          # Clone the above repository into '/mnt/data/src'
-          # See https://docs.onepanel.ai/docs/reference/workflows/artifacts#git for private repositories
-          git:
-            repo: '{{workflow.parameters.source}}'
-            revision: '{{workflow.parameters.revision}}'
-          path: /mnt/data/src
-        # [CHANGE] Path where config.yaml will be generated or already exists
-        # Update the path below so that config.yaml is written to the same directory as your main.py file
-        # Note that your source code is cloned to /mnt/data/src
-        - name: config
-          path: /mnt/data/src/workflows/hyperparameter-tuning/mnist/config.yaml
-          raw:
-            data: '{{workflow.parameters.config}}'
-        # [CHANGE] Path where search_space.json will be generated or already exists
-        # Update the path below so that search_space.json is written to the same directory as your main.py file
-        # Note that your source code is cloned to /mnt/data/src
-        - name: search-space
-          path: /mnt/data/src/workflows/hyperparameter-tuning/mnist/search_space.json
-          raw:
-            data: '{{workflow.parameters.search-space}}'
-    outputs:
-      artifacts:
-        - name: output
-          path: /mnt/output
-          optional: true
-    container:
-      image: onepanel/dl:0.17.0
-      command:
-        - sh
-        - -c
-      args:
-        # [CHANGE] Update the config path below to point to config.yaml path as described above
-        # Note that you can `pip install` additional tools here if necessary
-        - |
-          python -u /opt/onepanel/nni/start.py \
-            --config /mnt/data/src/workflows/hyperparameter-tuning/mnist/config.yaml
-      workingDir: /mnt
-      volumeMounts:
-        - name: hyperparamtuning-data
-          mountPath: /mnt/data
-        - name: hyperparamtuning-output
-          mountPath: /mnt/output
-    nodeSelector:
-      {{.NodePoolLabel}}: '{{workflow.parameters.sys-node-pool}}'
-    sidecars:
-      - name: nni-web-ui
-        image: onepanel/nni-web-ui:0.17.0
-        env:
-          - name: ONEPANEL_INTERACTIVE_SIDECAR
-            value: 'true'
-        ports:
-          - containerPort: 9000
-            name: nni
-      - name: tensorboard
+  templates:
+    - name: main
+      dag:
+        tasks:
+          - name: hyperparameter-tuning
+            template: hyperparameter-tuning
+          - name: metrics-writer
+            template: metrics-writer
+            dependencies: [hyperparameter-tuning]
+            arguments:
+              # Use sys-metrics artifact output from hyperparameter-tuning Task
+              # This writes the best metrics to the Workflow
+              artifacts:
+                - name: sys-metrics
+                  from: "{{tasks.hyperparameter-tuning.outputs.artifacts.sys-metrics}}"
+    - name: hyperparameter-tuning
+      inputs:
+        artifacts:
+          - name: src
+            # Clone the above repository into '/mnt/data/src'
+            # See https://docs.onepanel.ai/docs/reference/workflows/artifacts#git for private repositories
+            git:
+              repo: '{{workflow.parameters.source}}'
+              revision: '{{workflow.parameters.revision}}'
+            path: /mnt/data/src
+          # [CHANGE] Path where config.yaml will be generated or already exists
+          # Update the path below so that config.yaml is written to the same directory as your main.py file
+          # Note that your source code is cloned to /mnt/data/src
+          - name: config
+            path: /mnt/data/src/workflows/hyperparameter-tuning/mnist/config.yaml
+            raw:
+              data: '{{workflow.parameters.config}}'
+          # [CHANGE] Path where search_space.json will be generated or already exists
+          # Update the path below so that search_space.json is written to the same directory as your main.py file
+          # Note that your source code is cloned to /mnt/data/src
+          - name: search-space
+            path: /mnt/data/src/workflows/hyperparameter-tuning/mnist/search_space.json
+            raw:
+              data: '{{workflow.parameters.search-space}}'
+      outputs:
+        artifacts:
+          - name: output
+            path: /mnt/output
+            optional: true
+      container:
         image: onepanel/dl:0.17.0
         command:
           - sh
-          - '-c'
-        env:
-          - name: ONEPANEL_INTERACTIVE_SIDECAR
-            value: 'true'
+          - -c
         args:
-          # Read logs from /mnt/output/tensorboard - /mnt/output is auto-mounted from volumeMounts
-          - tensorboard --logdir /mnt/output/tensorboard
-        ports:
-          - containerPort: 6006
-            name: tensorboard
-  # Use the metrics-writer tasks to write best metrics to Workflow
-  - name: metrics-writer
-    inputs:
-      artifacts:
-        - name: sys-metrics
-          path: /tmp/sys-metrics.json
-        - git:
-            repo: https://github.com/onepanelio/templates.git
-            revision: v0.18.0
-          name: src
-          path: /mnt/src
-    container:
-      image: onepanel/python-sdk:v0.16.0
-      command:
-        - python
-        - -u
-      args:
-        - /mnt/src/tasks/metrics-writer/main.py
-        - --from_file=/tmp/sys-metrics.json
+          # [CHANGE] Update the config path below to point to config.yaml path as described above
+          # Note that you can `pip install` additional tools here if necessary
+          - |
+            python -u /opt/onepanel/nni/start.py \
+              --config /mnt/data/src/workflows/hyperparameter-tuning/mnist/config.yaml
+        workingDir: /mnt
+        volumeMounts:
+          - name: hyperparamtuning-data
+            mountPath: /mnt/data
+          - name: hyperparamtuning-output
+            mountPath: /mnt/output
+      nodeSelector:
+        "{{.NodePoolLabel}}": '{{workflow.parameters.sys-node-pool}}'
+      sidecars:
+        - name: nni-web-ui
+          image: onepanel/nni-web-ui:0.17.0
+          env:
+            - name: ONEPANEL_INTERACTIVE_SIDECAR
+              value: 'true'
+          ports:
+            - containerPort: 9000
+              name: nni
+        - name: tensorboard
+          image: onepanel/dl:0.17.0
+          command:
+            - sh
+            - '-c'
+          env:
+            - name: ONEPANEL_INTERACTIVE_SIDECAR
+              value: 'true'
+          args:
+            # Read logs from /mnt/output/tensorboard - /mnt/output is auto-mounted from volumeMounts
+            - tensorboard --logdir /mnt/output/tensorboard
+          ports:
+            - containerPort: 6006
+              name: tensorboard
+    # Use the metrics-writer tasks to write best metrics to Workflow
+    - name: metrics-writer
+      inputs:
+        artifacts:
+          - name: sys-metrics
+            path: /tmp/sys-metrics.json
+          - git:
+              repo: https://github.com/onepanelio/templates.git
+              revision: v0.18.0
+            name: src
+            path: /mnt/src
+      container:
+        image: onepanel/python-sdk:v0.16.0
+        command:
+          - python
+          - -u
+        args:
+          - /mnt/src/tasks/metrics-writer/main.py
+          - --from_file=/tmp/sys-metrics.json
 
-# [CHANGE] Volumes that will mount to /mnt/data (annotated data) and /mnt/output (models, checkpoints, logs)
-# Update this depending on your annotation data, model, checkpoint, logs, etc. sizes
-# Example values: 250Mi, 500Gi, 1Ti
-volumeClaimTemplates:
-  - metadata:
-      name: hyperparamtuning-data
-    spec:
-      accessModes: [ "ReadWriteOnce" ]
-      resources:
-        requests:
-          storage: 20Gi
-  - metadata:
-      name: hyperparamtuning-output
-    spec:
-      accessModes: [ "ReadWriteOnce" ]
-      resources:
-        requests:
-          storage: 20Gi
+  # [CHANGE] Volumes that will mount to /mnt/data (annotated data) and /mnt/output (models, checkpoints, logs)
+  # Update this depending on your annotation data, model, checkpoint, logs, etc. sizes
+  # Example values: 250Mi, 500Gi, 1Ti
+  volumeClaimTemplates:
+    - metadata:
+        name: hyperparamtuning-data
+      spec:
+        accessModes: [ "ReadWriteOnce" ]
+        resources:
+          requests:
+            storage: 20Gi
+    - metadata:
+        name: hyperparamtuning-output
+      spec:
+        accessModes: [ "ReadWriteOnce" ]
+        resources:
+          requests:
+            storage: 20Gi

db/yaml/workflows/maskrcnn-training/20200812104328.yaml

@@ -0,0 +1,197 @@
+metadata:
+  name: "MaskRCNN Training"
+  kind: Workflow
+  version: 20200812104328
+  action: create
+  labels:
+    "used-by": "cvat"
+    "created-by": "system"
+spec:
+  arguments:
+    parameters:
+      - name: source
+        value: https://github.com/onepanelio/Mask_RCNN.git
+        displayName: Model source code
+        type: hidden
+        visibility: private
+
+      - name: sys-annotation-path
+        value: annotation-dump/sample_dataset
+        hint: Path to annotated data in default object storage (i.e S3). In CVAT, this parameter will be pre-populated.
+        displayName: Dataset path
+        visibility: private
+
+      - name: sys-output-path
+        value: workflow-data/output/sample_output
+        hint: Path to store output artifacts in default object storage (i.e s3). In CVAT, this parameter will be pre-populated.
+        displayName: Workflow output path
+        visibility: private
+
+      - name: sys-finetune-checkpoint
+        value: ''
+        hint: Select the last fine-tune checkpoint for this model. It may take up to 5 minutes for a recent checkpoint show here. Leave empty if this is the first time you're training this model.
+        displayName: Checkpoint path
+        visibility: public
+
+      - name: sys-num-classes
+        displayName: Number of classes
+        hint: Number of classes (i.e in CVAT taks) + 1 for background
+        value: '81'
+        visibility: private
+
+      - name: extras
+        displayName: Hyperparameters
+        visibility: public
+        type: textarea.textarea
+        value: |-
+          stage-1-epochs=1    #  Epochs for network heads
+          stage-2-epochs=2    #  Epochs for finetune layers
+          stage-3-epochs=3    #  Epochs for all layers
+        hint: "Please refer to our <a href='https://docs.onepanel.ai/docs/getting-started/use-cases/computervision/annotation/cvat/cvat_annotation_model#arguments-optional' target='_blank'>documentation</a> for more information on parameters."
+
+      - name: dump-format
+        type: select.select
+        value: cvat_coco
+        displayName: CVAT dump format
+        visibility: public
+        options:
+          - name: 'MS COCO'
+            value: 'cvat_coco'
+          - name: 'TF Detection API'
+            value: 'cvat_tfrecord'
+
+      - name: tf-image
+        visibility: public
+        value: tensorflow/tensorflow:1.13.1-py3
+        type: select.select
+        displayName: Select TensorFlow image
+        hint: Select the GPU image if you are running on a GPU node pool
+        options:
+          - name: 'TensorFlow 1.13.1 CPU Image'
+            value: 'tensorflow/tensorflow:1.13.1-py3'
+          - name: 'TensorFlow 1.13.1 GPU Image'
+            value: 'tensorflow/tensorflow:1.13.1-gpu-py3'
+
+      - displayName: Node pool
+        hint: Name of node pool or group to run this workflow task
+        type: select.select
+        visibility: public
+        name: sys-node-pool
+        value: Standard_D4s_v3
+        required: true
+        options:
+          - name: 'CPU: 2, RAM: 8GB'
+            value: Standard_D2s_v3
+          - name: 'CPU: 4, RAM: 16GB'
+            value: Standard_D4s_v3
+          - name: 'GPU: 1xK80, CPU: 6, RAM: 56GB'
+            value: Standard_NC6
+
+  entrypoint: main
+  templates:
+    - dag:
+        tasks:
+          - name: train-model
+            template: tensorflow
+      # Uncomment the lines below if you want to send Slack notifications
+      #    - arguments:
+      #        artifacts:
+      #        - from: '{{tasks.train-model.outputs.artifacts.sys-metrics}}'
+      #          name: metrics
+      #        parameters:
+      #        - name: status
+      #          value: '{{tasks.train-model.status}}'
+      #      dependencies:
+      #      - train-model
+      #      name: notify-in-slack
+      #      template: slack-notify-success
+      name: main
+    - container:
+        args:
+          - |
+            apt-get update \
+            && apt-get install -y git wget libglib2.0-0 libsm6 libxext6 libxrender-dev \
+            && pip install -r requirements.txt \
+            && pip install boto3 pyyaml google-cloud-storage \
+            && git clone https://github.com/waleedka/coco \
+            && cd coco/PythonAPI \
+            && python setup.py build_ext install \
+            && rm -rf build \
+            && cd ../../ \
+            && wget https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5 \
+            && python setup.py install && ls \
+            && python samples/coco/cvat.py train --dataset=/mnt/data/datasets \
+              --model=workflow_maskrcnn \
+              --extras="{{workflow.parameters.extras}}"  \
+              --ref_model_path="{{workflow.parameters.sys-finetune-checkpoint}}"  \
+              --num_classes="{{workflow.parameters.sys-num-classes}}" \
+            && cd /mnt/src/ \
+            && python prepare_dataset.py /mnt/data/datasets/annotations/instances_default.json
+        command:
+          - sh
+          - -c
+        image: '{{workflow.parameters.tf-image}}'
+        volumeMounts:
+          - mountPath: /mnt/data
+            name: data
+          - mountPath: /mnt/output
+            name: output
+        workingDir: /mnt/src
+      nodeSelector:
+        beta.kubernetes.io/instance-type: '{{workflow.parameters.sys-node-pool}}'
+      inputs:
+        artifacts:
+          - name: data
+            path: /mnt/data/datasets/
+            "{{.ArtifactRepositoryType}}":
+              key: '{{workflow.namespace}}/{{workflow.parameters.sys-annotation-path}}'
+          - git:
+              repo: '{{workflow.parameters.source}}'
+              revision: "no-boto"
+            name: src
+            path: /mnt/src
+      name: tensorflow
+      outputs:
+        artifacts:
+          - name: model
+            optional: true
+            path: /mnt/output
+            "{{.ArtifactRepositoryType}}":
+              key: '{{workflow.namespace}}/{{workflow.parameters.sys-output-path}}'
+  # Uncomment the lines below if you want to send Slack notifications
+  #- container:
+  #    args:
+  #    - SLACK_USERNAME=Onepanel SLACK_TITLE="{{workflow.name}} {{inputs.parameters.status}}"
+  #      SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd
+  #      SLACK_MESSAGE=$(cat /tmp/metrics.json)} ./slack-notify
+  #    command:
+  #    - sh
+  #    - -c
+  #    image: technosophos/slack-notify
+  #  inputs:
+  #    artifacts:
+  #    - name: metrics
+  #      optional: true
+  #      path: /tmp/metrics.json
+  #    parameters:
+  #    - name: status
+  #  name: slack-notify-success
+  volumeClaimTemplates:
+    - metadata:
+        creationTimestamp: null
+        name: data
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi
+    - metadata:
+        creationTimestamp: null
+        name: output
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi

db/yaml/workflows/maskrcnn-training/20200824095513.yaml

@@ -0,0 +1,191 @@
+metadata:
+  name: "MaskRCNN Training"
+  kind: Workflow
+  version: 20200824095513
+  action: update
+  labels:
+    "used-by": "cvat"
+    "created-by": "system"
+spec:
+  arguments:
+    parameters:
+      - name: source
+        value: https://github.com/onepanelio/Mask_RCNN.git
+        displayName: Model source code
+        type: hidden
+        visibility: private
+
+      - name: cvat-annotation-path
+        value: annotation-dump/sample_dataset
+        hint: Path to annotated data in default object storage (i.e S3). In CVAT, this parameter will be pre-populated.
+        displayName: Dataset path
+        visibility: private
+
+      - name: cvat-output-path
+        value: workflow-data/output/sample_output
+        hint: Path to store output artifacts in default object storage (i.e s3). In CVAT, this parameter will be pre-populated.
+        displayName: Workflow output path
+        visibility: private
+
+      - name: cvat-finetune-checkpoint
+        value: ''
+        hint: Select the last fine-tune checkpoint for this model. It may take up to 5 minutes for a recent checkpoint show here. Leave empty if this is the first time you're training this model.
+        displayName: Checkpoint path
+        visibility: public
+
+      - name: cvat-num-classes
+        displayName: Number of classes
+        hint: Number of classes (i.e in CVAT taks) + 1 for background
+        value: '81'
+        visibility: private
+
+      - name: hyperparameters
+        displayName: Hyperparameters
+        visibility: public
+        type: textarea.textarea
+        value: |-
+          stage-1-epochs=1    #  Epochs for network heads
+          stage-2-epochs=2    #  Epochs for finetune layers
+          stage-3-epochs=3    #  Epochs for all layers
+        hint: "Please refer to our <a href='https://docs.onepanel.ai/docs/getting-started/use-cases/computervision/annotation/cvat/cvat_annotation_model#arguments-optional' target='_blank'>documentation</a> for more information on parameters. Number of classes will be automatically populated if you had 'sys-num-classes' parameter in a workflow."
+
+      - name: dump-format
+        value: cvat_coco
+        displayName: CVAT dump format
+        visibility: public
+
+      - name: tf-image
+        visibility: public
+        value: tensorflow/tensorflow:1.13.1-py3
+        type: select.select
+        displayName: Select TensorFlow image
+        hint: Select the GPU image if you are running on a GPU node pool
+        options:
+          - name: 'TensorFlow 1.13.1 CPU Image'
+            value: 'tensorflow/tensorflow:1.13.1-py3'
+          - name: 'TensorFlow 1.13.1 GPU Image'
+            value: 'tensorflow/tensorflow:1.13.1-gpu-py3'
+
+      - displayName: Node pool
+        hint: Name of node pool or group to run this workflow task
+        type: select.select
+        visibility: public
+        name: sys-node-pool
+        value: Standard_D4s_v3
+        required: true
+        options:
+          - name: 'CPU: 2, RAM: 8GB'
+            value: Standard_D2s_v3
+          - name: 'CPU: 4, RAM: 16GB'
+            value: Standard_D4s_v3
+          - name: 'GPU: 1xK80, CPU: 6, RAM: 56GB'
+            value: Standard_NC6
+
+  entrypoint: main
+  templates:
+    - dag:
+        tasks:
+          - name: train-model
+            template: tensorflow
+      # Uncomment the lines below if you want to send Slack notifications
+      #    - arguments:
+      #        artifacts:
+      #        - from: '{{tasks.train-model.outputs.artifacts.sys-metrics}}'
+      #          name: metrics
+      #        parameters:
+      #        - name: status
+      #          value: '{{tasks.train-model.status}}'
+      #      dependencies:
+      #      - train-model
+      #      name: notify-in-slack
+      #      template: slack-notify-success
+      name: main
+    - container:
+        args:
+          - |
+            apt-get update \
+            && apt-get install -y git wget libglib2.0-0 libsm6 libxext6 libxrender-dev \
+            && pip install -r requirements.txt \
+            && pip install boto3 pyyaml google-cloud-storage \
+            && git clone https://github.com/waleedka/coco \
+            && cd coco/PythonAPI \
+            && python setup.py build_ext install \
+            && rm -rf build \
+            && cd ../../ \
+            && wget https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5 \
+            && python setup.py install && ls \
+            && python samples/coco/cvat.py train --dataset=/mnt/data/datasets \
+              --model=workflow_maskrcnn \
+              --extras="{{workflow.parameters.hyperparameters}}"  \
+              --ref_model_path="{{workflow.parameters.cvat-finetune-checkpoint}}"  \
+              --num_classes="{{workflow.parameters.cvat-num-classes}}" \
+            && cd /mnt/src/ \
+            && python prepare_dataset.py /mnt/data/datasets/annotations/instances_default.json
+        command:
+          - sh
+          - -c
+        image: '{{workflow.parameters.tf-image}}'
+        volumeMounts:
+          - mountPath: /mnt/data
+            name: data
+          - mountPath: /mnt/output
+            name: output
+        workingDir: /mnt/src
+      nodeSelector:
+        beta.kubernetes.io/instance-type: '{{workflow.parameters.sys-node-pool}}'
+      inputs:
+        artifacts:
+          - name: data
+            path: /mnt/data/datasets/
+            "{{.ArtifactRepositoryType}}":
+              key: '{{workflow.namespace}}/{{workflow.parameters.cvat-annotation-path}}'
+          - git:
+              repo: '{{workflow.parameters.source}}'
+              revision: "no-boto"
+            name: src
+            path: /mnt/src
+      name: tensorflow
+      outputs:
+        artifacts:
+          - name: model
+            optional: true
+            path: /mnt/output
+            "{{.ArtifactRepositoryType}}":
+              key: '{{workflow.namespace}}/{{workflow.parameters.cvat-output-path}}/{{workflow.name}}'
+  # Uncomment the lines below if you want to send Slack notifications
+  #- container:
+  #    args:
+  #    - SLACK_USERNAME=Onepanel SLACK_TITLE="{{workflow.name}} {{inputs.parameters.status}}"
+  #      SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd
+  #      SLACK_MESSAGE=$(cat /tmp/metrics.json)} ./slack-notify
+  #    command:
+  #    - sh
+  #    - -c
+  #    image: technosophos/slack-notify
+  #  inputs:
+  #    artifacts:
+  #    - name: metrics
+  #      optional: true
+  #      path: /tmp/metrics.json
+  #    parameters:
+  #    - name: status
+  #  name: slack-notify-success
+  volumeClaimTemplates:
+    - metadata:
+        creationTimestamp: null
+        name: data
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi
+    - metadata:
+        creationTimestamp: null
+        name: output
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi

db/yaml/workflows/maskrcnn-training/20201115145814.yaml

@@ -1,190 +1,199 @@
-entrypoint: main
-arguments:
-  parameters:
-    - name: source
-      value: https://github.com/onepanelio/Mask_RCNN.git
-      displayName: Model source code
-      type: hidden
-      visibility: private
+metadata:
+  name: "MaskRCNN Training"
+  kind: Workflow
+  version: 20201115145814
+  action: update
+  labels:
+    "used-by": "cvat"
+    "created-by": "system"
+spec:
+  entrypoint: main
+  arguments:
+    parameters:
+      - name: source
+        value: https://github.com/onepanelio/Mask_RCNN.git
+        displayName: Model source code
+        type: hidden
+        visibility: private
 
-    - name: cvat-annotation-path
-      value: annotation-dump/sample_dataset
-      hint: Path to annotated data in default object storage (i.e S3). In CVAT, this parameter will be pre-populated.
-      displayName: Dataset path
-      visibility: private
+      - name: cvat-annotation-path
+        value: annotation-dump/sample_dataset
+        hint: Path to annotated data in default object storage (i.e S3). In CVAT, this parameter will be pre-populated.
+        displayName: Dataset path
+        visibility: private
 
-    - name: cvat-output-path
-      value: workflow-data/output/sample_output
-      hint: Path to store output artifacts in default object storage (i.e s3). In CVAT, this parameter will be pre-populated.
-      displayName: Workflow output path
-      visibility: private
+      - name: cvat-output-path
+        value: workflow-data/output/sample_output
+        hint: Path to store output artifacts in default object storage (i.e s3). In CVAT, this parameter will be pre-populated.
+        displayName: Workflow output path
+        visibility: private
 
-    - name: cvat-finetune-checkpoint
-      value: ''
-      hint: Select the last fine-tune checkpoint for this model. It may take up to 5 minutes for a recent checkpoint show here. Leave empty if this is the first time you're training this model.
-      displayName: Checkpoint path
-      visibility: public
+      - name: cvat-finetune-checkpoint
+        value: ''
+        hint: Select the last fine-tune checkpoint for this model. It may take up to 5 minutes for a recent checkpoint show here. Leave empty if this is the first time you're training this model.
+        displayName: Checkpoint path
+        visibility: public
 
-    - name: cvat-num-classes
-      displayName: Number of classes
-      hint: Number of classes (i.e in CVAT taks) + 1 for background
-      value: '81'
-      visibility: private
+      - name: cvat-num-classes
+        displayName: Number of classes
+        hint: Number of classes (i.e in CVAT taks) + 1 for background
+        value: '81'
+        visibility: private
 
-    - name: hyperparameters
-      displayName: Hyperparameters
-      visibility: public
-      type: textarea.textarea
-      value: |-
-        stage-1-epochs=1    #  Epochs for network heads
-        stage-2-epochs=2    #  Epochs for finetune layers
-        stage-3-epochs=3    #  Epochs for all layers
-      hint: "Please refer to our <a href='https://docs.onepanel.ai/docs/getting-started/use-cases/computervision/annotation/cvat/cvat_annotation_model#arguments-optional' target='_blank'>documentation</a> for more information on parameters. Number of classes will be automatically populated if you had 'sys-num-classes' parameter in a workflow."
+      - name: hyperparameters
+        displayName: Hyperparameters
+        visibility: public
+        type: textarea.textarea
+        value: |-
+          stage-1-epochs=1    #  Epochs for network heads
+          stage-2-epochs=2    #  Epochs for finetune layers
+          stage-3-epochs=3    #  Epochs for all layers
+        hint: "Please refer to our <a href='https://docs.onepanel.ai/docs/getting-started/use-cases/computervision/annotation/cvat/cvat_annotation_model#arguments-optional' target='_blank'>documentation</a> for more information on parameters. Number of classes will be automatically populated if you had 'sys-num-classes' parameter in a workflow."
 
-    - name: dump-format
-      value: cvat_coco
-      displayName: CVAT dump format
-      visibility: public
+      - name: dump-format
+        value: cvat_coco
+        displayName: CVAT dump format
+        visibility: public
 
-    - name: tf-image
-      visibility: public
-      value: tensorflow/tensorflow:1.13.1-py3
-      type: select.select
-      displayName: Select TensorFlow image
-      hint: Select the GPU image if you are running on a GPU node pool
-      options:
-        - name: 'TensorFlow 1.13.1 CPU Image'
-          value: 'tensorflow/tensorflow:1.13.1-py3'
-        - name: 'TensorFlow 1.13.1 GPU Image'
-          value: 'tensorflow/tensorflow:1.13.1-gpu-py3'
+      - name: tf-image
+        visibility: public
+        value: tensorflow/tensorflow:1.13.1-py3
+        type: select.select
+        displayName: Select TensorFlow image
+        hint: Select the GPU image if you are running on a GPU node pool
+        options:
+          - name: 'TensorFlow 1.13.1 CPU Image'
+            value: 'tensorflow/tensorflow:1.13.1-py3'
+          - name: 'TensorFlow 1.13.1 GPU Image'
+            value: 'tensorflow/tensorflow:1.13.1-gpu-py3'
 
-    - displayName: Node pool
-      hint: Name of node pool or group to run this workflow task
-      type: select.select
-      visibility: public
-      name: sys-node-pool
-      value: Standard_D4s_v3
-      required: true
-      options:
-        - name: 'CPU: 2, RAM: 8GB'
-          value: Standard_D2s_v3
-        - name: 'CPU: 4, RAM: 16GB'
-          value: Standard_D4s_v3
-        - name: 'GPU: 1xK80, CPU: 6, RAM: 56GB'
-          value: Standard_NC6
-templates:
-  - name: main
-    dag:
-      tasks:
-        - name: train-model
-          template: tensorflow
-    # Uncomment the lines below if you want to send Slack notifications
-    #    - arguments:
-    #        artifacts:
-    #        - from: '{{tasks.train-model.outputs.artifacts.sys-metrics}}'
-    #          name: metrics
-    #        parameters:
-    #        - name: status
-    #          value: '{{tasks.train-model.status}}'
-    #      dependencies:
-    #      - train-model
-    #      name: notify-in-slack
-    #      template: slack-notify-success
-  - name: tensorflow
-    container:
-      args:
-        - |
-          apt-get update \
-          && apt-get install -y git wget libglib2.0-0 libsm6 libxext6 libxrender-dev \
-          && pip install -r requirements.txt \
-          && pip install boto3 pyyaml google-cloud-storage \
-          && git clone https://github.com/waleedka/coco \
-          && cd coco/PythonAPI \
-          && python setup.py build_ext install \
-          && rm -rf build \
-          && cd ../../ \
-          && wget https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5 \
-          && python setup.py install && ls \
-          && python samples/coco/cvat.py train --dataset=/mnt/data/datasets \
-            --model=workflow_maskrcnn \
-            --extras="{{workflow.parameters.hyperparameters}}"  \
-            --ref_model_path="{{workflow.parameters.cvat-finetune-checkpoint}}"  \
-            --num_classes="{{workflow.parameters.cvat-num-classes}}" \
-          && cd /mnt/src/ \
-          && python prepare_dataset.py /mnt/data/datasets/annotations/instances_default.json
-      command:
-        - sh
-        - -c
-      image: '{{workflow.parameters.tf-image}}'
-      volumeMounts:
-        - mountPath: /mnt/data
-          name: data
-        - mountPath: /mnt/output
-          name: output
-      workingDir: /mnt/src
-    nodeSelector:
-      beta.kubernetes.io/instance-type: '{{workflow.parameters.sys-node-pool}}'
-    sidecars:
-      - name: tensorboard
-        image: tensorflow/tensorflow:2.3.0
-        command: [sh, -c]
-        tty: true
-        args: ["tensorboard --logdir /mnt/output/"]
-        ports:
-          - containerPort: 6006
-            name: tensorboard
-    inputs:
-      artifacts:
-        - name: data
-          path: /mnt/data/datasets/
-          {{.ArtifactRepositoryType}}:
-            key: '{{workflow.namespace}}/{{workflow.parameters.cvat-annotation-path}}'
-        - git:
-            repo: '{{workflow.parameters.source}}'
-            revision: "no-boto"
-          name: src
-          path: /mnt/src
-    outputs:
-      artifacts:
-        - name: model
-          optional: true
-          path: /mnt/output
-          {{.ArtifactRepositoryType}}:
-            key: '{{workflow.namespace}}/{{workflow.parameters.cvat-output-path}}/{{workflow.name}}'
-# Uncomment the lines below if you want to send Slack notifications
-#- container:
-#    args:
-#    - SLACK_USERNAME=Onepanel SLACK_TITLE="{{workflow.name}} {{inputs.parameters.status}}"
-#      SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd
-#      SLACK_MESSAGE=$(cat /tmp/metrics.json)} ./slack-notify
-#    command:
-#    - sh
-#    - -c
-#    image: technosophos/slack-notify
-#  inputs:
-#    artifacts:
-#    - name: metrics
-#      optional: true
-#      path: /tmp/metrics.json
-#    parameters:
-#    - name: status
-#  name: slack-notify-success
-volumeClaimTemplates:
-  - metadata:
-      creationTimestamp: null
-      name: data
-    spec:
-      accessModes:
-        - ReadWriteOnce
-      resources:
-        requests:
-          storage: 200Gi
-  - metadata:
-      creationTimestamp: null
-      name: output
-    spec:
-      accessModes:
-        - ReadWriteOnce
-      resources:
-        requests:
-          storage: 200Gi
+      - displayName: Node pool
+        hint: Name of node pool or group to run this workflow task
+        type: select.select
+        visibility: public
+        name: sys-node-pool
+        value: Standard_D4s_v3
+        required: true
+        options:
+          - name: 'CPU: 2, RAM: 8GB'
+            value: Standard_D2s_v3
+          - name: 'CPU: 4, RAM: 16GB'
+            value: Standard_D4s_v3
+          - name: 'GPU: 1xK80, CPU: 6, RAM: 56GB'
+            value: Standard_NC6
+  templates:
+    - name: main
+      dag:
+        tasks:
+          - name: train-model
+            template: tensorflow
+      # Uncomment the lines below if you want to send Slack notifications
+      #    - arguments:
+      #        artifacts:
+      #        - from: '{{tasks.train-model.outputs.artifacts.sys-metrics}}'
+      #          name: metrics
+      #        parameters:
+      #        - name: status
+      #          value: '{{tasks.train-model.status}}'
+      #      dependencies:
+      #      - train-model
+      #      name: notify-in-slack
+      #      template: slack-notify-success
+    - name: tensorflow
+      container:
+        args:
+          - |
+            apt-get update \
+            && apt-get install -y git wget libglib2.0-0 libsm6 libxext6 libxrender-dev \
+            && pip install -r requirements.txt \
+            && pip install boto3 pyyaml google-cloud-storage \
+            && git clone https://github.com/waleedka/coco \
+            && cd coco/PythonAPI \
+            && python setup.py build_ext install \
+            && rm -rf build \
+            && cd ../../ \
+            && wget https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5 \
+            && python setup.py install && ls \
+            && python samples/coco/cvat.py train --dataset=/mnt/data/datasets \
+              --model=workflow_maskrcnn \
+              --extras="{{workflow.parameters.hyperparameters}}"  \
+              --ref_model_path="{{workflow.parameters.cvat-finetune-checkpoint}}"  \
+              --num_classes="{{workflow.parameters.cvat-num-classes}}" \
+            && cd /mnt/src/ \
+            && python prepare_dataset.py /mnt/data/datasets/annotations/instances_default.json
+        command:
+          - sh
+          - -c
+        image: '{{workflow.parameters.tf-image}}'
+        volumeMounts:
+          - mountPath: /mnt/data
+            name: data
+          - mountPath: /mnt/output
+            name: output
+        workingDir: /mnt/src
+      nodeSelector:
+        beta.kubernetes.io/instance-type: '{{workflow.parameters.sys-node-pool}}'
+      sidecars:
+        - name: tensorboard
+          image: tensorflow/tensorflow:2.3.0
+          command: [sh, -c]
+          tty: true
+          args: ["tensorboard --logdir /mnt/output/"]
+          ports:
+            - containerPort: 6006
+              name: tensorboard
+      inputs:
+        artifacts:
+          - name: data
+            path: /mnt/data/datasets/
+            "{{.ArtifactRepositoryType}}":
+              key: '{{workflow.namespace}}/{{workflow.parameters.cvat-annotation-path}}'
+          - git:
+              repo: '{{workflow.parameters.source}}'
+              revision: "no-boto"
+            name: src
+            path: /mnt/src
+      outputs:
+        artifacts:
+          - name: model
+            optional: true
+            path: /mnt/output
+            "{{.ArtifactRepositoryType}}":
+              key: '{{workflow.namespace}}/{{workflow.parameters.cvat-output-path}}/{{workflow.name}}'
+  # Uncomment the lines below if you want to send Slack notifications
+  #- container:
+  #    args:
+  #    - SLACK_USERNAME=Onepanel SLACK_TITLE="{{workflow.name}} {{inputs.parameters.status}}"
+  #      SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd
+  #      SLACK_MESSAGE=$(cat /tmp/metrics.json)} ./slack-notify
+  #    command:
+  #    - sh
+  #    - -c
+  #    image: technosophos/slack-notify
+  #  inputs:
+  #    artifacts:
+  #    - name: metrics
+  #      optional: true
+  #      path: /tmp/metrics.json
+  #    parameters:
+  #    - name: status
+  #  name: slack-notify-success
+  volumeClaimTemplates:
+    - metadata:
+        creationTimestamp: null
+        name: data
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi
+    - metadata:
+        creationTimestamp: null
+        name: output
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi

db/yaml/workflows/maskrcnn-training/20201208155115.yaml

@@ -1,192 +1,201 @@
-entrypoint: main
-arguments:
-  parameters:
-    - name: source
-      value: https://github.com/onepanelio/Mask_RCNN.git
-      displayName: Model source code
-      type: hidden
-      visibility: private
+metadata:
+  name: "MaskRCNN Training"
+  kind: Workflow
+  version: 20201208155115
+  action: update
+  labels:
+    "used-by": "cvat"
+    "created-by": "system"
+spec:
+  entrypoint: main
+  arguments:
+    parameters:
+      - name: source
+        value: https://github.com/onepanelio/Mask_RCNN.git
+        displayName: Model source code
+        type: hidden
+        visibility: private
 
-    - name: cvat-annotation-path
-      value: annotation-dump/sample_dataset
-      hint: Path to annotated data in default object storage (i.e S3). In CVAT, this parameter will be pre-populated.
-      displayName: Dataset path
-      visibility: private
+      - name: cvat-annotation-path
+        value: annotation-dump/sample_dataset
+        hint: Path to annotated data in default object storage (i.e S3). In CVAT, this parameter will be pre-populated.
+        displayName: Dataset path
+        visibility: private
 
-    - name: cvat-output-path
-      value: workflow-data/output/sample_output
-      hint: Path to store output artifacts in default object storage (i.e s3). In CVAT, this parameter will be pre-populated.
-      displayName: Workflow output path
-      visibility: private
+      - name: cvat-output-path
+        value: workflow-data/output/sample_output
+        hint: Path to store output artifacts in default object storage (i.e s3). In CVAT, this parameter will be pre-populated.
+        displayName: Workflow output path
+        visibility: private
 
-    - name: cvat-finetune-checkpoint
-      value: ''
-      hint: Select the last fine-tune checkpoint for this model. It may take up to 5 minutes for a recent checkpoint show here. Leave empty if this is the first time you're training this model.
-      displayName: Checkpoint path
-      visibility: public
+      - name: cvat-finetune-checkpoint
+        value: ''
+        hint: Select the last fine-tune checkpoint for this model. It may take up to 5 minutes for a recent checkpoint show here. Leave empty if this is the first time you're training this model.
+        displayName: Checkpoint path
+        visibility: public
 
-    - name: cvat-num-classes
-      displayName: Number of classes
-      hint: Number of classes (i.e in CVAT taks) + 1 for background
-      value: '81'
-      visibility: private
+      - name: cvat-num-classes
+        displayName: Number of classes
+        hint: Number of classes (i.e in CVAT taks) + 1 for background
+        value: '81'
+        visibility: private
 
-    - name: hyperparameters
-      displayName: Hyperparameters
-      visibility: public
-      type: textarea.textarea
-      value: |-
-        stage-1-epochs=1    #  Epochs for network heads
-        stage-2-epochs=2    #  Epochs for finetune layers
-        stage-3-epochs=3    #  Epochs for all layers
-      hint: "Please refer to our <a href='https://docs.onepanel.ai/docs/getting-started/use-cases/computervision/annotation/cvat/cvat_annotation_model#arguments-optional' target='_blank'>documentation</a> for more information on parameters. Number of classes will be automatically populated if you had 'sys-num-classes' parameter in a workflow."
+      - name: hyperparameters
+        displayName: Hyperparameters
+        visibility: public
+        type: textarea.textarea
+        value: |-
+          stage-1-epochs=1    #  Epochs for network heads
+          stage-2-epochs=2    #  Epochs for finetune layers
+          stage-3-epochs=3    #  Epochs for all layers
+        hint: "Please refer to our <a href='https://docs.onepanel.ai/docs/getting-started/use-cases/computervision/annotation/cvat/cvat_annotation_model#arguments-optional' target='_blank'>documentation</a> for more information on parameters. Number of classes will be automatically populated if you had 'sys-num-classes' parameter in a workflow."
 
-    - name: dump-format
-      value: cvat_coco
-      displayName: CVAT dump format
-      visibility: public
+      - name: dump-format
+        value: cvat_coco
+        displayName: CVAT dump format
+        visibility: public
 
-    - name: tf-image
-      visibility: public
-      value: tensorflow/tensorflow:1.13.1-py3
-      type: select.select
-      displayName: Select TensorFlow image
-      hint: Select the GPU image if you are running on a GPU node pool
-      options:
-        - name: 'TensorFlow 1.13.1 CPU Image'
-          value: 'tensorflow/tensorflow:1.13.1-py3'
-        - name: 'TensorFlow 1.13.1 GPU Image'
-          value: 'tensorflow/tensorflow:1.13.1-gpu-py3'
+      - name: tf-image
+        visibility: public
+        value: tensorflow/tensorflow:1.13.1-py3
+        type: select.select
+        displayName: Select TensorFlow image
+        hint: Select the GPU image if you are running on a GPU node pool
+        options:
+          - name: 'TensorFlow 1.13.1 CPU Image'
+            value: 'tensorflow/tensorflow:1.13.1-py3'
+          - name: 'TensorFlow 1.13.1 GPU Image'
+            value: 'tensorflow/tensorflow:1.13.1-gpu-py3'
 
-    - displayName: Node pool
-      hint: Name of node pool or group to run this workflow task
-      type: select.select
-      visibility: public
-      name: sys-node-pool
-      value: Standard_D4s_v3
-      required: true
-      options:
-        - name: 'CPU: 2, RAM: 8GB'
-          value: Standard_D2s_v3
-        - name: 'CPU: 4, RAM: 16GB'
-          value: Standard_D4s_v3
-        - name: 'GPU: 1xK80, CPU: 6, RAM: 56GB'
-          value: Standard_NC6
-templates:
-  - name: main
-    dag:
-      tasks:
-        - name: train-model
-          template: tensorflow
-    # Uncomment the lines below if you want to send Slack notifications
-    #    - arguments:
-    #        artifacts:
-    #        - from: '{{tasks.train-model.outputs.artifacts.sys-metrics}}'
-    #          name: metrics
-    #        parameters:
-    #        - name: status
-    #          value: '{{tasks.train-model.status}}'
-    #      dependencies:
-    #      - train-model
-    #      name: notify-in-slack
-    #      template: slack-notify-success
-  - name: tensorflow
-    container:
-      args:
-        - |
-          apt-get update \
-          && apt-get install -y git wget libglib2.0-0 libsm6 libxext6 libxrender-dev \
-          && pip install -r requirements.txt \
-          && pip install boto3 pyyaml google-cloud-storage \
-          && git clone https://github.com/waleedka/coco \
-          && cd coco/PythonAPI \
-          && python setup.py build_ext install \
-          && rm -rf build \
-          && cd ../../ \
-          && wget https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5 \
-          && python setup.py install && ls \
-          && python samples/coco/cvat.py train --dataset=/mnt/data/datasets \
-            --model=workflow_maskrcnn \
-            --extras="{{workflow.parameters.hyperparameters}}"  \
-            --ref_model_path="{{workflow.parameters.cvat-finetune-checkpoint}}"  \
-            --num_classes="{{workflow.parameters.cvat-num-classes}}" \
-          && cd /mnt/src/ \
-          && python prepare_dataset.py /mnt/data/datasets/annotations/instances_default.json
-      command:
-        - sh
-        - -c
-      image: '{{workflow.parameters.tf-image}}'
-      volumeMounts:
-        - mountPath: /mnt/data
-          name: data
-        - mountPath: /mnt/output
-          name: output
-      workingDir: /mnt/src
-    nodeSelector:
-      beta.kubernetes.io/instance-type: '{{workflow.parameters.sys-node-pool}}'
-    sidecars:
-      - name: tensorboard
-        image: tensorflow/tensorflow:2.3.0
-        command: [sh, -c]
-        env:
-          - name: ONEPANEL_INTERACTIVE_SIDECAR
-            value: 'true'
-        args: ["tensorboard --logdir /mnt/output/"]
-        ports:
-          - containerPort: 6006
-            name: tensorboard
-    inputs:
-      artifacts:
-        - name: data
-          path: /mnt/data/datasets/
-          {{.ArtifactRepositoryType}}:
-            key: '{{workflow.namespace}}/{{workflow.parameters.cvat-annotation-path}}'
-        - git:
-            repo: '{{workflow.parameters.source}}'
-            revision: "no-boto"
-          name: src
-          path: /mnt/src
-    outputs:
-      artifacts:
-        - name: model
-          optional: true
-          path: /mnt/output
-          {{.ArtifactRepositoryType}}:
-            key: '{{workflow.namespace}}/{{workflow.parameters.cvat-output-path}}/{{workflow.name}}'
-# Uncomment the lines below if you want to send Slack notifications
-#- container:
-#    args:
-#    - SLACK_USERNAME=Onepanel SLACK_TITLE="{{workflow.name}} {{inputs.parameters.status}}"
-#      SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd
-#      SLACK_MESSAGE=$(cat /tmp/metrics.json)} ./slack-notify
-#    command:
-#    - sh
-#    - -c
-#    image: technosophos/slack-notify
-#  inputs:
-#    artifacts:
-#    - name: metrics
-#      optional: true
-#      path: /tmp/metrics.json
-#    parameters:
-#    - name: status
-#  name: slack-notify-success
-volumeClaimTemplates:
-  - metadata:
-      creationTimestamp: null
-      name: data
-    spec:
-      accessModes:
-        - ReadWriteOnce
-      resources:
-        requests:
-          storage: 200Gi
-  - metadata:
-      creationTimestamp: null
-      name: output
-    spec:
-      accessModes:
-        - ReadWriteOnce
-      resources:
-        requests:
-          storage: 200Gi
+      - displayName: Node pool
+        hint: Name of node pool or group to run this workflow task
+        type: select.select
+        visibility: public
+        name: sys-node-pool
+        value: Standard_D4s_v3
+        required: true
+        options:
+          - name: 'CPU: 2, RAM: 8GB'
+            value: Standard_D2s_v3
+          - name: 'CPU: 4, RAM: 16GB'
+            value: Standard_D4s_v3
+          - name: 'GPU: 1xK80, CPU: 6, RAM: 56GB'
+            value: Standard_NC6
+  templates:
+    - name: main
+      dag:
+        tasks:
+          - name: train-model
+            template: tensorflow
+      # Uncomment the lines below if you want to send Slack notifications
+      #    - arguments:
+      #        artifacts:
+      #        - from: '{{tasks.train-model.outputs.artifacts.sys-metrics}}'
+      #          name: metrics
+      #        parameters:
+      #        - name: status
+      #          value: '{{tasks.train-model.status}}'
+      #      dependencies:
+      #      - train-model
+      #      name: notify-in-slack
+      #      template: slack-notify-success
+    - name: tensorflow
+      container:
+        args:
+          - |
+            apt-get update \
+            && apt-get install -y git wget libglib2.0-0 libsm6 libxext6 libxrender-dev \
+            && pip install -r requirements.txt \
+            && pip install boto3 pyyaml google-cloud-storage \
+            && git clone https://github.com/waleedka/coco \
+            && cd coco/PythonAPI \
+            && python setup.py build_ext install \
+            && rm -rf build \
+            && cd ../../ \
+            && wget https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5 \
+            && python setup.py install && ls \
+            && python samples/coco/cvat.py train --dataset=/mnt/data/datasets \
+              --model=workflow_maskrcnn \
+              --extras="{{workflow.parameters.hyperparameters}}"  \
+              --ref_model_path="{{workflow.parameters.cvat-finetune-checkpoint}}"  \
+              --num_classes="{{workflow.parameters.cvat-num-classes}}" \
+            && cd /mnt/src/ \
+            && python prepare_dataset.py /mnt/data/datasets/annotations/instances_default.json
+        command:
+          - sh
+          - -c
+        image: '{{workflow.parameters.tf-image}}'
+        volumeMounts:
+          - mountPath: /mnt/data
+            name: data
+          - mountPath: /mnt/output
+            name: output
+        workingDir: /mnt/src
+      nodeSelector:
+        beta.kubernetes.io/instance-type: '{{workflow.parameters.sys-node-pool}}'
+      sidecars:
+        - name: tensorboard
+          image: tensorflow/tensorflow:2.3.0
+          command: [sh, -c]
+          env:
+            - name: ONEPANEL_INTERACTIVE_SIDECAR
+              value: 'true'
+          args: ["tensorboard --logdir /mnt/output/"]
+          ports:
+            - containerPort: 6006
+              name: tensorboard
+      inputs:
+        artifacts:
+          - name: data
+            path: /mnt/data/datasets/
+            "{{.ArtifactRepositoryType}}":
+              key: '{{workflow.namespace}}/{{workflow.parameters.cvat-annotation-path}}'
+          - git:
+              repo: '{{workflow.parameters.source}}'
+              revision: "no-boto"
+            name: src
+            path: /mnt/src
+      outputs:
+        artifacts:
+          - name: model
+            optional: true
+            path: /mnt/output
+            "{{.ArtifactRepositoryType}}":
+              key: '{{workflow.namespace}}/{{workflow.parameters.cvat-output-path}}/{{workflow.name}}'
+  # Uncomment the lines below if you want to send Slack notifications
+  #- container:
+  #    args:
+  #    - SLACK_USERNAME=Onepanel SLACK_TITLE="{{workflow.name}} {{inputs.parameters.status}}"
+  #      SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd
+  #      SLACK_MESSAGE=$(cat /tmp/metrics.json)} ./slack-notify
+  #    command:
+  #    - sh
+  #    - -c
+  #    image: technosophos/slack-notify
+  #  inputs:
+  #    artifacts:
+  #    - name: metrics
+  #      optional: true
+  #      path: /tmp/metrics.json
+  #    parameters:
+  #    - name: status
+  #  name: slack-notify-success
+  volumeClaimTemplates:
+    - metadata:
+        creationTimestamp: null
+        name: data
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi
+    - metadata:
+        creationTimestamp: null
+        name: output
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi

db/yaml/workflows/maskrcnn-training/20201221195937.yaml

@@ -1,149 +1,158 @@
-# source: https://github.com/onepanelio/templates/blob/master/workflows/maskrcnn-training/
-arguments:
-  parameters:
-    - name: cvat-annotation-path
-      value: annotation-dump/sample_dataset
-      hint: Path to annotated data in default object storage. In CVAT, this parameter will be pre-populated.
-      displayName: Dataset path
-      visibility: internal
+metadata:
+  name: "MaskRCNN Training"
+  kind: Workflow
+  version: 20201221195937
+  action: update
+  source: "https://github.com/onepanelio/templates/blob/master/workflows/maskrcnn-training/"
+  labels:
+    "used-by": "cvat"
+    "created-by": "system"
+spec:
+  arguments:
+    parameters:
+      - name: cvat-annotation-path
+        value: annotation-dump/sample_dataset
+        hint: Path to annotated data in default object storage. In CVAT, this parameter will be pre-populated.
+        displayName: Dataset path
+        visibility: internal
 
-    - name: cvat-output-path
-      value: workflow-data/output/sample_output
-      hint: Path to store output artifacts in default object storage. In CVAT, this parameter will be pre-populated.
-      displayName: Workflow output path
-      visibility: internal
+      - name: cvat-output-path
+        value: workflow-data/output/sample_output
+        hint: Path to store output artifacts in default object storage. In CVAT, this parameter will be pre-populated.
+        displayName: Workflow output path
+        visibility: internal
 
-    - name: cvat-finetune-checkpoint
-      value: ''
-      hint: Select the last fine-tune checkpoint for this model. It may take up to 5 minutes for a recent checkpoint show here. Leave empty if this is the first time you're training this model.
-      displayName: Checkpoint path
-      visibility: public
+      - name: cvat-finetune-checkpoint
+        value: ''
+        hint: Select the last fine-tune checkpoint for this model. It may take up to 5 minutes for a recent checkpoint show here. Leave empty if this is the first time you're training this model.
+        displayName: Checkpoint path
+        visibility: public
 
-    - name: cvat-num-classes
-      displayName: Number of classes
-      hint: Number of classes + 1 for background. In CVAT, this parameter will be pre-populated.
-      value: '11'
-      visibility: internal
+      - name: cvat-num-classes
+        displayName: Number of classes
+        hint: Number of classes + 1 for background. In CVAT, this parameter will be pre-populated.
+        value: '11'
+        visibility: internal
 
-    - name: hyperparameters
-      displayName: Hyperparameters
-      visibility: public
-      type: textarea.textarea
-      value: |-
-        stage-1-epochs=1    #  Epochs for network heads
-        stage-2-epochs=2    #  Epochs for finetune layers
-        stage-3-epochs=3    #  Epochs for all layers
-      hint: "See <a href='https://docs.onepanel.ai/docs/getting-started/use-cases/computervision/annotation/cvat/cvat_annotation_model#maskrcnn-hyperparameters' target='_blank'>documentation</a> for more information on parameters."
+      - name: hyperparameters
+        displayName: Hyperparameters
+        visibility: public
+        type: textarea.textarea
+        value: |-
+          stage-1-epochs=1    #  Epochs for network heads
+          stage-2-epochs=2    #  Epochs for finetune layers
+          stage-3-epochs=3    #  Epochs for all layers
+        hint: "See <a href='https://docs.onepanel.ai/docs/getting-started/use-cases/computervision/annotation/cvat/cvat_annotation_model#maskrcnn-hyperparameters' target='_blank'>documentation</a> for more information on parameters."
 
-    - name: dump-format
-      value: cvat_coco
-      displayName: CVAT dump format
-      visibility: public
+      - name: dump-format
+        value: cvat_coco
+        displayName: CVAT dump format
+        visibility: public
 
-    - name: tf-image
-      visibility: public
-      value: tensorflow/tensorflow:1.13.1-py3
-      type: select.select
-      displayName: Select TensorFlow image
-      hint: Select the GPU image if you are running on a GPU node pool
-      options:
-        - name: 'TensorFlow 1.13.1 CPU Image'
-          value: 'tensorflow/tensorflow:1.13.1-py3'
-        - name: 'TensorFlow 1.13.1 GPU Image'
-          value: 'tensorflow/tensorflow:1.13.1-gpu-py3'
+      - name: tf-image
+        visibility: public
+        value: tensorflow/tensorflow:1.13.1-py3
+        type: select.select
+        displayName: Select TensorFlow image
+        hint: Select the GPU image if you are running on a GPU node pool
+        options:
+          - name: 'TensorFlow 1.13.1 CPU Image'
+            value: 'tensorflow/tensorflow:1.13.1-py3'
+          - name: 'TensorFlow 1.13.1 GPU Image'
+            value: 'tensorflow/tensorflow:1.13.1-gpu-py3'
 
-    - displayName: Node pool
-      hint: Name of node pool or group to run this workflow task
-      type: select.nodepool
-      visibility: public
-      name: sys-node-pool
-      value: {{.DefaultNodePoolOption}}
-      required: true
+      - displayName: Node pool
+        hint: Name of node pool or group to run this workflow task
+        type: select.nodepool
+        visibility: public
+        name: sys-node-pool
+        value: "{{.DefaultNodePoolOption}}"
+        required: true
 
-entrypoint: main
-templates:
-  - dag:
-      tasks:
-        - name: train-model
-          template: tensorflow
-    name: main
-  - container:
-      args:
-        - |
-          apt-get update \
-          && apt-get install -y git wget libglib2.0-0 libsm6 libxext6 libxrender-dev \
-          && pip install -r requirements.txt \
-          && pip install boto3 pyyaml google-cloud-storage \
-          && git clone https://github.com/waleedka/coco \
-          && cd coco/PythonAPI \
-          && python setup.py build_ext install \
-          && rm -rf build \
-          && cd ../../ \
-          && wget https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5 \
-          && python setup.py install && ls \
-          && python samples/coco/cvat.py train --dataset=/mnt/data/datasets \
-            --model=workflow_maskrcnn \
-            --extras="{{workflow.parameters.hyperparameters}}"  \
-            --ref_model_path="{{workflow.parameters.cvat-finetune-checkpoint}}"  \
-            --num_classes="{{workflow.parameters.cvat-num-classes}}" \
-          && cd /mnt/src/ \
-          && python prepare_dataset.py /mnt/data/datasets/annotations/instances_default.json
-      command:
-        - sh
-        - -c
-      image: '{{workflow.parameters.tf-image}}'
-      volumeMounts:
-        - mountPath: /mnt/data
-          name: data
-        - mountPath: /mnt/output
-          name: output
-      workingDir: /mnt/src
-    sidecars:
-      - name: tensorboard
-        image: tensorflow/tensorflow:2.3.0
-        command: [ sh, -c ]
-        env:
-          - name: ONEPANEL_INTERACTIVE_SIDECAR
-            value: 'true'
-        args: [ "tensorboard --logdir /mnt/output/" ]
-        ports:
-          - containerPort: 6006
-            name: tensorboard
-    nodeSelector:
-      beta.kubernetes.io/instance-type: '{{workflow.parameters.sys-node-pool}}'
-    inputs:
-      artifacts:
-        - name: data
-          path: /mnt/data/datasets/
-          s3:
-            key: '{{workflow.namespace}}/{{workflow.parameters.cvat-annotation-path}}'
-        - git:
-            repo: 'https://github.com/onepanelio/Mask_RCNN.git'
-            revision: 'no-boto'
-          name: src
-          path: /mnt/src
-    name: tensorflow
-    outputs:
-      artifacts:
-        - name: model
-          optional: true
-          path: /mnt/output
-          s3:
-            key: '{{workflow.namespace}}/{{workflow.parameters.cvat-output-path}}/{{workflow.name}}'
-volumeClaimTemplates:
-  - metadata:
-      name: data
-    spec:
-      accessModes:
-        - ReadWriteOnce
-      resources:
-        requests:
-          storage: 200Gi
-  - metadata:
-      name: output
-    spec:
-      accessModes:
-        - ReadWriteOnce
-      resources:
-        requests:
-          storage: 200Gi
+  entrypoint: main
+  templates:
+    - dag:
+        tasks:
+          - name: train-model
+            template: tensorflow
+      name: main
+    - container:
+        args:
+          - |
+            apt-get update \
+            && apt-get install -y git wget libglib2.0-0 libsm6 libxext6 libxrender-dev \
+            && pip install -r requirements.txt \
+            && pip install boto3 pyyaml google-cloud-storage \
+            && git clone https://github.com/waleedka/coco \
+            && cd coco/PythonAPI \
+            && python setup.py build_ext install \
+            && rm -rf build \
+            && cd ../../ \
+            && wget https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5 \
+            && python setup.py install && ls \
+            && python samples/coco/cvat.py train --dataset=/mnt/data/datasets \
+              --model=workflow_maskrcnn \
+              --extras="{{workflow.parameters.hyperparameters}}"  \
+              --ref_model_path="{{workflow.parameters.cvat-finetune-checkpoint}}"  \
+              --num_classes="{{workflow.parameters.cvat-num-classes}}" \
+            && cd /mnt/src/ \
+            && python prepare_dataset.py /mnt/data/datasets/annotations/instances_default.json
+        command:
+          - sh
+          - -c
+        image: '{{workflow.parameters.tf-image}}'
+        volumeMounts:
+          - mountPath: /mnt/data
+            name: data
+          - mountPath: /mnt/output
+            name: output
+        workingDir: /mnt/src
+      sidecars:
+        - name: tensorboard
+          image: tensorflow/tensorflow:2.3.0
+          command: [ sh, -c ]
+          env:
+            - name: ONEPANEL_INTERACTIVE_SIDECAR
+              value: 'true'
+          args: [ "tensorboard --logdir /mnt/output/" ]
+          ports:
+            - containerPort: 6006
+              name: tensorboard
+      nodeSelector:
+        beta.kubernetes.io/instance-type: '{{workflow.parameters.sys-node-pool}}'
+      inputs:
+        artifacts:
+          - name: data
+            path: /mnt/data/datasets/
+            s3:
+              key: '{{workflow.namespace}}/{{workflow.parameters.cvat-annotation-path}}'
+          - git:
+              repo: 'https://github.com/onepanelio/Mask_RCNN.git'
+              revision: 'no-boto'
+            name: src
+            path: /mnt/src
+      name: tensorflow
+      outputs:
+        artifacts:
+          - name: model
+            optional: true
+            path: /mnt/output
+            s3:
+              key: '{{workflow.namespace}}/{{workflow.parameters.cvat-output-path}}/{{workflow.name}}'
+  volumeClaimTemplates:
+    - metadata:
+        name: data
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi
+    - metadata:
+        name: output
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi

db/yaml/workflows/maskrcnn-training/20210118175809.yaml

@@ -1,208 +1,217 @@
-# source: https://github.com/onepanelio/templates/blob/master/workflows/maskrcnn-training/
-arguments:
-  parameters:
-    - name: cvat-annotation-path
-      value: 'artifacts/{{workflow.namespace}}/annotations/'
-      hint: Path to annotated data (COCO format) in default object storage. In CVAT, this parameter will be pre-populated.
-      displayName: Dataset path
-      visibility: internal
+metadata:
+  name: "MaskRCNN Training"
+  kind: Workflow
+  version: 20210118175809
+  action: update
+  source: "https://github.com/onepanelio/templates/blob/master/workflows/maskrcnn-training/"
+  labels:
+    "used-by": "cvat"
+    "created-by": "system"
+spec:
+  arguments:
+    parameters:
+      - name: cvat-annotation-path
+        value: 'artifacts/{{workflow.namespace}}/annotations/'
+        hint: Path to annotated data (COCO format) in default object storage. In CVAT, this parameter will be pre-populated.
+        displayName: Dataset path
+        visibility: internal
 
-    - name: val-split
-      value: 10
-      displayName: Validation split size
-      type: input.number
-      visibility: public
-      hint: Enter validation set size in percentage of full dataset. (0 - 100)
+      - name: val-split
+        value: 10
+        displayName: Validation split size
+        type: input.number
+        visibility: public
+        hint: Enter validation set size in percentage of full dataset. (0 - 100)
 
-    - name: num-augmentation-cycles
-      value: 1
-      displayName: Number of augmentation cycles
-      type: input.number
-      visibility: public
-      hint: Number of augmentation cycles, zero means no data augmentation
+      - name: num-augmentation-cycles
+        value: 1
+        displayName: Number of augmentation cycles
+        type: input.number
+        visibility: public
+        hint: Number of augmentation cycles, zero means no data augmentation
 
-    - name: preprocessing-parameters
-      value: |-
-        RandomBrightnessContrast:
-            p: 0.2
-        GaussianBlur:
-            p: 0.3
-        GaussNoise:
-            p: 0.4
-        HorizontalFlip:
-            p: 0.5
-        VerticalFlip:
-            p: 0.3
-      displayName: Preprocessing parameters
-      visibility: public
-      type: textarea.textarea
-      hint: 'See <a href="https://albumentations.ai/docs/api_reference/augmentations/transforms/" target="_blank">documentation</a> for more information on parameters.'
+      - name: preprocessing-parameters
+        value: |-
+          RandomBrightnessContrast:
+              p: 0.2
+          GaussianBlur:
+              p: 0.3
+          GaussNoise:
+              p: 0.4
+          HorizontalFlip:
+              p: 0.5
+          VerticalFlip:
+              p: 0.3
+        displayName: Preprocessing parameters
+        visibility: public
+        type: textarea.textarea
+        hint: 'See <a href="https://albumentations.ai/docs/api_reference/augmentations/transforms/" target="_blank">documentation</a> for more information on parameters.'
 
-    - name: cvat-num-classes
-      displayName: Number of classes
-      hint: Number of classes. In CVAT, this parameter will be pre-populated.
-      value: '10'
-      visibility: internal
+      - name: cvat-num-classes
+        displayName: Number of classes
+        hint: Number of classes. In CVAT, this parameter will be pre-populated.
+        value: '10'
+        visibility: internal
 
-    - name: hyperparameters
-      displayName: Hyperparameters
-      visibility: public
-      type: textarea.textarea
-      value: |-
-        stage_1_epochs: 1    #  Epochs for network heads
-        stage_2_epochs: 1    #  Epochs for finetune layers
-        stage_3_epochs: 1    #  Epochs for all layers
-        num_steps: 1000     #  Num steps per epoch
-      hint: 'See <a href="https://docs.onepanel.ai/docs/reference/workflows/training#maskrcnn-hyperparameters" target="_blank">documentation</a> for more information on parameters.'
+      - name: hyperparameters
+        displayName: Hyperparameters
+        visibility: public
+        type: textarea.textarea
+        value: |-
+          stage_1_epochs: 1    #  Epochs for network heads
+          stage_2_epochs: 1    #  Epochs for finetune layers
+          stage_3_epochs: 1    #  Epochs for all layers
+          num_steps: 1000     #  Num steps per epoch
+        hint: 'See <a href="https://docs.onepanel.ai/docs/reference/workflows/training#maskrcnn-hyperparameters" target="_blank">documentation</a> for more information on parameters.'
 
-    - name: dump-format
-      value: cvat_coco
-      displayName: CVAT dump format
-      visibility: private
+      - name: dump-format
+        value: cvat_coco
+        displayName: CVAT dump format
+        visibility: private
 
-    - name: cvat-finetune-checkpoint
-      value: ''
-      hint: Path to the last fine-tune checkpoint for this model in default object storage. Leave empty if this is the first time you're training this model.
-      displayName: Checkpoint path
-      visibility: public
+      - name: cvat-finetune-checkpoint
+        value: ''
+        hint: Path to the last fine-tune checkpoint for this model in default object storage. Leave empty if this is the first time you're training this model.
+        displayName: Checkpoint path
+        visibility: public
 
-    - displayName: Node pool
-      hint: Name of node pool or group to run this workflow task
-      type: select.nodepool
-      visibility: public
-      name: sys-node-pool
-      value: {{.DefaultNodePoolOption}}
-      required: true
+      - displayName: Node pool
+        hint: Name of node pool or group to run this workflow task
+        type: select.nodepool
+        visibility: public
+        name: sys-node-pool
+        value: "{{.DefaultNodePoolOption}}"
+        required: true
 
-entrypoint: main
-templates:
-  - dag:
-      tasks:
-        - name: preprocessing
-          template: preprocessing
-        - name: train-model
-          template: tensorflow
-          dependencies: [preprocessing]
-          arguments:
-            artifacts:
-              - name: data
-                from: "{{tasks.preprocessing.outputs.artifacts.processed-data}}"
-    name: main
-  - container:
-      args:
-        - |
-          pip install pycocotools scikit-image==0.16.2 && \
-          cd /mnt/src/train/workflows/maskrcnn-training && \
-          python -u main.py train --dataset=/mnt/data/datasets/train_set/ \
-            --model=workflow_maskrcnn \
-            --extras="{{workflow.parameters.hyperparameters}}"  \
-            --ref_model_path="{{workflow.parameters.cvat-finetune-checkpoint}}"  \
-            --num_classes="{{workflow.parameters.cvat-num-classes}}" \
-            --val_dataset=/mnt/data/datasets/eval_set/ \
-            --use_validation=True
-      command:
-        - sh
-        - -c
-      image: onepanel/dl:v0.20.0
-      volumeMounts:
-        - mountPath: /mnt/data
-          name: processed-data
-        - mountPath: /mnt/output
-          name: output
-      workingDir: /mnt/src
-    sidecars:
-      - name: tensorboard
+  entrypoint: main
+  templates:
+    - dag:
+        tasks:
+          - name: preprocessing
+            template: preprocessing
+          - name: train-model
+            template: tensorflow
+            dependencies: [preprocessing]
+            arguments:
+              artifacts:
+                - name: data
+                  from: "{{tasks.preprocessing.outputs.artifacts.processed-data}}"
+      name: main
+    - container:
+        args:
+          - |
+            pip install pycocotools scikit-image==0.16.2 && \
+            cd /mnt/src/train/workflows/maskrcnn-training && \
+            python -u main.py train --dataset=/mnt/data/datasets/train_set/ \
+              --model=workflow_maskrcnn \
+              --extras="{{workflow.parameters.hyperparameters}}"  \
+              --ref_model_path="{{workflow.parameters.cvat-finetune-checkpoint}}"  \
+              --num_classes="{{workflow.parameters.cvat-num-classes}}" \
+              --val_dataset=/mnt/data/datasets/eval_set/ \
+              --use_validation=True
+        command:
+          - sh
+          - -c
         image: onepanel/dl:v0.20.0
-        command: [ sh, -c ]
-        env:
-          - name: ONEPANEL_INTERACTIVE_SIDECAR
-            value: 'true'
-        args: [ "tensorboard --logdir /mnt/output/tensorboard" ]
-        ports:
-          - containerPort: 6006
-            name: tensorboard
-    nodeSelector:
-        {{.NodePoolLabel}}: '{{workflow.parameters.sys-node-pool}}'
-    inputs:
-      artifacts:
-        - name: data
-          path: /mnt/data/datasets/
-        - name: models
-          path: /mnt/data/models/
-          optional: true
-          s3:
-            key: '{{workflow.parameters.cvat-finetune-checkpoint}}'
-        - git:
-            repo: https://github.com/onepanelio/templates.git
-            revision: v0.18.0
-          name: src
-          path: /mnt/src/train
-    name: tensorflow
-    outputs:
-      artifacts:
-        - name: model
-          optional: true
-          path: /mnt/output
-  - container:
-      args:
-        - |
-          pip install pycocotools && \
-          cd /mnt/src/preprocessing/workflows/albumentations-preprocessing && \
-          python -u main.py \
-            --data_aug_params="{{workflow.parameters.preprocessing-parameters}}" \
-            --val_split={{workflow.parameters.val-split}} \
-            --aug_steps={{workflow.parameters.num-augmentation-cycles}}
-      command:
-        - sh
-        - -c
-      image: onepanel/dl:v0.20.0
-      volumeMounts:
-        - mountPath: /mnt/data
-          name: data
-        - mountPath: /mnt/output
-          name: processed-data
-      workingDir: /mnt/src
-    nodeSelector:
-        {{.NodePoolLabel}}: '{{workflow.parameters.sys-node-pool}}'
-    inputs:
-      artifacts:
-        - name: data
-          path: /mnt/data/datasets/
-          s3:
-            key: '{{workflow.parameters.cvat-annotation-path}}'
-        - git:
-            repo: https://github.com/onepanelio/templates.git
-            revision: v0.18.0
-          name: src
-          path: /mnt/src/preprocessing
-    name: preprocessing
-    outputs:
-      artifacts:
-        - name: processed-data
-          optional: true
-          path: /mnt/output
-volumeClaimTemplates:
-  - metadata:
-      name: data
-    spec:
-      accessModes:
-        - ReadWriteOnce
-      resources:
-        requests:
-          storage: 200Gi
-  - metadata:
-      name: processed-data
-    spec:
-      accessModes:
-        - ReadWriteOnce
-      resources:
-        requests:
-          storage: 200Gi
-  - metadata:
-      name: output
-    spec:
-      accessModes:
-        - ReadWriteOnce
-      resources:
-        requests:
-          storage: 200Gi
+        volumeMounts:
+          - mountPath: /mnt/data
+            name: processed-data
+          - mountPath: /mnt/output
+            name: output
+        workingDir: /mnt/src
+      sidecars:
+        - name: tensorboard
+          image: onepanel/dl:v0.20.0
+          command: [ sh, -c ]
+          env:
+            - name: ONEPANEL_INTERACTIVE_SIDECAR
+              value: 'true'
+          args: [ "tensorboard --logdir /mnt/output/tensorboard" ]
+          ports:
+            - containerPort: 6006
+              name: tensorboard
+      nodeSelector:
+          "{{.NodePoolLabel}}": '{{workflow.parameters.sys-node-pool}}'
+      inputs:
+        artifacts:
+          - name: data
+            path: /mnt/data/datasets/
+          - name: models
+            path: /mnt/data/models/
+            optional: true
+            s3:
+              key: '{{workflow.parameters.cvat-finetune-checkpoint}}'
+          - git:
+              repo: https://github.com/onepanelio/templates.git
+              revision: v0.18.0
+            name: src
+            path: /mnt/src/train
+      name: tensorflow
+      outputs:
+        artifacts:
+          - name: model
+            optional: true
+            path: /mnt/output
+    - container:
+        args:
+          - |
+            pip install pycocotools && \
+            cd /mnt/src/preprocessing/workflows/albumentations-preprocessing && \
+            python -u main.py \
+              --data_aug_params="{{workflow.parameters.preprocessing-parameters}}" \
+              --val_split={{workflow.parameters.val-split}} \
+              --aug_steps={{workflow.parameters.num-augmentation-cycles}}
+        command:
+          - sh
+          - -c
+        image: onepanel/dl:v0.20.0
+        volumeMounts:
+          - mountPath: /mnt/data
+            name: data
+          - mountPath: /mnt/output
+            name: processed-data
+        workingDir: /mnt/src
+      nodeSelector:
+          "{{.NodePoolLabel}}": '{{workflow.parameters.sys-node-pool}}'
+      inputs:
+        artifacts:
+          - name: data
+            path: /mnt/data/datasets/
+            s3:
+              key: '{{workflow.parameters.cvat-annotation-path}}'
+          - git:
+              repo: https://github.com/onepanelio/templates.git
+              revision: v0.18.0
+            name: src
+            path: /mnt/src/preprocessing
+      name: preprocessing
+      outputs:
+        artifacts:
+          - name: processed-data
+            optional: true
+            path: /mnt/output
+  volumeClaimTemplates:
+    - metadata:
+        name: data
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi
+    - metadata:
+        name: processed-data
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi
+    - metadata:
+        name: output
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi

db/yaml/workflows/pytorch-mnist-training/20200605090509.yaml

@@ -1,75 +1,84 @@
-entrypoint: main
-arguments:
-  parameters:
-    - name: source
-      value: https://github.com/onepanelio/pytorch-examples.git
-    - name: command
-      value: "python mnist/main.py --epochs=1"
-volumeClaimTemplates:
-  - metadata:
-      name: data
-    spec:
-      accessModes: [ "ReadWriteOnce" ]
-      resources:
-        requests:
-          storage: 2Gi
-  - metadata:
-      name: output
-    spec:
-      accessModes: [ "ReadWriteOnce" ]
-      resources:
-        requests:
-          storage: 2Gi
-templates:
-  - name: main
-    dag:
-      tasks:
-        - name: train-model
-          template: pytorch
-  # Uncomment section below to send metrics to Slack
-  #      - name: notify-in-slack
-  #        dependencies: [train-model]
-  #        template: slack-notify-success
-  #        arguments:
-  #          parameters:
-  #          - name: status
-  #            value: "{{tasks.train-model.status}}"
-  #          artifacts:
-  #          - name: metrics
-  #            from: "{{tasks.train-model.outputs.artifacts.sys-metrics}}"
-  - name: pytorch
-    inputs:
-      artifacts:
-        - name: src
-          path: /mnt/src
-          git:
-            repo: "{{workflow.parameters.source}}"
-    outputs:
-      artifacts:
-        - name: model
-          path: /mnt/output
-          optional: true
-          archive:
-            none: {}
-    container:
-      image: pytorch/pytorch:latest
-      command: [sh,-c]
-      args: ["{{workflow.parameters.command}}"]
-      workingDir: /mnt/src
-      volumeMounts:
-        - name: data
-          mountPath: /mnt/data
-        - name: output
-          mountPath: /mnt/output
-  - name: slack-notify-success
-    container:
-      image: technosophos/slack-notify
-      command: [sh,-c]
-      args: ['SLACK_USERNAME=Worker SLACK_TITLE="{{workflow.name}} {{inputs.parameters.status}}" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE=$(cat /tmp/metrics.json)} ./slack-notify']
-    inputs:
-      parameters:
-        - name: status
-      artifacts:
-        - name: metrics
-          path: /tmp/metrics.json
-          optional: true
+metadata:
+  name: "PyTorch Training"
+  kind: Workflow
+  version: 20200605090509
+  action: create
+  labels:
+    "created-by": "system"
+    framework: pytorch
+spec:
+  entrypoint: main
+  arguments:
+    parameters:
+      - name: source
+        value: https://github.com/onepanelio/pytorch-examples.git
+      - name: command
+        value: "python mnist/main.py --epochs=1"
+  volumeClaimTemplates:
+    - metadata:
+        name: data
+      spec:
+        accessModes: [ "ReadWriteOnce" ]
+        resources:
+          requests:
+            storage: 2Gi
+    - metadata:
+        name: output
+      spec:
+        accessModes: [ "ReadWriteOnce" ]
+        resources:
+          requests:
+            storage: 2Gi
+  templates:
+    - name: main
+      dag:
+        tasks:
+          - name: train-model
+            template: pytorch
+    # Uncomment section below to send metrics to Slack
+    #      - name: notify-in-slack
+    #        dependencies: [train-model]
+    #        template: slack-notify-success
+    #        arguments:
+    #          parameters:
+    #          - name: status
+    #            value: "{{tasks.train-model.status}}"
+    #          artifacts:
+    #          - name: metrics
+    #            from: "{{tasks.train-model.outputs.artifacts.sys-metrics}}"
+    - name: pytorch
+      inputs:
+        artifacts:
+          - name: src
+            path: /mnt/src
+            git:
+              repo: "{{workflow.parameters.source}}"
+      outputs:
+        artifacts:
+          - name: model
+            path: /mnt/output
+            optional: true
+            archive:
+              none: {}
+      container:
+        image: pytorch/pytorch:latest
+        command: [sh,-c]
+        args: ["{{workflow.parameters.command}}"]
+        workingDir: /mnt/src
+        volumeMounts:
+          - name: data
+            mountPath: /mnt/data
+          - name: output
+            mountPath: /mnt/output
+    - name: slack-notify-success
+      container:
+        image: technosophos/slack-notify
+        command: [sh,-c]
+        args: ['SLACK_USERNAME=Worker SLACK_TITLE="{{workflow.name}} {{inputs.parameters.status}}" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE=$(cat /tmp/metrics.json)} ./slack-notify']
+      inputs:
+        parameters:
+          - name: status
+        artifacts:
+          - name: metrics
+            path: /tmp/metrics.json
+            optional: true

db/yaml/workflows/pytorch-mnist-training/20201221194344.yaml

@@ -1,207 +1,216 @@
-# source: https://github.com/onepanelio/templates/blob/master/workflows/pytorch-mnist-training/
-arguments:
-  parameters:
-    - name: epochs
-      value: '10'
-    - displayName: Node pool
-      hint: Name of node pool or group to run this workflow task
-      type: select.nodepool
-      name: sys-node-pool
-      value: {{.DefaultNodePoolOption}}
-      visibility: public
-      required: true
-entrypoint: main
-templates:
-  - name: main
-    dag:
-      tasks:
-        - name: train-model
-          template: train-model
-  - name: train-model
-    # Indicates that we want to push files in /mnt/output to object storage
-    outputs:
-      artifacts:
-        - name: output
-          path: /mnt/output
-          optional: true
-    script:
-      image: onepanel/dl:0.17.0
-      command:
-        - python
-        - '-u'
-      source: |
-        import json
-        import torch
-        import torch.nn as nn
-        import torch.nn.functional as F
-        import torch.optim as optim
-        from torchvision import datasets, transforms
-        from torch.optim.lr_scheduler import StepLR
-        from torch.utils.tensorboard import SummaryWriter
-
-
-        class Net(nn.Module):
-            def __init__(self):
-                super(Net, self).__init__()
-                self.conv1 = nn.Conv2d(1, 32, 3, 1)
-                self.conv2 = nn.Conv2d(32, 64, 3, 1)
-                self.dropout1 = nn.Dropout(0.25)
-                self.dropout2 = nn.Dropout(0.5)
-                self.fc1 = nn.Linear(9216, 128)
-                self.fc2 = nn.Linear(128, 10)
-
-            def forward(self, x):
-                x = self.conv1(x)
-                x = F.relu(x)
-                x = self.conv2(x)
-                x = F.relu(x)
-                x = F.max_pool2d(x, 2)
-                x = self.dropout1(x)
-                x = torch.flatten(x, 1)
-                x = self.fc1(x)
-                x = F.relu(x)
-                x = self.dropout2(x)
-                x = self.fc2(x)
-                output = F.log_softmax(x, dim=1)
-                return output
-
-
-        def train(model, device, train_loader, optimizer, epoch, batch_size, writer):
-            model.train()
-            for batch_idx, (data, target) in enumerate(train_loader):
-                data, target = data.to(device), target.to(device)
-                optimizer.zero_grad()
-                output = model(data)
-                loss = F.nll_loss(output, target)
-                loss.backward()
-                optimizer.step()
-                if batch_idx % 10 == 0:
-                    print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
-                        epoch, batch_idx * len(data), len(train_loader.dataset),
-                        100. * batch_idx / len(train_loader), loss.item()))
-
-                    writer.add_scalar('training loss', loss.item(), epoch)
-
-
-        def test(model, device, test_loader, epoch, writer):
-            model.eval()
-            test_loss = 0
-            correct = 0
-            with torch.no_grad():
-                for data, target in test_loader:
-                    data, target = data.to(device), target.to(device)
-                    output = model(data)
-                    test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss
-                    pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
-                    correct += pred.eq(target.view_as(pred)).sum().item()
-
-            loss = test_loss / len(test_loader.dataset)
-            accuracy = correct / len(test_loader.dataset)
-
-            print('\nTest set: Average loss: {}, Accuracy: {}\n'.format(
-                loss, accuracy))
-
-             # Store metrics for this task
-            metrics = [
-              {'name': 'accuracy', 'value': accuracy},
-              {'name': 'loss', 'value': loss}
-            ]
-            with open('/tmp/sys-metrics.json', 'w') as f:
-              json.dump(metrics, f)
-
-
-        def main(params):
-            writer = SummaryWriter(log_dir='/mnt/output/tensorboard')
-
-            use_cuda = torch.cuda.is_available()
-
-            torch.manual_seed(params['seed'])
-
-            device = torch.device('cuda' if use_cuda else 'cpu')
-
-            train_kwargs = {'batch_size': params['batch_size']}
-            test_kwargs = {'batch_size': params['test_batch_size']}
-            if use_cuda:
-                cuda_kwargs = {'num_workers': 1,
-                               'pin_memory': True,
-                               'shuffle': True}
-                train_kwargs.update(cuda_kwargs)
-                test_kwargs.update(cuda_kwargs)
-
-            transform=transforms.Compose([
-                transforms.ToTensor(),
-                transforms.Normalize((0.1307,), (0.3081,))
-            ])
-            dataset1 = datasets.MNIST('/mnt/data', train=True, download=True,
-                               transform=transform)
-            dataset2 = datasets.MNIST('/mnt/data', train=False,
-                               transform=transform)
-            train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs)
-            test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
-
-            model = Net().to(device)
-            optimizer = optim.Adadelta(model.parameters(), lr=params['lr'])
-
-            scheduler = StepLR(optimizer, step_size=1, gamma=params['gamma'])
-            for epoch in range(1, params['epochs'] + 1):
-                train(model, device, train_loader, optimizer, epoch, params['batch_size'], writer)
-                test(model, device, test_loader, epoch, writer)
-                scheduler.step()
-
-            # Save model
-            torch.save(model.state_dict(), '/mnt/output/model.pt')
-
-            writer.close()
-
-
-        if __name__ == '__main__':
-            params = {
-                'seed': 1,
-                'batch_size': 64,
-                'test_batch_size': 1000,
-                'epochs': {{workflow.parameters.epochs}},
-                'lr': 0.001,
-                'gamma': 0.7,
-            }
-            main(params)
-      volumeMounts:
-        # TensorBoard sidecar will automatically mount these volumes
-        # The `data` volume is mounted for saving datasets
-        # The `output` volume is mounted to save model output and share TensorBoard logs
-        - name: data
-          mountPath: /mnt/data
-        - name: output
-          mountPath: /mnt/output
-    nodeSelector:
-      beta.kubernetes.io/instance-type: '{{workflow.parameters.sys-node-pool}}'
-    sidecars:
-      - name: tensorboard
-        image: tensorflow/tensorflow:2.3.0
+metadata:
+  name: "PyTorch Training"
+  kind: Workflow
+  version: 20201221194344
+  action: update
+  source: "https://github.com/onepanelio/templates/blob/master/workflows/pytorch-mnist-training/"
+  labels:
+    "created-by": "system"
+    framework: pytorch
+spec:
+  arguments:
+    parameters:
+      - name: epochs
+        value: '10'
+      - displayName: Node pool
+        hint: Name of node pool or group to run this workflow task
+        type: select.nodepool
+        name: sys-node-pool
+        value: "{{.DefaultNodePoolOption}}"
+        visibility: public
+        required: true
+  entrypoint: main
+  templates:
+    - name: main
+      dag:
+        tasks:
+          - name: train-model
+            template: train-model
+    - name: train-model
+      # Indicates that we want to push files in /mnt/output to object storage
+      outputs:
+        artifacts:
+          - name: output
+            path: /mnt/output
+            optional: true
+      script:
+        image: onepanel/dl:0.17.0
         command:
-          - sh
-          - '-c'
-        env:
-          - name: ONEPANEL_INTERACTIVE_SIDECAR
-            value: 'true'
-        args:
-          # Read logs from /mnt/output - this directory is auto-mounted from volumeMounts
-          - tensorboard --logdir /mnt/output/tensorboard
-        ports:
-          - containerPort: 6006
-            name: tensorboard
-volumeClaimTemplates:
-  # Provision volumes for storing data and output
-  - metadata:
-      name: data
-    spec:
-      accessModes: [ "ReadWriteOnce" ]
-      resources:
-        requests:
-          storage: 2Gi
-  - metadata:
-      name: output
-    spec:
-      accessModes: [ "ReadWriteOnce" ]
-      resources:
-        requests:
-          storage: 2Gi
+          - python
+          - '-u'
+        source: |
+          import json
+          import torch
+          import torch.nn as nn
+          import torch.nn.functional as F
+          import torch.optim as optim
+          from torchvision import datasets, transforms
+          from torch.optim.lr_scheduler import StepLR
+          from torch.utils.tensorboard import SummaryWriter
+
+
+          class Net(nn.Module):
+              def __init__(self):
+                  super(Net, self).__init__()
+                  self.conv1 = nn.Conv2d(1, 32, 3, 1)
+                  self.conv2 = nn.Conv2d(32, 64, 3, 1)
+                  self.dropout1 = nn.Dropout(0.25)
+                  self.dropout2 = nn.Dropout(0.5)
+                  self.fc1 = nn.Linear(9216, 128)
+                  self.fc2 = nn.Linear(128, 10)
+
+              def forward(self, x):
+                  x = self.conv1(x)
+                  x = F.relu(x)
+                  x = self.conv2(x)
+                  x = F.relu(x)
+                  x = F.max_pool2d(x, 2)
+                  x = self.dropout1(x)
+                  x = torch.flatten(x, 1)
+                  x = self.fc1(x)
+                  x = F.relu(x)
+                  x = self.dropout2(x)
+                  x = self.fc2(x)
+                  output = F.log_softmax(x, dim=1)
+                  return output
+
+
+          def train(model, device, train_loader, optimizer, epoch, batch_size, writer):
+              model.train()
+              for batch_idx, (data, target) in enumerate(train_loader):
+                  data, target = data.to(device), target.to(device)
+                  optimizer.zero_grad()
+                  output = model(data)
+                  loss = F.nll_loss(output, target)
+                  loss.backward()
+                  optimizer.step()
+                  if batch_idx % 10 == 0:
+                      print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                          epoch, batch_idx * len(data), len(train_loader.dataset),
+                          100. * batch_idx / len(train_loader), loss.item()))
+
+                      writer.add_scalar('training loss', loss.item(), epoch)
+
+
+          def test(model, device, test_loader, epoch, writer):
+              model.eval()
+              test_loss = 0
+              correct = 0
+              with torch.no_grad():
+                  for data, target in test_loader:
+                      data, target = data.to(device), target.to(device)
+                      output = model(data)
+                      test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss
+                      pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
+                      correct += pred.eq(target.view_as(pred)).sum().item()
+
+              loss = test_loss / len(test_loader.dataset)
+              accuracy = correct / len(test_loader.dataset)
+
+              print('\nTest set: Average loss: {}, Accuracy: {}\n'.format(
+                  loss, accuracy))
+
+               # Store metrics for this task
+              metrics = [
+                {'name': 'accuracy', 'value': accuracy},
+                {'name': 'loss', 'value': loss}
+              ]
+              with open('/tmp/sys-metrics.json', 'w') as f:
+                json.dump(metrics, f)
+
+
+          def main(params):
+              writer = SummaryWriter(log_dir='/mnt/output/tensorboard')
+
+              use_cuda = torch.cuda.is_available()
+
+              torch.manual_seed(params['seed'])
+
+              device = torch.device('cuda' if use_cuda else 'cpu')
+
+              train_kwargs = {'batch_size': params['batch_size']}
+              test_kwargs = {'batch_size': params['test_batch_size']}
+              if use_cuda:
+                  cuda_kwargs = {'num_workers': 1,
+                                 'pin_memory': True,
+                                 'shuffle': True}
+                  train_kwargs.update(cuda_kwargs)
+                  test_kwargs.update(cuda_kwargs)
+
+              transform=transforms.Compose([
+                  transforms.ToTensor(),
+                  transforms.Normalize((0.1307,), (0.3081,))
+              ])
+              dataset1 = datasets.MNIST('/mnt/data', train=True, download=True,
+                                 transform=transform)
+              dataset2 = datasets.MNIST('/mnt/data', train=False,
+                                 transform=transform)
+              train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs)
+              test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
+
+              model = Net().to(device)
+              optimizer = optim.Adadelta(model.parameters(), lr=params['lr'])
+
+              scheduler = StepLR(optimizer, step_size=1, gamma=params['gamma'])
+              for epoch in range(1, params['epochs'] + 1):
+                  train(model, device, train_loader, optimizer, epoch, params['batch_size'], writer)
+                  test(model, device, test_loader, epoch, writer)
+                  scheduler.step()
+
+              # Save model
+              torch.save(model.state_dict(), '/mnt/output/model.pt')
+
+              writer.close()
+
+
+          if __name__ == '__main__':
+              params = {
+                  'seed': 1,
+                  'batch_size': 64,
+                  'test_batch_size': 1000,
+                  'epochs': {{workflow.parameters.epochs}},
+                  'lr': 0.001,
+                  'gamma': 0.7,
+              }
+              main(params)
+        volumeMounts:
+          # TensorBoard sidecar will automatically mount these volumes
+          # The `data` volume is mounted for saving datasets
+          # The `output` volume is mounted to save model output and share TensorBoard logs
+          - name: data
+            mountPath: /mnt/data
+          - name: output
+            mountPath: /mnt/output
+      nodeSelector:
+        beta.kubernetes.io/instance-type: '{{workflow.parameters.sys-node-pool}}'
+      sidecars:
+        - name: tensorboard
+          image: tensorflow/tensorflow:2.3.0
+          command:
+            - sh
+            - '-c'
+          env:
+            - name: ONEPANEL_INTERACTIVE_SIDECAR
+              value: 'true'
+          args:
+            # Read logs from /mnt/output - this directory is auto-mounted from volumeMounts
+            - tensorboard --logdir /mnt/output/tensorboard
+          ports:
+            - containerPort: 6006
+              name: tensorboard
+  volumeClaimTemplates:
+    # Provision volumes for storing data and output
+    - metadata:
+        name: data
+      spec:
+        accessModes: [ "ReadWriteOnce" ]
+        resources:
+          requests:
+            storage: 2Gi
+    - metadata:
+        name: output
+      spec:
+        accessModes: [ "ReadWriteOnce" ]
+        resources:
+          requests:
+            storage: 2Gi

db/yaml/workflows/pytorch-mnist-training/20210118175809.yaml

@@ -1,207 +1,216 @@
-# source: https://github.com/onepanelio/templates/blob/master/workflows/pytorch-mnist-training/
-arguments:
-  parameters:
-    - name: epochs
-      value: '10'
-    - displayName: Node pool
-      hint: Name of node pool or group to run this workflow task
-      type: select.nodepool
-      name: sys-node-pool
-      value: {{.DefaultNodePoolOption}}
-      visibility: public
-      required: true
-entrypoint: main
-templates:
-  - name: main
-    dag:
-      tasks:
-        - name: train-model
-          template: train-model
-  - name: train-model
-    # Indicates that we want to push files in /mnt/output to object storage
-    outputs:
-      artifacts:
-        - name: output
-          path: /mnt/output
-          optional: true
-    script:
-      image: onepanel/dl:0.17.0
-      command:
-        - python
-        - '-u'
-      source: |
-        import json
-        import torch
-        import torch.nn as nn
-        import torch.nn.functional as F
-        import torch.optim as optim
-        from torchvision import datasets, transforms
-        from torch.optim.lr_scheduler import StepLR
-        from torch.utils.tensorboard import SummaryWriter
-
-
-        class Net(nn.Module):
-            def __init__(self):
-                super(Net, self).__init__()
-                self.conv1 = nn.Conv2d(1, 32, 3, 1)
-                self.conv2 = nn.Conv2d(32, 64, 3, 1)
-                self.dropout1 = nn.Dropout(0.25)
-                self.dropout2 = nn.Dropout(0.5)
-                self.fc1 = nn.Linear(9216, 128)
-                self.fc2 = nn.Linear(128, 10)
-
-            def forward(self, x):
-                x = self.conv1(x)
-                x = F.relu(x)
-                x = self.conv2(x)
-                x = F.relu(x)
-                x = F.max_pool2d(x, 2)
-                x = self.dropout1(x)
-                x = torch.flatten(x, 1)
-                x = self.fc1(x)
-                x = F.relu(x)
-                x = self.dropout2(x)
-                x = self.fc2(x)
-                output = F.log_softmax(x, dim=1)
-                return output
-
-
-        def train(model, device, train_loader, optimizer, epoch, batch_size, writer):
-            model.train()
-            for batch_idx, (data, target) in enumerate(train_loader):
-                data, target = data.to(device), target.to(device)
-                optimizer.zero_grad()
-                output = model(data)
-                loss = F.nll_loss(output, target)
-                loss.backward()
-                optimizer.step()
-                if batch_idx % 10 == 0:
-                    print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
-                        epoch, batch_idx * len(data), len(train_loader.dataset),
-                        100. * batch_idx / len(train_loader), loss.item()))
-
-                    writer.add_scalar('training loss', loss.item(), epoch)
-
-
-        def test(model, device, test_loader, epoch, writer):
-            model.eval()
-            test_loss = 0
-            correct = 0
-            with torch.no_grad():
-                for data, target in test_loader:
-                    data, target = data.to(device), target.to(device)
-                    output = model(data)
-                    test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss
-                    pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
-                    correct += pred.eq(target.view_as(pred)).sum().item()
-
-            loss = test_loss / len(test_loader.dataset)
-            accuracy = correct / len(test_loader.dataset)
-
-            print('\nTest set: Average loss: {}, Accuracy: {}\n'.format(
-                loss, accuracy))
-
-             # Store metrics for this task
-            metrics = [
-              {'name': 'accuracy', 'value': accuracy},
-              {'name': 'loss', 'value': loss}
-            ]
-            with open('/tmp/sys-metrics.json', 'w') as f:
-              json.dump(metrics, f)
-
-
-        def main(params):
-            writer = SummaryWriter(log_dir='/mnt/output/tensorboard')
-
-            use_cuda = torch.cuda.is_available()
-
-            torch.manual_seed(params['seed'])
-
-            device = torch.device('cuda' if use_cuda else 'cpu')
-
-            train_kwargs = {'batch_size': params['batch_size']}
-            test_kwargs = {'batch_size': params['test_batch_size']}
-            if use_cuda:
-                cuda_kwargs = {'num_workers': 1,
-                               'pin_memory': True,
-                               'shuffle': True}
-                train_kwargs.update(cuda_kwargs)
-                test_kwargs.update(cuda_kwargs)
-
-            transform=transforms.Compose([
-                transforms.ToTensor(),
-                transforms.Normalize((0.1307,), (0.3081,))
-            ])
-            dataset1 = datasets.MNIST('/mnt/data', train=True, download=True,
-                               transform=transform)
-            dataset2 = datasets.MNIST('/mnt/data', train=False,
-                               transform=transform)
-            train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs)
-            test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
-
-            model = Net().to(device)
-            optimizer = optim.Adadelta(model.parameters(), lr=params['lr'])
-
-            scheduler = StepLR(optimizer, step_size=1, gamma=params['gamma'])
-            for epoch in range(1, params['epochs'] + 1):
-                train(model, device, train_loader, optimizer, epoch, params['batch_size'], writer)
-                test(model, device, test_loader, epoch, writer)
-                scheduler.step()
-
-            # Save model
-            torch.save(model.state_dict(), '/mnt/output/model.pt')
-
-            writer.close()
-
-
-        if __name__ == '__main__':
-            params = {
-                'seed': 1,
-                'batch_size': 64,
-                'test_batch_size': 1000,
-                'epochs': {{workflow.parameters.epochs}},
-                'lr': 0.001,
-                'gamma': 0.7,
-            }
-            main(params)
-      volumeMounts:
-        # TensorBoard sidecar will automatically mount these volumes
-        # The `data` volume is mounted for saving datasets
-        # The `output` volume is mounted to save model output and share TensorBoard logs
-        - name: data
-          mountPath: /mnt/data
-        - name: output
-          mountPath: /mnt/output
-    nodeSelector:
-      {{.NodePoolLabel}}: '{{workflow.parameters.sys-node-pool}}'
-    sidecars:
-      - name: tensorboard
+metadata:
+  name: "PyTorch Training"
+  kind: Workflow
+  version: 20210118175809
+  action: update
+  source: "https://github.com/onepanelio/templates/blob/master/workflows/pytorch-mnist-training/"
+  labels:
+    "created-by": "system"
+    framework: pytorch
+spec:
+  arguments:
+    parameters:
+      - name: epochs
+        value: '10'
+      - displayName: Node pool
+        hint: Name of node pool or group to run this workflow task
+        type: select.nodepool
+        name: sys-node-pool
+        value: "{{.DefaultNodePoolOption}}"
+        visibility: public
+        required: true
+  entrypoint: main
+  templates:
+    - name: main
+      dag:
+        tasks:
+          - name: train-model
+            template: train-model
+    - name: train-model
+      # Indicates that we want to push files in /mnt/output to object storage
+      outputs:
+        artifacts:
+          - name: output
+            path: /mnt/output
+            optional: true
+      script:
         image: onepanel/dl:0.17.0
         command:
-          - sh
-          - '-c'
-        env:
-          - name: ONEPANEL_INTERACTIVE_SIDECAR
-            value: 'true'
-        args:
-          # Read logs from /mnt/output - this directory is auto-mounted from volumeMounts
-          - tensorboard --logdir /mnt/output/tensorboard
-        ports:
-          - containerPort: 6006
-            name: tensorboard
-volumeClaimTemplates:
-  # Provision volumes for storing data and output
-  - metadata:
-      name: data
-    spec:
-      accessModes: [ "ReadWriteOnce" ]
-      resources:
-        requests:
-          storage: 2Gi
-  - metadata:
-      name: output
-    spec:
-      accessModes: [ "ReadWriteOnce" ]
-      resources:
-        requests:
-          storage: 2Gi
+          - python
+          - '-u'
+        source: |
+          import json
+          import torch
+          import torch.nn as nn
+          import torch.nn.functional as F
+          import torch.optim as optim
+          from torchvision import datasets, transforms
+          from torch.optim.lr_scheduler import StepLR
+          from torch.utils.tensorboard import SummaryWriter
+
+
+          class Net(nn.Module):
+              def __init__(self):
+                  super(Net, self).__init__()
+                  self.conv1 = nn.Conv2d(1, 32, 3, 1)
+                  self.conv2 = nn.Conv2d(32, 64, 3, 1)
+                  self.dropout1 = nn.Dropout(0.25)
+                  self.dropout2 = nn.Dropout(0.5)
+                  self.fc1 = nn.Linear(9216, 128)
+                  self.fc2 = nn.Linear(128, 10)
+
+              def forward(self, x):
+                  x = self.conv1(x)
+                  x = F.relu(x)
+                  x = self.conv2(x)
+                  x = F.relu(x)
+                  x = F.max_pool2d(x, 2)
+                  x = self.dropout1(x)
+                  x = torch.flatten(x, 1)
+                  x = self.fc1(x)
+                  x = F.relu(x)
+                  x = self.dropout2(x)
+                  x = self.fc2(x)
+                  output = F.log_softmax(x, dim=1)
+                  return output
+
+
+          def train(model, device, train_loader, optimizer, epoch, batch_size, writer):
+              model.train()
+              for batch_idx, (data, target) in enumerate(train_loader):
+                  data, target = data.to(device), target.to(device)
+                  optimizer.zero_grad()
+                  output = model(data)
+                  loss = F.nll_loss(output, target)
+                  loss.backward()
+                  optimizer.step()
+                  if batch_idx % 10 == 0:
+                      print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                          epoch, batch_idx * len(data), len(train_loader.dataset),
+                          100. * batch_idx / len(train_loader), loss.item()))
+
+                      writer.add_scalar('training loss', loss.item(), epoch)
+
+
+          def test(model, device, test_loader, epoch, writer):
+              model.eval()
+              test_loss = 0
+              correct = 0
+              with torch.no_grad():
+                  for data, target in test_loader:
+                      data, target = data.to(device), target.to(device)
+                      output = model(data)
+                      test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss
+                      pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
+                      correct += pred.eq(target.view_as(pred)).sum().item()
+
+              loss = test_loss / len(test_loader.dataset)
+              accuracy = correct / len(test_loader.dataset)
+
+              print('\nTest set: Average loss: {}, Accuracy: {}\n'.format(
+                  loss, accuracy))
+
+               # Store metrics for this task
+              metrics = [
+                {'name': 'accuracy', 'value': accuracy},
+                {'name': 'loss', 'value': loss}
+              ]
+              with open('/tmp/sys-metrics.json', 'w') as f:
+                json.dump(metrics, f)
+
+
+          def main(params):
+              writer = SummaryWriter(log_dir='/mnt/output/tensorboard')
+
+              use_cuda = torch.cuda.is_available()
+
+              torch.manual_seed(params['seed'])
+
+              device = torch.device('cuda' if use_cuda else 'cpu')
+
+              train_kwargs = {'batch_size': params['batch_size']}
+              test_kwargs = {'batch_size': params['test_batch_size']}
+              if use_cuda:
+                  cuda_kwargs = {'num_workers': 1,
+                                 'pin_memory': True,
+                                 'shuffle': True}
+                  train_kwargs.update(cuda_kwargs)
+                  test_kwargs.update(cuda_kwargs)
+
+              transform=transforms.Compose([
+                  transforms.ToTensor(),
+                  transforms.Normalize((0.1307,), (0.3081,))
+              ])
+              dataset1 = datasets.MNIST('/mnt/data', train=True, download=True,
+                                 transform=transform)
+              dataset2 = datasets.MNIST('/mnt/data', train=False,
+                                 transform=transform)
+              train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs)
+              test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
+
+              model = Net().to(device)
+              optimizer = optim.Adadelta(model.parameters(), lr=params['lr'])
+
+              scheduler = StepLR(optimizer, step_size=1, gamma=params['gamma'])
+              for epoch in range(1, params['epochs'] + 1):
+                  train(model, device, train_loader, optimizer, epoch, params['batch_size'], writer)
+                  test(model, device, test_loader, epoch, writer)
+                  scheduler.step()
+
+              # Save model
+              torch.save(model.state_dict(), '/mnt/output/model.pt')
+
+              writer.close()
+
+
+          if __name__ == '__main__':
+              params = {
+                  'seed': 1,
+                  'batch_size': 64,
+                  'test_batch_size': 1000,
+                  'epochs': {{workflow.parameters.epochs}},
+                  'lr': 0.001,
+                  'gamma': 0.7,
+              }
+              main(params)
+        volumeMounts:
+          # TensorBoard sidecar will automatically mount these volumes
+          # The `data` volume is mounted for saving datasets
+          # The `output` volume is mounted to save model output and share TensorBoard logs
+          - name: data
+            mountPath: /mnt/data
+          - name: output
+            mountPath: /mnt/output
+      nodeSelector:
+        "{{.NodePoolLabel}}": '{{workflow.parameters.sys-node-pool}}'
+      sidecars:
+        - name: tensorboard
+          image: onepanel/dl:0.17.0
+          command:
+            - sh
+            - '-c'
+          env:
+            - name: ONEPANEL_INTERACTIVE_SIDECAR
+              value: 'true'
+          args:
+            # Read logs from /mnt/output - this directory is auto-mounted from volumeMounts
+            - tensorboard --logdir /mnt/output/tensorboard
+          ports:
+            - containerPort: 6006
+              name: tensorboard
+  volumeClaimTemplates:
+    # Provision volumes for storing data and output
+    - metadata:
+        name: data
+      spec:
+        accessModes: [ "ReadWriteOnce" ]
+        resources:
+          requests:
+            storage: 2Gi
+    - metadata:
+        name: output
+      spec:
+        accessModes: [ "ReadWriteOnce" ]
+        resources:
+          requests:
+            storage: 2Gi

db/yaml/workflows/pytorch-mnist-training/20210323175655.yaml

@@ -1,207 +1,216 @@
-# source: https://github.com/onepanelio/templates/blob/master/workflows/pytorch-mnist-training/
-arguments:
-  parameters:
-    - name: epochs
-      value: '10'
-    - displayName: Node pool
-      hint: Name of node pool or group to run this workflow task
-      type: select.nodepool
-      name: sys-node-pool
-      value: {{.DefaultNodePoolOption}}
-      visibility: public
-      required: true
-entrypoint: main
-templates:
-  - name: main
-    dag:
-      tasks:
-        - name: train-model
-          template: train-model
-  - name: train-model
-    # Indicates that we want to push files in /mnt/output to object storage
-    outputs:
-      artifacts:
-        - name: output
-          path: /mnt/output
-          optional: true
-    script:
-      image: onepanel/dl:v0.20.0
-      command:
-        - python
-        - '-u'
-      source: |
-        import json
-        import torch
-        import torch.nn as nn
-        import torch.nn.functional as F
-        import torch.optim as optim
-        from torchvision import datasets, transforms
-        from torch.optim.lr_scheduler import StepLR
-        from torch.utils.tensorboard import SummaryWriter
-
-
-        class Net(nn.Module):
-            def __init__(self):
-                super(Net, self).__init__()
-                self.conv1 = nn.Conv2d(1, 32, 3, 1)
-                self.conv2 = nn.Conv2d(32, 64, 3, 1)
-                self.dropout1 = nn.Dropout(0.25)
-                self.dropout2 = nn.Dropout(0.5)
-                self.fc1 = nn.Linear(9216, 128)
-                self.fc2 = nn.Linear(128, 10)
-
-            def forward(self, x):
-                x = self.conv1(x)
-                x = F.relu(x)
-                x = self.conv2(x)
-                x = F.relu(x)
-                x = F.max_pool2d(x, 2)
-                x = self.dropout1(x)
-                x = torch.flatten(x, 1)
-                x = self.fc1(x)
-                x = F.relu(x)
-                x = self.dropout2(x)
-                x = self.fc2(x)
-                output = F.log_softmax(x, dim=1)
-                return output
-
-
-        def train(model, device, train_loader, optimizer, epoch, batch_size, writer):
-            model.train()
-            for batch_idx, (data, target) in enumerate(train_loader):
-                data, target = data.to(device), target.to(device)
-                optimizer.zero_grad()
-                output = model(data)
-                loss = F.nll_loss(output, target)
-                loss.backward()
-                optimizer.step()
-                if batch_idx % 10 == 0:
-                    print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
-                        epoch, batch_idx * len(data), len(train_loader.dataset),
-                        100. * batch_idx / len(train_loader), loss.item()))
-
-                    writer.add_scalar('training loss', loss.item(), epoch)
-
-
-        def test(model, device, test_loader, epoch, writer):
-            model.eval()
-            test_loss = 0
-            correct = 0
-            with torch.no_grad():
-                for data, target in test_loader:
-                    data, target = data.to(device), target.to(device)
-                    output = model(data)
-                    test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss
-                    pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
-                    correct += pred.eq(target.view_as(pred)).sum().item()
-
-            loss = test_loss / len(test_loader.dataset)
-            accuracy = correct / len(test_loader.dataset)
-
-            print('\nTest set: Average loss: {}, Accuracy: {}\n'.format(
-                loss, accuracy))
-
-             # Store metrics for this task
-            metrics = [
-              {'name': 'accuracy', 'value': accuracy},
-              {'name': 'loss', 'value': loss}
-            ]
-            with open('/mnt/tmp/sys-metrics.json', 'w') as f:
-              json.dump(metrics, f)
-
-
-        def main(params):
-            writer = SummaryWriter(log_dir='/mnt/output/tensorboard')
-
-            use_cuda = torch.cuda.is_available()
-
-            torch.manual_seed(params['seed'])
-
-            device = torch.device('cuda' if use_cuda else 'cpu')
-
-            train_kwargs = {'batch_size': params['batch_size']}
-            test_kwargs = {'batch_size': params['test_batch_size']}
-            if use_cuda:
-                cuda_kwargs = {'num_workers': 1,
-                               'pin_memory': True,
-                               'shuffle': True}
-                train_kwargs.update(cuda_kwargs)
-                test_kwargs.update(cuda_kwargs)
-
-            transform=transforms.Compose([
-                transforms.ToTensor(),
-                transforms.Normalize((0.1307,), (0.3081,))
-            ])
-            dataset1 = datasets.MNIST('/mnt/data', train=True, download=True,
-                               transform=transform)
-            dataset2 = datasets.MNIST('/mnt/data', train=False,
-                               transform=transform)
-            train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs)
-            test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
-
-            model = Net().to(device)
-            optimizer = optim.Adadelta(model.parameters(), lr=params['lr'])
-
-            scheduler = StepLR(optimizer, step_size=1, gamma=params['gamma'])
-            for epoch in range(1, params['epochs'] + 1):
-                train(model, device, train_loader, optimizer, epoch, params['batch_size'], writer)
-                test(model, device, test_loader, epoch, writer)
-                scheduler.step()
-
-            # Save model
-            torch.save(model.state_dict(), '/mnt/output/model.pt')
-
-            writer.close()
-
-
-        if __name__ == '__main__':
-            params = {
-                'seed': 1,
-                'batch_size': 64,
-                'test_batch_size': 1000,
-                'epochs': {{workflow.parameters.epochs}},
-                'lr': 0.001,
-                'gamma': 0.7,
-            }
-            main(params)
-      volumeMounts:
-        # TensorBoard sidecar will automatically mount these volumes
-        # The `data` volume is mounted for saving datasets
-        # The `output` volume is mounted to save model output and share TensorBoard logs
-        - name: data
-          mountPath: /mnt/data
-        - name: output
-          mountPath: /mnt/output
-    nodeSelector:
-        {{.NodePoolLabel}}: '{{workflow.parameters.sys-node-pool}}'
-    sidecars:
-      - name: tensorboard
+metadata:
+  name: "PyTorch Training"
+  kind: Workflow
+  version: 20210323175655
+  action: update
+  source: "https://github.com/onepanelio/templates/blob/master/workflows/pytorch-mnist-training/"
+  labels:
+    "created-by": "system"
+    framework: pytorch
+spec:
+  arguments:
+    parameters:
+      - name: epochs
+        value: '10'
+      - displayName: Node pool
+        hint: Name of node pool or group to run this workflow task
+        type: select.nodepool
+        name: sys-node-pool
+        value: "{{.DefaultNodePoolOption}}"
+        visibility: public
+        required: true
+  entrypoint: main
+  templates:
+    - name: main
+      dag:
+        tasks:
+          - name: train-model
+            template: train-model
+    - name: train-model
+      # Indicates that we want to push files in /mnt/output to object storage
+      outputs:
+        artifacts:
+          - name: output
+            path: /mnt/output
+            optional: true
+      script:
         image: onepanel/dl:v0.20.0
         command:
-          - sh
-          - '-c'
-        env:
-          - name: ONEPANEL_INTERACTIVE_SIDECAR
-            value: 'true'
-        args:
-          # Read logs from /mnt/output - this directory is auto-mounted from volumeMounts
-          - tensorboard --logdir /mnt/output/tensorboard
-        ports:
-          - containerPort: 6006
-            name: tensorboard
-volumeClaimTemplates:
-  # Provision volumes for storing data and output
-  - metadata:
-      name: data
-    spec:
-      accessModes: [ "ReadWriteOnce" ]
-      resources:
-        requests:
-          storage: 2Gi
-  - metadata:
-      name: output
-    spec:
-      accessModes: [ "ReadWriteOnce" ]
-      resources:
-        requests:
-          storage: 2Gi
+          - python
+          - '-u'
+        source: |
+          import json
+          import torch
+          import torch.nn as nn
+          import torch.nn.functional as F
+          import torch.optim as optim
+          from torchvision import datasets, transforms
+          from torch.optim.lr_scheduler import StepLR
+          from torch.utils.tensorboard import SummaryWriter
+
+
+          class Net(nn.Module):
+              def __init__(self):
+                  super(Net, self).__init__()
+                  self.conv1 = nn.Conv2d(1, 32, 3, 1)
+                  self.conv2 = nn.Conv2d(32, 64, 3, 1)
+                  self.dropout1 = nn.Dropout(0.25)
+                  self.dropout2 = nn.Dropout(0.5)
+                  self.fc1 = nn.Linear(9216, 128)
+                  self.fc2 = nn.Linear(128, 10)
+
+              def forward(self, x):
+                  x = self.conv1(x)
+                  x = F.relu(x)
+                  x = self.conv2(x)
+                  x = F.relu(x)
+                  x = F.max_pool2d(x, 2)
+                  x = self.dropout1(x)
+                  x = torch.flatten(x, 1)
+                  x = self.fc1(x)
+                  x = F.relu(x)
+                  x = self.dropout2(x)
+                  x = self.fc2(x)
+                  output = F.log_softmax(x, dim=1)
+                  return output
+
+
+          def train(model, device, train_loader, optimizer, epoch, batch_size, writer):
+              model.train()
+              for batch_idx, (data, target) in enumerate(train_loader):
+                  data, target = data.to(device), target.to(device)
+                  optimizer.zero_grad()
+                  output = model(data)
+                  loss = F.nll_loss(output, target)
+                  loss.backward()
+                  optimizer.step()
+                  if batch_idx % 10 == 0:
+                      print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                          epoch, batch_idx * len(data), len(train_loader.dataset),
+                          100. * batch_idx / len(train_loader), loss.item()))
+
+                      writer.add_scalar('training loss', loss.item(), epoch)
+
+
+          def test(model, device, test_loader, epoch, writer):
+              model.eval()
+              test_loss = 0
+              correct = 0
+              with torch.no_grad():
+                  for data, target in test_loader:
+                      data, target = data.to(device), target.to(device)
+                      output = model(data)
+                      test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss
+                      pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
+                      correct += pred.eq(target.view_as(pred)).sum().item()
+
+              loss = test_loss / len(test_loader.dataset)
+              accuracy = correct / len(test_loader.dataset)
+
+              print('\nTest set: Average loss: {}, Accuracy: {}\n'.format(
+                  loss, accuracy))
+
+               # Store metrics for this task
+              metrics = [
+                {'name': 'accuracy', 'value': accuracy},
+                {'name': 'loss', 'value': loss}
+              ]
+              with open('/mnt/tmp/sys-metrics.json', 'w') as f:
+                json.dump(metrics, f)
+
+
+          def main(params):
+              writer = SummaryWriter(log_dir='/mnt/output/tensorboard')
+
+              use_cuda = torch.cuda.is_available()
+
+              torch.manual_seed(params['seed'])
+
+              device = torch.device('cuda' if use_cuda else 'cpu')
+
+              train_kwargs = {'batch_size': params['batch_size']}
+              test_kwargs = {'batch_size': params['test_batch_size']}
+              if use_cuda:
+                  cuda_kwargs = {'num_workers': 1,
+                                 'pin_memory': True,
+                                 'shuffle': True}
+                  train_kwargs.update(cuda_kwargs)
+                  test_kwargs.update(cuda_kwargs)
+
+              transform=transforms.Compose([
+                  transforms.ToTensor(),
+                  transforms.Normalize((0.1307,), (0.3081,))
+              ])
+              dataset1 = datasets.MNIST('/mnt/data', train=True, download=True,
+                                 transform=transform)
+              dataset2 = datasets.MNIST('/mnt/data', train=False,
+                                 transform=transform)
+              train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs)
+              test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
+
+              model = Net().to(device)
+              optimizer = optim.Adadelta(model.parameters(), lr=params['lr'])
+
+              scheduler = StepLR(optimizer, step_size=1, gamma=params['gamma'])
+              for epoch in range(1, params['epochs'] + 1):
+                  train(model, device, train_loader, optimizer, epoch, params['batch_size'], writer)
+                  test(model, device, test_loader, epoch, writer)
+                  scheduler.step()
+
+              # Save model
+              torch.save(model.state_dict(), '/mnt/output/model.pt')
+
+              writer.close()
+
+
+          if __name__ == '__main__':
+              params = {
+                  'seed': 1,
+                  'batch_size': 64,
+                  'test_batch_size': 1000,
+                  'epochs': {{workflow.parameters.epochs}},
+                  'lr': 0.001,
+                  'gamma': 0.7,
+              }
+              main(params)
+        volumeMounts:
+          # TensorBoard sidecar will automatically mount these volumes
+          # The `data` volume is mounted for saving datasets
+          # The `output` volume is mounted to save model output and share TensorBoard logs
+          - name: data
+            mountPath: /mnt/data
+          - name: output
+            mountPath: /mnt/output
+      nodeSelector:
+          "{{.NodePoolLabel}}": '{{workflow.parameters.sys-node-pool}}'
+      sidecars:
+        - name: tensorboard
+          image: onepanel/dl:v0.20.0
+          command:
+            - sh
+            - '-c'
+          env:
+            - name: ONEPANEL_INTERACTIVE_SIDECAR
+              value: 'true'
+          args:
+            # Read logs from /mnt/output - this directory is auto-mounted from volumeMounts
+            - tensorboard --logdir /mnt/output/tensorboard
+          ports:
+            - containerPort: 6006
+              name: tensorboard
+  volumeClaimTemplates:
+    # Provision volumes for storing data and output
+    - metadata:
+        name: data
+      spec:
+        accessModes: [ "ReadWriteOnce" ]
+        resources:
+          requests:
+            storage: 2Gi
+    - metadata:
+        name: output
+      spec:
+        accessModes: [ "ReadWriteOnce" ]
+        resources:
+          requests:
+            storage: 2Gi

db/yaml/workflows/tensorflow-mnist-training/20200605090535.yaml

@@ -1,76 +1,85 @@
-# source: https://github.com/onepanelio/templates/blob/master/workflows/tensorflow-mnist-training/template.yaml
-entrypoint: main
-arguments:
-  parameters:
-    - name: source
-      value: https://github.com/onepanelio/tensorflow-examples.git
-    - name: command
-      value: "python mnist/main.py --epochs=5"
-volumeClaimTemplates:
-  - metadata:
-      name: data
-    spec:
-      accessModes: [ "ReadWriteOnce" ]
-      resources:
-        requests:
-          storage: 2Gi
-  - metadata:
-      name: output
-    spec:
-      accessModes: [ "ReadWriteOnce" ]
-      resources:
-        requests:
-          storage: 2Gi
-templates:
-  - name: main
-    dag:
-      tasks:
-        - name: train-model
-          template: pytorch
-  # Uncomment section below to send metrics to Slack
-  #      - name: notify-in-slack
-  #        dependencies: [train-model]
-  #        template: slack-notify-success
-  #        arguments:
-  #          parameters:
-  #          - name: status
-  #            value: "{{tasks.train-model.status}}"
-  #          artifacts:
-  #          - name: metrics
-  #            from: "{{tasks.train-model.outputs.artifacts.sys-metrics}}"
-  - name: pytorch
-    inputs:
-      artifacts:
-        - name: src
-          path: /mnt/src
-          git:
-            repo: "{{workflow.parameters.source}}"
-    outputs:
-      artifacts:
-        - name: model
-          path: /mnt/output
-          optional: true
-          archive:
-            none: {}
-    container:
-      image: tensorflow/tensorflow:latest
-      command: [sh,-c]
-      args: ["{{workflow.parameters.command}}"]
-      workingDir: /mnt/src
-      volumeMounts:
-        - name: data
-          mountPath: /mnt/data
-        - name: output
-          mountPath: /mnt/output
-  - name: slack-notify-success
-    container:
-      image: technosophos/slack-notify
-      command: [sh,-c]
-      args: ['SLACK_USERNAME=Worker SLACK_TITLE="{{workflow.name}} {{inputs.parameters.status}}" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE=$(cat /tmp/metrics.json)} ./slack-notify']
-    inputs:
-      parameters:
-        - name: status
-      artifacts:
-        - name: metrics
-          path: /tmp/metrics.json
-          optional: true
+metadata:
+  name: "TensorFlow Training"
+  kind: Workflow
+  version: 20200605090535
+  action: create
+  source: "https://github.com/onepanelio/templates/blob/master/workflows/tensorflow-mnist-training/template.yaml"
+  labels:
+    "created-by": "system"
+    framework: tensorflow
+spec:
+  entrypoint: main
+  arguments:
+    parameters:
+      - name: source
+        value: https://github.com/onepanelio/tensorflow-examples.git
+      - name: command
+        value: "python mnist/main.py --epochs=5"
+  volumeClaimTemplates:
+    - metadata:
+        name: data
+      spec:
+        accessModes: [ "ReadWriteOnce" ]
+        resources:
+          requests:
+            storage: 2Gi
+    - metadata:
+        name: output
+      spec:
+        accessModes: [ "ReadWriteOnce" ]
+        resources:
+          requests:
+            storage: 2Gi
+  templates:
+    - name: main
+      dag:
+        tasks:
+          - name: train-model
+            template: pytorch
+    # Uncomment section below to send metrics to Slack
+    #      - name: notify-in-slack
+    #        dependencies: [train-model]
+    #        template: slack-notify-success
+    #        arguments:
+    #          parameters:
+    #          - name: status
+    #            value: "{{tasks.train-model.status}}"
+    #          artifacts:
+    #          - name: metrics
+    #            from: "{{tasks.train-model.outputs.artifacts.sys-metrics}}"
+    - name: pytorch
+      inputs:
+        artifacts:
+          - name: src
+            path: /mnt/src
+            git:
+              repo: "{{workflow.parameters.source}}"
+      outputs:
+        artifacts:
+          - name: model
+            path: /mnt/output
+            optional: true
+            archive:
+              none: {}
+      container:
+        image: tensorflow/tensorflow:latest
+        command: [sh,-c]
+        args: ["{{workflow.parameters.command}}"]
+        workingDir: /mnt/src
+        volumeMounts:
+          - name: data
+            mountPath: /mnt/data
+          - name: output
+            mountPath: /mnt/output
+    - name: slack-notify-success
+      container:
+        image: technosophos/slack-notify
+        command: [sh,-c]
+        args: ['SLACK_USERNAME=Worker SLACK_TITLE="{{workflow.name}} {{inputs.parameters.status}}" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE=$(cat /tmp/metrics.json)} ./slack-notify']
+      inputs:
+        parameters:
+          - name: status
+        artifacts:
+          - name: metrics
+            path: /tmp/metrics.json
+            optional: true

db/yaml/workflows/tensorflow-mnist-training/20201209124226.yaml

@@ -1,71 +1,80 @@
-# source: https://github.com/onepanelio/templates/blob/master/workflows/tensorflow-mnist-training/template.yaml
-arguments:
-  parameters:
-    - name: epochs
-      value: '10'
-entrypoint: main
-templates:
-  - name: main
-    dag:
-      tasks:
-        - name: train-model
-          template: tf-dense
-  - name: tf-dense
-    script:
-      image: tensorflow/tensorflow:2.3.0
-      command:
-        - python
-        - '-u'
-      source: |
-        import tensorflow as tf
-        import datetime
-        mnist = tf.keras.datasets.mnist
-        (x_train, y_train),(x_test, y_test) = mnist.load_data()
-        x_train, x_test = x_train / 255.0, x_test / 255.0
-        def create_model():
-          return tf.keras.models.Sequential([
-            tf.keras.layers.Flatten(input_shape=(28, 28)),
-            tf.keras.layers.Dense(512, activation='relu'),
-            tf.keras.layers.Dropout(0.2),
-            tf.keras.layers.Dense(10, activation='softmax')
-          ])
-        model = create_model()
-        model.compile(optimizer='adam',
-                      loss='sparse_categorical_crossentropy',
-                      metrics=['accuracy'])
-        # Write logs to /mnt/output
-        log_dir = "/mnt/output/logs/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
-        tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
-        history = model.fit(x=x_train,
-                  y=y_train,
-                  epochs={{workflow.parameters.epochs}},
-                  validation_data=(x_test, y_test),
-                  callbacks=[tensorboard_callback])
-      volumeMounts:
-        # TensorBoard sidecar will automatically mount this volume
-        - name: output
-          mountPath: /mnt/output
-    sidecars:
-      - name: tensorboard
-        image: 'tensorflow/tensorflow:2.3.0'
+metadata:
+  name: "TensorFlow Training"
+  kind: Workflow
+  version: 20201209124226
+  action: update
+  source: "https://github.com/onepanelio/templates/blob/master/workflows/tensorflow-mnist-training/template.yaml"
+  labels:
+    "created-by": "system"
+    framework: tensorflow
+spec:
+  arguments:
+    parameters:
+      - name: epochs
+        value: '10'
+  entrypoint: main
+  templates:
+    - name: main
+      dag:
+        tasks:
+          - name: train-model
+            template: tf-dense
+    - name: tf-dense
+      script:
+        image: tensorflow/tensorflow:2.3.0
         command:
-          - sh
-          - '-c'
-        env:
-          - name: ONEPANEL_INTERACTIVE_SIDECAR
-            value: 'true'
-        args:
-          # Read logs from /mnt/output - this directory is auto-mounted from volumeMounts
-          - tensorboard --logdir /mnt/output/
-        ports:
-          - containerPort: 6006
-            name: tensorboard
-volumeClaimTemplates:
-  # Provision a volume that can be shared between main container and TensorBoard side car
-  - metadata:
-      name: output
-    spec:
-      accessModes: [ "ReadWriteOnce" ]
-      resources:
-        requests:
-          storage: 2Gi
+          - python
+          - '-u'
+        source: |
+          import tensorflow as tf
+          import datetime
+          mnist = tf.keras.datasets.mnist
+          (x_train, y_train),(x_test, y_test) = mnist.load_data()
+          x_train, x_test = x_train / 255.0, x_test / 255.0
+          def create_model():
+            return tf.keras.models.Sequential([
+              tf.keras.layers.Flatten(input_shape=(28, 28)),
+              tf.keras.layers.Dense(512, activation='relu'),
+              tf.keras.layers.Dropout(0.2),
+              tf.keras.layers.Dense(10, activation='softmax')
+            ])
+          model = create_model()
+          model.compile(optimizer='adam',
+                        loss='sparse_categorical_crossentropy',
+                        metrics=['accuracy'])
+          # Write logs to /mnt/output
+          log_dir = "/mnt/output/logs/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
+          tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
+          history = model.fit(x=x_train,
+                    y=y_train,
+                    epochs={{workflow.parameters.epochs}},
+                    validation_data=(x_test, y_test),
+                    callbacks=[tensorboard_callback])
+        volumeMounts:
+          # TensorBoard sidecar will automatically mount this volume
+          - name: output
+            mountPath: /mnt/output
+      sidecars:
+        - name: tensorboard
+          image: 'tensorflow/tensorflow:2.3.0'
+          command:
+            - sh
+            - '-c'
+          env:
+            - name: ONEPANEL_INTERACTIVE_SIDECAR
+              value: 'true'
+          args:
+            # Read logs from /mnt/output - this directory is auto-mounted from volumeMounts
+            - tensorboard --logdir /mnt/output/
+          ports:
+            - containerPort: 6006
+              name: tensorboard
+  volumeClaimTemplates:
+    # Provision a volume that can be shared between main container and TensorBoard side car
+    - metadata:
+        name: output
+      spec:
+        accessModes: [ "ReadWriteOnce" ]
+        resources:
+          requests:
+            storage: 2Gi

db/yaml/workflows/tensorflow-mnist-training/20201223062947.yaml

@@ -1,118 +1,127 @@
-# source: https://github.com/onepanelio/templates/blob/master/workflows/tensorflow-mnist-training/
-arguments:
-  parameters:
-    - name: epochs
-      value: '10'
-    - displayName: Node pool
-      hint: Name of node pool or group to run this workflow task
-      type: select.nodepool
-      name: sys-node-pool
-      value: {{.DefaultNodePoolOption}}
-      visibility: public
-      required: true
-entrypoint: main
-templates:
-  - name: main
-    dag:
-      tasks:
-        - name: train-model
-          template: train-model
-  - name: train-model
-    # Indicates that we want to push files in /mnt/output to object storage
-    outputs:
-      artifacts:
-        - name: output
-          path: /mnt/output
-          optional: true
-    script:
-      image: onepanel/dl:0.17.0
-      command:
-        - python
-        - '-u'
-      source: |
-        import json
-        import tensorflow as tf
-
-        mnist = tf.keras.datasets.mnist
-
-        (x_train, y_train),(x_test, y_test) = mnist.load_data()
-        x_train, x_test = x_train / 255.0, x_test / 255.0
-        x_train = x_train[..., tf.newaxis]
-        x_test = x_test[..., tf.newaxis]
-
-        model = tf.keras.Sequential([
-          tf.keras.layers.Conv2D(filters=32, kernel_size=5, activation='relu'),
-          tf.keras.layers.MaxPool2D(pool_size=2),
-          tf.keras.layers.Conv2D(filters=64, kernel_size=5, activation='relu'),
-          tf.keras.layers.MaxPool2D(pool_size=2),
-          tf.keras.layers.Flatten(),
-          tf.keras.layers.Dense(units=124, activation='relu'),
-          tf.keras.layers.Dropout(rate=0.75),
-          tf.keras.layers.Dense(units=10, activation='softmax')
-        ])
-        model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.001),
-                      loss='sparse_categorical_crossentropy',
-                      metrics=['accuracy'])
-
-        # Write TensorBoard logs to /mnt/output
-        log_dir = '/mnt/output/tensorboard/'
-        tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
-
-        model.fit(x=x_train,
-                  y=y_train,
-                  epochs={{workflow.parameters.epochs}},
-                  validation_data=(x_test, y_test),
-                  callbacks=[tensorboard_callback])
-
-        # Store metrics for this task
-        loss, accuracy = model.evaluate(x_test, y_test)
-        metrics = [
-          {'name': 'accuracy', 'value': accuracy},
-          {'name': 'loss', 'value': loss}
-        ]
-        with open('/tmp/sys-metrics.json', 'w') as f:
-          json.dump(metrics, f)
-
-        # Save model
-        model.save('/mnt/output/model.h5')
-      volumeMounts:
-        # TensorBoard sidecar will automatically mount these volumes
-        # The `data` volume is mounted to support Keras datasets
-        # The `output` volume is mounted to save model output and share TensorBoard logs
-        - name: data
-          mountPath: /home/root/.keras/datasets
-        - name: output
-          mountPath: /mnt/output
-    nodeSelector:
-      beta.kubernetes.io/instance-type: '{{workflow.parameters.sys-node-pool}}'
-    sidecars:
-      - name: tensorboard
-        image: tensorflow/tensorflow:2.3.0
+metadata:
+  name: "TensorFlow Training"
+  kind: Workflow
+  version: 20201223062947
+  action: update
+  source: "https://github.com/onepanelio/templates/blob/master/workflows/tensorflow-mnist-training/"
+  labels:
+    "created-by": "system"
+    framework: tensorflow
+spec:
+  arguments:
+    parameters:
+      - name: epochs
+        value: '10'
+      - displayName: Node pool
+        hint: Name of node pool or group to run this workflow task
+        type: select.nodepool
+        name: sys-node-pool
+        value: "{{.DefaultNodePoolOption}}"
+        visibility: public
+        required: true
+  entrypoint: main
+  templates:
+    - name: main
+      dag:
+        tasks:
+          - name: train-model
+            template: train-model
+    - name: train-model
+      # Indicates that we want to push files in /mnt/output to object storage
+      outputs:
+        artifacts:
+          - name: output
+            path: /mnt/output
+            optional: true
+      script:
+        image: onepanel/dl:0.17.0
         command:
-          - sh
-          - '-c'
-        env:
-          - name: ONEPANEL_INTERACTIVE_SIDECAR
-            value: 'true'
-        args:
-          # Read logs from /mnt/output - this directory is auto-mounted from volumeMounts
-          - tensorboard --logdir /mnt/output/tensorboard
-        ports:
-          - containerPort: 6006
-            name: tensorboard
-volumeClaimTemplates:
-  # Provision volumes for storing data and output
-  - metadata:
-      name: data
-    spec:
-      accessModes: [ "ReadWriteOnce" ]
-      resources:
-        requests:
-          storage: 2Gi
-  - metadata:
-      name: output
-    spec:
-      accessModes: [ "ReadWriteOnce" ]
-      resources:
-        requests:
-          storage: 2Gi
+          - python
+          - '-u'
+        source: |
+          import json
+          import tensorflow as tf
+
+          mnist = tf.keras.datasets.mnist
+
+          (x_train, y_train),(x_test, y_test) = mnist.load_data()
+          x_train, x_test = x_train / 255.0, x_test / 255.0
+          x_train = x_train[..., tf.newaxis]
+          x_test = x_test[..., tf.newaxis]
+
+          model = tf.keras.Sequential([
+            tf.keras.layers.Conv2D(filters=32, kernel_size=5, activation='relu'),
+            tf.keras.layers.MaxPool2D(pool_size=2),
+            tf.keras.layers.Conv2D(filters=64, kernel_size=5, activation='relu'),
+            tf.keras.layers.MaxPool2D(pool_size=2),
+            tf.keras.layers.Flatten(),
+            tf.keras.layers.Dense(units=124, activation='relu'),
+            tf.keras.layers.Dropout(rate=0.75),
+            tf.keras.layers.Dense(units=10, activation='softmax')
+          ])
+          model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.001),
+                        loss='sparse_categorical_crossentropy',
+                        metrics=['accuracy'])
+
+          # Write TensorBoard logs to /mnt/output
+          log_dir = '/mnt/output/tensorboard/'
+          tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
+
+          model.fit(x=x_train,
+                    y=y_train,
+                    epochs={{workflow.parameters.epochs}},
+                    validation_data=(x_test, y_test),
+                    callbacks=[tensorboard_callback])
+
+          # Store metrics for this task
+          loss, accuracy = model.evaluate(x_test, y_test)
+          metrics = [
+            {'name': 'accuracy', 'value': accuracy},
+            {'name': 'loss', 'value': loss}
+          ]
+          with open('/tmp/sys-metrics.json', 'w') as f:
+            json.dump(metrics, f)
+
+          # Save model
+          model.save('/mnt/output/model.h5')
+        volumeMounts:
+          # TensorBoard sidecar will automatically mount these volumes
+          # The `data` volume is mounted to support Keras datasets
+          # The `output` volume is mounted to save model output and share TensorBoard logs
+          - name: data
+            mountPath: /home/root/.keras/datasets
+          - name: output
+            mountPath: /mnt/output
+      nodeSelector:
+        beta.kubernetes.io/instance-type: '{{workflow.parameters.sys-node-pool}}'
+      sidecars:
+        - name: tensorboard
+          image: tensorflow/tensorflow:2.3.0
+          command:
+            - sh
+            - '-c'
+          env:
+            - name: ONEPANEL_INTERACTIVE_SIDECAR
+              value: 'true'
+          args:
+            # Read logs from /mnt/output - this directory is auto-mounted from volumeMounts
+            - tensorboard --logdir /mnt/output/tensorboard
+          ports:
+            - containerPort: 6006
+              name: tensorboard
+  volumeClaimTemplates:
+    # Provision volumes for storing data and output
+    - metadata:
+        name: data
+      spec:
+        accessModes: [ "ReadWriteOnce" ]
+        resources:
+          requests:
+            storage: 2Gi
+    - metadata:
+        name: output
+      spec:
+        accessModes: [ "ReadWriteOnce" ]
+        resources:
+          requests:
+            storage: 2Gi

db/yaml/workflows/tensorflow-mnist-training/20210118175809.yaml

@@ -1,118 +1,127 @@
-# source: https://github.com/onepanelio/templates/blob/master/workflows/tensorflow-mnist-training/
-arguments:
-  parameters:
-    - name: epochs
-      value: '10'
-    - displayName: Node pool
-      hint: Name of node pool or group to run this workflow task
-      type: select.nodepool
-      name: sys-node-pool
-      value: {{.DefaultNodePoolOption}}
-      visibility: public
-      required: true
-entrypoint: main
-templates:
-  - name: main
-    dag:
-      tasks:
-        - name: train-model
-          template: train-model
-  - name: train-model
-    # Indicates that we want to push files in /mnt/output to object storage
-    outputs:
-      artifacts:
-        - name: output
-          path: /mnt/output
-          optional: true
-    script:
-      image: onepanel/dl:0.17.0
-      command:
-        - python
-        - '-u'
-      source: |
-        import json
-        import tensorflow as tf
-
-        mnist = tf.keras.datasets.mnist
-
-        (x_train, y_train),(x_test, y_test) = mnist.load_data()
-        x_train, x_test = x_train / 255.0, x_test / 255.0
-        x_train = x_train[..., tf.newaxis]
-        x_test = x_test[..., tf.newaxis]
-
-        model = tf.keras.Sequential([
-          tf.keras.layers.Conv2D(filters=32, kernel_size=5, activation='relu'),
-          tf.keras.layers.MaxPool2D(pool_size=2),
-          tf.keras.layers.Conv2D(filters=64, kernel_size=5, activation='relu'),
-          tf.keras.layers.MaxPool2D(pool_size=2),
-          tf.keras.layers.Flatten(),
-          tf.keras.layers.Dense(units=124, activation='relu'),
-          tf.keras.layers.Dropout(rate=0.75),
-          tf.keras.layers.Dense(units=10, activation='softmax')
-        ])
-        model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.001),
-                      loss='sparse_categorical_crossentropy',
-                      metrics=['accuracy'])
-
-        # Write TensorBoard logs to /mnt/output
-        log_dir = '/mnt/output/tensorboard/'
-        tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
-
-        model.fit(x=x_train,
-                  y=y_train,
-                  epochs={{workflow.parameters.epochs}},
-                  validation_data=(x_test, y_test),
-                  callbacks=[tensorboard_callback])
-
-        # Store metrics for this task
-        loss, accuracy = model.evaluate(x_test, y_test)
-        metrics = [
-          {'name': 'accuracy', 'value': accuracy},
-          {'name': 'loss', 'value': loss}
-        ]
-        with open('/tmp/sys-metrics.json', 'w') as f:
-          json.dump(metrics, f)
-
-        # Save model
-        model.save('/mnt/output/model.h5')
-      volumeMounts:
-        # TensorBoard sidecar will automatically mount these volumes
-        # The `data` volume is mounted to support Keras datasets
-        # The `output` volume is mounted to save model output and share TensorBoard logs
-        - name: data
-          mountPath: /home/root/.keras/datasets
-        - name: output
-          mountPath: /mnt/output
-    nodeSelector:
-      {{.NodePoolLabel}}: '{{workflow.parameters.sys-node-pool}}'
-    sidecars:
-      - name: tensorboard
+metadata:
+  name: "TensorFlow Training"
+  kind: Workflow
+  version: 20210118175809
+  action: update
+  source: "https://github.com/onepanelio/templates/blob/master/workflows/tensorflow-mnist-training/"
+  labels:
+    "created-by": "system"
+    framework: tensorflow
+spec:
+  arguments:
+    parameters:
+      - name: epochs
+        value: '10'
+      - displayName: Node pool
+        hint: Name of node pool or group to run this workflow task
+        type: select.nodepool
+        name: sys-node-pool
+        value: "{{.DefaultNodePoolOption}}"
+        visibility: public
+        required: true
+  entrypoint: main
+  templates:
+    - name: main
+      dag:
+        tasks:
+          - name: train-model
+            template: train-model
+    - name: train-model
+      # Indicates that we want to push files in /mnt/output to object storage
+      outputs:
+        artifacts:
+          - name: output
+            path: /mnt/output
+            optional: true
+      script:
         image: onepanel/dl:0.17.0
         command:
-          - sh
-          - '-c'
-        env:
-          - name: ONEPANEL_INTERACTIVE_SIDECAR
-            value: 'true'
-        args:
-          # Read logs from /mnt/output - this directory is auto-mounted from volumeMounts
-          - tensorboard --logdir /mnt/output/tensorboard
-        ports:
-          - containerPort: 6006
-            name: tensorboard
-volumeClaimTemplates:
-  # Provision volumes for storing data and output
-  - metadata:
-      name: data
-    spec:
-      accessModes: [ "ReadWriteOnce" ]
-      resources:
-        requests:
-          storage: 2Gi
-  - metadata:
-      name: output
-    spec:
-      accessModes: [ "ReadWriteOnce" ]
-      resources:
-        requests:
-          storage: 2Gi
+          - python
+          - '-u'
+        source: |
+          import json
+          import tensorflow as tf
+
+          mnist = tf.keras.datasets.mnist
+
+          (x_train, y_train),(x_test, y_test) = mnist.load_data()
+          x_train, x_test = x_train / 255.0, x_test / 255.0
+          x_train = x_train[..., tf.newaxis]
+          x_test = x_test[..., tf.newaxis]
+
+          model = tf.keras.Sequential([
+            tf.keras.layers.Conv2D(filters=32, kernel_size=5, activation='relu'),
+            tf.keras.layers.MaxPool2D(pool_size=2),
+            tf.keras.layers.Conv2D(filters=64, kernel_size=5, activation='relu'),
+            tf.keras.layers.MaxPool2D(pool_size=2),
+            tf.keras.layers.Flatten(),
+            tf.keras.layers.Dense(units=124, activation='relu'),
+            tf.keras.layers.Dropout(rate=0.75),
+            tf.keras.layers.Dense(units=10, activation='softmax')
+          ])
+          model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.001),
+                        loss='sparse_categorical_crossentropy',
+                        metrics=['accuracy'])
+
+          # Write TensorBoard logs to /mnt/output
+          log_dir = '/mnt/output/tensorboard/'
+          tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
+
+          model.fit(x=x_train,
+                    y=y_train,
+                    epochs={{workflow.parameters.epochs}},
+                    validation_data=(x_test, y_test),
+                    callbacks=[tensorboard_callback])
+
+          # Store metrics for this task
+          loss, accuracy = model.evaluate(x_test, y_test)
+          metrics = [
+            {'name': 'accuracy', 'value': accuracy},
+            {'name': 'loss', 'value': loss}
+          ]
+          with open('/tmp/sys-metrics.json', 'w') as f:
+            json.dump(metrics, f)
+
+          # Save model
+          model.save('/mnt/output/model.h5')
+        volumeMounts:
+          # TensorBoard sidecar will automatically mount these volumes
+          # The `data` volume is mounted to support Keras datasets
+          # The `output` volume is mounted to save model output and share TensorBoard logs
+          - name: data
+            mountPath: /home/root/.keras/datasets
+          - name: output
+            mountPath: /mnt/output
+      nodeSelector:
+        "{{.NodePoolLabel}}": '{{workflow.parameters.sys-node-pool}}'
+      sidecars:
+        - name: tensorboard
+          image: onepanel/dl:0.17.0
+          command:
+            - sh
+            - '-c'
+          env:
+            - name: ONEPANEL_INTERACTIVE_SIDECAR
+              value: 'true'
+          args:
+            # Read logs from /mnt/output - this directory is auto-mounted from volumeMounts
+            - tensorboard --logdir /mnt/output/tensorboard
+          ports:
+            - containerPort: 6006
+              name: tensorboard
+  volumeClaimTemplates:
+    # Provision volumes for storing data and output
+    - metadata:
+        name: data
+      spec:
+        accessModes: [ "ReadWriteOnce" ]
+        resources:
+          requests:
+            storage: 2Gi
+    - metadata:
+        name: output
+      spec:
+        accessModes: [ "ReadWriteOnce" ]
+        resources:
+          requests:
+            storage: 2Gi

db/yaml/workflows/tensorflow-mnist-training/20210323175655.yaml

@@ -1,118 +1,127 @@
-# source: https://github.com/onepanelio/templates/blob/master/workflows/tensorflow-mnist-training/
-arguments:
-  parameters:
-    - name: epochs
-      value: '10'
-    - displayName: Node pool
-      hint: Name of node pool or group to run this workflow task
-      type: select.nodepool
-      name: sys-node-pool
-      value: {{.DefaultNodePoolOption}}
-      visibility: public
-      required: true
-entrypoint: main
-templates:
-  - name: main
-    dag:
-      tasks:
-        - name: train-model
-          template: train-model
-  - name: train-model
-    # Indicates that we want to push files in /mnt/output to object storage
-    outputs:
-      artifacts:
-        - name: output
-          path: /mnt/output
-          optional: true
-    script:
-      image: onepanel/dl:v0.20.0
-      command:
-        - python
-        - '-u'
-      source: |
-        import json
-        import tensorflow as tf
-
-        mnist = tf.keras.datasets.mnist
-
-        (x_train, y_train),(x_test, y_test) = mnist.load_data()
-        x_train, x_test = x_train / 255.0, x_test / 255.0
-        x_train = x_train[..., tf.newaxis]
-        x_test = x_test[..., tf.newaxis]
-
-        model = tf.keras.Sequential([
-          tf.keras.layers.Conv2D(filters=32, kernel_size=5, activation='relu'),
-          tf.keras.layers.MaxPool2D(pool_size=2),
-          tf.keras.layers.Conv2D(filters=64, kernel_size=5, activation='relu'),
-          tf.keras.layers.MaxPool2D(pool_size=2),
-          tf.keras.layers.Flatten(),
-          tf.keras.layers.Dense(units=124, activation='relu'),
-          tf.keras.layers.Dropout(rate=0.75),
-          tf.keras.layers.Dense(units=10, activation='softmax')
-        ])
-        model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.001),
-                      loss='sparse_categorical_crossentropy',
-                      metrics=['accuracy'])
-
-        # Write TensorBoard logs to /mnt/output
-        log_dir = '/mnt/output/tensorboard/'
-        tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
-
-        model.fit(x=x_train,
-                  y=y_train,
-                  epochs={{workflow.parameters.epochs}},
-                  validation_data=(x_test, y_test),
-                  callbacks=[tensorboard_callback])
-
-        # Store metrics for this task
-        loss, accuracy = model.evaluate(x_test, y_test)
-        metrics = [
-          {'name': 'accuracy', 'value': accuracy},
-          {'name': 'loss', 'value': loss}
-        ]
-        with open('/mnt/tmp/sys-metrics.json', 'w') as f:
-          json.dump(metrics, f)
-
-        # Save model
-        model.save('/mnt/output/model.h5')
-      volumeMounts:
-        # TensorBoard sidecar will automatically mount these volumes
-        # The `data` volume is mounted to support Keras datasets
-        # The `output` volume is mounted to save model output and share TensorBoard logs
-        - name: data
-          mountPath: /home/root/.keras/datasets
-        - name: output
-          mountPath: /mnt/output
-    nodeSelector:
-        {{.NodePoolLabel}}: '{{workflow.parameters.sys-node-pool}}'
-    sidecars:
-      - name: tensorboard
+metadata:
+  name: "TensorFlow Training"
+  kind: Workflow
+  version: 20210323175655
+  action: update
+  source: "https://github.com/onepanelio/templates/blob/master/workflows/tensorflow-mnist-training/"
+  labels:
+    "created-by": "system"
+    framework: tensorflow
+spec:
+  arguments:
+    parameters:
+      - name: epochs
+        value: '10'
+      - displayName: Node pool
+        hint: Name of node pool or group to run this workflow task
+        type: select.nodepool
+        name: sys-node-pool
+        value: "{{.DefaultNodePoolOption}}"
+        visibility: public
+        required: true
+  entrypoint: main
+  templates:
+    - name: main
+      dag:
+        tasks:
+          - name: train-model
+            template: train-model
+    - name: train-model
+      # Indicates that we want to push files in /mnt/output to object storage
+      outputs:
+        artifacts:
+          - name: output
+            path: /mnt/output
+            optional: true
+      script:
         image: onepanel/dl:v0.20.0
         command:
-          - sh
-          - '-c'
-        env:
-          - name: ONEPANEL_INTERACTIVE_SIDECAR
-            value: 'true'
-        args:
-          # Read logs from /mnt/output - this directory is auto-mounted from volumeMounts
-          - tensorboard --logdir /mnt/output/tensorboard
-        ports:
-          - containerPort: 6006
-            name: tensorboard
-volumeClaimTemplates:
-  # Provision volumes for storing data and output
-  - metadata:
-      name: data
-    spec:
-      accessModes: [ "ReadWriteOnce" ]
-      resources:
-        requests:
-          storage: 2Gi
-  - metadata:
-      name: output
-    spec:
-      accessModes: [ "ReadWriteOnce" ]
-      resources:
-        requests:
-          storage: 2Gi
+          - python
+          - '-u'
+        source: |
+          import json
+          import tensorflow as tf
+
+          mnist = tf.keras.datasets.mnist
+
+          (x_train, y_train),(x_test, y_test) = mnist.load_data()
+          x_train, x_test = x_train / 255.0, x_test / 255.0
+          x_train = x_train[..., tf.newaxis]
+          x_test = x_test[..., tf.newaxis]
+
+          model = tf.keras.Sequential([
+            tf.keras.layers.Conv2D(filters=32, kernel_size=5, activation='relu'),
+            tf.keras.layers.MaxPool2D(pool_size=2),
+            tf.keras.layers.Conv2D(filters=64, kernel_size=5, activation='relu'),
+            tf.keras.layers.MaxPool2D(pool_size=2),
+            tf.keras.layers.Flatten(),
+            tf.keras.layers.Dense(units=124, activation='relu'),
+            tf.keras.layers.Dropout(rate=0.75),
+            tf.keras.layers.Dense(units=10, activation='softmax')
+          ])
+          model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.001),
+                        loss='sparse_categorical_crossentropy',
+                        metrics=['accuracy'])
+
+          # Write TensorBoard logs to /mnt/output
+          log_dir = '/mnt/output/tensorboard/'
+          tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
+
+          model.fit(x=x_train,
+                    y=y_train,
+                    epochs={{workflow.parameters.epochs}},
+                    validation_data=(x_test, y_test),
+                    callbacks=[tensorboard_callback])
+
+          # Store metrics for this task
+          loss, accuracy = model.evaluate(x_test, y_test)
+          metrics = [
+            {'name': 'accuracy', 'value': accuracy},
+            {'name': 'loss', 'value': loss}
+          ]
+          with open('/mnt/tmp/sys-metrics.json', 'w') as f:
+            json.dump(metrics, f)
+
+          # Save model
+          model.save('/mnt/output/model.h5')
+        volumeMounts:
+          # TensorBoard sidecar will automatically mount these volumes
+          # The `data` volume is mounted to support Keras datasets
+          # The `output` volume is mounted to save model output and share TensorBoard logs
+          - name: data
+            mountPath: /home/root/.keras/datasets
+          - name: output
+            mountPath: /mnt/output
+      nodeSelector:
+          "{{.NodePoolLabel}}": '{{workflow.parameters.sys-node-pool}}'
+      sidecars:
+        - name: tensorboard
+          image: onepanel/dl:v0.20.0
+          command:
+            - sh
+            - '-c'
+          env:
+            - name: ONEPANEL_INTERACTIVE_SIDECAR
+              value: 'true'
+          args:
+            # Read logs from /mnt/output - this directory is auto-mounted from volumeMounts
+            - tensorboard --logdir /mnt/output/tensorboard
+          ports:
+            - containerPort: 6006
+              name: tensorboard
+  volumeClaimTemplates:
+    # Provision volumes for storing data and output
+    - metadata:
+        name: data
+      spec:
+        accessModes: [ "ReadWriteOnce" ]
+        resources:
+          requests:
+            storage: 2Gi
+    - metadata:
+        name: output
+      spec:
+        accessModes: [ "ReadWriteOnce" ]
+        resources:
+          requests:
+            storage: 2Gi

db/yaml/workflows/tf-object-detection-training/20200812104328.yaml

@@ -0,0 +1,221 @@
+metadata:
+  name: "TF Object Detection Training"
+  kind: Workflow
+  version: 20200812104328
+  action: create
+  source: "https://github.com/onepanelio/templates/blob/master/workflows/tf-object-detection-training/"
+  labels:
+    "created-by": "system"
+    "used-by": "cvat"
+spec:
+  arguments:
+    parameters:
+      - name: source
+        value: https://github.com/tensorflow/models.git
+        displayName: Model source code
+        type: hidden
+        visibility: private
+
+      - name: trainingsource
+        value: https://github.com/onepanelio/cvat-training.git
+        type: hidden
+        visibility: private
+
+      - name: revision
+        value: v1.13.0
+        type: hidden
+        visibility: private
+
+      - name: sys-annotation-path
+        value: annotation-dump/sample_dataset
+        displayName: Dataset path
+        hint: Path to annotated data in default object storage (i.e S3). In CVAT, this parameter will be pre-populated.
+
+      - name: sys-output-path
+        value: workflow-data/output/sample_output
+        hint: Path to store output artifacts in default object storage (i.e s3). In CVAT, this parameter will be pre-populated.
+        displayName: Workflow output path
+        visibility: private
+
+      - name: ref-model
+        value: frcnn-res50-coco
+        displayName: Model
+        hint: TF Detection API's model to use for training.
+        type: select.select
+        visibility: public
+        options:
+          - name: 'Faster RCNN-ResNet 101-COCO'
+            value: frcnn-res101-coco
+          - name: 'Faster RCNN-ResNet 101-Low Proposal-COCO'
+            value: frcnn-res101-low
+          - name: 'Faster RCNN-ResNet 50-COCO'
+            value: frcnn-res50-coco
+          - name: 'Faster RCNN-NAS-COCO'
+            value: frcnn-nas-coco
+          - name: 'SSD MobileNet V1-COCO'
+            value: ssd-mobilenet-v1-coco2
+          - name: 'SSD MobileNet V2-COCO'
+            value: ssd-mobilenet-v2-coco
+          - name: 'SSDLite MobileNet-COCO'
+            value: ssdlite-mobilenet-coco
+
+      - name: extras
+        value: |-
+          epochs=1000
+        displayName: Hyperparameters
+        visibility: public
+        type: textarea.textarea
+        hint: "Please refer to our <a href='https://docs.onepanel.ai/docs/getting-started/use-cases/computervision/annotation/cvat/cvat_annotation_model#arguments-optional' target='_blank'>documentation</a> for more information on parameters. Number of classes will be automatically populated if you had 'sys-num-classes' parameter in a workflow."
+
+      - name: sys-finetune-checkpoint
+        value: ''
+        hint: Select the last fine-tune checkpoint for this model. It may take up to 5 minutes for a recent checkpoint show here. Leave empty if this is the first time you're training this model.
+        displayName: Checkpoint path
+        visibility: public
+
+      - name: sys-num-classes
+        value: '81'
+        hint: Number of classes
+        displayName: Number of classes
+        visibility: private
+
+      - name: tf-image
+        value: tensorflow/tensorflow:1.13.1-py3
+        type: select.select
+        displayName: Select TensorFlow image
+        visibility: public
+        hint: Select the GPU image if you are running on a GPU node pool
+        options:
+          - name: 'TensorFlow 1.13.1 CPU Image'
+            value: 'tensorflow/tensorflow:1.13.1-py3'
+          - name: 'TensorFlow 1.13.1 GPU Image'
+            value: 'tensorflow/tensorflow:1.13.1-gpu-py3'
+
+      - displayName: Node pool
+        hint: Name of node pool or group to run this workflow task
+        type: select.select
+        name: sys-node-pool
+        value: Standard_D4s_v3
+        visibility: public
+        required: true
+        options:
+          - name: 'CPU: 2, RAM: 8GB'
+            value: Standard_D2s_v3
+          - name: 'CPU: 4, RAM: 16GB'
+            value: Standard_D4s_v3
+          - name: 'GPU: 1xK80, CPU: 6, RAM: 56GB'
+            value: Standard_NC6
+      - name: dump-format
+        value: cvat_tfrecord
+        visibility: public
+  entrypoint: main
+  templates:
+    - dag:
+        tasks:
+          - name: train-model
+            template: tensorflow
+      # Uncomment the lines below if you want to send Slack notifications
+      #    - arguments:
+      #        artifacts:
+      #        - from: '{{tasks.train-model.outputs.artifacts.sys-metrics}}'
+      #          name: metrics
+      #        parameters:
+      #        - name: status
+      #          value: '{{tasks.train-model.status}}'
+      #      dependencies:
+      #      - train-model
+      #      name: notify-in-slack
+      #      template: slack-notify-success
+      name: main
+    - container:
+        args:
+          - |
+            apt-get update && \
+            apt-get install -y python3-pip git wget unzip libglib2.0-0 libsm6 libxext6 libxrender-dev && \
+            pip install pillow lxml Cython contextlib2 jupyter matplotlib numpy scipy boto3 pycocotools pyyaml google-cloud-storage && \
+            cd /mnt/src/tf/research && \
+            export PYTHONPATH=$PYTHONPATH:` + "`pwd`:`pwd`/slim" + ` && \
+            cd /mnt/src/train && \
+            python convert_workflow.py \
+              --extras="{{workflow.parameters.extras}}" \
+              --model="{{workflow.parameters.ref-model}}" \
+              --num_classes="{{workflow.parameters.sys-num-classes}}" \
+              --sys_finetune_checkpoint={{workflow.parameters.sys-finetune-checkpoint}}
+        command:
+          - sh
+          - -c
+        image: '{{workflow.parameters.tf-image}}'
+        volumeMounts:
+          - mountPath: /mnt/data
+            name: data
+          - mountPath: /mnt/output
+            name: output
+        workingDir: /mnt/src
+      nodeSelector:
+        beta.kubernetes.io/instance-type: '{{workflow.parameters.sys-node-pool}}'
+      inputs:
+        artifacts:
+          - name: data
+            path: /mnt/data/datasets/
+            "{{.ArtifactRepositoryType}}":
+              key: '{{workflow.namespace}}/{{workflow.parameters.sys-annotation-path}}'
+          - name: models
+            path: /mnt/data/models/
+            optional: true
+            "{{.ArtifactRepositoryType}}":
+              key: '{{workflow.namespace}}/{{workflow.parameters.sys-finetune-checkpoint}}'
+          - git:
+              repo: '{{workflow.parameters.source}}'
+              revision: '{{workflow.parameters.revision}}'
+            name: src
+            path: /mnt/src/tf
+          - git:
+              repo: '{{workflow.parameters.trainingsource}}'
+              revision: 'optional-artifacts'
+            name: tsrc
+            path: /mnt/src/train
+      name: tensorflow
+      outputs:
+        artifacts:
+          - name: model
+            optional: true
+            path: /mnt/output
+            "{{.ArtifactRepositoryType}}":
+              key: '{{workflow.namespace}}/{{workflow.parameters.sys-output-path}}'
+  # Uncomment the lines below if you want to send Slack notifications
+  #- container:
+  #    args:
+  #    - SLACK_USERNAME=Onepanel SLACK_TITLE="{{workflow.name}} {{inputs.parameters.status}}"
+  #      SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd
+  #      SLACK_MESSAGE=$(cat /tmp/metrics.json)} ./slack-notify
+  #    command:
+  #    - sh
+  #    - -c
+  #    image: technosophos/slack-notify
+  #  inputs:
+  #    artifacts:
+  #    - name: metrics
+  #      optional: true
+  #      path: /tmp/metrics.json
+  #    parameters:
+  #    - name: status
+  #  name: slack-notify-success
+  volumeClaimTemplates:
+    - metadata:
+        creationTimestamp: null
+        name: data
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi
+    - metadata:
+        creationTimestamp: null
+        name: output
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi

db/yaml/workflows/tf-object-detection-training/20200824101019.yaml

@@ -0,0 +1,222 @@
+metadata:
+  name: "TF Object Detection Training"
+  kind: Workflow
+  version: 20200824101019
+  action: update
+  source: "https://github.com/onepanelio/templates/blob/master/workflows/tf-object-detection-training/"
+  labels:
+    "created-by": "system"
+    "used-by": "cvat"
+spec:
+  arguments:
+    parameters:
+      - name: source
+        value: https://github.com/tensorflow/models.git
+        displayName: Model source code
+        type: hidden
+        visibility: private
+
+      - name: trainingsource
+        value: https://github.com/onepanelio/cvat-training.git
+        type: hidden
+        visibility: private
+
+      - name: revision
+        value: v1.13.0
+        type: hidden
+        visibility: private
+
+      - name: cvat-annotation-path
+        value: annotation-dump/sample_dataset
+        displayName: Dataset path
+        hint: Path to annotated data in default object storage (i.e S3). In CVAT, this parameter will be pre-populated.
+        visibility: private
+
+      - name: cvat-output-path
+        value: workflow-data/output/sample_output
+        hint: Path to store output artifacts in default object storage (i.e s3). In CVAT, this parameter will be pre-populated.
+        displayName: Workflow output path
+        visibility: private
+
+      - name: cvat-model
+        value: frcnn-res50-coco
+        displayName: Model
+        hint: TF Detection API's model to use for training.
+        type: select.select
+        visibility: public
+        options:
+          - name: 'Faster RCNN-ResNet 101-COCO'
+            value: frcnn-res101-coco
+          - name: 'Faster RCNN-ResNet 101-Low Proposal-COCO'
+            value: frcnn-res101-low
+          - name: 'Faster RCNN-ResNet 50-COCO'
+            value: frcnn-res50-coco
+          - name: 'Faster RCNN-NAS-COCO'
+            value: frcnn-nas-coco
+          - name: 'SSD MobileNet V1-COCO'
+            value: ssd-mobilenet-v1-coco2
+          - name: 'SSD MobileNet V2-COCO'
+            value: ssd-mobilenet-v2-coco
+          - name: 'SSDLite MobileNet-COCO'
+            value: ssdlite-mobilenet-coco
+
+      - name: hyperparameters
+        value: |-
+          num-steps=10000
+        displayName: Hyperparameters
+        visibility: public
+        type: textarea.textarea
+        hint: "Please refer to our <a href='https://docs.onepanel.ai/docs/getting-started/use-cases/computervision/annotation/cvat/cvat_annotation_model#arguments-optional' target='_blank'>documentation</a> for more information on parameters. Number of classes will be automatically populated if you had 'sys-num-classes' parameter in a workflow."
+
+      - name: cvat-finetune-checkpoint
+        value: ''
+        hint: Select the last fine-tune checkpoint for this model. It may take up to 5 minutes for a recent checkpoint show here. Leave empty if this is the first time you're training this model.
+        displayName: Checkpoint path
+        visibility: public
+
+      - name: cvat-num-classes
+        value: '81'
+        hint: Number of classes
+        displayName: Number of classes
+        visibility: private
+
+      - name: tf-image
+        value: tensorflow/tensorflow:1.13.1-py3
+        type: select.select
+        displayName: Select TensorFlow image
+        visibility: public
+        hint: Select the GPU image if you are running on a GPU node pool
+        options:
+          - name: 'TensorFlow 1.13.1 CPU Image'
+            value: 'tensorflow/tensorflow:1.13.1-py3'
+          - name: 'TensorFlow 1.13.1 GPU Image'
+            value: 'tensorflow/tensorflow:1.13.1-gpu-py3'
+
+      - displayName: Node pool
+        hint: Name of node pool or group to run this workflow task
+        type: select.select
+        name: sys-node-pool
+        value: Standard_D4s_v3
+        visibility: public
+        required: true
+        options:
+          - name: 'CPU: 2, RAM: 8GB'
+            value: Standard_D2s_v3
+          - name: 'CPU: 4, RAM: 16GB'
+            value: Standard_D4s_v3
+          - name: 'GPU: 1xK80, CPU: 6, RAM: 56GB'
+            value: Standard_NC6
+      - name: dump-format
+        value: cvat_tfrecord
+        visibility: public
+  entrypoint: main
+  templates:
+    - dag:
+        tasks:
+          - name: train-model
+            template: tensorflow
+      # Uncomment the lines below if you want to send Slack notifications
+      #    - arguments:
+      #        artifacts:
+      #        - from: '{{tasks.train-model.outputs.artifacts.sys-metrics}}'
+      #          name: metrics
+      #        parameters:
+      #        - name: status
+      #          value: '{{tasks.train-model.status}}'
+      #      dependencies:
+      #      - train-model
+      #      name: notify-in-slack
+      #      template: slack-notify-success
+      name: main
+    - container:
+        args:
+          - |
+            apt-get update && \
+            apt-get install -y python3-pip git wget unzip libglib2.0-0 libsm6 libxext6 libxrender-dev && \
+            pip install pillow lxml Cython contextlib2 jupyter matplotlib numpy scipy boto3 pycocotools pyyaml google-cloud-storage && \
+            cd /mnt/src/tf/research && \
+            export PYTHONPATH=$PYTHONPATH:` + "`pwd`:`pwd`" + `/slim && \
+            cd /mnt/src/train && \
+            python convert_workflow.py \
+              --extras="{{workflow.parameters.hyperparameters}}" \
+              --model="{{workflow.parameters.cvat-model}}" \
+              --num_classes="{{workflow.parameters.cvat-num-classes}}" \
+              --sys_finetune_checkpoint={{workflow.parameters.cvat-finetune-checkpoint}}
+        command:
+          - sh
+          - -c
+        image: '{{workflow.parameters.tf-image}}'
+        volumeMounts:
+          - mountPath: /mnt/data
+            name: data
+          - mountPath: /mnt/output
+            name: output
+        workingDir: /mnt/src
+      nodeSelector:
+        beta.kubernetes.io/instance-type: '{{workflow.parameters.sys-node-pool}}'
+      inputs:
+        artifacts:
+          - name: data
+            path: /mnt/data/datasets/
+            "{{.ArtifactRepositoryType}}":
+              key: '{{workflow.namespace}}/{{workflow.parameters.cvat-annotation-path}}'
+          - name: models
+            path: /mnt/data/models/
+            optional: true
+            "{{.ArtifactRepositoryType}}":
+              key: '{{workflow.namespace}}/{{workflow.parameters.cvat-finetune-checkpoint}}'
+          - git:
+              repo: '{{workflow.parameters.source}}'
+              revision: '{{workflow.parameters.revision}}'
+            name: src
+            path: /mnt/src/tf
+          - git:
+              repo: '{{workflow.parameters.trainingsource}}'
+              revision: 'optional-artifacts'
+            name: tsrc
+            path: /mnt/src/train
+      name: tensorflow
+      outputs:
+        artifacts:
+          - name: model
+            optional: true
+            path: /mnt/output
+            "{{.ArtifactRepositoryType}}":
+              key: '{{workflow.namespace}}/{{workflow.parameters.cvat-output-path}}/{{workflow.name}}'
+  # Uncomment the lines below if you want to send Slack notifications
+  #- container:
+  #    args:
+  #    - SLACK_USERNAME=Onepanel SLACK_TITLE="{{workflow.name}} {{inputs.parameters.status}}"
+  #      SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd
+  #      SLACK_MESSAGE=$(cat /tmp/metrics.json)} ./slack-notify
+  #    command:
+  #    - sh
+  #    - -c
+  #    image: technosophos/slack-notify
+  #  inputs:
+  #    artifacts:
+  #    - name: metrics
+  #      optional: true
+  #      path: /tmp/metrics.json
+  #    parameters:
+  #    - name: status
+  #  name: slack-notify-success
+  volumeClaimTemplates:
+    - metadata:
+        creationTimestamp: null
+        name: data
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi
+    - metadata:
+        creationTimestamp: null
+        name: output
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi

db/yaml/workflows/tf-object-detection-training/20201115134934.yaml

@@ -1,221 +1,231 @@
-entrypoint: main
-arguments:
-  parameters:
-    - name: source
-      value: https://github.com/tensorflow/models.git
-      displayName: Model source code
-      type: hidden
-      visibility: private
+metadata:
+  name: "TF Object Detection Training"
+  kind: Workflow
+  version: 20201115134934
+  action: update
+  source: "https://github.com/onepanelio/templates/blob/master/workflows/tf-object-detection-training/"
+  labels:
+    "created-by": "system"
+    "used-by": "cvat"
+spec:
+  entrypoint: main
+  arguments:
+    parameters:
+      - name: source
+        value: https://github.com/tensorflow/models.git
+        displayName: Model source code
+        type: hidden
+        visibility: private
 
-    - name: trainingsource
-      value: https://github.com/onepanelio/cvat-training.git
-      type: hidden
-      visibility: private
+      - name: trainingsource
+        value: https://github.com/onepanelio/cvat-training.git
+        type: hidden
+        visibility: private
 
-    - name: revision
-      value: v1.13.0
-      type: hidden
-      visibility: private
+      - name: revision
+        value: v1.13.0
+        type: hidden
+        visibility: private
 
-    - name: cvat-annotation-path
-      value: annotation-dump/sample_dataset
-      displayName: Dataset path
-      hint: Path to annotated data in default object storage (i.e S3). In CVAT, this parameter will be pre-populated.
-      visibility: private
+      - name: cvat-annotation-path
+        value: annotation-dump/sample_dataset
+        displayName: Dataset path
+        hint: Path to annotated data in default object storage (i.e S3). In CVAT, this parameter will be pre-populated.
+        visibility: private
 
-    - name: cvat-output-path
-      value: workflow-data/output/sample_output
-      hint: Path to store output artifacts in default object storage (i.e s3). In CVAT, this parameter will be pre-populated.
-      displayName: Workflow output path
-      visibility: private
+      - name: cvat-output-path
+        value: workflow-data/output/sample_output
+        hint: Path to store output artifacts in default object storage (i.e s3). In CVAT, this parameter will be pre-populated.
+        displayName: Workflow output path
+        visibility: private
 
-    - name: cvat-model
-      value: frcnn-res50-coco
-      displayName: Model
-      hint: TF Detection API's model to use for training.
-      type: select.select
-      visibility: public
-      options:
-        - name: 'Faster RCNN-ResNet 101-COCO'
-          value: frcnn-res101-coco
-        - name: 'Faster RCNN-ResNet 101-Low Proposal-COCO'
-          value: frcnn-res101-low
-        - name: 'Faster RCNN-ResNet 50-COCO'
-          value: frcnn-res50-coco
-        - name: 'Faster RCNN-NAS-COCO'
-          value: frcnn-nas-coco
-        - name: 'SSD MobileNet V1-COCO'
-          value: ssd-mobilenet-v1-coco2
-        - name: 'SSD MobileNet V2-COCO'
-          value: ssd-mobilenet-v2-coco
-        - name: 'SSDLite MobileNet-COCO'
-          value: ssdlite-mobilenet-coco
+      - name: cvat-model
+        value: frcnn-res50-coco
+        displayName: Model
+        hint: TF Detection API's model to use for training.
+        type: select.select
+        visibility: public
+        options:
+          - name: 'Faster RCNN-ResNet 101-COCO'
+            value: frcnn-res101-coco
+          - name: 'Faster RCNN-ResNet 101-Low Proposal-COCO'
+            value: frcnn-res101-low
+          - name: 'Faster RCNN-ResNet 50-COCO'
+            value: frcnn-res50-coco
+          - name: 'Faster RCNN-NAS-COCO'
+            value: frcnn-nas-coco
+          - name: 'SSD MobileNet V1-COCO'
+            value: ssd-mobilenet-v1-coco2
+          - name: 'SSD MobileNet V2-COCO'
+            value: ssd-mobilenet-v2-coco
+          - name: 'SSDLite MobileNet-COCO'
+            value: ssdlite-mobilenet-coco
 
-    - name: hyperparameters
-      value: |-
-        num-steps=10000
-      displayName: Hyperparameters
-      visibility: public
-      type: textarea.textarea
-      hint: "Please refer to our <a href='https://docs.onepanel.ai/docs/getting-started/use-cases/computervision/annotation/cvat/cvat_annotation_model#arguments-optional' target='_blank'>documentation</a> for more information on parameters. Number of classes will be automatically populated if you had 'sys-num-classes' parameter in a workflow."
+      - name: hyperparameters
+        value: |-
+          num-steps=10000
+        displayName: Hyperparameters
+        visibility: public
+        type: textarea.textarea
+        hint: "Please refer to our <a href='https://docs.onepanel.ai/docs/getting-started/use-cases/computervision/annotation/cvat/cvat_annotation_model#arguments-optional' target='_blank'>documentation</a> for more information on parameters. Number of classes will be automatically populated if you had 'sys-num-classes' parameter in a workflow."
 
-    - name: cvat-finetune-checkpoint
-      value: ''
-      hint: Select the last fine-tune checkpoint for this model. It may take up to 5 minutes for a recent checkpoint show here. Leave empty if this is the first time you're training this model.
-      displayName: Checkpoint path
-      visibility: public
+      - name: cvat-finetune-checkpoint
+        value: ''
+        hint: Select the last fine-tune checkpoint for this model. It may take up to 5 minutes for a recent checkpoint show here. Leave empty if this is the first time you're training this model.
+        displayName: Checkpoint path
+        visibility: public
 
-    - name: cvat-num-classes
-      value: '81'
-      hint: Number of classes
-      displayName: Number of classes
-      visibility: private
+      - name: cvat-num-classes
+        value: '81'
+        hint: Number of classes
+        displayName: Number of classes
+        visibility: private
 
-    - name: tf-image
-      value: tensorflow/tensorflow:1.13.1-py3
-      type: select.select
-      displayName: Select TensorFlow image
-      visibility: public
-      hint: Select the GPU image if you are running on a GPU node pool
-      options:
-        - name: 'TensorFlow 1.13.1 CPU Image'
-          value: 'tensorflow/tensorflow:1.13.1-py3'
-        - name: 'TensorFlow 1.13.1 GPU Image'
-          value: 'tensorflow/tensorflow:1.13.1-gpu-py3'
+      - name: tf-image
+        value: tensorflow/tensorflow:1.13.1-py3
+        type: select.select
+        displayName: Select TensorFlow image
+        visibility: public
+        hint: Select the GPU image if you are running on a GPU node pool
+        options:
+          - name: 'TensorFlow 1.13.1 CPU Image'
+            value: 'tensorflow/tensorflow:1.13.1-py3'
+          - name: 'TensorFlow 1.13.1 GPU Image'
+            value: 'tensorflow/tensorflow:1.13.1-gpu-py3'
 
-    - displayName: Node pool
-      hint: Name of node pool or group to run this workflow task
-      type: select.select
-      name: sys-node-pool
-      value: Standard_D4s_v3
-      visibility: public
-      required: true
-      options:
-        - name: 'CPU: 2, RAM: 8GB'
-          value: Standard_D2s_v3
-        - name: 'CPU: 4, RAM: 16GB'
-          value: Standard_D4s_v3
-        - name: 'GPU: 1xK80, CPU: 6, RAM: 56GB'
-          value: Standard_NC6
-    - name: dump-format
-      value: cvat_tfrecord
-      visibility: public
-templates:
-  - name: main
-    dag:
-      tasks:
-        - name: train-model
-          template: tensorflow
-    # Uncomment the lines below if you want to send Slack notifications
-    #    - arguments:
-    #        artifacts:
-    #        - from: '{{tasks.train-model.outputs.artifacts.sys-metrics}}'
-    #          name: metrics
-    #        parameters:
-    #        - name: status
-    #          value: '{{tasks.train-model.status}}'
-    #      dependencies:
-    #      - train-model
-    #      name: notify-in-slack
-    #      template: slack-notify-success
-  - name: tensorflow
-    container:
-      args:
-        - |
-          apt-get update && \
-          apt-get install -y python3-pip git wget unzip libglib2.0-0 libsm6 libxext6 libxrender-dev && \
-          pip install pillow lxml Cython contextlib2 jupyter matplotlib numpy scipy boto3 pycocotools pyyaml google-cloud-storage && \
-          cd /mnt/src/tf/research && \
-          export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim && \
-          cd /mnt/src/train && \
-          python convert_workflow.py \
-            --extras="{{workflow.parameters.hyperparameters}}" \
-            --model="{{workflow.parameters.cvat-model}}" \
-            --num_classes="{{workflow.parameters.cvat-num-classes}}" \
-            --sys_finetune_checkpoint={{workflow.parameters.cvat-finetune-checkpoint}}
-      command:
-        - sh
-        - -c
-      image: '{{workflow.parameters.tf-image}}'
-      volumeMounts:
-        - mountPath: /mnt/data
-          name: data
-        - mountPath: /mnt/output
-          name: output
-      workingDir: /mnt/src
-    nodeSelector:
-      beta.kubernetes.io/instance-type: '{{workflow.parameters.sys-node-pool}}'
-    sidecars:
-      - name: tensorboard
-        image: tensorflow/tensorflow:2.3.0
-        command: [sh, -c]
-        tty: true
-        args: ["tensorboard --logdir /mnt/output/"]
-        ports:
-          - containerPort: 6006
-            name: tensorboard
-    inputs:
-      artifacts:
-        - name: data
-          path: /mnt/data/datasets/
-          {{.ArtifactRepositoryType}}:
-            key: '{{workflow.namespace}}/{{workflow.parameters.cvat-annotation-path}}'
-        - name: models
-          path: /mnt/data/models/
-          optional: true
-          {{.ArtifactRepositoryType}}:
-            key: '{{workflow.namespace}}/{{workflow.parameters.cvat-finetune-checkpoint}}'
-        - git:
-            repo: '{{workflow.parameters.source}}'
-            revision: '{{workflow.parameters.revision}}'
-          name: src
-          path: /mnt/src/tf
-        - git:
-            repo: '{{workflow.parameters.trainingsource}}'
-            revision: 'optional-artifacts'
-          name: tsrc
-          path: /mnt/src/train
-    outputs:
-      artifacts:
-        - name: model
-          optional: true
-          path: /mnt/output
-          {{.ArtifactRepositoryType}}:
-            key: '{{workflow.namespace}}/{{workflow.parameters.cvat-output-path}}/{{workflow.name}}'
-# Uncomment the lines below if you want to send Slack notifications
-#- container:
-#    args:
-#    - SLACK_USERNAME=Onepanel SLACK_TITLE="{{workflow.name}} {{inputs.parameters.status}}"
-#      SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd
-#      SLACK_MESSAGE=$(cat /tmp/metrics.json)} ./slack-notify
-#    command:
-#    - sh
-#    - -c
-#    image: technosophos/slack-notify
-#  inputs:
-#    artifacts:
-#    - name: metrics
-#      optional: true
-#      path: /tmp/metrics.json
-#    parameters:
-#    - name: status
-#  name: slack-notify-success
-volumeClaimTemplates:
-  - metadata:
-      creationTimestamp: null
-      name: data
-    spec:
-      accessModes:
-        - ReadWriteOnce
-      resources:
-        requests:
-          storage: 200Gi
-  - metadata:
-      creationTimestamp: null
-      name: output
-    spec:
-      accessModes:
-        - ReadWriteOnce
-      resources:
-        requests:
-          storage: 200Gi
+      - displayName: Node pool
+        hint: Name of node pool or group to run this workflow task
+        type: select.select
+        name: sys-node-pool
+        value: Standard_D4s_v3
+        visibility: public
+        required: true
+        options:
+          - name: 'CPU: 2, RAM: 8GB'
+            value: Standard_D2s_v3
+          - name: 'CPU: 4, RAM: 16GB'
+            value: Standard_D4s_v3
+          - name: 'GPU: 1xK80, CPU: 6, RAM: 56GB'
+            value: Standard_NC6
+      - name: dump-format
+        value: cvat_tfrecord
+        visibility: public
+  templates:
+    - name: main
+      dag:
+        tasks:
+          - name: train-model
+            template: tensorflow
+      # Uncomment the lines below if you want to send Slack notifications
+      #    - arguments:
+      #        artifacts:
+      #        - from: '{{tasks.train-model.outputs.artifacts.sys-metrics}}'
+      #          name: metrics
+      #        parameters:
+      #        - name: status
+      #          value: '{{tasks.train-model.status}}'
+      #      dependencies:
+      #      - train-model
+      #      name: notify-in-slack
+      #      template: slack-notify-success
+    - name: tensorflow
+      container:
+        args:
+          - |
+            apt-get update && \
+            apt-get install -y python3-pip git wget unzip libglib2.0-0 libsm6 libxext6 libxrender-dev && \
+            pip install pillow lxml Cython contextlib2 jupyter matplotlib numpy scipy boto3 pycocotools pyyaml google-cloud-storage && \
+            cd /mnt/src/tf/research && \
+            export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim && \
+            cd /mnt/src/train && \
+            python convert_workflow.py \
+              --extras="{{workflow.parameters.hyperparameters}}" \
+              --model="{{workflow.parameters.cvat-model}}" \
+              --num_classes="{{workflow.parameters.cvat-num-classes}}" \
+              --sys_finetune_checkpoint={{workflow.parameters.cvat-finetune-checkpoint}}
+        command:
+          - sh
+          - -c
+        image: '{{workflow.parameters.tf-image}}'
+        volumeMounts:
+          - mountPath: /mnt/data
+            name: data
+          - mountPath: /mnt/output
+            name: output
+        workingDir: /mnt/src
+      nodeSelector:
+        beta.kubernetes.io/instance-type: '{{workflow.parameters.sys-node-pool}}'
+      sidecars:
+        - name: tensorboard
+          image: tensorflow/tensorflow:2.3.0
+          command: [sh, -c]
+          tty: true
+          args: ["tensorboard --logdir /mnt/output/"]
+          ports:
+            - containerPort: 6006
+              name: tensorboard
+      inputs:
+        artifacts:
+          - name: data
+            path: /mnt/data/datasets/
+            "{{.ArtifactRepositoryType}}":
+              key: '{{workflow.namespace}}/{{workflow.parameters.cvat-annotation-path}}'
+          - name: models
+            path: /mnt/data/models/
+            optional: true
+            "{{.ArtifactRepositoryType}}":
+              key: '{{workflow.namespace}}/{{workflow.parameters.cvat-finetune-checkpoint}}'
+          - git:
+              repo: '{{workflow.parameters.source}}'
+              revision: '{{workflow.parameters.revision}}'
+            name: src
+            path: /mnt/src/tf
+          - git:
+              repo: '{{workflow.parameters.trainingsource}}'
+              revision: 'optional-artifacts'
+            name: tsrc
+            path: /mnt/src/train
+      outputs:
+        artifacts:
+          - name: model
+            optional: true
+            path: /mnt/output
+            "{{.ArtifactRepositoryType}}":
+              key: '{{workflow.namespace}}/{{workflow.parameters.cvat-output-path}}/{{workflow.name}}'
+  # Uncomment the lines below if you want to send Slack notifications
+  #- container:
+  #    args:
+  #    - SLACK_USERNAME=Onepanel SLACK_TITLE="{{workflow.name}} {{inputs.parameters.status}}"
+  #      SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd
+  #      SLACK_MESSAGE=$(cat /tmp/metrics.json)} ./slack-notify
+  #    command:
+  #    - sh
+  #    - -c
+  #    image: technosophos/slack-notify
+  #  inputs:
+  #    artifacts:
+  #    - name: metrics
+  #      optional: true
+  #      path: /tmp/metrics.json
+  #    parameters:
+  #    - name: status
+  #  name: slack-notify-success
+  volumeClaimTemplates:
+    - metadata:
+        creationTimestamp: null
+        name: data
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi
+    - metadata:
+        creationTimestamp: null
+        name: output
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi

db/yaml/workflows/tf-object-detection-training/20201130130433.yaml

@@ -1,221 +1,231 @@
-entrypoint: main
-arguments:
-  parameters:
-    - name: source
-      value: https://github.com/tensorflow/models.git
-      displayName: Model source code
-      type: hidden
-      visibility: private
+metadata:
+  name: "TF Object Detection Training"
+  kind: Workflow
+  version: 20201130130433
+  action: update
+  source: "https://github.com/onepanelio/templates/blob/master/workflows/tf-object-detection-training/"
+  labels:
+    "created-by": "system"
+    "used-by": "cvat"
+spec:
+  entrypoint: main
+  arguments:
+    parameters:
+      - name: source
+        value: https://github.com/tensorflow/models.git
+        displayName: Model source code
+        type: hidden
+        visibility: private
 
-    - name: trainingsource
-      value: https://github.com/onepanelio/cvat-training.git
-      type: hidden
-      visibility: private
+      - name: trainingsource
+        value: https://github.com/onepanelio/cvat-training.git
+        type: hidden
+        visibility: private
 
-    - name: revision
-      value: v1.13.0
-      type: hidden
-      visibility: private
+      - name: revision
+        value: v1.13.0
+        type: hidden
+        visibility: private
 
-    - name: cvat-annotation-path
-      value: annotation-dump/sample_dataset
-      displayName: Dataset path
-      hint: Path to annotated data in default object storage (i.e S3). In CVAT, this parameter will be pre-populated.
-      visibility: private
+      - name: cvat-annotation-path
+        value: annotation-dump/sample_dataset
+        displayName: Dataset path
+        hint: Path to annotated data in default object storage (i.e S3). In CVAT, this parameter will be pre-populated.
+        visibility: private
 
-    - name: cvat-output-path
-      value: workflow-data/output/sample_output
-      hint: Path to store output artifacts in default object storage (i.e s3). In CVAT, this parameter will be pre-populated.
-      displayName: Workflow output path
-      visibility: private
+      - name: cvat-output-path
+        value: workflow-data/output/sample_output
+        hint: Path to store output artifacts in default object storage (i.e s3). In CVAT, this parameter will be pre-populated.
+        displayName: Workflow output path
+        visibility: private
 
-    - name: cvat-model
-      value: frcnn-res50-coco
-      displayName: Model
-      hint: TF Detection API's model to use for training.
-      type: select.select
-      visibility: public
-      options:
-        - name: 'Faster RCNN-ResNet 101-COCO'
-          value: frcnn-res101-coco
-        - name: 'Faster RCNN-ResNet 101-Low Proposal-COCO'
-          value: frcnn-res101-low
-        - name: 'Faster RCNN-ResNet 50-COCO'
-          value: frcnn-res50-coco
-        - name: 'Faster RCNN-NAS-COCO'
-          value: frcnn-nas-coco
-        - name: 'SSD MobileNet V1-COCO'
-          value: ssd-mobilenet-v1-coco2
-        - name: 'SSD MobileNet V2-COCO'
-          value: ssd-mobilenet-v2-coco
-        - name: 'SSDLite MobileNet-COCO'
-          value: ssdlite-mobilenet-coco
+      - name: cvat-model
+        value: frcnn-res50-coco
+        displayName: Model
+        hint: TF Detection API's model to use for training.
+        type: select.select
+        visibility: public
+        options:
+          - name: 'Faster RCNN-ResNet 101-COCO'
+            value: frcnn-res101-coco
+          - name: 'Faster RCNN-ResNet 101-Low Proposal-COCO'
+            value: frcnn-res101-low
+          - name: 'Faster RCNN-ResNet 50-COCO'
+            value: frcnn-res50-coco
+          - name: 'Faster RCNN-NAS-COCO'
+            value: frcnn-nas-coco
+          - name: 'SSD MobileNet V1-COCO'
+            value: ssd-mobilenet-v1-coco2
+          - name: 'SSD MobileNet V2-COCO'
+            value: ssd-mobilenet-v2-coco
+          - name: 'SSDLite MobileNet-COCO'
+            value: ssdlite-mobilenet-coco
 
-    - name: hyperparameters
-      value: |-
-        num-steps=10000
-      displayName: Hyperparameters
-      visibility: public
-      type: textarea.textarea
-      hint: "Please refer to our <a href='https://docs.onepanel.ai/docs/getting-started/use-cases/computervision/annotation/cvat/cvat_annotation_model#arguments-optional' target='_blank'>documentation</a> for more information on parameters. Number of classes will be automatically populated if you had 'sys-num-classes' parameter in a workflow."
+      - name: hyperparameters
+        value: |-
+          num-steps=10000
+        displayName: Hyperparameters
+        visibility: public
+        type: textarea.textarea
+        hint: "Please refer to our <a href='https://docs.onepanel.ai/docs/getting-started/use-cases/computervision/annotation/cvat/cvat_annotation_model#arguments-optional' target='_blank'>documentation</a> for more information on parameters. Number of classes will be automatically populated if you had 'sys-num-classes' parameter in a workflow."
 
-    - name: cvat-finetune-checkpoint
-      value: ''
-      hint: Select the last fine-tune checkpoint for this model. It may take up to 5 minutes for a recent checkpoint show here. Leave empty if this is the first time you're training this model.
-      displayName: Checkpoint path
-      visibility: public
+      - name: cvat-finetune-checkpoint
+        value: ''
+        hint: Select the last fine-tune checkpoint for this model. It may take up to 5 minutes for a recent checkpoint show here. Leave empty if this is the first time you're training this model.
+        displayName: Checkpoint path
+        visibility: public
 
-    - name: cvat-num-classes
-      value: '81'
-      hint: Number of classes
-      displayName: Number of classes
-      visibility: private
+      - name: cvat-num-classes
+        value: '81'
+        hint: Number of classes
+        displayName: Number of classes
+        visibility: private
 
-    - name: tf-image
-      value: tensorflow/tensorflow:1.13.1-py3
-      type: select.select
-      displayName: Select TensorFlow image
-      visibility: public
-      hint: Select the GPU image if you are running on a GPU node pool
-      options:
-        - name: 'TensorFlow 1.13.1 CPU Image'
-          value: 'tensorflow/tensorflow:1.13.1-py3'
-        - name: 'TensorFlow 1.13.1 GPU Image'
-          value: 'tensorflow/tensorflow:1.13.1-gpu-py3'
+      - name: tf-image
+        value: tensorflow/tensorflow:1.13.1-py3
+        type: select.select
+        displayName: Select TensorFlow image
+        visibility: public
+        hint: Select the GPU image if you are running on a GPU node pool
+        options:
+          - name: 'TensorFlow 1.13.1 CPU Image'
+            value: 'tensorflow/tensorflow:1.13.1-py3'
+          - name: 'TensorFlow 1.13.1 GPU Image'
+            value: 'tensorflow/tensorflow:1.13.1-gpu-py3'
 
-    - displayName: Node pool
-      hint: Name of node pool or group to run this workflow task
-      type: select.select
-      name: sys-node-pool
-      value: Standard_D4s_v3
-      visibility: public
-      required: true
-      options:
-        - name: 'CPU: 2, RAM: 8GB'
-          value: Standard_D2s_v3
-        - name: 'CPU: 4, RAM: 16GB'
-          value: Standard_D4s_v3
-        - name: 'GPU: 1xK80, CPU: 6, RAM: 56GB'
-          value: Standard_NC6
-    - name: dump-format
-      value: cvat_tfrecord
-      visibility: public
-templates:
-  - name: main
-    dag:
-      tasks:
-        - name: train-model
-          template: tensorflow
-    # Uncomment the lines below if you want to send Slack notifications
-    #    - arguments:
-    #        artifacts:
-    #        - from: '{{tasks.train-model.outputs.artifacts.sys-metrics}}'
-    #          name: metrics
-    #        parameters:
-    #        - name: status
-    #          value: '{{tasks.train-model.status}}'
-    #      dependencies:
-    #      - train-model
-    #      name: notify-in-slack
-    #      template: slack-notify-success
-  - name: tensorflow
-    container:
-      args:
-        - |
-          apt-get update && \
-          apt-get install -y python3-pip git wget unzip libglib2.0-0 libsm6 libxext6 libxrender-dev && \
-          pip install pillow lxml Cython contextlib2 jupyter matplotlib numpy scipy boto3 pycocotools pyyaml google-cloud-storage && \
-          cd /mnt/src/tf/research && \
-          export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim && \
-          cd /mnt/src/train && \
-          python convert_workflow.py \
-            --extras="{{workflow.parameters.hyperparameters}}" \
-            --model="{{workflow.parameters.cvat-model}}" \
-            --num_classes="{{workflow.parameters.cvat-num-classes}}" \
-            --sys_finetune_checkpoint={{workflow.parameters.cvat-finetune-checkpoint}}
-      command:
-        - sh
-        - -c
-      image: '{{workflow.parameters.tf-image}}'
-      volumeMounts:
-        - mountPath: /mnt/data
-          name: data
-        - mountPath: /mnt/output
-          name: output
-      workingDir: /mnt/src
-    nodeSelector:
-      beta.kubernetes.io/instance-type: '{{workflow.parameters.sys-node-pool}}'
-    sidecars:
-      - name: tensorboard
-        image: tensorflow/tensorflow:2.3.0
-        command: [sh, -c]
-        tty: true
-        args: ["tensorboard --logdir /mnt/output/"]
-        ports:
-          - containerPort: 6006
-            name: tensorboard
-    inputs:
-      artifacts:
-        - name: data
-          path: /mnt/data/datasets/
-          {{.ArtifactRepositoryType}}:
-            key: '{{workflow.namespace}}/{{workflow.parameters.cvat-annotation-path}}'
-        - name: models
-          path: /mnt/data/models/
-          optional: true
-          {{.ArtifactRepositoryType}}:
-            key: '{{workflow.parameters.cvat-finetune-checkpoint}}'
-        - git:
-            repo: '{{workflow.parameters.source}}'
-            revision: '{{workflow.parameters.revision}}'
-          name: src
-          path: /mnt/src/tf
-        - git:
-            repo: '{{workflow.parameters.trainingsource}}'
-            revision: 'optional-artifacts'
-          name: tsrc
-          path: /mnt/src/train
-    outputs:
-      artifacts:
-        - name: model
-          optional: true
-          path: /mnt/output
-          {{.ArtifactRepositoryType}}:
-            key: '{{workflow.namespace}}/{{workflow.parameters.cvat-output-path}}/{{workflow.name}}'
-# Uncomment the lines below if you want to send Slack notifications
-#- container:
-#    args:
-#    - SLACK_USERNAME=Onepanel SLACK_TITLE="{{workflow.name}} {{inputs.parameters.status}}"
-#      SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd
-#      SLACK_MESSAGE=$(cat /tmp/metrics.json)} ./slack-notify
-#    command:
-#    - sh
-#    - -c
-#    image: technosophos/slack-notify
-#  inputs:
-#    artifacts:
-#    - name: metrics
-#      optional: true
-#      path: /tmp/metrics.json
-#    parameters:
-#    - name: status
-#  name: slack-notify-success
-volumeClaimTemplates:
-  - metadata:
-      creationTimestamp: null
-      name: data
-    spec:
-      accessModes:
-        - ReadWriteOnce
-      resources:
-        requests:
-          storage: 200Gi
-  - metadata:
-      creationTimestamp: null
-      name: output
-    spec:
-      accessModes:
-        - ReadWriteOnce
-      resources:
-        requests:
-          storage: 200Gi
+      - displayName: Node pool
+        hint: Name of node pool or group to run this workflow task
+        type: select.select
+        name: sys-node-pool
+        value: Standard_D4s_v3
+        visibility: public
+        required: true
+        options:
+          - name: 'CPU: 2, RAM: 8GB'
+            value: Standard_D2s_v3
+          - name: 'CPU: 4, RAM: 16GB'
+            value: Standard_D4s_v3
+          - name: 'GPU: 1xK80, CPU: 6, RAM: 56GB'
+            value: Standard_NC6
+      - name: dump-format
+        value: cvat_tfrecord
+        visibility: public
+  templates:
+    - name: main
+      dag:
+        tasks:
+          - name: train-model
+            template: tensorflow
+      # Uncomment the lines below if you want to send Slack notifications
+      #    - arguments:
+      #        artifacts:
+      #        - from: '{{tasks.train-model.outputs.artifacts.sys-metrics}}'
+      #          name: metrics
+      #        parameters:
+      #        - name: status
+      #          value: '{{tasks.train-model.status}}'
+      #      dependencies:
+      #      - train-model
+      #      name: notify-in-slack
+      #      template: slack-notify-success
+    - name: tensorflow
+      container:
+        args:
+          - |
+            apt-get update && \
+            apt-get install -y python3-pip git wget unzip libglib2.0-0 libsm6 libxext6 libxrender-dev && \
+            pip install pillow lxml Cython contextlib2 jupyter matplotlib numpy scipy boto3 pycocotools pyyaml google-cloud-storage && \
+            cd /mnt/src/tf/research && \
+            export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim && \
+            cd /mnt/src/train && \
+            python convert_workflow.py \
+              --extras="{{workflow.parameters.hyperparameters}}" \
+              --model="{{workflow.parameters.cvat-model}}" \
+              --num_classes="{{workflow.parameters.cvat-num-classes}}" \
+              --sys_finetune_checkpoint={{workflow.parameters.cvat-finetune-checkpoint}}
+        command:
+          - sh
+          - -c
+        image: '{{workflow.parameters.tf-image}}'
+        volumeMounts:
+          - mountPath: /mnt/data
+            name: data
+          - mountPath: /mnt/output
+            name: output
+        workingDir: /mnt/src
+      nodeSelector:
+        beta.kubernetes.io/instance-type: '{{workflow.parameters.sys-node-pool}}'
+      sidecars:
+        - name: tensorboard
+          image: tensorflow/tensorflow:2.3.0
+          command: [sh, -c]
+          tty: true
+          args: ["tensorboard --logdir /mnt/output/"]
+          ports:
+            - containerPort: 6006
+              name: tensorboard
+      inputs:
+        artifacts:
+          - name: data
+            path: /mnt/data/datasets/
+            "{{.ArtifactRepositoryType}}":
+              key: '{{workflow.namespace}}/{{workflow.parameters.cvat-annotation-path}}'
+          - name: models
+            path: /mnt/data/models/
+            optional: true
+            "{{.ArtifactRepositoryType}}":
+              key: '{{workflow.parameters.cvat-finetune-checkpoint}}'
+          - git:
+              repo: '{{workflow.parameters.source}}'
+              revision: '{{workflow.parameters.revision}}'
+            name: src
+            path: /mnt/src/tf
+          - git:
+              repo: '{{workflow.parameters.trainingsource}}'
+              revision: 'optional-artifacts'
+            name: tsrc
+            path: /mnt/src/train
+      outputs:
+        artifacts:
+          - name: model
+            optional: true
+            path: /mnt/output
+            "{{.ArtifactRepositoryType}}":
+              key: '{{workflow.namespace}}/{{workflow.parameters.cvat-output-path}}/{{workflow.name}}'
+  # Uncomment the lines below if you want to send Slack notifications
+  #- container:
+  #    args:
+  #    - SLACK_USERNAME=Onepanel SLACK_TITLE="{{workflow.name}} {{inputs.parameters.status}}"
+  #      SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd
+  #      SLACK_MESSAGE=$(cat /tmp/metrics.json)} ./slack-notify
+  #    command:
+  #    - sh
+  #    - -c
+  #    image: technosophos/slack-notify
+  #  inputs:
+  #    artifacts:
+  #    - name: metrics
+  #      optional: true
+  #      path: /tmp/metrics.json
+  #    parameters:
+  #    - name: status
+  #  name: slack-notify-success
+  volumeClaimTemplates:
+    - metadata:
+        creationTimestamp: null
+        name: data
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi
+    - metadata:
+        creationTimestamp: null
+        name: output
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi

db/yaml/workflows/tf-object-detection-training/20201208155115.yaml

@@ -1,223 +1,233 @@
-entrypoint: main
-arguments:
-  parameters:
-    - name: source
-      value: https://github.com/tensorflow/models.git
-      displayName: Model source code
-      type: hidden
-      visibility: private
+metadata:
+  name: "TF Object Detection Training"
+  kind: Workflow
+  version: 20201208155115
+  action: update
+  source: "https://github.com/onepanelio/templates/blob/master/workflows/tf-object-detection-training/"
+  labels:
+    "created-by": "system"
+    "used-by": "cvat"
+spec:
+  entrypoint: main
+  arguments:
+    parameters:
+      - name: source
+        value: https://github.com/tensorflow/models.git
+        displayName: Model source code
+        type: hidden
+        visibility: private
 
-    - name: trainingsource
-      value: https://github.com/onepanelio/cvat-training.git
-      type: hidden
-      visibility: private
+      - name: trainingsource
+        value: https://github.com/onepanelio/cvat-training.git
+        type: hidden
+        visibility: private
 
-    - name: revision
-      value: v1.13.0
-      type: hidden
-      visibility: private
+      - name: revision
+        value: v1.13.0
+        type: hidden
+        visibility: private
 
-    - name: cvat-annotation-path
-      value: annotation-dump/sample_dataset
-      displayName: Dataset path
-      hint: Path to annotated data in default object storage (i.e S3). In CVAT, this parameter will be pre-populated.
-      visibility: private
+      - name: cvat-annotation-path
+        value: annotation-dump/sample_dataset
+        displayName: Dataset path
+        hint: Path to annotated data in default object storage (i.e S3). In CVAT, this parameter will be pre-populated.
+        visibility: private
 
-    - name: cvat-output-path
-      value: workflow-data/output/sample_output
-      hint: Path to store output artifacts in default object storage (i.e s3). In CVAT, this parameter will be pre-populated.
-      displayName: Workflow output path
-      visibility: private
+      - name: cvat-output-path
+        value: workflow-data/output/sample_output
+        hint: Path to store output artifacts in default object storage (i.e s3). In CVAT, this parameter will be pre-populated.
+        displayName: Workflow output path
+        visibility: private
 
-    - name: cvat-model
-      value: frcnn-res50-coco
-      displayName: Model
-      hint: TF Detection API's model to use for training.
-      type: select.select
-      visibility: public
-      options:
-        - name: 'Faster RCNN-ResNet 101-COCO'
-          value: frcnn-res101-coco
-        - name: 'Faster RCNN-ResNet 101-Low Proposal-COCO'
-          value: frcnn-res101-low
-        - name: 'Faster RCNN-ResNet 50-COCO'
-          value: frcnn-res50-coco
-        - name: 'Faster RCNN-NAS-COCO'
-          value: frcnn-nas-coco
-        - name: 'SSD MobileNet V1-COCO'
-          value: ssd-mobilenet-v1-coco2
-        - name: 'SSD MobileNet V2-COCO'
-          value: ssd-mobilenet-v2-coco
-        - name: 'SSDLite MobileNet-COCO'
-          value: ssdlite-mobilenet-coco
+      - name: cvat-model
+        value: frcnn-res50-coco
+        displayName: Model
+        hint: TF Detection API's model to use for training.
+        type: select.select
+        visibility: public
+        options:
+          - name: 'Faster RCNN-ResNet 101-COCO'
+            value: frcnn-res101-coco
+          - name: 'Faster RCNN-ResNet 101-Low Proposal-COCO'
+            value: frcnn-res101-low
+          - name: 'Faster RCNN-ResNet 50-COCO'
+            value: frcnn-res50-coco
+          - name: 'Faster RCNN-NAS-COCO'
+            value: frcnn-nas-coco
+          - name: 'SSD MobileNet V1-COCO'
+            value: ssd-mobilenet-v1-coco2
+          - name: 'SSD MobileNet V2-COCO'
+            value: ssd-mobilenet-v2-coco
+          - name: 'SSDLite MobileNet-COCO'
+            value: ssdlite-mobilenet-coco
 
-    - name: hyperparameters
-      value: |-
-        num-steps=10000
-      displayName: Hyperparameters
-      visibility: public
-      type: textarea.textarea
-      hint: "Please refer to our <a href='https://docs.onepanel.ai/docs/getting-started/use-cases/computervision/annotation/cvat/cvat_annotation_model#arguments-optional' target='_blank'>documentation</a> for more information on parameters. Number of classes will be automatically populated if you had 'sys-num-classes' parameter in a workflow."
+      - name: hyperparameters
+        value: |-
+          num-steps=10000
+        displayName: Hyperparameters
+        visibility: public
+        type: textarea.textarea
+        hint: "Please refer to our <a href='https://docs.onepanel.ai/docs/getting-started/use-cases/computervision/annotation/cvat/cvat_annotation_model#arguments-optional' target='_blank'>documentation</a> for more information on parameters. Number of classes will be automatically populated if you had 'sys-num-classes' parameter in a workflow."
 
-    - name: cvat-finetune-checkpoint
-      value: ''
-      hint: Select the last fine-tune checkpoint for this model. It may take up to 5 minutes for a recent checkpoint show here. Leave empty if this is the first time you're training this model.
-      displayName: Checkpoint path
-      visibility: public
+      - name: cvat-finetune-checkpoint
+        value: ''
+        hint: Select the last fine-tune checkpoint for this model. It may take up to 5 minutes for a recent checkpoint show here. Leave empty if this is the first time you're training this model.
+        displayName: Checkpoint path
+        visibility: public
 
-    - name: cvat-num-classes
-      value: '81'
-      hint: Number of classes
-      displayName: Number of classes
-      visibility: private
+      - name: cvat-num-classes
+        value: '81'
+        hint: Number of classes
+        displayName: Number of classes
+        visibility: private
 
-    - name: tf-image
-      value: tensorflow/tensorflow:1.13.1-py3
-      type: select.select
-      displayName: Select TensorFlow image
-      visibility: public
-      hint: Select the GPU image if you are running on a GPU node pool
-      options:
-        - name: 'TensorFlow 1.13.1 CPU Image'
-          value: 'tensorflow/tensorflow:1.13.1-py3'
-        - name: 'TensorFlow 1.13.1 GPU Image'
-          value: 'tensorflow/tensorflow:1.13.1-gpu-py3'
+      - name: tf-image
+        value: tensorflow/tensorflow:1.13.1-py3
+        type: select.select
+        displayName: Select TensorFlow image
+        visibility: public
+        hint: Select the GPU image if you are running on a GPU node pool
+        options:
+          - name: 'TensorFlow 1.13.1 CPU Image'
+            value: 'tensorflow/tensorflow:1.13.1-py3'
+          - name: 'TensorFlow 1.13.1 GPU Image'
+            value: 'tensorflow/tensorflow:1.13.1-gpu-py3'
 
-    - displayName: Node pool
-      hint: Name of node pool or group to run this workflow task
-      type: select.select
-      name: sys-node-pool
-      value: Standard_D4s_v3
-      visibility: public
-      required: true
-      options:
-        - name: 'CPU: 2, RAM: 8GB'
-          value: Standard_D2s_v3
-        - name: 'CPU: 4, RAM: 16GB'
-          value: Standard_D4s_v3
-        - name: 'GPU: 1xK80, CPU: 6, RAM: 56GB'
-          value: Standard_NC6
-    - name: dump-format
-      value: cvat_tfrecord
-      visibility: public
-templates:
-  - name: main
-    dag:
-      tasks:
-        - name: train-model
-          template: tensorflow
-    # Uncomment the lines below if you want to send Slack notifications
-    #    - arguments:
-    #        artifacts:
-    #        - from: '{{tasks.train-model.outputs.artifacts.sys-metrics}}'
-    #          name: metrics
-    #        parameters:
-    #        - name: status
-    #          value: '{{tasks.train-model.status}}'
-    #      dependencies:
-    #      - train-model
-    #      name: notify-in-slack
-    #      template: slack-notify-success
-  - name: tensorflow
-    container:
-      args:
-        - |
-          apt-get update && \
-          apt-get install -y python3-pip git wget unzip libglib2.0-0 libsm6 libxext6 libxrender-dev && \
-          pip install pillow lxml Cython contextlib2 jupyter matplotlib numpy scipy boto3 pycocotools pyyaml google-cloud-storage && \
-          cd /mnt/src/tf/research && \
-          export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim && \
-          cd /mnt/src/train && \
-          python convert_workflow.py \
-            --extras="{{workflow.parameters.hyperparameters}}" \
-            --model="{{workflow.parameters.cvat-model}}" \
-            --num_classes="{{workflow.parameters.cvat-num-classes}}" \
-            --sys_finetune_checkpoint={{workflow.parameters.cvat-finetune-checkpoint}}
-      command:
-        - sh
-        - -c
-      image: '{{workflow.parameters.tf-image}}'
-      volumeMounts:
-        - mountPath: /mnt/data
-          name: data
-        - mountPath: /mnt/output
-          name: output
-      workingDir: /mnt/src
-    nodeSelector:
-      beta.kubernetes.io/instance-type: '{{workflow.parameters.sys-node-pool}}'
-    sidecars:
-      - name: tensorboard
-        image: tensorflow/tensorflow:2.3.0
-        command: [sh, -c]
-        env:
-          - name: ONEPANEL_INTERACTIVE_SIDECAR
-            value: 'true'
-        args: ["tensorboard --logdir /mnt/output/"]
-        ports:
-          - containerPort: 6006
-            name: tensorboard
-    inputs:
-      artifacts:
-        - name: data
-          path: /mnt/data/datasets/
-          {{.ArtifactRepositoryType}}:
-            key: '{{workflow.namespace}}/{{workflow.parameters.cvat-annotation-path}}'
-        - name: models
-          path: /mnt/data/models/
-          optional: true
-          {{.ArtifactRepositoryType}}:
-            key: '{{workflow.parameters.cvat-finetune-checkpoint}}'
-        - git:
-            repo: '{{workflow.parameters.source}}'
-            revision: '{{workflow.parameters.revision}}'
-          name: src
-          path: /mnt/src/tf
-        - git:
-            repo: '{{workflow.parameters.trainingsource}}'
-            revision: 'optional-artifacts'
-          name: tsrc
-          path: /mnt/src/train
-    outputs:
-      artifacts:
-        - name: model
-          optional: true
-          path: /mnt/output
-          {{.ArtifactRepositoryType}}:
-            key: '{{workflow.namespace}}/{{workflow.parameters.cvat-output-path}}/{{workflow.name}}'
-# Uncomment the lines below if you want to send Slack notifications
-#- container:
-#    args:
-#    - SLACK_USERNAME=Onepanel SLACK_TITLE="{{workflow.name}} {{inputs.parameters.status}}"
-#      SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd
-#      SLACK_MESSAGE=$(cat /tmp/metrics.json)} ./slack-notify
-#    command:
-#    - sh
-#    - -c
-#    image: technosophos/slack-notify
-#  inputs:
-#    artifacts:
-#    - name: metrics
-#      optional: true
-#      path: /tmp/metrics.json
-#    parameters:
-#    - name: status
-#  name: slack-notify-success
-volumeClaimTemplates:
-  - metadata:
-      creationTimestamp: null
-      name: data
-    spec:
-      accessModes:
-        - ReadWriteOnce
-      resources:
-        requests:
-          storage: 200Gi
-  - metadata:
-      creationTimestamp: null
-      name: output
-    spec:
-      accessModes:
-        - ReadWriteOnce
-      resources:
-        requests:
-          storage: 200Gi
+      - displayName: Node pool
+        hint: Name of node pool or group to run this workflow task
+        type: select.select
+        name: sys-node-pool
+        value: Standard_D4s_v3
+        visibility: public
+        required: true
+        options:
+          - name: 'CPU: 2, RAM: 8GB'
+            value: Standard_D2s_v3
+          - name: 'CPU: 4, RAM: 16GB'
+            value: Standard_D4s_v3
+          - name: 'GPU: 1xK80, CPU: 6, RAM: 56GB'
+            value: Standard_NC6
+      - name: dump-format
+        value: cvat_tfrecord
+        visibility: public
+  templates:
+    - name: main
+      dag:
+        tasks:
+          - name: train-model
+            template: tensorflow
+      # Uncomment the lines below if you want to send Slack notifications
+      #    - arguments:
+      #        artifacts:
+      #        - from: '{{tasks.train-model.outputs.artifacts.sys-metrics}}'
+      #          name: metrics
+      #        parameters:
+      #        - name: status
+      #          value: '{{tasks.train-model.status}}'
+      #      dependencies:
+      #      - train-model
+      #      name: notify-in-slack
+      #      template: slack-notify-success
+    - name: tensorflow
+      container:
+        args:
+          - |
+            apt-get update && \
+            apt-get install -y python3-pip git wget unzip libglib2.0-0 libsm6 libxext6 libxrender-dev && \
+            pip install pillow lxml Cython contextlib2 jupyter matplotlib numpy scipy boto3 pycocotools pyyaml google-cloud-storage && \
+            cd /mnt/src/tf/research && \
+            export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim && \
+            cd /mnt/src/train && \
+            python convert_workflow.py \
+              --extras="{{workflow.parameters.hyperparameters}}" \
+              --model="{{workflow.parameters.cvat-model}}" \
+              --num_classes="{{workflow.parameters.cvat-num-classes}}" \
+              --sys_finetune_checkpoint={{workflow.parameters.cvat-finetune-checkpoint}}
+        command:
+          - sh
+          - -c
+        image: '{{workflow.parameters.tf-image}}'
+        volumeMounts:
+          - mountPath: /mnt/data
+            name: data
+          - mountPath: /mnt/output
+            name: output
+        workingDir: /mnt/src
+      nodeSelector:
+        beta.kubernetes.io/instance-type: '{{workflow.parameters.sys-node-pool}}'
+      sidecars:
+        - name: tensorboard
+          image: tensorflow/tensorflow:2.3.0
+          command: [sh, -c]
+          env:
+            - name: ONEPANEL_INTERACTIVE_SIDECAR
+              value: 'true'
+          args: ["tensorboard --logdir /mnt/output/"]
+          ports:
+            - containerPort: 6006
+              name: tensorboard
+      inputs:
+        artifacts:
+          - name: data
+            path: /mnt/data/datasets/
+            "{{.ArtifactRepositoryType}}":
+              key: '{{workflow.namespace}}/{{workflow.parameters.cvat-annotation-path}}'
+          - name: models
+            path: /mnt/data/models/
+            optional: true
+            "{{.ArtifactRepositoryType}}":
+              key: '{{workflow.parameters.cvat-finetune-checkpoint}}'
+          - git:
+              repo: '{{workflow.parameters.source}}'
+              revision: '{{workflow.parameters.revision}}'
+            name: src
+            path: /mnt/src/tf
+          - git:
+              repo: '{{workflow.parameters.trainingsource}}'
+              revision: 'optional-artifacts'
+            name: tsrc
+            path: /mnt/src/train
+      outputs:
+        artifacts:
+          - name: model
+            optional: true
+            path: /mnt/output
+            "{{.ArtifactRepositoryType}}":
+              key: '{{workflow.namespace}}/{{workflow.parameters.cvat-output-path}}/{{workflow.name}}'
+  # Uncomment the lines below if you want to send Slack notifications
+  #- container:
+  #    args:
+  #    - SLACK_USERNAME=Onepanel SLACK_TITLE="{{workflow.name}} {{inputs.parameters.status}}"
+  #      SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd
+  #      SLACK_MESSAGE=$(cat /tmp/metrics.json)} ./slack-notify
+  #    command:
+  #    - sh
+  #    - -c
+  #    image: technosophos/slack-notify
+  #  inputs:
+  #    artifacts:
+  #    - name: metrics
+  #      optional: true
+  #      path: /tmp/metrics.json
+  #    parameters:
+  #    - name: status
+  #  name: slack-notify-success
+  volumeClaimTemplates:
+    - metadata:
+        creationTimestamp: null
+        name: data
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi
+    - metadata:
+        creationTimestamp: null
+        name: output
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi

db/yaml/workflows/tf-object-detection-training/20201223202929.yaml

@@ -1,165 +1,174 @@
-# source: https://github.com/onepanelio/templates/blob/master/workflows/tf-object-detection-training/
-arguments:
-  parameters:
-    - name: cvat-annotation-path
-      value: annotation-dump/sample_dataset
-      displayName: Dataset path
-      hint: Path to annotated data (TFRecord format) in default object storage. In CVAT, this parameter will be pre-populated.
-      visibility: internal
+metadata:
+  name: "TF Object Detection Training"
+  kind: Workflow
+  version: 20201223202929
+  action: update
+  source: "https://github.com/onepanelio/templates/blob/master/workflows/tf-object-detection-training/"
+  labels:
+    "created-by": "system"
+    "used-by": "cvat"
+spec:
+  arguments:
+    parameters:
+      - name: cvat-annotation-path
+        value: annotation-dump/sample_dataset
+        displayName: Dataset path
+        hint: Path to annotated data (TFRecord format) in default object storage. In CVAT, this parameter will be pre-populated.
+        visibility: internal
 
-    - name: cvat-output-path
-      value: workflow-data/output/sample_output
-      hint: Path to store output artifacts in default object storage (i.e s3). In CVAT, this parameter will be pre-populated.
-      displayName: Workflow output path
-      visibility: internal
+      - name: cvat-output-path
+        value: workflow-data/output/sample_output
+        hint: Path to store output artifacts in default object storage (i.e s3). In CVAT, this parameter will be pre-populated.
+        displayName: Workflow output path
+        visibility: internal
 
-    - name: cvat-model
-      value: frcnn-res50-coco
-      displayName: Model
-      hint: TF Detection API's model to use for training.
-      type: select.select
-      visibility: public
-      options:
-        - name: 'Faster RCNN-ResNet 101-COCO'
-          value: frcnn-res101-coco
-        - name: 'Faster RCNN-ResNet 101-Low Proposal-COCO'
-          value: frcnn-res101-low
-        - name: 'Faster RCNN-ResNet 50-COCO'
-          value: frcnn-res50-coco
-        - name: 'Faster RCNN-NAS-COCO'
-          value: frcnn-nas-coco
-        - name: 'SSD MobileNet V1-COCO'
-          value: ssd-mobilenet-v1-coco2
-        - name: 'SSD MobileNet V2-COCO'
-          value: ssd-mobilenet-v2-coco
-        - name: 'SSDLite MobileNet-COCO'
-          value: ssdlite-mobilenet-coco
+      - name: cvat-model
+        value: frcnn-res50-coco
+        displayName: Model
+        hint: TF Detection API's model to use for training.
+        type: select.select
+        visibility: public
+        options:
+          - name: 'Faster RCNN-ResNet 101-COCO'
+            value: frcnn-res101-coco
+          - name: 'Faster RCNN-ResNet 101-Low Proposal-COCO'
+            value: frcnn-res101-low
+          - name: 'Faster RCNN-ResNet 50-COCO'
+            value: frcnn-res50-coco
+          - name: 'Faster RCNN-NAS-COCO'
+            value: frcnn-nas-coco
+          - name: 'SSD MobileNet V1-COCO'
+            value: ssd-mobilenet-v1-coco2
+          - name: 'SSD MobileNet V2-COCO'
+            value: ssd-mobilenet-v2-coco
+          - name: 'SSDLite MobileNet-COCO'
+            value: ssdlite-mobilenet-coco
 
-    - name: hyperparameters
-      value: |-
-        num-steps=10000
-      displayName: Hyperparameters
-      visibility: public
-      type: textarea.textarea
-      hint: 'See <a href="https://docs.onepanel.ai/docs/getting-started/use-cases/computervision/annotation/cvat/cvat_annotation_model/#tfod-hyperparameters" target="_blank">documentation</a> for more information on parameters.'
+      - name: hyperparameters
+        value: |-
+          num-steps=10000
+        displayName: Hyperparameters
+        visibility: public
+        type: textarea.textarea
+        hint: 'See <a href="https://docs.onepanel.ai/docs/getting-started/use-cases/computervision/annotation/cvat/cvat_annotation_model/#tfod-hyperparameters" target="_blank">documentation</a> for more information on parameters.'
 
-    - name: cvat-finetune-checkpoint
-      value: ''
-      hint: Select the last fine-tune checkpoint for this model. It may take up to 5 minutes for a recent checkpoint show here. Leave empty if this is the first time you're training this model.
-      displayName: Checkpoint path
-      visibility: public
+      - name: cvat-finetune-checkpoint
+        value: ''
+        hint: Select the last fine-tune checkpoint for this model. It may take up to 5 minutes for a recent checkpoint show here. Leave empty if this is the first time you're training this model.
+        displayName: Checkpoint path
+        visibility: public
 
-    - name: cvat-num-classes
-      value: '10'
-      hint: Number of classes. In CVAT, this parameter will be pre-populated.
-      displayName: Number of classes
-      visibility: internal
+      - name: cvat-num-classes
+        value: '10'
+        hint: Number of classes. In CVAT, this parameter will be pre-populated.
+        displayName: Number of classes
+        visibility: internal
 
-    - name: tf-image
-      value: tensorflow/tensorflow:1.13.1-py3
-      type: select.select
-      displayName: Select TensorFlow image
-      visibility: public
-      hint: Select the GPU image if you are running on a GPU node pool
-      options:
-        - name: 'TensorFlow 1.13.1 CPU Image'
-          value: 'tensorflow/tensorflow:1.13.1-py3'
-        - name: 'TensorFlow 1.13.1 GPU Image'
-          value: 'tensorflow/tensorflow:1.13.1-gpu-py3'
+      - name: tf-image
+        value: tensorflow/tensorflow:1.13.1-py3
+        type: select.select
+        displayName: Select TensorFlow image
+        visibility: public
+        hint: Select the GPU image if you are running on a GPU node pool
+        options:
+          - name: 'TensorFlow 1.13.1 CPU Image'
+            value: 'tensorflow/tensorflow:1.13.1-py3'
+          - name: 'TensorFlow 1.13.1 GPU Image'
+            value: 'tensorflow/tensorflow:1.13.1-gpu-py3'
 
-    - name: dump-format
-      value: cvat_tfrecord
-      visibility: public
+      - name: dump-format
+        value: cvat_tfrecord
+        visibility: public
 
-    - displayName: Node pool
-      hint: Name of node pool or group to run this workflow task
-      type: select.nodepool
-      name: sys-node-pool
-      value: {{.DefaultNodePoolOption}}
-      visibility: public
-      required: true
+      - displayName: Node pool
+        hint: Name of node pool or group to run this workflow task
+        type: select.nodepool
+        name: sys-node-pool
+        value: "{{.DefaultNodePoolOption}}"
+        visibility: public
+        required: true
 
-entrypoint: main
-templates:
-  - dag:
-      tasks:
-        - name: train-model
-          template: tensorflow
-    name: main
-  - container:
-      args:
-        - |
-          apt-get update && \
-          apt-get install -y python3-pip git wget unzip libglib2.0-0 libsm6 libxext6 libxrender-dev && \
-          pip install pillow lxml Cython contextlib2 matplotlib numpy scipy pycocotools pyyaml test-generator && \
-          cd /mnt/src/tf/research && \
-          export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim && \
-          mkdir -p /mnt/src/protoc && \
-          wget -P /mnt/src/protoc https://github.com/protocolbuffers/protobuf/releases/download/v3.10.1/protoc-3.10.1-linux-x86_64.zip && \
-          cd /mnt/src/protoc/ && \
-          unzip protoc-3.10.1-linux-x86_64.zip && \
-          cd /mnt/src/tf/research/ && \
-          /mnt/src/protoc/bin/protoc object_detection/protos/*.proto --python_out=. && \
-          cd /mnt/src/train/workflows/tf-object-detection-training && \
-          python main.py \
-            --extras="{{workflow.parameters.hyperparameters}}" \
-            --model="{{workflow.parameters.cvat-model}}" \
-            --num_classes="{{workflow.parameters.cvat-num-classes}}" \
-            --sys_finetune_checkpoint={{workflow.parameters.cvat-finetune-checkpoint}}
-      command:
-        - sh
-        - -c
-      image: '{{workflow.parameters.tf-image}}'
-      volumeMounts:
-        - mountPath: /mnt/data
-          name: data
-        - mountPath: /mnt/output
-          name: output
-      workingDir: /mnt/src
-    nodeSelector:
-      beta.kubernetes.io/instance-type: '{{workflow.parameters.sys-node-pool}}'
-    inputs:
-      artifacts:
-        - name: data
-          path: /mnt/data/datasets/
-          s3:
-            key: '{{workflow.namespace}}/{{workflow.parameters.cvat-annotation-path}}'
-        - name: models
-          path: /mnt/data/models/
-          optional: true
-          s3:
-            key: '{{workflow.parameters.cvat-finetune-checkpoint}}'
-        - git:
-            repo: https://github.com/tensorflow/models.git
-            revision: v1.13.0
-          name: src
-          path: /mnt/src/tf
-        - git:
-            repo: https://github.com/onepanelio/templates.git
-          name: tsrc
-          path: /mnt/src/train
-    name: tensorflow
-    outputs:
-      artifacts:
-        - name: model
-          optional: true
-          path: /mnt/output
-          s3:
-            key: '{{workflow.namespace}}/{{workflow.parameters.cvat-output-path}}/{{workflow.name}}'
-volumeClaimTemplates:
-  - metadata:
-      name: data
-    spec:
-      accessModes:
-        - ReadWriteOnce
-      resources:
-        requests:
-          storage: 200Gi
-  - metadata:
-      name: output
-    spec:
-      accessModes:
-        - ReadWriteOnce
-      resources:
-        requests:
-          storage: 200Gi
+  entrypoint: main
+  templates:
+    - dag:
+        tasks:
+          - name: train-model
+            template: tensorflow
+      name: main
+    - container:
+        args:
+          - |
+            apt-get update && \
+            apt-get install -y python3-pip git wget unzip libglib2.0-0 libsm6 libxext6 libxrender-dev && \
+            pip install pillow lxml Cython contextlib2 matplotlib numpy scipy pycocotools pyyaml test-generator && \
+            cd /mnt/src/tf/research && \
+            export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim && \
+            mkdir -p /mnt/src/protoc && \
+            wget -P /mnt/src/protoc https://github.com/protocolbuffers/protobuf/releases/download/v3.10.1/protoc-3.10.1-linux-x86_64.zip && \
+            cd /mnt/src/protoc/ && \
+            unzip protoc-3.10.1-linux-x86_64.zip && \
+            cd /mnt/src/tf/research/ && \
+            /mnt/src/protoc/bin/protoc object_detection/protos/*.proto --python_out=. && \
+            cd /mnt/src/train/workflows/tf-object-detection-training && \
+            python main.py \
+              --extras="{{workflow.parameters.hyperparameters}}" \
+              --model="{{workflow.parameters.cvat-model}}" \
+              --num_classes="{{workflow.parameters.cvat-num-classes}}" \
+              --sys_finetune_checkpoint={{workflow.parameters.cvat-finetune-checkpoint}}
+        command:
+          - sh
+          - -c
+        image: '{{workflow.parameters.tf-image}}'
+        volumeMounts:
+          - mountPath: /mnt/data
+            name: data
+          - mountPath: /mnt/output
+            name: output
+        workingDir: /mnt/src
+      nodeSelector:
+        beta.kubernetes.io/instance-type: '{{workflow.parameters.sys-node-pool}}'
+      inputs:
+        artifacts:
+          - name: data
+            path: /mnt/data/datasets/
+            s3:
+              key: '{{workflow.namespace}}/{{workflow.parameters.cvat-annotation-path}}'
+          - name: models
+            path: /mnt/data/models/
+            optional: true
+            s3:
+              key: '{{workflow.parameters.cvat-finetune-checkpoint}}'
+          - git:
+              repo: https://github.com/tensorflow/models.git
+              revision: v1.13.0
+            name: src
+            path: /mnt/src/tf
+          - git:
+              repo: https://github.com/onepanelio/templates.git
+            name: tsrc
+            path: /mnt/src/train
+      name: tensorflow
+      outputs:
+        artifacts:
+          - name: model
+            optional: true
+            path: /mnt/output
+            s3:
+              key: '{{workflow.namespace}}/{{workflow.parameters.cvat-output-path}}/{{workflow.name}}'
+  volumeClaimTemplates:
+    - metadata:
+        name: data
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi
+    - metadata:
+        name: output
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi

db/yaml/workflows/tf-object-detection-training/20210118175809.yaml

@@ -1,260 +1,269 @@
-# source: https://github.com/onepanelio/templates/blob/master/workflows/tf-object-detection-training/
-arguments:
-  parameters:
-    - name: cvat-annotation-path
-      value: 'artifacts/{{workflow.namespace}}/annotations/'
-      hint: Path to annotated data (COCO format) in default object storage. In CVAT, this parameter will be pre-populated.
-      displayName: Dataset path
-      visibility: internal
+metadata:
+  name: "TF Object Detection Training"
+  kind: Workflow
+  version: 20210118175809
+  action: update
+  source: "https://github.com/onepanelio/templates/blob/master/workflows/tf-object-detection-training/"
+  labels:
+    "created-by": "system"
+    "used-by": "cvat"
+spec:
+  arguments:
+    parameters:
+      - name: cvat-annotation-path
+        value: 'artifacts/{{workflow.namespace}}/annotations/'
+        hint: Path to annotated data (COCO format) in default object storage. In CVAT, this parameter will be pre-populated.
+        displayName: Dataset path
+        visibility: internal
 
-    - name: val-split
-      value: 10
-      displayName: Validation split size
-      type: input.number
-      visibility: public
-      hint: Enter validation set size in percentage of full dataset. (0 - 100)
+      - name: val-split
+        value: 10
+        displayName: Validation split size
+        type: input.number
+        visibility: public
+        hint: Enter validation set size in percentage of full dataset. (0 - 100)
 
-    - name: num-augmentation-cycles
-      value: 1
-      displayName: Number of augmentation cycles
-      type: input.number
-      visibility: public
-      hint: Number of augmentation cycles, zero means no data augmentation
+      - name: num-augmentation-cycles
+        value: 1
+        displayName: Number of augmentation cycles
+        type: input.number
+        visibility: public
+        hint: Number of augmentation cycles, zero means no data augmentation
 
-    - name: preprocessing-parameters
-      value: |-
-        RandomBrightnessContrast:
-            p: 0.2
-        GaussianBlur:
-            p: 0.3
-        GaussNoise:
-            p: 0.4
-        HorizontalFlip:
-            p: 0.5
-        VerticalFlip:
-            p: 0.3
-      displayName: Preprocessing parameters
-      visibility: public
-      type: textarea.textarea
-      hint: 'See <a href="https://albumentations.ai/docs/api_reference/augmentations/transforms/" target="_blank">documentation</a> for more information on parameters.'
+      - name: preprocessing-parameters
+        value: |-
+          RandomBrightnessContrast:
+              p: 0.2
+          GaussianBlur:
+              p: 0.3
+          GaussNoise:
+              p: 0.4
+          HorizontalFlip:
+              p: 0.5
+          VerticalFlip:
+              p: 0.3
+        displayName: Preprocessing parameters
+        visibility: public
+        type: textarea.textarea
+        hint: 'See <a href="https://albumentations.ai/docs/api_reference/augmentations/transforms/" target="_blank">documentation</a> for more information on parameters.'
 
-    - name: cvat-model
-      value: frcnn-res50-coco
-      displayName: Model
-      hint: TF Detection API's model to use for training.
-      type: select.select
-      visibility: public
-      options:
-        - name: 'Faster RCNN-ResNet 101-COCO'
-          value: frcnn-res101-coco
-        - name: 'Faster RCNN-ResNet 101-Low Proposal-COCO'
-          value: frcnn-res101-low
-        - name: 'Faster RCNN-ResNet 50-COCO'
-          value: frcnn-res50-coco
-        - name: 'Faster RCNN-NAS-COCO'
-          value: frcnn-nas-coco
-        - name: 'SSD MobileNet V1-COCO'
-          value: ssd-mobilenet-v1-coco2
-        - name: 'SSD MobileNet V2-COCO'
-          value: ssd-mobilenet-v2-coco
-        - name: 'SSDLite MobileNet-COCO'
-          value: ssdlite-mobilenet-coco
+      - name: cvat-model
+        value: frcnn-res50-coco
+        displayName: Model
+        hint: TF Detection API's model to use for training.
+        type: select.select
+        visibility: public
+        options:
+          - name: 'Faster RCNN-ResNet 101-COCO'
+            value: frcnn-res101-coco
+          - name: 'Faster RCNN-ResNet 101-Low Proposal-COCO'
+            value: frcnn-res101-low
+          - name: 'Faster RCNN-ResNet 50-COCO'
+            value: frcnn-res50-coco
+          - name: 'Faster RCNN-NAS-COCO'
+            value: frcnn-nas-coco
+          - name: 'SSD MobileNet V1-COCO'
+            value: ssd-mobilenet-v1-coco2
+          - name: 'SSD MobileNet V2-COCO'
+            value: ssd-mobilenet-v2-coco
+          - name: 'SSDLite MobileNet-COCO'
+            value: ssdlite-mobilenet-coco
 
-    - name: cvat-num-classes
-      value: '10'
-      hint: Number of classes. In CVAT, this parameter will be pre-populated.
-      displayName: Number of classes
-      visibility: internal
+      - name: cvat-num-classes
+        value: '10'
+        hint: Number of classes. In CVAT, this parameter will be pre-populated.
+        displayName: Number of classes
+        visibility: internal
 
-    - name: hyperparameters
-      value: |-
-        num_steps: 10000
-      displayName: Hyperparameters
-      visibility: public
-      type: textarea.textarea
-      hint: 'See <a href="https://docs.onepanel.ai/docs/reference/workflows/training#tfod-hyperparameters" target="_blank">documentation</a> for more information on parameters.'
+      - name: hyperparameters
+        value: |-
+          num_steps: 10000
+        displayName: Hyperparameters
+        visibility: public
+        type: textarea.textarea
+        hint: 'See <a href="https://docs.onepanel.ai/docs/reference/workflows/training#tfod-hyperparameters" target="_blank">documentation</a> for more information on parameters.'
 
-    - name: dump-format
-      value: cvat_coco
-      displayName: CVAT dump format
-      visibility: private
+      - name: dump-format
+        value: cvat_coco
+        displayName: CVAT dump format
+        visibility: private
 
-    - name: cvat-finetune-checkpoint
-      value: ''
-      hint: Path to the last fine-tune checkpoint for this model in default object storage. Leave empty if this is the first time you're training this model.
-      displayName: Checkpoint path
-      visibility: public
+      - name: cvat-finetune-checkpoint
+        value: ''
+        hint: Path to the last fine-tune checkpoint for this model in default object storage. Leave empty if this is the first time you're training this model.
+        displayName: Checkpoint path
+        visibility: public
 
-    - name: tf-image
-      value: tensorflow/tensorflow:1.13.1-py3
-      type: select.select
-      displayName: Select TensorFlow image
-      visibility: public
-      hint: Select the GPU image if you are running on a GPU node pool
-      options:
-        - name: 'TensorFlow 1.13.1 CPU Image'
-          value: 'tensorflow/tensorflow:1.13.1-py3'
-        - name: 'TensorFlow 1.13.1 GPU Image'
-          value: 'tensorflow/tensorflow:1.13.1-gpu-py3'
+      - name: tf-image
+        value: tensorflow/tensorflow:1.13.1-py3
+        type: select.select
+        displayName: Select TensorFlow image
+        visibility: public
+        hint: Select the GPU image if you are running on a GPU node pool
+        options:
+          - name: 'TensorFlow 1.13.1 CPU Image'
+            value: 'tensorflow/tensorflow:1.13.1-py3'
+          - name: 'TensorFlow 1.13.1 GPU Image'
+            value: 'tensorflow/tensorflow:1.13.1-gpu-py3'
 
-    - displayName: Node pool
-      hint: Name of node pool or group to run this workflow task
-      type: select.nodepool
-      name: sys-node-pool
-      value: {{.DefaultNodePoolOption}}
-      visibility: public
-      required: true
+      - displayName: Node pool
+        hint: Name of node pool or group to run this workflow task
+        type: select.nodepool
+        name: sys-node-pool
+        value: "{{.DefaultNodePoolOption}}"
+        visibility: public
+        required: true
 
-entrypoint: main
-templates:
-  - dag:
-      tasks:
-        - name: preprocessing
-          template: preprocessing
-        - name: train-model
-          template: tensorflow
-          dependencies: [preprocessing]
-          arguments:
-            artifacts:
-              - name: data
-                from: "{{tasks.preprocessing.outputs.artifacts.processed-data}}"
-    name: main
-  - container:
-      args:
-        - |
-          apt-get update && \
-          apt-get install -y python3-pip git wget unzip libglib2.0-0 libsm6 libxext6 libxrender-dev && \
-          pip install --upgrade pip && \
-          pip install pillow lxml Cython contextlib2 matplotlib numpy scipy pycocotools pyyaml test-generator && \
-          cd /mnt/src/tf/research && \
-          export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim && \
-          mkdir -p /mnt/src/protoc && \
-          wget -P /mnt/src/protoc https://github.com/protocolbuffers/protobuf/releases/download/v3.10.1/protoc-3.10.1-linux-x86_64.zip && \
-          cd /mnt/src/protoc/ && \
-          unzip protoc-3.10.1-linux-x86_64.zip && \
-          cd /mnt/src/tf/research/ && \
-          /mnt/src/protoc/bin/protoc object_detection/protos/*.proto --python_out=. && \
-          cd /mnt/src/train/workflows/tf-object-detection-training && \
-          python main.py \
-            --extras="{{workflow.parameters.hyperparameters}}" \
-            --model="{{workflow.parameters.cvat-model}}" \
-            --num_classes="{{workflow.parameters.cvat-num-classes}}" \
-            --sys_finetune_checkpoint="{{workflow.parameters.cvat-finetune-checkpoint}}" \
-            --from_preprocessing=True
-      command:
-        - sh
-        - -c
-      image: '{{workflow.parameters.tf-image}}'
-      volumeMounts:
-        - mountPath: /mnt/data
-          name: processed-data
-        - mountPath: /mnt/output
-          name: output
-      workingDir: /mnt/src
-    nodeSelector:
-      {{.NodePoolLabel}}: '{{workflow.parameters.sys-node-pool}}'
-    inputs:
-      artifacts:
-        - name: data
-          path: /mnt/data/datasets/
-        - name: models
-          path: /mnt/data/models/
-          optional: true
-          s3:
-            key: '{{workflow.parameters.cvat-finetune-checkpoint}}'
-        - git:
-            repo: https://github.com/tensorflow/models.git
-            revision: v1.13.0
-          name: src
-          path: /mnt/src/tf
-        - git:
-            repo: https://github.com/onepanelio/templates.git
-            revision: v0.18.0
-          name: tsrc
-          path: /mnt/src/train
-    name: tensorflow
-    outputs:
-      artifacts:
-        - name: model
-          optional: true
-          path: /mnt/output
-    sidecars:
-      - name: tensorboard
-        image: '{{workflow.parameters.tf-image}}'
+  entrypoint: main
+  templates:
+    - dag:
+        tasks:
+          - name: preprocessing
+            template: preprocessing
+          - name: train-model
+            template: tensorflow
+            dependencies: [preprocessing]
+            arguments:
+              artifacts:
+                - name: data
+                  from: "{{tasks.preprocessing.outputs.artifacts.processed-data}}"
+      name: main
+    - container:
+        args:
+          - |
+            apt-get update && \
+            apt-get install -y python3-pip git wget unzip libglib2.0-0 libsm6 libxext6 libxrender-dev && \
+            pip install --upgrade pip && \
+            pip install pillow lxml Cython contextlib2 matplotlib numpy scipy pycocotools pyyaml test-generator && \
+            cd /mnt/src/tf/research && \
+            export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim && \
+            mkdir -p /mnt/src/protoc && \
+            wget -P /mnt/src/protoc https://github.com/protocolbuffers/protobuf/releases/download/v3.10.1/protoc-3.10.1-linux-x86_64.zip && \
+            cd /mnt/src/protoc/ && \
+            unzip protoc-3.10.1-linux-x86_64.zip && \
+            cd /mnt/src/tf/research/ && \
+            /mnt/src/protoc/bin/protoc object_detection/protos/*.proto --python_out=. && \
+            cd /mnt/src/train/workflows/tf-object-detection-training && \
+            python main.py \
+              --extras="{{workflow.parameters.hyperparameters}}" \
+              --model="{{workflow.parameters.cvat-model}}" \
+              --num_classes="{{workflow.parameters.cvat-num-classes}}" \
+              --sys_finetune_checkpoint="{{workflow.parameters.cvat-finetune-checkpoint}}" \
+              --from_preprocessing=True
         command:
           - sh
-          - '-c'
-        env:
-          - name: ONEPANEL_INTERACTIVE_SIDECAR
-            value: 'true'
+          - -c
+        image: '{{workflow.parameters.tf-image}}'
+        volumeMounts:
+          - mountPath: /mnt/data
+            name: processed-data
+          - mountPath: /mnt/output
+            name: output
+        workingDir: /mnt/src
+      nodeSelector:
+        "{{.NodePoolLabel}}": '{{workflow.parameters.sys-node-pool}}'
+      inputs:
+        artifacts:
+          - name: data
+            path: /mnt/data/datasets/
+          - name: models
+            path: /mnt/data/models/
+            optional: true
+            s3:
+              key: '{{workflow.parameters.cvat-finetune-checkpoint}}'
+          - git:
+              repo: https://github.com/tensorflow/models.git
+              revision: v1.13.0
+            name: src
+            path: /mnt/src/tf
+          - git:
+              repo: https://github.com/onepanelio/templates.git
+              revision: v0.18.0
+            name: tsrc
+            path: /mnt/src/train
+      name: tensorflow
+      outputs:
+        artifacts:
+          - name: model
+            optional: true
+            path: /mnt/output
+      sidecars:
+        - name: tensorboard
+          image: '{{workflow.parameters.tf-image}}'
+          command:
+            - sh
+            - '-c'
+          env:
+            - name: ONEPANEL_INTERACTIVE_SIDECAR
+              value: 'true'
+          args:
+            # Read logs from /mnt/output - this directory is auto-mounted from volumeMounts
+            - tensorboard --logdir /mnt/output/checkpoints/
+          ports:
+            - containerPort: 6006
+              name: tensorboard
+    - container:
         args:
-          # Read logs from /mnt/output - this directory is auto-mounted from volumeMounts
-          - tensorboard --logdir /mnt/output/checkpoints/
-        ports:
-          - containerPort: 6006
-            name: tensorboard
-  - container:
-      args:
-        - |
-          pip install --upgrade pip &&\
-          pip install opencv-python albumentations tqdm pyyaml pycocotools && \
-          cd /mnt/src/preprocessing/workflows/albumentations-preprocessing && \
-          python -u main.py \
-            --data_aug_params="{{workflow.parameters.preprocessing-parameters}}" \
-            --format="tfrecord" \
-            --val_split={{workflow.parameters.val-split}} \
-            --aug_steps={{workflow.parameters.num-augmentation-cycles}}
-      command:
-        - sh
-        - -c
-      image: '{{workflow.parameters.tf-image}}'
-      volumeMounts:
-        - mountPath: /mnt/data
-          name: data
-        - mountPath: /mnt/output
-          name: processed-data
-      workingDir: /mnt/src
-    nodeSelector:
-      {{.NodePoolLabel}}: '{{workflow.parameters.sys-node-pool}}'
-    inputs:
-      artifacts:
-        - name: data
-          path: /mnt/data/datasets/
-          s3:
-            key: '{{workflow.parameters.cvat-annotation-path}}'
-        - git:
-            repo: https://github.com/onepanelio/templates.git
-            revision: v0.18.0
-          name: src
-          path: /mnt/src/preprocessing
-    name: preprocessing
-    outputs:
-      artifacts:
-        - name: processed-data
-          optional: true
-          path: /mnt/output
-volumeClaimTemplates:
-  - metadata:
-      name: data
-    spec:
-      accessModes:
-        - ReadWriteOnce
-      resources:
-        requests:
-          storage: 200Gi
-  - metadata:
-      name: processed-data
-    spec:
-      accessModes:
-        - ReadWriteOnce
-      resources:
-        requests:
-          storage: 200Gi
-  - metadata:
-      name: output
-    spec:
-      accessModes:
-        - ReadWriteOnce
-      resources:
-        requests:
-          storage: 200Gi
+          - |
+            pip install --upgrade pip &&\
+            pip install opencv-python albumentations tqdm pyyaml pycocotools && \
+            cd /mnt/src/preprocessing/workflows/albumentations-preprocessing && \
+            python -u main.py \
+              --data_aug_params="{{workflow.parameters.preprocessing-parameters}}" \
+              --format="tfrecord" \
+              --val_split={{workflow.parameters.val-split}} \
+              --aug_steps={{workflow.parameters.num-augmentation-cycles}}
+        command:
+          - sh
+          - -c
+        image: '{{workflow.parameters.tf-image}}'
+        volumeMounts:
+          - mountPath: /mnt/data
+            name: data
+          - mountPath: /mnt/output
+            name: processed-data
+        workingDir: /mnt/src
+      nodeSelector:
+        "{{.NodePoolLabel}}": '{{workflow.parameters.sys-node-pool}}'
+      inputs:
+        artifacts:
+          - name: data
+            path: /mnt/data/datasets/
+            s3:
+              key: '{{workflow.parameters.cvat-annotation-path}}'
+          - git:
+              repo: https://github.com/onepanelio/templates.git
+              revision: v0.18.0
+            name: src
+            path: /mnt/src/preprocessing
+      name: preprocessing
+      outputs:
+        artifacts:
+          - name: processed-data
+            optional: true
+            path: /mnt/output
+  volumeClaimTemplates:
+    - metadata:
+        name: data
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi
+    - metadata:
+        name: processed-data
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi
+    - metadata:
+        name: output
+      spec:
+        accessModes:
+          - ReadWriteOnce
+        resources:
+          requests:
+            storage: 200Gi

db/yaml/workspaces/cvat/20200528140124.yaml

@@ -0,0 +1,105 @@
+metadata:
+  name: CVAT
+  kind: Workspace
+  version: 20200528140124
+  action: create
+  description: "Powerful and efficient Computer Vision Annotation Tool (CVAT)"
+spec:
+  # Docker containers that are part of the Workspace
+  containers:
+    - name: cvat-db
+      image: postgres:10-alpine
+      env:
+        - name: POSTGRES_USER
+          value: root
+        - name: POSTGRES_DB
+          value: cvat
+        - name: POSTGRES_HOST_AUTH_METHOD
+          value: trust
+        - name: PGDATA
+          value: /var/lib/psql/data
+      ports:
+        - containerPort: 5432
+          name: tcp
+      volumeMounts:
+        - name: db
+          mountPath: /var/lib/psql
+    - name: cvat-redis
+      image: redis:4.0-alpine
+      ports:
+        - containerPort: 6379
+          name: tcp
+    - name: cvat
+      image: onepanel/cvat:v0.7.0
+      env:
+        - name: DJANGO_MODWSGI_EXTRA_ARGS
+          value: ""
+        - name: ALLOWED_HOSTS
+          value: '*'
+        - name: CVAT_REDIS_HOST
+          value: localhost
+        - name: CVAT_POSTGRES_HOST
+          value: localhost
+        - name: CVAT_SHARE_URL
+          value: /home/django/data
+      ports:
+        - containerPort: 8080
+          name: http
+      volumeMounts:
+        - name: data
+          mountPath: /home/django/data
+        - name: keys
+          mountPath: /home/django/keys
+        - name: logs
+          mountPath: /home/django/logs
+        - name: models
+          mountPath: /home/django/models
+    - name: cvat-ui
+      image: onepanel/cvat-ui:v0.7.0
+      ports:
+        - containerPort: 80
+          name: http
+  ports:
+    - name: cvat-ui
+      port: 80
+      protocol: TCP
+      targetPort: 80
+    - name: cvat
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+  routes:
+    - match:
+        - uri:
+            regex: /api/.*|/git/.*|/tensorflow/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
+        - queryParams:
+            id:
+              regex: \d+.*
+      route:
+        - destination:
+            port:
+              number: 8080
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80
+  # DAG Workflow to be executed once a Workspace action completes
+  # postExecutionWorkflow:
+  #   entrypoint: main
+  #   templates:
+  #   - name: main
+  #     dag:
+  #        tasks:
+  #        - name: slack-notify
+  #          template: slack-notify
+  #   - name: slack-notify
+  #     container:
+  #       image: technosophos/slack-notify
+  #       args:
+  #       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #       command:
+  #       - sh
+  #       - -c

db/yaml/workspaces/cvat/20200626113635.yaml

@@ -0,0 +1,116 @@
+metadata:
+  name: CVAT
+  kind: Workspace
+  version: 20200626113635
+  action: update
+  description: "Powerful and efficient Computer Vision Annotation Tool (CVAT)"
+spec:
+  # Docker containers that are part of the Workspace
+  containers:
+    - name: cvat-db
+      image: postgres:10-alpine
+      env:
+        - name: POSTGRES_USER
+          value: root
+        - name: POSTGRES_DB
+          value: cvat
+        - name: POSTGRES_HOST_AUTH_METHOD
+          value: trust
+        - name: PGDATA
+          value: /var/lib/psql/data
+      ports:
+        - containerPort: 5432
+          name: tcp
+      volumeMounts:
+        - name: db
+          mountPath: /var/lib/psql
+    - name: cvat-redis
+      image: redis:4.0-alpine
+      ports:
+        - containerPort: 6379
+          name: tcp
+    - name: cvat
+      image: onepanel/cvat:v0.7.6
+      env:
+        - name: DJANGO_MODWSGI_EXTRA_ARGS
+          value: ""
+        - name: ALLOWED_HOSTS
+          value: '*'
+        - name: CVAT_REDIS_HOST
+          value: localhost
+        - name: CVAT_POSTGRES_HOST
+          value: localhost
+        - name: CVAT_SHARE_URL
+          value: /home/django/data
+      ports:
+        - containerPort: 8080
+          name: http
+      volumeMounts:
+        - name: data
+          mountPath: /home/django/data
+        - name: keys
+          mountPath: /home/django/keys
+        - name: logs
+          mountPath: /home/django/logs
+        - name: models
+          mountPath: /home/django/models
+        - name: share
+          mountPath: /home/django/share
+    - name: cvat-ui
+      image: onepanel/cvat-ui:v0.7.5
+      ports:
+        - containerPort: 80
+          name: http
+    - name: filesyncer
+      image: onepanel/filesyncer:v0.0.4
+      command: ['python3', 'main.py']
+      volumeMounts:
+        - name: share
+          mountPath: /mnt/share
+  ports:
+    - name: cvat-ui
+      port: 80
+      protocol: TCP
+      targetPort: 80
+    - name: cvat
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+  routes:
+    - match:
+        - uri:
+            regex: /api/.*|/git/.*|/tensorflow/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
+        - queryParams:
+            id:
+              regex: \d+.*
+      route:
+        - destination:
+            port:
+              number: 8080
+      timeout: 600s
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80
+      timeout: 600s
+  # DAG Workflow to be executed once a Workspace action completes (optional)
+  # Uncomment the lines below if you want to send Slack notifications
+  #postExecutionWorkflow:
+  #  entrypoint: main
+  #  templates:
+  #  - name: main
+  #    dag:
+  #       tasks:
+  #       - name: slack-notify
+  #         template: slack-notify
+  #  - name: slack-notify
+  #     container:
+  #       image: technosophos/slack-notify
+  #       args:
+  #       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #       command:
+  #       - sh
+  #       - -c

db/yaml/workspaces/cvat/20200704151301.yaml

@@ -0,0 +1,118 @@
+metadata:
+  name: CVAT
+  kind: Workspace
+  version: 20200704151301
+  action: update
+  description: "Powerful and efficient Computer Vision Annotation Tool (CVAT)"
+spec:
+  # Docker containers that are part of the Workspace
+  containers:
+    - name: cvat-db
+      image: postgres:10-alpine
+      env:
+        - name: POSTGRES_USER
+          value: root
+        - name: POSTGRES_DB
+          value: cvat
+        - name: POSTGRES_HOST_AUTH_METHOD
+          value: trust
+        - name: PGDATA
+          value: /var/lib/psql/data
+      ports:
+        - containerPort: 5432
+          name: tcp
+      volumeMounts:
+        - name: db
+          mountPath: /var/lib/psql
+    - name: cvat-redis
+      image: redis:4.0-alpine
+      ports:
+        - containerPort: 6379
+          name: tcp
+    - name: cvat
+      image: onepanel/cvat:v0.7.10-stable
+      env:
+        - name: DJANGO_MODWSGI_EXTRA_ARGS
+          value: ""
+        - name: ALLOWED_HOSTS
+          value: '*'
+        - name: CVAT_REDIS_HOST
+          value: localhost
+        - name: CVAT_POSTGRES_HOST
+          value: localhost
+        - name: CVAT_SHARE_URL
+          value: /home/django/data
+      ports:
+        - containerPort: 8080
+          name: http
+      volumeMounts:
+        - name: data
+          mountPath: /home/django/data
+        - name: keys
+          mountPath: /home/django/keys
+        - name: logs
+          mountPath: /home/django/logs
+        - name: models
+          mountPath: /home/django/models
+        - name: share
+          mountPath: /home/django/share
+    - name: cvat-ui
+      image: onepanel/cvat-ui:v0.7.10-stable
+      ports:
+        - containerPort: 80
+          name: http
+  # Uncomment following lines to enable S3 FileSyncer
+  # Refer to https://docs.onepanel.ai/docs/getting-started/use-cases/computervision/annotation/cvat/cvat_quick_guide#setting-up-environment-variables
+  #- name: filesyncer
+  #  image: onepanel/filesyncer:v0.0.4
+  #  command: ['python3', 'main.py']
+  #  volumeMounts:
+  #  - name: share
+  #    mountPath: /mnt/share
+  ports:
+    - name: cvat-ui
+      port: 80
+      protocol: TCP
+      targetPort: 80
+    - name: cvat
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+  routes:
+    - match:
+        - uri:
+            regex: /api/.*|/git/.*|/tensorflow/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
+        - queryParams:
+            id:
+              regex: \d+.*
+      route:
+        - destination:
+            port:
+              number: 8080
+      timeout: 600s
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80
+      timeout: 600s
+  # DAG Workflow to be executed once a Workspace action completes (optional)
+  # Uncomment the lines below if you want to send Slack notifications
+  #postExecutionWorkflow:
+  #  entrypoint: main
+  #  templates:
+  #  - name: main
+  #    dag:
+  #       tasks:
+  #       - name: slack-notify
+  #         template: slack-notify
+  #  - name: slack-notify
+  #     container:
+  #       image: technosophos/slack-notify
+  #       args:
+  #       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #       command:
+  #       - sh
+  #       - -c

db/yaml/workspaces/cvat/20200724220450.yaml

@@ -0,0 +1,135 @@
+metadata:
+  name: CVAT
+  kind: Workspace
+  version: 20200724220450
+  action: update
+  description: "Powerful and efficient Computer Vision Annotation Tool (CVAT)"
+spec:
+  # Workspace arguments
+  arguments:
+    parameters:
+      - name: storage-prefix
+        displayName: Directory in default object storage
+        value: data
+        hint: Location of data and models in default object storage, will continuously sync to '/mnt/share'
+  containers:
+    - name: cvat-db
+      image: postgres:10-alpine
+      env:
+        - name: POSTGRES_USER
+          value: root
+        - name: POSTGRES_DB
+          value: cvat
+        - name: POSTGRES_HOST_AUTH_METHOD
+          value: trust
+        - name: PGDATA
+          value: /var/lib/psql/data
+      ports:
+        - containerPort: 5432
+          name: tcp
+      volumeMounts:
+        - name: db
+          mountPath: /var/lib/psql
+    - name: cvat-redis
+      image: redis:4.0-alpine
+      ports:
+        - containerPort: 6379
+          name: tcp
+    - name: cvat
+      image: onepanel/cvat:v0.7.10-stable
+      env:
+        - name: DJANGO_MODWSGI_EXTRA_ARGS
+          value: ""
+        - name: ALLOWED_HOSTS
+          value: '*'
+        - name: CVAT_REDIS_HOST
+          value: localhost
+        - name: CVAT_POSTGRES_HOST
+          value: localhost
+        - name: CVAT_SHARE_URL
+          value: /home/django/data
+      ports:
+        - containerPort: 8080
+          name: http
+      volumeMounts:
+        - name: data
+          mountPath: /home/django/data
+        - name: keys
+          mountPath: /home/django/keys
+        - name: logs
+          mountPath: /home/django/logs
+        - name: models
+          mountPath: /home/django/models
+        - name: share
+          mountPath: /home/django/share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+    - name: cvat-ui
+      image: onepanel/cvat-ui:v0.7.10-stable
+      ports:
+        - containerPort: 80
+          name: http
+    # You can add multiple FileSyncer sidecar containers if needed
+    - name: filesyncer
+      image: "onepanel/filesyncer:{{.ArtifactRepositoryType}}"
+      args:
+        - download
+      env:
+        - name: FS_PATH
+          value: /mnt/share
+        - name: FS_PREFIX
+          value: '{{workspace.parameters.storage-prefix}}'
+      volumeMounts:
+        - name: share
+          mountPath: /mnt/share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: cvat-ui
+      port: 80
+      protocol: TCP
+      targetPort: 80
+    - name: cvat
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+  routes:
+    - match:
+        - uri:
+            regex: /api/.*|/git/.*|/tensorflow/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
+        - queryParams:
+            id:
+              regex: \d+.*
+      route:
+        - destination:
+            port:
+              number: 8080
+      timeout: 600s
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80
+      timeout: 600s
+  # DAG Workflow to be executed once a Workspace action completes (optional)
+  # Uncomment the lines below if you want to send Slack notifications
+  #postExecutionWorkflow:
+  #  entrypoint: main
+  #  templates:
+  #  - name: main
+  #    dag:
+  #       tasks:
+  #       - name: slack-notify
+  #         template: slack-notify
+  #  - name: slack-notify
+  #     container:
+  #       image: technosophos/slack-notify
+  #       args:
+  #       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #       command:
+  #       - sh
+  #       - -c

db/yaml/workspaces/cvat/20200812113316.yaml

@@ -0,0 +1,144 @@
+metadata:
+  name: CVAT
+  kind: Workspace
+  version: 20200812113316
+  action: update
+  description: "Powerful and efficient Computer Vision Annotation Tool (CVAT)"
+spec:
+  # Workspace arguments
+  arguments:
+    parameters:
+      - name: sync-directory
+        displayName: Directory to sync raw input and training output
+        value: workflow-data
+        hint: Location to sync raw input, models and checkpoints from default object storage. Note that this will be relative to the current namespace.
+  containers:
+    - name: cvat-db
+      image: postgres:10-alpine
+      env:
+        - name: POSTGRES_USER
+          value: root
+        - name: POSTGRES_DB
+          value: cvat
+        - name: POSTGRES_HOST_AUTH_METHOD
+          value: trust
+        - name: PGDATA
+          value: /var/lib/psql/data
+      ports:
+        - containerPort: 5432
+          name: tcp
+      volumeMounts:
+        - name: db
+          mountPath: /var/lib/psql
+    - name: cvat-redis
+      image: redis:4.0-alpine
+      ports:
+        - containerPort: 6379
+          name: tcp
+    - name: cvat
+      image: onepanel/cvat:0.12.0_cvat.1.0.0-beta.2-cuda
+      env:
+        - name: DJANGO_MODWSGI_EXTRA_ARGS
+          value: ""
+        - name: ALLOWED_HOSTS
+          value: '*'
+        - name: CVAT_REDIS_HOST
+          value: localhost
+        - name: CVAT_POSTGRES_HOST
+          value: localhost
+        - name: CVAT_SHARE_URL
+          value: /home/django/data
+        - name: ONEPANEL_SYNC_DIRECTORY
+          value: '{{workspace.parameters.sync-directory}}'
+        - name: NVIDIA_VISIBLE_DEVICES
+          value: all
+        - name: NVIDIA_DRIVER_CAPABILITIES
+          value: compute,utility
+        - name: NVIDIA_REQUIRE_CUDA
+          value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
+      ports:
+        - containerPort: 8080
+          name: http
+      volumeMounts:
+        - name: data
+          mountPath: /home/django/data
+        - name: keys
+          mountPath: /home/django/keys
+        - name: logs
+          mountPath: /home/django/logs
+        - name: models
+          mountPath: /home/django/models
+        - name: share
+          mountPath: /home/django/share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+    - name: cvat-ui
+      image: onepanel/cvat-ui:0.12.0_cvat.1.0.0-beta.2
+      ports:
+        - containerPort: 80
+          name: http
+    # You can add multiple FileSyncer sidecar containers if needed
+    - name: filesyncer
+      image: "onepanel/filesyncer:{{.ArtifactRepositoryType}}"
+      imagePullPolicy: Always
+      args:
+        - download
+      env:
+        - name: FS_PATH
+          value: /mnt/share
+        - name: FS_PREFIX
+          value: '{{workflow.namespace}}/{{workspace.parameters.sync-directory}}'
+      volumeMounts:
+        - name: share
+          mountPath: /mnt/share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: cvat-ui
+      port: 80
+      protocol: TCP
+      targetPort: 80
+    - name: cvat
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+  routes:
+    - match:
+        - uri:
+            regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
+        - queryParams:
+            id:
+              regex: \d+.*
+      route:
+        - destination:
+            port:
+              number: 8080
+      timeout: 600s
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80
+      timeout: 600s
+  # DAG Workflow to be executed once a Workspace action completes (optional)
+  # Uncomment the lines below if you want to send Slack notifications
+  #postExecutionWorkflow:
+  #  entrypoint: main
+  #  templates:
+  #  - name: main
+  #    dag:
+  #       tasks:
+  #       - name: slack-notify
+  #         template: slack-notify
+  #  - name: slack-notify
+  #     container:
+  #       image: technosophos/slack-notify
+  #       args:
+  #       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #       command:
+  #       - sh
+  #       - -c

db/yaml/workspaces/cvat/20200824101905.yaml

@@ -0,0 +1,144 @@
+metadata:
+  name: CVAT
+  kind: Workspace
+  version: 20200824101905
+  action: update
+  description: "Powerful and efficient Computer Vision Annotation Tool (CVAT)"
+spec:
+  # Workspace arguments
+  arguments:
+    parameters:
+      - name: sync-directory
+        displayName: Directory to sync raw input and training output
+        value: workflow-data
+        hint: Location to sync raw input, models and checkpoints from default object storage. Note that this will be relative to the current namespace.
+  containers:
+    - name: cvat-db
+      image: postgres:10-alpine
+      env:
+        - name: POSTGRES_USER
+          value: root
+        - name: POSTGRES_DB
+          value: cvat
+        - name: POSTGRES_HOST_AUTH_METHOD
+          value: trust
+        - name: PGDATA
+          value: /var/lib/psql/data
+      ports:
+        - containerPort: 5432
+          name: tcp
+      volumeMounts:
+        - name: db
+          mountPath: /var/lib/psql
+    - name: cvat-redis
+      image: redis:4.0-alpine
+      ports:
+        - containerPort: 6379
+          name: tcp
+    - name: cvat
+      image: onepanel/cvat:0.12.0-rc.6_cvat.1.0.0
+      env:
+        - name: DJANGO_MODWSGI_EXTRA_ARGS
+          value: ""
+        - name: ALLOWED_HOSTS
+          value: '*'
+        - name: CVAT_REDIS_HOST
+          value: localhost
+        - name: CVAT_POSTGRES_HOST
+          value: localhost
+        - name: CVAT_SHARE_URL
+          value: /home/django/data
+        - name: ONEPANEL_SYNC_DIRECTORY
+          value: '{{workspace.parameters.sync-directory}}'
+        - name: NVIDIA_VISIBLE_DEVICES
+          value: all
+        - name: NVIDIA_DRIVER_CAPABILITIES
+          value: compute,utility
+        - name: NVIDIA_REQUIRE_CUDA
+          value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
+      ports:
+        - containerPort: 8080
+          name: http
+      volumeMounts:
+        - name: data
+          mountPath: /home/django/data
+        - name: keys
+          mountPath: /home/django/keys
+        - name: logs
+          mountPath: /home/django/logs
+        - name: models
+          mountPath: /home/django/models
+        - name: share
+          mountPath: /home/django/share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+    - name: cvat-ui
+      image: onepanel/cvat-ui:0.12.0-rc.1_cvat.1.0.0
+      ports:
+        - containerPort: 80
+          name: http
+    # You can add multiple FileSyncer sidecar containers if needed
+    - name: filesyncer
+      image: "onepanel/filesyncer:{{.ArtifactRepositoryType}}"
+      imagePullPolicy: Always
+      args:
+        - download
+      env:
+        - name: FS_PATH
+          value: /mnt/share
+        - name: FS_PREFIX
+          value: '{{workflow.namespace}}/{{workspace.parameters.sync-directory}}'
+      volumeMounts:
+        - name: share
+          mountPath: /mnt/share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: cvat-ui
+      port: 80
+      protocol: TCP
+      targetPort: 80
+    - name: cvat
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+  routes:
+    - match:
+        - uri:
+            regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
+        - queryParams:
+            id:
+              regex: \d+.*
+      route:
+        - destination:
+            port:
+              number: 8080
+      timeout: 600s
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80
+      timeout: 600s
+  # DAG Workflow to be executed once a Workspace action completes (optional)
+  # Uncomment the lines below if you want to send Slack notifications
+  #postExecutionWorkflow:
+  #  entrypoint: main
+  #  templates:
+  #  - name: main
+  #    dag:
+  #       tasks:
+  #       - name: slack-notify
+  #         template: slack-notify
+  #  - name: slack-notify
+  #     container:
+  #       image: technosophos/slack-notify
+  #       args:
+  #       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #       command:
+  #       - sh
+  #       - -c

db/yaml/workspaces/cvat/20200825154403.yaml

@@ -0,0 +1,144 @@
+metadata:
+  name: CVAT
+  kind: Workspace
+  version: 20200825154403
+  action: update
+  description: "Powerful and efficient Computer Vision Annotation Tool (CVAT)"
+spec:
+  # Workspace arguments
+  arguments:
+    parameters:
+      - name: sync-directory
+        displayName: Directory to sync raw input and training output
+        value: workflow-data
+        hint: Location to sync raw input, models and checkpoints from default object storage. Note that this will be relative to the current namespace.
+  containers:
+    - name: cvat-db
+      image: postgres:10-alpine
+      env:
+        - name: POSTGRES_USER
+          value: root
+        - name: POSTGRES_DB
+          value: cvat
+        - name: POSTGRES_HOST_AUTH_METHOD
+          value: trust
+        - name: PGDATA
+          value: /var/lib/psql/data
+      ports:
+        - containerPort: 5432
+          name: tcp
+      volumeMounts:
+        - name: db
+          mountPath: /var/lib/psql
+    - name: cvat-redis
+      image: redis:4.0-alpine
+      ports:
+        - containerPort: 6379
+          name: tcp
+    - name: cvat
+      image: onepanel/cvat:0.12.0_cvat.1.0.0
+      env:
+        - name: DJANGO_MODWSGI_EXTRA_ARGS
+          value: ""
+        - name: ALLOWED_HOSTS
+          value: '*'
+        - name: CVAT_REDIS_HOST
+          value: localhost
+        - name: CVAT_POSTGRES_HOST
+          value: localhost
+        - name: CVAT_SHARE_URL
+          value: /home/django/data
+        - name: ONEPANEL_SYNC_DIRECTORY
+          value: '{{workspace.parameters.sync-directory}}'
+        - name: NVIDIA_VISIBLE_DEVICES
+          value: all
+        - name: NVIDIA_DRIVER_CAPABILITIES
+          value: compute,utility
+        - name: NVIDIA_REQUIRE_CUDA
+          value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
+      ports:
+        - containerPort: 8080
+          name: http
+      volumeMounts:
+        - name: data
+          mountPath: /home/django/data
+        - name: keys
+          mountPath: /home/django/keys
+        - name: logs
+          mountPath: /home/django/logs
+        - name: models
+          mountPath: /home/django/models
+        - name: share
+          mountPath: /home/django/share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+    - name: cvat-ui
+      image: onepanel/cvat-ui:0.12.0_cvat.1.0.0
+      ports:
+        - containerPort: 80
+          name: http
+    # You can add multiple FileSyncer sidecar containers if needed
+    - name: filesyncer
+      image: "onepanel/filesyncer:{{.ArtifactRepositoryType}}"
+      imagePullPolicy: Always
+      args:
+        - download
+      env:
+        - name: FS_PATH
+          value: /mnt/share
+        - name: FS_PREFIX
+          value: '{{workflow.namespace}}/{{workspace.parameters.sync-directory}}'
+      volumeMounts:
+        - name: share
+          mountPath: /mnt/share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: cvat-ui
+      port: 80
+      protocol: TCP
+      targetPort: 80
+    - name: cvat
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+  routes:
+    - match:
+        - uri:
+            regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
+        - queryParams:
+            id:
+              regex: \d+.*
+      route:
+        - destination:
+            port:
+              number: 8080
+      timeout: 600s
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80
+      timeout: 600s
+  # DAG Workflow to be executed once a Workspace action completes (optional)
+  # Uncomment the lines below if you want to send Slack notifications
+  #postExecutionWorkflow:
+  #  entrypoint: main
+  #  templates:
+  #  - name: main
+  #    dag:
+  #       tasks:
+  #       - name: slack-notify
+  #         template: slack-notify
+  #  - name: slack-notify
+  #     container:
+  #       image: technosophos/slack-notify
+  #       args:
+  #       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #       command:
+  #       - sh
+  #       - -c

db/yaml/workspaces/cvat/20200826185926.yaml

@@ -0,0 +1,156 @@
+metadata:
+  name: CVAT
+  kind: Workspace
+  version: 20200826185926
+  action: update
+  description: "Powerful and efficient Computer Vision Annotation Tool (CVAT)"
+spec:
+  # Workspace arguments
+  arguments:
+    parameters:
+      - name: sync-directory
+        displayName: Directory to sync raw input and training output
+        value: workflow-data
+        hint: Location to sync raw input, models and checkpoints from default object storage. Note that this will be relative to the current namespace.
+  containers:
+    - name: cvat-db
+      image: postgres:10-alpine
+      env:
+        - name: POSTGRES_USER
+          value: root
+        - name: POSTGRES_DB
+          value: cvat
+        - name: POSTGRES_HOST_AUTH_METHOD
+          value: trust
+        - name: PGDATA
+          value: /var/lib/psql/data
+      ports:
+        - containerPort: 5432
+          name: tcp
+      volumeMounts:
+        - name: db
+          mountPath: /var/lib/psql
+    - name: cvat-redis
+      image: redis:4.0-alpine
+      ports:
+        - containerPort: 6379
+          name: tcp
+    - name: cvat
+      image: onepanel/cvat:0.12.0_cvat.1.0.0
+      env:
+        - name: DJANGO_MODWSGI_EXTRA_ARGS
+          value: ""
+        - name: ALLOWED_HOSTS
+          value: '*'
+        - name: CVAT_REDIS_HOST
+          value: localhost
+        - name: CVAT_POSTGRES_HOST
+          value: localhost
+        - name: CVAT_SHARE_URL
+          value: /home/django/data
+        - name: ONEPANEL_SYNC_DIRECTORY
+          value: '{{workspace.parameters.sync-directory}}'
+        - name: NVIDIA_VISIBLE_DEVICES
+          value: all
+        - name: NVIDIA_DRIVER_CAPABILITIES
+          value: compute,utility
+        - name: NVIDIA_REQUIRE_CUDA
+          value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
+      ports:
+        - containerPort: 8080
+          name: http
+      volumeMounts:
+        - name: data
+          mountPath: /home/django/data
+        - name: keys
+          mountPath: /home/django/keys
+        - name: logs
+          mountPath: /home/django/logs
+        - name: models
+          mountPath: /home/django/models
+        - name: share
+          mountPath: /home/django/share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+    - name: cvat-ui
+      image: onepanel/cvat-ui:0.12.0_cvat.1.0.0
+      ports:
+        - containerPort: 80
+          name: http
+    # You can add multiple FileSyncer sidecar containers if needed
+    - name: filesyncer
+      image: "onepanel/filesyncer:{{.ArtifactRepositoryType}}"
+      imagePullPolicy: Always
+      args:
+        - download
+        - -server-prefix=/sys/filesyncer
+      env:
+        - name: FS_PATH
+          value: /mnt/share
+        - name: FS_PREFIX
+          value: '{{workflow.namespace}}/{{workspace.parameters.sync-directory}}'
+      volumeMounts:
+        - name: share
+          mountPath: /mnt/share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: cvat-ui
+      port: 80
+      protocol: TCP
+      targetPort: 80
+    - name: cvat
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+    - name: fs
+      port: 8888
+      protocol: TCP
+      targetPort: 8888
+  routes:
+    - match:
+        - uri:
+            prefix: /sys/filesyncer
+      route:
+        - destination:
+            port:
+              number: 8888
+    - match:
+        - uri:
+            regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
+        - queryParams:
+            id:
+              regex: \d+.*
+      route:
+        - destination:
+            port:
+              number: 8080
+      timeout: 600s
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80
+      timeout: 600s
+  # DAG Workflow to be executed once a Workspace action completes (optional)
+  # Uncomment the lines below if you want to send Slack notifications
+  #postExecutionWorkflow:
+  #  entrypoint: main
+  #  templates:
+  #  - name: main
+  #    dag:
+  #       tasks:
+  #       - name: slack-notify
+  #         template: slack-notify
+  #  - name: slack-notify
+  #     container:
+  #       image: technosophos/slack-notify
+  #       args:
+  #       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #       command:
+  #       - sh
+  #       - -c

db/yaml/workspaces/cvat/20201001070806.yaml

@@ -0,0 +1,154 @@
+metadata:
+  name: CVAT
+  kind: Workspace
+  version: 20201001070806
+  action: update
+  description: "Powerful and efficient Computer Vision Annotation Tool (CVAT)"
+spec:
+  # Workspace arguments
+  arguments:
+    parameters:
+      - name: sync-directory
+        displayName: Directory to sync raw input and training output
+        value: workflow-data
+        hint: Location to sync raw input, models and checkpoints from default object storage. Note that this will be relative to the current namespace.
+  containers:
+    - name: cvat-db
+      image: postgres:10-alpine
+      env:
+        - name: POSTGRES_USER
+          value: root
+        - name: POSTGRES_DB
+          value: cvat
+        - name: POSTGRES_HOST_AUTH_METHOD
+          value: trust
+        - name: PGDATA
+          value: /var/lib/psql/data
+      ports:
+        - containerPort: 5432
+          name: tcp
+      volumeMounts:
+        - name: db
+          mountPath: /var/lib/psql
+    - name: cvat-redis
+      image: redis:4.0-alpine
+      ports:
+        - containerPort: 6379
+          name: tcp
+    - name: cvat
+      image: onepanel/cvat:0.12.1_cvat.1.0.0
+      env:
+        - name: DJANGO_MODWSGI_EXTRA_ARGS
+          value: ""
+        - name: ALLOWED_HOSTS
+          value: '*'
+        - name: CVAT_REDIS_HOST
+          value: localhost
+        - name: CVAT_POSTGRES_HOST
+          value: localhost
+        - name: CVAT_SHARE_URL
+          value: /home/django/data
+        - name: ONEPANEL_SYNC_DIRECTORY
+          value: '{{workspace.parameters.sync-directory}}'
+        - name: NVIDIA_VISIBLE_DEVICES
+          value: all
+        - name: NVIDIA_DRIVER_CAPABILITIES
+          value: compute,utility
+        - name: NVIDIA_REQUIRE_CUDA
+          value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
+      ports:
+        - containerPort: 8080
+          name: http
+      volumeMounts:
+        - name: data
+          mountPath: /home/django/data
+        - name: keys
+          mountPath: /home/django/keys
+        - name: logs
+          mountPath: /home/django/logs
+        - name: models
+          mountPath: /home/django/models
+        - name: share
+          mountPath: /home/django/share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+    - name: cvat-ui
+      image: onepanel/cvat-ui:0.12.1_cvat.1.0.0
+      ports:
+        - containerPort: 80
+          name: http
+    # You can add multiple FileSyncer sidecar containers if needed
+    - name: filesyncer
+      image: "onepanel/filesyncer:{{.ArtifactRepositoryType}}"
+      imagePullPolicy: Always
+      args:
+        - download
+        - -server-prefix=/sys/filesyncer
+      env:
+        - name: FS_PATH
+          value: /mnt/share
+        - name: FS_PREFIX
+          value: '{{workflow.namespace}}/{{workspace.parameters.sync-directory}}'
+      volumeMounts:
+        - name: share
+          mountPath: /mnt/share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: cvat-ui
+      port: 80
+      protocol: TCP
+      targetPort: 80
+    - name: cvat
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+    - name: fs
+      port: 8888
+      protocol: TCP
+      targetPort: 8888
+  routes:
+    - match:
+        - uri:
+            prefix: /sys/filesyncer
+      route:
+        - destination:
+            port:
+              number: 8888
+    - match:
+        - uri:
+            regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
+        - queryParams:
+            id:
+              regex: \d+.*
+      route:
+        - destination:
+            port:
+              number: 8080
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80
+  # DAG Workflow to be executed once a Workspace action completes (optional)
+  # Uncomment the lines below if you want to send Slack notifications
+  #postExecutionWorkflow:
+  #  entrypoint: main
+  #  templates:
+  #  - name: main
+  #    dag:
+  #       tasks:
+  #       - name: slack-notify
+  #         template: slack-notify
+  #  - name: slack-notify
+  #     container:
+  #       image: technosophos/slack-notify
+  #       args:
+  #       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #       command:
+  #       - sh
+  #       - -c

db/yaml/workspaces/cvat/20201016170415.yaml

@@ -1,147 +1,154 @@
-# Workspace arguments
-arguments:
-  parameters:
-    - name: sync-directory
-      displayName: Directory to sync raw input and training output
-      value: workflow-data
-      hint: Location to sync raw input, models and checkpoints from default object storage. Note that this will be relative to the current namespace.
-containers:
-  - name: cvat-db
-    image: postgres:10-alpine
-    env:
-      - name: POSTGRES_USER
-        value: root
-      - name: POSTGRES_DB
-        value: cvat
-      - name: POSTGRES_HOST_AUTH_METHOD
-        value: trust
-      - name: PGDATA
-        value: /var/lib/psql/data
-    ports:
-      - containerPort: 5432
-        name: tcp
-    volumeMounts:
-      - name: db
-        mountPath: /var/lib/psql
-  - name: cvat-redis
-    image: redis:4.0-alpine
-    ports:
-      - containerPort: 6379
-        name: tcp
-  - name: cvat
-    image: onepanel/cvat:0.14.0_cvat.1.0.0
-    env:
-      - name: DJANGO_MODWSGI_EXTRA_ARGS
-        value: ""
-      - name: ALLOWED_HOSTS
-        value: '*'
-      - name: CVAT_REDIS_HOST
-        value: localhost
-      - name: CVAT_POSTGRES_HOST
-        value: localhost
-      - name: CVAT_SHARE_URL
-        value: /home/django/data
-      - name: ONEPANEL_SYNC_DIRECTORY
-        value: '{{workspace.parameters.sync-directory}}'
-      - name: NVIDIA_VISIBLE_DEVICES
-        value: all
-      - name: NVIDIA_DRIVER_CAPABILITIES
-        value: compute,utility
-      - name: NVIDIA_REQUIRE_CUDA
-        value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
-    ports:
-      - containerPort: 8080
-        name: http
-    volumeMounts:
-      - name: data
-        mountPath: /home/django/data
-      - name: keys
-        mountPath: /home/django/keys
-      - name: logs
-        mountPath: /home/django/logs
-      - name: models
-        mountPath: /home/django/models
-      - name: share
-        mountPath: /home/django/share
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-  - name: cvat-ui
-    image: onepanel/cvat-ui:0.14.0_cvat.1.0.0
-    ports:
-      - containerPort: 80
-        name: http
-  # You can add multiple FileSyncer sidecar containers if needed
-  - name: filesyncer
-    image: onepanel/filesyncer:{{.ArtifactRepositoryType}}
-    imagePullPolicy: Always
-    args:
-      - download
-      - -server-prefix=/sys/filesyncer
-    env:
-      - name: FS_PATH
-        value: /mnt/share
-      - name: FS_PREFIX
-        value: '{{workflow.namespace}}/{{workspace.parameters.sync-directory}}'
-    volumeMounts:
-      - name: share
-        mountPath: /mnt/share
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-ports:
-  - name: cvat-ui
-    port: 80
-    protocol: TCP
-    targetPort: 80
-  - name: cvat
-    port: 8080
-    protocol: TCP
-    targetPort: 8080
-  - name: fs
-    port: 8888
-    protocol: TCP
-    targetPort: 8888
-routes:
-  - match:
-      - uri:
-          prefix: /sys/filesyncer
-    route:
-      - destination:
-          port:
-            number: 8888
-  - match:
-      - uri:
-          regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
-      - queryParams:
-          id:
-            regex: \d+.*
-    route:
-      - destination:
-          port:
-            number: 8080
-  - match:
-      - uri:
-          prefix: /
-    route:
-      - destination:
-          port:
-            number: 80
-# DAG Workflow to be executed once a Workspace action completes (optional)
-# Uncomment the lines below if you want to send Slack notifications
-#postExecutionWorkflow:
-#  entrypoint: main
-#  templates:
-#  - name: main
-#    dag:
-#       tasks:
-#       - name: slack-notify
-#         template: slack-notify
-#  - name: slack-notify
-#     container:
-#       image: technosophos/slack-notify
-#       args:
-#       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
-#       command:
-#       - sh
-#       - -c
+metadata:
+  name: CVAT
+  kind: Workspace
+  version: 20201016170415
+  action: update
+  description: "Powerful and efficient Computer Vision Annotation Tool (CVAT)"
+spec:
+  # Workspace arguments
+  arguments:
+    parameters:
+      - name: sync-directory
+        displayName: Directory to sync raw input and training output
+        value: workflow-data
+        hint: Location to sync raw input, models and checkpoints from default object storage. Note that this will be relative to the current namespace.
+  containers:
+    - name: cvat-db
+      image: postgres:10-alpine
+      env:
+        - name: POSTGRES_USER
+          value: root
+        - name: POSTGRES_DB
+          value: cvat
+        - name: POSTGRES_HOST_AUTH_METHOD
+          value: trust
+        - name: PGDATA
+          value: /var/lib/psql/data
+      ports:
+        - containerPort: 5432
+          name: tcp
+      volumeMounts:
+        - name: db
+          mountPath: /var/lib/psql
+    - name: cvat-redis
+      image: redis:4.0-alpine
+      ports:
+        - containerPort: 6379
+          name: tcp
+    - name: cvat
+      image: onepanel/cvat:0.14.0_cvat.1.0.0
+      env:
+        - name: DJANGO_MODWSGI_EXTRA_ARGS
+          value: ""
+        - name: ALLOWED_HOSTS
+          value: '*'
+        - name: CVAT_REDIS_HOST
+          value: localhost
+        - name: CVAT_POSTGRES_HOST
+          value: localhost
+        - name: CVAT_SHARE_URL
+          value: /home/django/data
+        - name: ONEPANEL_SYNC_DIRECTORY
+          value: '{{workspace.parameters.sync-directory}}'
+        - name: NVIDIA_VISIBLE_DEVICES
+          value: all
+        - name: NVIDIA_DRIVER_CAPABILITIES
+          value: compute,utility
+        - name: NVIDIA_REQUIRE_CUDA
+          value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
+      ports:
+        - containerPort: 8080
+          name: http
+      volumeMounts:
+        - name: data
+          mountPath: /home/django/data
+        - name: keys
+          mountPath: /home/django/keys
+        - name: logs
+          mountPath: /home/django/logs
+        - name: models
+          mountPath: /home/django/models
+        - name: share
+          mountPath: /home/django/share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+    - name: cvat-ui
+      image: onepanel/cvat-ui:0.14.0_cvat.1.0.0
+      ports:
+        - containerPort: 80
+          name: http
+    # You can add multiple FileSyncer sidecar containers if needed
+    - name: filesyncer
+      image: "onepanel/filesyncer:{{.ArtifactRepositoryType}}"
+      imagePullPolicy: Always
+      args:
+        - download
+        - -server-prefix=/sys/filesyncer
+      env:
+        - name: FS_PATH
+          value: /mnt/share
+        - name: FS_PREFIX
+          value: '{{workflow.namespace}}/{{workspace.parameters.sync-directory}}'
+      volumeMounts:
+        - name: share
+          mountPath: /mnt/share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: cvat-ui
+      port: 80
+      protocol: TCP
+      targetPort: 80
+    - name: cvat
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+    - name: fs
+      port: 8888
+      protocol: TCP
+      targetPort: 8888
+  routes:
+    - match:
+        - uri:
+            prefix: /sys/filesyncer
+      route:
+        - destination:
+            port:
+              number: 8888
+    - match:
+        - uri:
+            regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
+        - queryParams:
+            id:
+              regex: \d+.*
+      route:
+        - destination:
+            port:
+              number: 8080
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80
+  # DAG Workflow to be executed once a Workspace action completes (optional)
+  # Uncomment the lines below if you want to send Slack notifications
+  #postExecutionWorkflow:
+  #  entrypoint: main
+  #  templates:
+  #  - name: main
+  #    dag:
+  #       tasks:
+  #       - name: slack-notify
+  #         template: slack-notify
+  #  - name: slack-notify
+  #     container:
+  #       image: technosophos/slack-notify
+  #       args:
+  #       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #       command:
+  #       - sh
+  #       - -c

db/yaml/workspaces/cvat/20201102104048.yaml

@@ -1,159 +1,166 @@
-# Workspace arguments
-arguments:
-  parameters:
-    - name: sync-directory
-      displayName: Directory to sync raw input and training output
-      value: workflow-data
-      hint: Location (relative to current namespace) to sync raw input, models and checkpoints from default object storage to '/share'.
-containers:
-  - name: cvat-db
-    image: postgres:10-alpine
-    env:
-      - name: POSTGRES_USER
-        value: root
-      - name: POSTGRES_DB
-        value: cvat
-      - name: POSTGRES_HOST_AUTH_METHOD
-        value: trust
-      - name: PGDATA
-        value: /var/lib/psql/data
-    ports:
-      - containerPort: 5432
-        name: tcp
-    volumeMounts:
-      - name: db
-        mountPath: /var/lib/psql
-  - name: cvat-redis
-    image: redis:4.0-alpine
-    ports:
-      - containerPort: 6379
-        name: tcp
-  - name: cvat
-    image: onepanel/cvat:0.15.0_cvat.1.0.0
-    env:
-      - name: DJANGO_MODWSGI_EXTRA_ARGS
-        value: ""
-      - name: ALLOWED_HOSTS
-        value: '*'
-      - name: CVAT_REDIS_HOST
-        value: localhost
-      - name: CVAT_POSTGRES_HOST
-        value: localhost
-      - name: CVAT_SHARE_URL
-        value: /cvat/data
-      - name: CVAT_SHARE_DIR
-        value: /share
-      - name: CVAT_KEYS_DIR
-        value: /cvat/keys
-      - name: CVAT_DATA_DIR
-        value: /cvat/data
-      - name: CVAT_MODELS_DIR
-        value: /cvat/models
-      - name: CVAT_LOGS_DIR
-        value: /cvat/logs
-      - name: ONEPANEL_SYNC_DIRECTORY
-        value: '{{workspace.parameters.sync-directory}}'
-      - name: NVIDIA_VISIBLE_DEVICES
-        value: all
-      - name: NVIDIA_DRIVER_CAPABILITIES
-        value: compute,utility
-      - name: NVIDIA_REQUIRE_CUDA
-        value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
-    ports:
-      - containerPort: 8080
-        name: http
-    volumeMounts:
-      - name: cvat-data
-        mountPath: /cvat
-      - name: share
-        mountPath: /share
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-  - name: cvat-ui
-    image: onepanel/cvat-ui:0.15.0_cvat.1.0.0
-    ports:
-      - containerPort: 80
-        name: http
-  # You can add multiple FileSyncer sidecar containers if needed
-  - name: filesyncer
-    image: onepanel/filesyncer:s3
-    imagePullPolicy: Always
-    args:
-      - download
-      - -server-prefix=/sys/filesyncer
-    env:
-      - name: FS_PATH
-        value: /mnt/share
-      - name: FS_PREFIX
-        value: '{{workflow.namespace}}/{{workspace.parameters.sync-directory}}'
-    volumeMounts:
-      - name: share
-        mountPath: /mnt/share
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-ports:
-  - name: cvat-ui
-    port: 80
-    protocol: TCP
-    targetPort: 80
-  - name: cvat
-    port: 8080
-    protocol: TCP
-    targetPort: 8080
-  - name: fs
-    port: 8888
-    protocol: TCP
-    targetPort: 8888
-routes:
-  - match:
-      - uri:
-          prefix: /sys/filesyncer
-    route:
-      - destination:
-          port:
-            number: 8888
-  - match:
-      - uri:
-          regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
-      - queryParams:
-          id:
-            regex: \d+.*
-    route:
-      - destination:
-          port:
-            number: 8080
-  - match:
-      - uri:
-          prefix: /
-    route:
-      - destination:
-          port:
-            number: 80
-volumeClaimTemplates:
-  - metadata:
-      name: db
-    spec:
-      accessModes: ["ReadWriteOnce"]
-      resources:
-        requests:
-          storage: 20Gi
-# DAG Workflow to be executed once a Workspace action completes (optional)
-# Uncomment the lines below if you want to send Slack notifications
-#postExecutionWorkflow:
-#  entrypoint: main
-#  templates:
-#  - name: main
-#    dag:
-#       tasks:
-#       - name: slack-notify
-#         template: slack-notify
-#  - name: slack-notify
-#     container:
-#       image: technosophos/slack-notify
-#       args:
-#       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
-#       command:
-#       - sh
-#       - -c
+metadata:
+  name: CVAT
+  kind: Workspace
+  version: 20201102104048
+  action: update
+  description: "Powerful and efficient Computer Vision Annotation Tool (CVAT)"
+spec:
+  # Workspace arguments
+  arguments:
+    parameters:
+      - name: sync-directory
+        displayName: Directory to sync raw input and training output
+        value: workflow-data
+        hint: Location (relative to current namespace) to sync raw input, models and checkpoints from default object storage to '/share'.
+  containers:
+    - name: cvat-db
+      image: postgres:10-alpine
+      env:
+        - name: POSTGRES_USER
+          value: root
+        - name: POSTGRES_DB
+          value: cvat
+        - name: POSTGRES_HOST_AUTH_METHOD
+          value: trust
+        - name: PGDATA
+          value: /var/lib/psql/data
+      ports:
+        - containerPort: 5432
+          name: tcp
+      volumeMounts:
+        - name: db
+          mountPath: /var/lib/psql
+    - name: cvat-redis
+      image: redis:4.0-alpine
+      ports:
+        - containerPort: 6379
+          name: tcp
+    - name: cvat
+      image: onepanel/cvat:0.15.0_cvat.1.0.0
+      env:
+        - name: DJANGO_MODWSGI_EXTRA_ARGS
+          value: ""
+        - name: ALLOWED_HOSTS
+          value: '*'
+        - name: CVAT_REDIS_HOST
+          value: localhost
+        - name: CVAT_POSTGRES_HOST
+          value: localhost
+        - name: CVAT_SHARE_URL
+          value: /cvat/data
+        - name: CVAT_SHARE_DIR
+          value: /share
+        - name: CVAT_KEYS_DIR
+          value: /cvat/keys
+        - name: CVAT_DATA_DIR
+          value: /cvat/data
+        - name: CVAT_MODELS_DIR
+          value: /cvat/models
+        - name: CVAT_LOGS_DIR
+          value: /cvat/logs
+        - name: ONEPANEL_SYNC_DIRECTORY
+          value: '{{workspace.parameters.sync-directory}}'
+        - name: NVIDIA_VISIBLE_DEVICES
+          value: all
+        - name: NVIDIA_DRIVER_CAPABILITIES
+          value: compute,utility
+        - name: NVIDIA_REQUIRE_CUDA
+          value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
+      ports:
+        - containerPort: 8080
+          name: http
+      volumeMounts:
+        - name: cvat-data
+          mountPath: /cvat
+        - name: share
+          mountPath: /share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+    - name: cvat-ui
+      image: onepanel/cvat-ui:0.15.0_cvat.1.0.0
+      ports:
+        - containerPort: 80
+          name: http
+    # You can add multiple FileSyncer sidecar containers if needed
+    - name: filesyncer
+      image: onepanel/filesyncer:s3
+      imagePullPolicy: Always
+      args:
+        - download
+        - -server-prefix=/sys/filesyncer
+      env:
+        - name: FS_PATH
+          value: /mnt/share
+        - name: FS_PREFIX
+          value: '{{workflow.namespace}}/{{workspace.parameters.sync-directory}}'
+      volumeMounts:
+        - name: share
+          mountPath: /mnt/share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: cvat-ui
+      port: 80
+      protocol: TCP
+      targetPort: 80
+    - name: cvat
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+    - name: fs
+      port: 8888
+      protocol: TCP
+      targetPort: 8888
+  routes:
+    - match:
+        - uri:
+            prefix: /sys/filesyncer
+      route:
+        - destination:
+            port:
+              number: 8888
+    - match:
+        - uri:
+            regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
+        - queryParams:
+            id:
+              regex: \d+.*
+      route:
+        - destination:
+            port:
+              number: 8080
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80
+  volumeClaimTemplates:
+    - metadata:
+        name: db
+      spec:
+        accessModes: ["ReadWriteOnce"]
+        resources:
+          requests:
+            storage: 20Gi
+  # DAG Workflow to be executed once a Workspace action completes (optional)
+  # Uncomment the lines below if you want to send Slack notifications
+  #postExecutionWorkflow:
+  #  entrypoint: main
+  #  templates:
+  #  - name: main
+  #    dag:
+  #       tasks:
+  #       - name: slack-notify
+  #         template: slack-notify
+  #  - name: slack-notify
+  #     container:
+  #       image: technosophos/slack-notify
+  #       args:
+  #       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #       command:
+  #       - sh
+  #       - -c

db/yaml/workspaces/cvat/20201113094916.yaml

@@ -1,159 +1,166 @@
-# Workspace arguments
-arguments:
-  parameters:
-    - name: sync-directory
-      displayName: Directory to sync raw input and training output
-      value: workflow-data
-      hint: Location (relative to current namespace) to sync raw input, models and checkpoints from default object storage to '/share'.
-containers:
-  - name: cvat-db
-    image: postgres:10-alpine
-    env:
-      - name: POSTGRES_USER
-        value: root
-      - name: POSTGRES_DB
-        value: cvat
-      - name: POSTGRES_HOST_AUTH_METHOD
-        value: trust
-      - name: PGDATA
-        value: /var/lib/psql/data
-    ports:
-      - containerPort: 5432
-        name: tcp
-    volumeMounts:
-      - name: db
-        mountPath: /var/lib/psql
-  - name: cvat-redis
-    image: redis:4.0-alpine
-    ports:
-      - containerPort: 6379
-        name: tcp
-  - name: cvat
-    image: onepanel/cvat:0.16.0_cvat.1.0.0
-    env:
-      - name: DJANGO_MODWSGI_EXTRA_ARGS
-        value: ""
-      - name: ALLOWED_HOSTS
-        value: '*'
-      - name: CVAT_REDIS_HOST
-        value: localhost
-      - name: CVAT_POSTGRES_HOST
-        value: localhost
-      - name: CVAT_SHARE_URL
-        value: /cvat/data
-      - name: CVAT_SHARE_DIR
-        value: /share
-      - name: CVAT_KEYS_DIR
-        value: /cvat/keys
-      - name: CVAT_DATA_DIR
-        value: /cvat/data
-      - name: CVAT_MODELS_DIR
-        value: /cvat/models
-      - name: CVAT_LOGS_DIR
-        value: /cvat/logs
-      - name: ONEPANEL_SYNC_DIRECTORY
-        value: '{{workspace.parameters.sync-directory}}'
-      - name: NVIDIA_VISIBLE_DEVICES
-        value: all
-      - name: NVIDIA_DRIVER_CAPABILITIES
-        value: compute,utility
-      - name: NVIDIA_REQUIRE_CUDA
-        value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
-    ports:
-      - containerPort: 8080
-        name: http
-    volumeMounts:
-      - name: cvat-data
-        mountPath: /cvat
-      - name: share
-        mountPath: /share
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-  - name: cvat-ui
-    image: onepanel/cvat-ui:0.16.0_cvat.1.0.0
-    ports:
-      - containerPort: 80
-        name: http
-  # You can add multiple FileSyncer sidecar containers if needed
-  - name: filesyncer
-    image: onepanel/filesyncer:s3
-    imagePullPolicy: Always
-    args:
-      - download
-      - -server-prefix=/sys/filesyncer
-    env:
-      - name: FS_PATH
-        value: /mnt/share
-      - name: FS_PREFIX
-        value: '{{workflow.namespace}}/{{workspace.parameters.sync-directory}}'
-    volumeMounts:
-      - name: share
-        mountPath: /mnt/share
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-ports:
-  - name: cvat-ui
-    port: 80
-    protocol: TCP
-    targetPort: 80
-  - name: cvat
-    port: 8080
-    protocol: TCP
-    targetPort: 8080
-  - name: fs
-    port: 8888
-    protocol: TCP
-    targetPort: 8888
-routes:
-  - match:
-      - uri:
-          prefix: /sys/filesyncer
-    route:
-      - destination:
-          port:
-            number: 8888
-  - match:
-      - uri:
-          regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
-      - queryParams:
-          id:
-            regex: \d+.*
-    route:
-      - destination:
-          port:
-            number: 8080
-  - match:
-      - uri:
-          prefix: /
-    route:
-      - destination:
-          port:
-            number: 80
-volumeClaimTemplates:
-  - metadata:
-      name: db
-    spec:
-      accessModes: ["ReadWriteOnce"]
-      resources:
-        requests:
-          storage: 20Gi
-# DAG Workflow to be executed once a Workspace action completes (optional)
-# Uncomment the lines below if you want to send Slack notifications
-#postExecutionWorkflow:
-#  entrypoint: main
-#  templates:
-#  - name: main
-#    dag:
-#       tasks:
-#       - name: slack-notify
-#         template: slack-notify
-#  - name: slack-notify
-#     container:
-#       image: technosophos/slack-notify
-#       args:
-#       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
-#       command:
-#       - sh
-#       - -c
+metadata:
+  name: CVAT
+  kind: Workspace
+  version: 20201113094916
+  action: update
+  description: "Powerful and efficient Computer Vision Annotation Tool (CVAT)"
+spec:
+  # Workspace arguments
+  arguments:
+    parameters:
+      - name: sync-directory
+        displayName: Directory to sync raw input and training output
+        value: workflow-data
+        hint: Location (relative to current namespace) to sync raw input, models and checkpoints from default object storage to '/share'.
+  containers:
+    - name: cvat-db
+      image: postgres:10-alpine
+      env:
+        - name: POSTGRES_USER
+          value: root
+        - name: POSTGRES_DB
+          value: cvat
+        - name: POSTGRES_HOST_AUTH_METHOD
+          value: trust
+        - name: PGDATA
+          value: /var/lib/psql/data
+      ports:
+        - containerPort: 5432
+          name: tcp
+      volumeMounts:
+        - name: db
+          mountPath: /var/lib/psql
+    - name: cvat-redis
+      image: redis:4.0-alpine
+      ports:
+        - containerPort: 6379
+          name: tcp
+    - name: cvat
+      image: onepanel/cvat:0.16.0_cvat.1.0.0
+      env:
+        - name: DJANGO_MODWSGI_EXTRA_ARGS
+          value: ""
+        - name: ALLOWED_HOSTS
+          value: '*'
+        - name: CVAT_REDIS_HOST
+          value: localhost
+        - name: CVAT_POSTGRES_HOST
+          value: localhost
+        - name: CVAT_SHARE_URL
+          value: /cvat/data
+        - name: CVAT_SHARE_DIR
+          value: /share
+        - name: CVAT_KEYS_DIR
+          value: /cvat/keys
+        - name: CVAT_DATA_DIR
+          value: /cvat/data
+        - name: CVAT_MODELS_DIR
+          value: /cvat/models
+        - name: CVAT_LOGS_DIR
+          value: /cvat/logs
+        - name: ONEPANEL_SYNC_DIRECTORY
+          value: '{{workspace.parameters.sync-directory}}'
+        - name: NVIDIA_VISIBLE_DEVICES
+          value: all
+        - name: NVIDIA_DRIVER_CAPABILITIES
+          value: compute,utility
+        - name: NVIDIA_REQUIRE_CUDA
+          value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
+      ports:
+        - containerPort: 8080
+          name: http
+      volumeMounts:
+        - name: cvat-data
+          mountPath: /cvat
+        - name: share
+          mountPath: /share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+    - name: cvat-ui
+      image: onepanel/cvat-ui:0.16.0_cvat.1.0.0
+      ports:
+        - containerPort: 80
+          name: http
+    # You can add multiple FileSyncer sidecar containers if needed
+    - name: filesyncer
+      image: onepanel/filesyncer:s3
+      imagePullPolicy: Always
+      args:
+        - download
+        - -server-prefix=/sys/filesyncer
+      env:
+        - name: FS_PATH
+          value: /mnt/share
+        - name: FS_PREFIX
+          value: '{{workflow.namespace}}/{{workspace.parameters.sync-directory}}'
+      volumeMounts:
+        - name: share
+          mountPath: /mnt/share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: cvat-ui
+      port: 80
+      protocol: TCP
+      targetPort: 80
+    - name: cvat
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+    - name: fs
+      port: 8888
+      protocol: TCP
+      targetPort: 8888
+  routes:
+    - match:
+        - uri:
+            prefix: /sys/filesyncer
+      route:
+        - destination:
+            port:
+              number: 8888
+    - match:
+        - uri:
+            regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
+        - queryParams:
+            id:
+              regex: \d+.*
+      route:
+        - destination:
+            port:
+              number: 8080
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80
+  volumeClaimTemplates:
+    - metadata:
+        name: db
+      spec:
+        accessModes: ["ReadWriteOnce"]
+        resources:
+          requests:
+            storage: 20Gi
+  # DAG Workflow to be executed once a Workspace action completes (optional)
+  # Uncomment the lines below if you want to send Slack notifications
+  #postExecutionWorkflow:
+  #  entrypoint: main
+  #  templates:
+  #  - name: main
+  #    dag:
+  #       tasks:
+  #       - name: slack-notify
+  #         template: slack-notify
+  #  - name: slack-notify
+  #     container:
+  #       image: technosophos/slack-notify
+  #       args:
+  #       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #       command:
+  #       - sh
+  #       - -c

db/yaml/workspaces/cvat/20201115133046.yaml

@@ -1,161 +1,168 @@
-# Workspace arguments
-arguments:
-  parameters:
-    - name: sync-directory
-      displayName: Directory to sync raw input and training output
-      value: workflow-data
-      hint: Location (relative to current namespace) to sync raw input, models and checkpoints from default object storage to '/share'.
-containers:
-  - name: cvat-db
-    image: postgres:10-alpine
-    env:
-      - name: POSTGRES_USER
-        value: root
-      - name: POSTGRES_DB
-        value: cvat
-      - name: POSTGRES_HOST_AUTH_METHOD
-        value: trust
-      - name: PGDATA
-        value: /var/lib/psql/data
-    ports:
-      - containerPort: 5432
-        name: tcp
-    volumeMounts:
-      - name: db
-        mountPath: /var/lib/psql
-  - name: cvat-redis
-    image: redis:4.0-alpine
-    ports:
-      - containerPort: 6379
-        name: tcp
-  - name: cvat
-    image: onepanel/cvat:0.16.0_cvat.1.0.0
-    env:
-      - name: DJANGO_MODWSGI_EXTRA_ARGS
-        value: ""
-      - name: ALLOWED_HOSTS
-        value: '*'
-      - name: CVAT_REDIS_HOST
-        value: localhost
-      - name: CVAT_POSTGRES_HOST
-        value: localhost
-      - name: CVAT_SHARE_URL
-        value: /cvat/data
-      - name: CVAT_SHARE_DIR
-        value: /share
-      - name: CVAT_DATA_DIR
-        value: /cvat/data
-      - name: CVAT_MEDIA_DATA_DIR
-        value: /cvat/data/data
-      - name: CVAT_KEYS_DIR
-        value: /cvat/data/keys
-      - name: CVAT_MODELS_DIR
-        value: /cvat/data/models
-      - name: CVAT_LOGS_DIR
-        value: /cvat/logs
-      - name: ONEPANEL_SYNC_DIRECTORY
-        value: '{{workspace.parameters.sync-directory}}'
-      - name: NVIDIA_VISIBLE_DEVICES
-        value: all
-      - name: NVIDIA_DRIVER_CAPABILITIES
-        value: compute,utility
-      - name: NVIDIA_REQUIRE_CUDA
-        value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
-    ports:
-      - containerPort: 8080
-        name: http
-    volumeMounts:
-      - name: cvat-data
-        mountPath: /cvat
-      - name: share
-        mountPath: /share
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-  - name: cvat-ui
-    image: onepanel/cvat-ui:0.16.0_cvat.1.0.0
-    ports:
-      - containerPort: 80
-        name: http
-  # You can add multiple FileSyncer sidecar containers if needed
-  - name: filesyncer
-    image: onepanel/filesyncer:s3
-    imagePullPolicy: Always
-    args:
-      - download
-      - -server-prefix=/sys/filesyncer
-    env:
-      - name: FS_PATH
-        value: /mnt/share
-      - name: FS_PREFIX
-        value: '{{workflow.namespace}}/{{workspace.parameters.sync-directory}}'
-    volumeMounts:
-      - name: share
-        mountPath: /mnt/share
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-ports:
-  - name: cvat-ui
-    port: 80
-    protocol: TCP
-    targetPort: 80
-  - name: cvat
-    port: 8080
-    protocol: TCP
-    targetPort: 8080
-  - name: fs
-    port: 8888
-    protocol: TCP
-    targetPort: 8888
-routes:
-  - match:
-      - uri:
-          prefix: /sys/filesyncer
-    route:
-      - destination:
-          port:
-            number: 8888
-  - match:
-      - uri:
-          regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
-      - queryParams:
-          id:
-            regex: \d+.*
-    route:
-      - destination:
-          port:
-            number: 8080
-  - match:
-      - uri:
-          prefix: /
-    route:
-      - destination:
-          port:
-            number: 80
-volumeClaimTemplates:
-  - metadata:
-      name: db
-    spec:
-      accessModes: ["ReadWriteOnce"]
-      resources:
-        requests:
-          storage: 20Gi
-# DAG Workflow to be executed once a Workspace action completes (optional)
-# Uncomment the lines below if you want to send Slack notifications
-#postExecutionWorkflow:
-#  entrypoint: main
-#  templates:
-#  - name: main
-#    dag:
-#       tasks:
-#       - name: slack-notify
-#         template: slack-notify
-#  - name: slack-notify
-#     container:
-#       image: technosophos/slack-notify
-#       args:
-#       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
-#       command:
-#       - sh
-#       - -c
+metadata:
+  name: CVAT
+  kind: Workspace
+  version: 20201115133046
+  action: update
+  description: "Powerful and efficient Computer Vision Annotation Tool (CVAT)"
+spec:
+  # Workspace arguments
+  arguments:
+    parameters:
+      - name: sync-directory
+        displayName: Directory to sync raw input and training output
+        value: workflow-data
+        hint: Location (relative to current namespace) to sync raw input, models and checkpoints from default object storage to '/share'.
+  containers:
+    - name: cvat-db
+      image: postgres:10-alpine
+      env:
+        - name: POSTGRES_USER
+          value: root
+        - name: POSTGRES_DB
+          value: cvat
+        - name: POSTGRES_HOST_AUTH_METHOD
+          value: trust
+        - name: PGDATA
+          value: /var/lib/psql/data
+      ports:
+        - containerPort: 5432
+          name: tcp
+      volumeMounts:
+        - name: db
+          mountPath: /var/lib/psql
+    - name: cvat-redis
+      image: redis:4.0-alpine
+      ports:
+        - containerPort: 6379
+          name: tcp
+    - name: cvat
+      image: onepanel/cvat:0.16.0_cvat.1.0.0
+      env:
+        - name: DJANGO_MODWSGI_EXTRA_ARGS
+          value: ""
+        - name: ALLOWED_HOSTS
+          value: '*'
+        - name: CVAT_REDIS_HOST
+          value: localhost
+        - name: CVAT_POSTGRES_HOST
+          value: localhost
+        - name: CVAT_SHARE_URL
+          value: /cvat/data
+        - name: CVAT_SHARE_DIR
+          value: /share
+        - name: CVAT_DATA_DIR
+          value: /cvat/data
+        - name: CVAT_MEDIA_DATA_DIR
+          value: /cvat/data/data
+        - name: CVAT_KEYS_DIR
+          value: /cvat/data/keys
+        - name: CVAT_MODELS_DIR
+          value: /cvat/data/models
+        - name: CVAT_LOGS_DIR
+          value: /cvat/logs
+        - name: ONEPANEL_SYNC_DIRECTORY
+          value: '{{workspace.parameters.sync-directory}}'
+        - name: NVIDIA_VISIBLE_DEVICES
+          value: all
+        - name: NVIDIA_DRIVER_CAPABILITIES
+          value: compute,utility
+        - name: NVIDIA_REQUIRE_CUDA
+          value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
+      ports:
+        - containerPort: 8080
+          name: http
+      volumeMounts:
+        - name: cvat-data
+          mountPath: /cvat
+        - name: share
+          mountPath: /share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+    - name: cvat-ui
+      image: onepanel/cvat-ui:0.16.0_cvat.1.0.0
+      ports:
+        - containerPort: 80
+          name: http
+    # You can add multiple FileSyncer sidecar containers if needed
+    - name: filesyncer
+      image: onepanel/filesyncer:s3
+      imagePullPolicy: Always
+      args:
+        - download
+        - -server-prefix=/sys/filesyncer
+      env:
+        - name: FS_PATH
+          value: /mnt/share
+        - name: FS_PREFIX
+          value: '{{workflow.namespace}}/{{workspace.parameters.sync-directory}}'
+      volumeMounts:
+        - name: share
+          mountPath: /mnt/share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: cvat-ui
+      port: 80
+      protocol: TCP
+      targetPort: 80
+    - name: cvat
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+    - name: fs
+      port: 8888
+      protocol: TCP
+      targetPort: 8888
+  routes:
+    - match:
+        - uri:
+            prefix: /sys/filesyncer
+      route:
+        - destination:
+            port:
+              number: 8888
+    - match:
+        - uri:
+            regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
+        - queryParams:
+            id:
+              regex: \d+.*
+      route:
+        - destination:
+            port:
+              number: 8080
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80
+  volumeClaimTemplates:
+    - metadata:
+        name: db
+      spec:
+        accessModes: ["ReadWriteOnce"]
+        resources:
+          requests:
+            storage: 20Gi
+  # DAG Workflow to be executed once a Workspace action completes (optional)
+  # Uncomment the lines below if you want to send Slack notifications
+  #postExecutionWorkflow:
+  #  entrypoint: main
+  #  templates:
+  #  - name: main
+  #    dag:
+  #       tasks:
+  #       - name: slack-notify
+  #         template: slack-notify
+  #  - name: slack-notify
+  #     container:
+  #       image: technosophos/slack-notify
+  #       args:
+  #       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #       command:
+  #       - sh
+  #       - -c

db/yaml/workspaces/cvat/20201211161117.yaml

@@ -1,163 +1,170 @@
-# Workspace arguments
-arguments:
-  parameters:
-    - name: sync-directory
-      displayName: Directory to sync raw input and training output
-      value: workflow-data
-      hint: Location (relative to current namespace) to sync raw input, models and checkpoints from default object storage to '/share'.
-containers:
-  - name: cvat-db
-    image: postgres:10-alpine
-    env:
-      - name: POSTGRES_USER
-        value: root
-      - name: POSTGRES_DB
-        value: cvat
-      - name: POSTGRES_HOST_AUTH_METHOD
-        value: trust
-      - name: PGDATA
-        value: /var/lib/psql/data
-    ports:
-      - containerPort: 5432
-        name: tcp
-    volumeMounts:
-      - name: db
-        mountPath: /var/lib/psql
-  - name: cvat-redis
-    image: redis:4.0-alpine
-    ports:
-      - containerPort: 6379
-        name: tcp
-  - name: cvat
-    image: onepanel/cvat:0.16.0_cvat.1.0.0
-    env:
-      - name: DJANGO_MODWSGI_EXTRA_ARGS
-        value: ""
-      - name: ALLOWED_HOSTS
-        value: '*'
-      - name: CVAT_REDIS_HOST
-        value: localhost
-      - name: CVAT_POSTGRES_HOST
-        value: localhost
-      - name: CVAT_SHARE_URL
-        value: /cvat/data
-      - name: CVAT_SHARE_DIR
-        value: /share
-      - name: CVAT_DATA_DIR
-        value: /cvat/data
-      - name: CVAT_MEDIA_DATA_DIR
-        value: /cvat/data/data
-      - name: CVAT_KEYS_DIR
-        value: /cvat/data/keys
-      - name: CVAT_MODELS_DIR
-        value: /cvat/data/models
-      - name: CVAT_LOGS_DIR
-        value: /cvat/logs
-      - name: ONEPANEL_SYNC_DIRECTORY
-        value: '{{workspace.parameters.sync-directory}}'
-      - name: NVIDIA_VISIBLE_DEVICES
-        value: all
-      - name: NVIDIA_DRIVER_CAPABILITIES
-        value: compute,utility
-      - name: NVIDIA_REQUIRE_CUDA
-        value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
-      - name: ONEPANEL_MAIN_CONTAINER
-        value: 'true'
-    ports:
-      - containerPort: 8080
-        name: http
-    volumeMounts:
-      - name: cvat-data
-        mountPath: /cvat
-      - name: share
-        mountPath: /share
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-  - name: cvat-ui
-    image: onepanel/cvat-ui:0.16.0_cvat.1.0.0
-    ports:
-      - containerPort: 80
-        name: http
-  # You can add multiple FileSyncer sidecar containers if needed
-  - name: filesyncer
-    image: onepanel/filesyncer:s3
-    imagePullPolicy: Always
-    args:
-      - download
-      - -server-prefix=/sys/filesyncer
-    env:
-      - name: FS_PATH
-        value: /mnt/share
-      - name: FS_PREFIX
-        value: '{{workflow.namespace}}/{{workspace.parameters.sync-directory}}'
-    volumeMounts:
-      - name: share
-        mountPath: /mnt/share
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-ports:
-  - name: cvat-ui
-    port: 80
-    protocol: TCP
-    targetPort: 80
-  - name: cvat
-    port: 8080
-    protocol: TCP
-    targetPort: 8080
-  - name: fs
-    port: 8888
-    protocol: TCP
-    targetPort: 8888
-routes:
-  - match:
-      - uri:
-          prefix: /sys/filesyncer
-    route:
-      - destination:
-          port:
-            number: 8888
-  - match:
-      - uri:
-          regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
-      - queryParams:
-          id:
-            regex: \d+.*
-    route:
-      - destination:
-          port:
-            number: 8080
-  - match:
-      - uri:
-          prefix: /
-    route:
-      - destination:
-          port:
-            number: 80
-volumeClaimTemplates:
-  - metadata:
-      name: db
-    spec:
-      accessModes: ["ReadWriteOnce"]
-      resources:
-        requests:
-          storage: 20Gi
-# DAG Workflow to be executed once a Workspace action completes (optional)
-# Uncomment the lines below if you want to send Slack notifications
-#postExecutionWorkflow:
-#  entrypoint: main
-#  templates:
-#  - name: main
-#    dag:
-#       tasks:
-#       - name: slack-notify
-#         template: slack-notify
-#  - name: slack-notify
-#     container:
-#       image: technosophos/slack-notify
-#       args:
-#       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
-#       command:
-#       - sh
-#       - -c
+metadata:
+  name: CVAT
+  kind: Workspace
+  version: 20201211161117
+  action: update
+  description: "Powerful and efficient Computer Vision Annotation Tool (CVAT)"
+spec:
+  # Workspace arguments
+  arguments:
+    parameters:
+      - name: sync-directory
+        displayName: Directory to sync raw input and training output
+        value: workflow-data
+        hint: Location (relative to current namespace) to sync raw input, models and checkpoints from default object storage to '/share'.
+  containers:
+    - name: cvat-db
+      image: postgres:10-alpine
+      env:
+        - name: POSTGRES_USER
+          value: root
+        - name: POSTGRES_DB
+          value: cvat
+        - name: POSTGRES_HOST_AUTH_METHOD
+          value: trust
+        - name: PGDATA
+          value: /var/lib/psql/data
+      ports:
+        - containerPort: 5432
+          name: tcp
+      volumeMounts:
+        - name: db
+          mountPath: /var/lib/psql
+    - name: cvat-redis
+      image: redis:4.0-alpine
+      ports:
+        - containerPort: 6379
+          name: tcp
+    - name: cvat
+      image: onepanel/cvat:0.16.0_cvat.1.0.0
+      env:
+        - name: DJANGO_MODWSGI_EXTRA_ARGS
+          value: ""
+        - name: ALLOWED_HOSTS
+          value: '*'
+        - name: CVAT_REDIS_HOST
+          value: localhost
+        - name: CVAT_POSTGRES_HOST
+          value: localhost
+        - name: CVAT_SHARE_URL
+          value: /cvat/data
+        - name: CVAT_SHARE_DIR
+          value: /share
+        - name: CVAT_DATA_DIR
+          value: /cvat/data
+        - name: CVAT_MEDIA_DATA_DIR
+          value: /cvat/data/data
+        - name: CVAT_KEYS_DIR
+          value: /cvat/data/keys
+        - name: CVAT_MODELS_DIR
+          value: /cvat/data/models
+        - name: CVAT_LOGS_DIR
+          value: /cvat/logs
+        - name: ONEPANEL_SYNC_DIRECTORY
+          value: '{{workspace.parameters.sync-directory}}'
+        - name: NVIDIA_VISIBLE_DEVICES
+          value: all
+        - name: NVIDIA_DRIVER_CAPABILITIES
+          value: compute,utility
+        - name: NVIDIA_REQUIRE_CUDA
+          value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
+        - name: ONEPANEL_MAIN_CONTAINER
+          value: 'true'
+      ports:
+        - containerPort: 8080
+          name: http
+      volumeMounts:
+        - name: cvat-data
+          mountPath: /cvat
+        - name: share
+          mountPath: /share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+    - name: cvat-ui
+      image: onepanel/cvat-ui:0.16.0_cvat.1.0.0
+      ports:
+        - containerPort: 80
+          name: http
+    # You can add multiple FileSyncer sidecar containers if needed
+    - name: filesyncer
+      image: onepanel/filesyncer:s3
+      imagePullPolicy: Always
+      args:
+        - download
+        - -server-prefix=/sys/filesyncer
+      env:
+        - name: FS_PATH
+          value: /mnt/share
+        - name: FS_PREFIX
+          value: '{{workflow.namespace}}/{{workspace.parameters.sync-directory}}'
+      volumeMounts:
+        - name: share
+          mountPath: /mnt/share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: cvat-ui
+      port: 80
+      protocol: TCP
+      targetPort: 80
+    - name: cvat
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+    - name: fs
+      port: 8888
+      protocol: TCP
+      targetPort: 8888
+  routes:
+    - match:
+        - uri:
+            prefix: /sys/filesyncer
+      route:
+        - destination:
+            port:
+              number: 8888
+    - match:
+        - uri:
+            regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
+        - queryParams:
+            id:
+              regex: \d+.*
+      route:
+        - destination:
+            port:
+              number: 8080
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80
+  volumeClaimTemplates:
+    - metadata:
+        name: db
+      spec:
+        accessModes: ["ReadWriteOnce"]
+        resources:
+          requests:
+            storage: 20Gi
+  # DAG Workflow to be executed once a Workspace action completes (optional)
+  # Uncomment the lines below if you want to send Slack notifications
+  #postExecutionWorkflow:
+  #  entrypoint: main
+  #  templates:
+  #  - name: main
+  #    dag:
+  #       tasks:
+  #       - name: slack-notify
+  #         template: slack-notify
+  #  - name: slack-notify
+  #     container:
+  #       image: technosophos/slack-notify
+  #       args:
+  #       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #       command:
+  #       - sh
+  #       - -c

db/yaml/workspaces/cvat/20210107094725.yaml

@@ -1,163 +1,170 @@
-# Workspace arguments
-arguments:
-  parameters:
-    - name: sync-directory
-      displayName: Directory to sync raw input and training output
-      value: workflow-data
-      hint: Location (relative to current namespace) to sync raw input, models and checkpoints from default object storage to '/share'.
-containers:
-  - name: cvat-db
-    image: postgres:10-alpine
-    env:
-      - name: POSTGRES_USER
-        value: root
-      - name: POSTGRES_DB
-        value: cvat
-      - name: POSTGRES_HOST_AUTH_METHOD
-        value: trust
-      - name: PGDATA
-        value: /var/lib/psql/data
-    ports:
-      - containerPort: 5432
-        name: tcp
-    volumeMounts:
-      - name: db
-        mountPath: /var/lib/psql
-  - name: cvat-redis
-    image: redis:4.0-alpine
-    ports:
-      - containerPort: 6379
-        name: tcp
-  - name: cvat
-    image: onepanel/cvat:0.17.0_cvat.1.0.0
-    env:
-      - name: DJANGO_MODWSGI_EXTRA_ARGS
-        value: ""
-      - name: ALLOWED_HOSTS
-        value: '*'
-      - name: CVAT_REDIS_HOST
-        value: localhost
-      - name: CVAT_POSTGRES_HOST
-        value: localhost
-      - name: CVAT_SHARE_URL
-        value: /cvat/data
-      - name: CVAT_SHARE_DIR
-        value: /share
-      - name: CVAT_DATA_DIR
-        value: /cvat/data
-      - name: CVAT_MEDIA_DATA_DIR
-        value: /cvat/data/data
-      - name: CVAT_KEYS_DIR
-        value: /cvat/data/keys
-      - name: CVAT_MODELS_DIR
-        value: /cvat/data/models
-      - name: CVAT_LOGS_DIR
-        value: /cvat/logs
-      - name: ONEPANEL_SYNC_DIRECTORY
-        value: '{{workspace.parameters.sync-directory}}'
-      - name: NVIDIA_VISIBLE_DEVICES
-        value: all
-      - name: NVIDIA_DRIVER_CAPABILITIES
-        value: compute,utility
-      - name: NVIDIA_REQUIRE_CUDA
-        value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
-      - name: ONEPANEL_MAIN_CONTAINER
-        value: 'true'
-    ports:
-      - containerPort: 8080
-        name: http
-    volumeMounts:
-      - name: cvat-data
-        mountPath: /cvat
-      - name: share
-        mountPath: /share
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-  - name: cvat-ui
-    image: onepanel/cvat-ui:0.17.0_cvat.1.0.0
-    ports:
-      - containerPort: 80
-        name: http
-  # You can add multiple FileSyncer sidecar containers if needed
-  - name: filesyncer
-    image: onepanel/filesyncer:0.17.0
-    imagePullPolicy: Always
-    args:
-      - download
-      - -server-prefix=/sys/filesyncer
-    env:
-      - name: FS_PATH
-        value: /mnt/share
-      - name: FS_PREFIX
-        value: '{{workflow.namespace}}/{{workspace.parameters.sync-directory}}'
-    volumeMounts:
-      - name: share
-        mountPath: /mnt/share
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-ports:
-  - name: cvat-ui
-    port: 80
-    protocol: TCP
-    targetPort: 80
-  - name: cvat
-    port: 8080
-    protocol: TCP
-    targetPort: 8080
-  - name: fs
-    port: 8888
-    protocol: TCP
-    targetPort: 8888
-routes:
-  - match:
-      - uri:
-          prefix: /sys/filesyncer
-    route:
-      - destination:
-          port:
-            number: 8888
-  - match:
-      - uri:
-          regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
-      - queryParams:
-          id:
-            regex: \d+.*
-    route:
-      - destination:
-          port:
-            number: 8080
-  - match:
-      - uri:
-          prefix: /
-    route:
-      - destination:
-          port:
-            number: 80
-volumeClaimTemplates:
-  - metadata:
-      name: db
-    spec:
-      accessModes: ["ReadWriteOnce"]
-      resources:
-        requests:
-          storage: 20Gi
-# DAG Workflow to be executed once a Workspace action completes (optional)
-# Uncomment the lines below if you want to send Slack notifications
-#postExecutionWorkflow:
-#  entrypoint: main
-#  templates:
-#  - name: main
-#    dag:
-#       tasks:
-#       - name: slack-notify
-#         template: slack-notify
-#  - name: slack-notify
-#     container:
-#       image: technosophos/slack-notify
-#       args:
-#       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
-#       command:
-#       - sh
-#       - -c
+metadata:
+  name: CVAT
+  kind: Workspace
+  version: 20210107094725
+  action: update
+  description: "Powerful and efficient Computer Vision Annotation Tool (CVAT)"
+spec:
+  # Workspace arguments
+  arguments:
+    parameters:
+      - name: sync-directory
+        displayName: Directory to sync raw input and training output
+        value: workflow-data
+        hint: Location (relative to current namespace) to sync raw input, models and checkpoints from default object storage to '/share'.
+  containers:
+    - name: cvat-db
+      image: postgres:10-alpine
+      env:
+        - name: POSTGRES_USER
+          value: root
+        - name: POSTGRES_DB
+          value: cvat
+        - name: POSTGRES_HOST_AUTH_METHOD
+          value: trust
+        - name: PGDATA
+          value: /var/lib/psql/data
+      ports:
+        - containerPort: 5432
+          name: tcp
+      volumeMounts:
+        - name: db
+          mountPath: /var/lib/psql
+    - name: cvat-redis
+      image: redis:4.0-alpine
+      ports:
+        - containerPort: 6379
+          name: tcp
+    - name: cvat
+      image: onepanel/cvat:0.17.0_cvat.1.0.0
+      env:
+        - name: DJANGO_MODWSGI_EXTRA_ARGS
+          value: ""
+        - name: ALLOWED_HOSTS
+          value: '*'
+        - name: CVAT_REDIS_HOST
+          value: localhost
+        - name: CVAT_POSTGRES_HOST
+          value: localhost
+        - name: CVAT_SHARE_URL
+          value: /cvat/data
+        - name: CVAT_SHARE_DIR
+          value: /share
+        - name: CVAT_DATA_DIR
+          value: /cvat/data
+        - name: CVAT_MEDIA_DATA_DIR
+          value: /cvat/data/data
+        - name: CVAT_KEYS_DIR
+          value: /cvat/data/keys
+        - name: CVAT_MODELS_DIR
+          value: /cvat/data/models
+        - name: CVAT_LOGS_DIR
+          value: /cvat/logs
+        - name: ONEPANEL_SYNC_DIRECTORY
+          value: '{{workspace.parameters.sync-directory}}'
+        - name: NVIDIA_VISIBLE_DEVICES
+          value: all
+        - name: NVIDIA_DRIVER_CAPABILITIES
+          value: compute,utility
+        - name: NVIDIA_REQUIRE_CUDA
+          value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
+        - name: ONEPANEL_MAIN_CONTAINER
+          value: 'true'
+      ports:
+        - containerPort: 8080
+          name: http
+      volumeMounts:
+        - name: cvat-data
+          mountPath: /cvat
+        - name: share
+          mountPath: /share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+    - name: cvat-ui
+      image: onepanel/cvat-ui:0.17.0_cvat.1.0.0
+      ports:
+        - containerPort: 80
+          name: http
+    # You can add multiple FileSyncer sidecar containers if needed
+    - name: filesyncer
+      image: onepanel/filesyncer:0.17.0
+      imagePullPolicy: Always
+      args:
+        - download
+        - -server-prefix=/sys/filesyncer
+      env:
+        - name: FS_PATH
+          value: /mnt/share
+        - name: FS_PREFIX
+          value: '{{workflow.namespace}}/{{workspace.parameters.sync-directory}}'
+      volumeMounts:
+        - name: share
+          mountPath: /mnt/share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: cvat-ui
+      port: 80
+      protocol: TCP
+      targetPort: 80
+    - name: cvat
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+    - name: fs
+      port: 8888
+      protocol: TCP
+      targetPort: 8888
+  routes:
+    - match:
+        - uri:
+            prefix: /sys/filesyncer
+      route:
+        - destination:
+            port:
+              number: 8888
+    - match:
+        - uri:
+            regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
+        - queryParams:
+            id:
+              regex: \d+.*
+      route:
+        - destination:
+            port:
+              number: 8080
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80
+  volumeClaimTemplates:
+    - metadata:
+        name: db
+      spec:
+        accessModes: ["ReadWriteOnce"]
+        resources:
+          requests:
+            storage: 20Gi
+  # DAG Workflow to be executed once a Workspace action completes (optional)
+  # Uncomment the lines below if you want to send Slack notifications
+  #postExecutionWorkflow:
+  #  entrypoint: main
+  #  templates:
+  #  - name: main
+  #    dag:
+  #       tasks:
+  #       - name: slack-notify
+  #         template: slack-notify
+  #  - name: slack-notify
+  #     container:
+  #       image: technosophos/slack-notify
+  #       args:
+  #       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #       command:
+  #       - sh
+  #       - -c

db/yaml/workspaces/cvat/20210129134326.yaml

@@ -1,134 +1,141 @@
-containers:
-  - name: cvat-db
-    image: postgres:10-alpine
-    env:
-      - name: POSTGRES_USER
-        value: root
-      - name: POSTGRES_DB
-        value: cvat
-      - name: POSTGRES_HOST_AUTH_METHOD
-        value: trust
-      - name: PGDATA
-        value: /var/lib/psql/data
-    ports:
-      - containerPort: 5432
-        name: tcp
-    volumeMounts:
-      - name: db
-        mountPath: /var/lib/psql
-  - name: cvat-redis
-    image: redis:4.0-alpine
-    ports:
-      - containerPort: 6379
-        name: tcp
-  - name: cvat
-    image: onepanel/cvat:v0.18.0_cvat.1.0.0
-    env:
-      - name: DJANGO_MODWSGI_EXTRA_ARGS
-        value: ""
-      - name: ALLOWED_HOSTS
-        value: '*'
-      - name: CVAT_REDIS_HOST
-        value: localhost
-      - name: CVAT_POSTGRES_HOST
-        value: localhost
-      - name: CVAT_SHARE_URL
-        value: /cvat/data
-      - name: CVAT_SHARE_DIR
-        value: /share
-      - name: CVAT_DATA_DIR
-        value: /cvat/data
-      - name: CVAT_MEDIA_DATA_DIR
-        value: /cvat/data/data
-      - name: CVAT_KEYS_DIR
-        value: /cvat/data/keys
-      - name: CVAT_MODELS_DIR
-        value: /cvat/data/models
-      - name: CVAT_LOGS_DIR
-        value: /cvat/logs
-      - name: CVAT_ANNOTATIONS_OBJECT_STORAGE_PREFIX
-        value: 'artifacts/$(ONEPANEL_RESOURCE_NAMESPACE)/annotations/'
-      - name: CVAT_ONEPANEL_WORKFLOWS_LABEL
-        value: 'key=used-by,value=cvat'
-      - name: NVIDIA_VISIBLE_DEVICES
-        value: all
-      - name: NVIDIA_DRIVER_CAPABILITIES
-        value: compute,utility
-      - name: NVIDIA_REQUIRE_CUDA
-        value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
-      - name: ONEPANEL_MAIN_CONTAINER
-        value: 'true'
-    ports:
-      - containerPort: 8080
-        name: http
-    volumeMounts:
-      - name: cvat-data
-        mountPath: /cvat
-      - name: share
-        mountPath: /share
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-  - name: cvat-ui
-    image: onepanel/cvat-ui:v0.18.0_cvat.1.0.0
-    ports:
-      - containerPort: 80
-        name: http
-  - name: sys-filesyncer
-    image: onepanel/filesyncer:v0.18.0
-    imagePullPolicy: Always
-    args:
-      - server
-      - -server-prefix=/sys/filesyncer
-    volumeMounts:
-      - name: share
-        mountPath: /share
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-ports:
-  - name: cvat-ui
-    port: 80
-    protocol: TCP
-    targetPort: 80
-  - name: cvat
-    port: 8080
-    protocol: TCP
-    targetPort: 8080
-  - name: fs
-    port: 8888
-    protocol: TCP
-    targetPort: 8888
-routes:
-  - match:
-      - uri:
-          prefix: /sys/filesyncer
-    route:
-      - destination:
-          port:
-            number: 8888
-  - match:
-      - uri:
-          regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
-      - queryParams:
-          id:
-            regex: \d+.*
-    route:
-      - destination:
-          port:
-            number: 8080
-  - match:
-      - uri:
-          prefix: /
-    route:
-      - destination:
-          port:
-            number: 80
-volumeClaimTemplates:
-  - metadata:
-      name: db
-    spec:
-      accessModes: ["ReadWriteOnce"]
-      resources:
-        requests:
-          storage: 20Gi
+metadata:
+  name: CVAT
+  kind: Workspace
+  version: 20210129134326
+  action: update
+  description: "Powerful and efficient Computer Vision Annotation Tool (CVAT)"
+spec:
+  containers:
+    - name: cvat-db
+      image: postgres:10-alpine
+      env:
+        - name: POSTGRES_USER
+          value: root
+        - name: POSTGRES_DB
+          value: cvat
+        - name: POSTGRES_HOST_AUTH_METHOD
+          value: trust
+        - name: PGDATA
+          value: /var/lib/psql/data
+      ports:
+        - containerPort: 5432
+          name: tcp
+      volumeMounts:
+        - name: db
+          mountPath: /var/lib/psql
+    - name: cvat-redis
+      image: redis:4.0-alpine
+      ports:
+        - containerPort: 6379
+          name: tcp
+    - name: cvat
+      image: onepanel/cvat:v0.18.0_cvat.1.0.0
+      env:
+        - name: DJANGO_MODWSGI_EXTRA_ARGS
+          value: ""
+        - name: ALLOWED_HOSTS
+          value: '*'
+        - name: CVAT_REDIS_HOST
+          value: localhost
+        - name: CVAT_POSTGRES_HOST
+          value: localhost
+        - name: CVAT_SHARE_URL
+          value: /cvat/data
+        - name: CVAT_SHARE_DIR
+          value: /share
+        - name: CVAT_DATA_DIR
+          value: /cvat/data
+        - name: CVAT_MEDIA_DATA_DIR
+          value: /cvat/data/data
+        - name: CVAT_KEYS_DIR
+          value: /cvat/data/keys
+        - name: CVAT_MODELS_DIR
+          value: /cvat/data/models
+        - name: CVAT_LOGS_DIR
+          value: /cvat/logs
+        - name: CVAT_ANNOTATIONS_OBJECT_STORAGE_PREFIX
+          value: 'artifacts/$(ONEPANEL_RESOURCE_NAMESPACE)/annotations/'
+        - name: CVAT_ONEPANEL_WORKFLOWS_LABEL
+          value: 'key=used-by,value=cvat'
+        - name: NVIDIA_VISIBLE_DEVICES
+          value: all
+        - name: NVIDIA_DRIVER_CAPABILITIES
+          value: compute,utility
+        - name: NVIDIA_REQUIRE_CUDA
+          value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
+        - name: ONEPANEL_MAIN_CONTAINER
+          value: 'true'
+      ports:
+        - containerPort: 8080
+          name: http
+      volumeMounts:
+        - name: cvat-data
+          mountPath: /cvat
+        - name: share
+          mountPath: /share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+    - name: cvat-ui
+      image: onepanel/cvat-ui:v0.18.0_cvat.1.0.0
+      ports:
+        - containerPort: 80
+          name: http
+    - name: sys-filesyncer
+      image: onepanel/filesyncer:v0.18.0
+      imagePullPolicy: Always
+      args:
+        - server
+        - -server-prefix=/sys/filesyncer
+      volumeMounts:
+        - name: share
+          mountPath: /share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: cvat-ui
+      port: 80
+      protocol: TCP
+      targetPort: 80
+    - name: cvat
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+    - name: fs
+      port: 8888
+      protocol: TCP
+      targetPort: 8888
+  routes:
+    - match:
+        - uri:
+            prefix: /sys/filesyncer
+      route:
+        - destination:
+            port:
+              number: 8888
+    - match:
+        - uri:
+            regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
+        - queryParams:
+            id:
+              regex: \d+.*
+      route:
+        - destination:
+            port:
+              number: 8080
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80
+  volumeClaimTemplates:
+    - metadata:
+        name: db
+      spec:
+        accessModes: ["ReadWriteOnce"]
+        resources:
+          requests:
+            storage: 20Gi

db/yaml/workspaces/cvat/20210224180017.yaml

@@ -1,134 +1,141 @@
-containers:
-  - name: cvat-db
-    image: postgres:10-alpine
-    env:
-      - name: POSTGRES_USER
-        value: root
-      - name: POSTGRES_DB
-        value: cvat
-      - name: POSTGRES_HOST_AUTH_METHOD
-        value: trust
-      - name: PGDATA
-        value: /var/lib/psql/data
-    ports:
-      - containerPort: 5432
-        name: tcp
-    volumeMounts:
-      - name: db
-        mountPath: /var/lib/psql
-  - name: cvat-redis
-    image: redis:4.0-alpine
-    ports:
-      - containerPort: 6379
-        name: tcp
-  - name: cvat
-    image: onepanel/cvat:v0.19.0_cvat.1.0.0
-    env:
-      - name: DJANGO_MODWSGI_EXTRA_ARGS
-        value: ""
-      - name: ALLOWED_HOSTS
-        value: '*'
-      - name: CVAT_REDIS_HOST
-        value: localhost
-      - name: CVAT_POSTGRES_HOST
-        value: localhost
-      - name: CVAT_SHARE_URL
-        value: /cvat/data
-      - name: CVAT_SHARE_DIR
-        value: /share
-      - name: CVAT_DATA_DIR
-        value: /cvat/data
-      - name: CVAT_MEDIA_DATA_DIR
-        value: /cvat/data/data
-      - name: CVAT_KEYS_DIR
-        value: /cvat/data/keys
-      - name: CVAT_MODELS_DIR
-        value: /cvat/data/models
-      - name: CVAT_LOGS_DIR
-        value: /cvat/logs
-      - name: CVAT_ANNOTATIONS_OBJECT_STORAGE_PREFIX
-        value: 'artifacts/$(ONEPANEL_RESOURCE_NAMESPACE)/annotations/'
-      - name: CVAT_ONEPANEL_WORKFLOWS_LABEL
-        value: 'key=used-by,value=cvat'
-      - name: NVIDIA_VISIBLE_DEVICES
-        value: all
-      - name: NVIDIA_DRIVER_CAPABILITIES
-        value: compute,utility
-      - name: NVIDIA_REQUIRE_CUDA
-        value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
-      - name: ONEPANEL_MAIN_CONTAINER
-        value: 'true'
-    ports:
-      - containerPort: 8080
-        name: http
-    volumeMounts:
-      - name: cvat-data
-        mountPath: /cvat
-      - name: share
-        mountPath: /share
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-  - name: cvat-ui
-    image: onepanel/cvat-ui:v0.19.0_cvat.1.0.0
-    ports:
-      - containerPort: 80
-        name: http
-  - name: sys-filesyncer
-    image: onepanel/filesyncer:v0.19.0
-    imagePullPolicy: Always
-    args:
-      - server
-      - -server-prefix=/sys/filesyncer
-    volumeMounts:
-      - name: share
-        mountPath: /share
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-ports:
-  - name: cvat-ui
-    port: 80
-    protocol: TCP
-    targetPort: 80
-  - name: cvat
-    port: 8080
-    protocol: TCP
-    targetPort: 8080
-  - name: fs
-    port: 8888
-    protocol: TCP
-    targetPort: 8888
-routes:
-  - match:
-      - uri:
-          prefix: /sys/filesyncer
-    route:
-      - destination:
-          port:
-            number: 8888
-  - match:
-      - uri:
-          regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
-      - queryParams:
-          id:
-            regex: \d+.*
-    route:
-      - destination:
-          port:
-            number: 8080
-  - match:
-      - uri:
-          prefix: /
-    route:
-      - destination:
-          port:
-            number: 80
-volumeClaimTemplates:
-  - metadata:
-      name: db
-    spec:
-      accessModes: ["ReadWriteOnce"]
-      resources:
-        requests:
-          storage: 20Gi
+metadata:
+  name: CVAT
+  kind: Workspace
+  version: 20210224180017
+  action: update
+  description: "Powerful and efficient Computer Vision Annotation Tool (CVAT)"
+spec:
+  containers:
+    - name: cvat-db
+      image: postgres:10-alpine
+      env:
+        - name: POSTGRES_USER
+          value: root
+        - name: POSTGRES_DB
+          value: cvat
+        - name: POSTGRES_HOST_AUTH_METHOD
+          value: trust
+        - name: PGDATA
+          value: /var/lib/psql/data
+      ports:
+        - containerPort: 5432
+          name: tcp
+      volumeMounts:
+        - name: db
+          mountPath: /var/lib/psql
+    - name: cvat-redis
+      image: redis:4.0-alpine
+      ports:
+        - containerPort: 6379
+          name: tcp
+    - name: cvat
+      image: onepanel/cvat:v0.19.0_cvat.1.0.0
+      env:
+        - name: DJANGO_MODWSGI_EXTRA_ARGS
+          value: ""
+        - name: ALLOWED_HOSTS
+          value: '*'
+        - name: CVAT_REDIS_HOST
+          value: localhost
+        - name: CVAT_POSTGRES_HOST
+          value: localhost
+        - name: CVAT_SHARE_URL
+          value: /cvat/data
+        - name: CVAT_SHARE_DIR
+          value: /share
+        - name: CVAT_DATA_DIR
+          value: /cvat/data
+        - name: CVAT_MEDIA_DATA_DIR
+          value: /cvat/data/data
+        - name: CVAT_KEYS_DIR
+          value: /cvat/data/keys
+        - name: CVAT_MODELS_DIR
+          value: /cvat/data/models
+        - name: CVAT_LOGS_DIR
+          value: /cvat/logs
+        - name: CVAT_ANNOTATIONS_OBJECT_STORAGE_PREFIX
+          value: 'artifacts/$(ONEPANEL_RESOURCE_NAMESPACE)/annotations/'
+        - name: CVAT_ONEPANEL_WORKFLOWS_LABEL
+          value: 'key=used-by,value=cvat'
+        - name: NVIDIA_VISIBLE_DEVICES
+          value: all
+        - name: NVIDIA_DRIVER_CAPABILITIES
+          value: compute,utility
+        - name: NVIDIA_REQUIRE_CUDA
+          value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
+        - name: ONEPANEL_MAIN_CONTAINER
+          value: 'true'
+      ports:
+        - containerPort: 8080
+          name: http
+      volumeMounts:
+        - name: cvat-data
+          mountPath: /cvat
+        - name: share
+          mountPath: /share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+    - name: cvat-ui
+      image: onepanel/cvat-ui:v0.19.0_cvat.1.0.0
+      ports:
+        - containerPort: 80
+          name: http
+    - name: sys-filesyncer
+      image: onepanel/filesyncer:v0.19.0
+      imagePullPolicy: Always
+      args:
+        - server
+        - -server-prefix=/sys/filesyncer
+      volumeMounts:
+        - name: share
+          mountPath: /share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: cvat-ui
+      port: 80
+      protocol: TCP
+      targetPort: 80
+    - name: cvat
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+    - name: fs
+      port: 8888
+      protocol: TCP
+      targetPort: 8888
+  routes:
+    - match:
+        - uri:
+            prefix: /sys/filesyncer
+      route:
+        - destination:
+            port:
+              number: 8888
+    - match:
+        - uri:
+            regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
+        - queryParams:
+            id:
+              regex: \d+.*
+      route:
+        - destination:
+            port:
+              number: 8080
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80
+  volumeClaimTemplates:
+    - metadata:
+        name: db
+      spec:
+        accessModes: ["ReadWriteOnce"]
+        resources:
+          requests:
+            storage: 20Gi

db/yaml/workspaces/cvat/20210323175655.yaml

@@ -1,134 +1,141 @@
-containers:
-  - name: cvat-db
-    image: postgres:10-alpine
-    env:
-      - name: POSTGRES_USER
-        value: root
-      - name: POSTGRES_DB
-        value: cvat
-      - name: POSTGRES_HOST_AUTH_METHOD
-        value: trust
-      - name: PGDATA
-        value: /var/lib/psql/data
-    ports:
-      - containerPort: 5432
-        name: tcp
-    volumeMounts:
-      - name: db
-        mountPath: /var/lib/psql
-  - name: cvat-redis
-    image: redis:4.0-alpine
-    ports:
-      - containerPort: 6379
-        name: tcp
-  - name: cvat
-    image: onepanel/cvat:v0.19.0_cvat.1.0.0
-    env:
-      - name: DJANGO_MODWSGI_EXTRA_ARGS
-        value: ""
-      - name: ALLOWED_HOSTS
-        value: '*'
-      - name: CVAT_REDIS_HOST
-        value: localhost
-      - name: CVAT_POSTGRES_HOST
-        value: localhost
-      - name: CVAT_SHARE_URL
-        value: /cvat/data
-      - name: CVAT_SHARE_DIR
-        value: /share
-      - name: CVAT_DATA_DIR
-        value: /cvat/data
-      - name: CVAT_MEDIA_DATA_DIR
-        value: /cvat/data/data
-      - name: CVAT_KEYS_DIR
-        value: /cvat/data/keys
-      - name: CVAT_MODELS_DIR
-        value: /cvat/data/models
-      - name: CVAT_LOGS_DIR
-        value: /cvat/logs
-      - name: CVAT_ANNOTATIONS_OBJECT_STORAGE_PREFIX
-        value: 'artifacts/$(ONEPANEL_RESOURCE_NAMESPACE)/annotations/'
-      - name: CVAT_ONEPANEL_WORKFLOWS_LABEL
-        value: 'key=used-by,value=cvat'
-      - name: NVIDIA_VISIBLE_DEVICES
-        value: all
-      - name: NVIDIA_DRIVER_CAPABILITIES
-        value: compute,utility
-      - name: NVIDIA_REQUIRE_CUDA
-        value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
-      - name: ONEPANEL_MAIN_CONTAINER
-        value: 'true'
-    ports:
-      - containerPort: 8080
-        name: http
-    volumeMounts:
-      - name: cvat-data
-        mountPath: /cvat
-      - name: share
-        mountPath: /share
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-  - name: cvat-ui
-    image: onepanel/cvat-ui:v0.19.0_cvat.1.0.0
-    ports:
-      - containerPort: 80
-        name: http
-  - name: sys-filesyncer
-    image: onepanel/filesyncer:v0.20.0
-    imagePullPolicy: Always
-    args:
-      - server
-      - -server-prefix=/sys/filesyncer
-    volumeMounts:
-      - name: share
-        mountPath: /share
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-ports:
-  - name: cvat-ui
-    port: 80
-    protocol: TCP
-    targetPort: 80
-  - name: cvat
-    port: 8080
-    protocol: TCP
-    targetPort: 8080
-  - name: fs
-    port: 8888
-    protocol: TCP
-    targetPort: 8888
-routes:
-  - match:
-      - uri:
-          prefix: /sys/filesyncer
-    route:
-      - destination:
-          port:
-            number: 8888
-  - match:
-      - uri:
-          regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
-      - queryParams:
-          id:
-            regex: \d+.*
-    route:
-      - destination:
-          port:
-            number: 8080
-  - match:
-      - uri:
-          prefix: /
-    route:
-      - destination:
-          port:
-            number: 80
-volumeClaimTemplates:
-  - metadata:
-      name: db
-    spec:
-      accessModes: ["ReadWriteOnce"]
-      resources:
-        requests:
-          storage: 20Gi
+metadata:
+  name: CVAT
+  kind: Workspace
+  version: 20210323175655
+  action: update
+  description: "Powerful and efficient Computer Vision Annotation Tool (CVAT)"
+spec:
+  containers:
+    - name: cvat-db
+      image: postgres:10-alpine
+      env:
+        - name: POSTGRES_USER
+          value: root
+        - name: POSTGRES_DB
+          value: cvat
+        - name: POSTGRES_HOST_AUTH_METHOD
+          value: trust
+        - name: PGDATA
+          value: /var/lib/psql/data
+      ports:
+        - containerPort: 5432
+          name: tcp
+      volumeMounts:
+        - name: db
+          mountPath: /var/lib/psql
+    - name: cvat-redis
+      image: redis:4.0-alpine
+      ports:
+        - containerPort: 6379
+          name: tcp
+    - name: cvat
+      image: onepanel/cvat:v0.19.0_cvat.1.0.0
+      env:
+        - name: DJANGO_MODWSGI_EXTRA_ARGS
+          value: ""
+        - name: ALLOWED_HOSTS
+          value: '*'
+        - name: CVAT_REDIS_HOST
+          value: localhost
+        - name: CVAT_POSTGRES_HOST
+          value: localhost
+        - name: CVAT_SHARE_URL
+          value: /cvat/data
+        - name: CVAT_SHARE_DIR
+          value: /share
+        - name: CVAT_DATA_DIR
+          value: /cvat/data
+        - name: CVAT_MEDIA_DATA_DIR
+          value: /cvat/data/data
+        - name: CVAT_KEYS_DIR
+          value: /cvat/data/keys
+        - name: CVAT_MODELS_DIR
+          value: /cvat/data/models
+        - name: CVAT_LOGS_DIR
+          value: /cvat/logs
+        - name: CVAT_ANNOTATIONS_OBJECT_STORAGE_PREFIX
+          value: 'artifacts/$(ONEPANEL_RESOURCE_NAMESPACE)/annotations/'
+        - name: CVAT_ONEPANEL_WORKFLOWS_LABEL
+          value: 'key=used-by,value=cvat'
+        - name: NVIDIA_VISIBLE_DEVICES
+          value: all
+        - name: NVIDIA_DRIVER_CAPABILITIES
+          value: compute,utility
+        - name: NVIDIA_REQUIRE_CUDA
+          value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
+        - name: ONEPANEL_MAIN_CONTAINER
+          value: 'true'
+      ports:
+        - containerPort: 8080
+          name: http
+      volumeMounts:
+        - name: cvat-data
+          mountPath: /cvat
+        - name: share
+          mountPath: /share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+    - name: cvat-ui
+      image: onepanel/cvat-ui:v0.19.0_cvat.1.0.0
+      ports:
+        - containerPort: 80
+          name: http
+    - name: sys-filesyncer
+      image: onepanel/filesyncer:v0.20.0
+      imagePullPolicy: Always
+      args:
+        - server
+        - -server-prefix=/sys/filesyncer
+      volumeMounts:
+        - name: share
+          mountPath: /share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: cvat-ui
+      port: 80
+      protocol: TCP
+      targetPort: 80
+    - name: cvat
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+    - name: fs
+      port: 8888
+      protocol: TCP
+      targetPort: 8888
+  routes:
+    - match:
+        - uri:
+            prefix: /sys/filesyncer
+      route:
+        - destination:
+            port:
+              number: 8888
+    - match:
+        - uri:
+            regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
+        - queryParams:
+            id:
+              regex: \d+.*
+      route:
+        - destination:
+            port:
+              number: 8080
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80
+  volumeClaimTemplates:
+    - metadata:
+        name: db
+      spec:
+        accessModes: ["ReadWriteOnce"]
+        resources:
+          requests:
+            storage: 20Gi

db/yaml/workspaces/cvat/20210719190719.yaml

@@ -1,134 +1,141 @@
-containers:
-  - name: cvat-db
-    image: postgres:10-alpine
-    env:
-      - name: POSTGRES_USER
-        value: root
-      - name: POSTGRES_DB
-        value: cvat
-      - name: POSTGRES_HOST_AUTH_METHOD
-        value: trust
-      - name: PGDATA
-        value: /var/lib/psql/data
-    ports:
-      - containerPort: 5432
-        name: tcp
-    volumeMounts:
-      - name: db
-        mountPath: /var/lib/psql
-  - name: cvat-redis
-    image: redis:4.0-alpine
-    ports:
-      - containerPort: 6379
-        name: tcp
-  - name: cvat
-    image: onepanel/cvat:v0.19.0_cvat.1.0.0
-    env:
-      - name: DJANGO_MODWSGI_EXTRA_ARGS
-        value: ""
-      - name: ALLOWED_HOSTS
-        value: '*'
-      - name: CVAT_REDIS_HOST
-        value: localhost
-      - name: CVAT_POSTGRES_HOST
-        value: localhost
-      - name: CVAT_SHARE_URL
-        value: /cvat/data
-      - name: CVAT_SHARE_DIR
-        value: /share
-      - name: CVAT_DATA_DIR
-        value: /cvat/data
-      - name: CVAT_MEDIA_DATA_DIR
-        value: /cvat/data/data
-      - name: CVAT_KEYS_DIR
-        value: /cvat/data/keys
-      - name: CVAT_MODELS_DIR
-        value: /cvat/data/models
-      - name: CVAT_LOGS_DIR
-        value: /cvat/logs
-      - name: CVAT_ANNOTATIONS_OBJECT_STORAGE_PREFIX
-        value: 'artifacts/$(ONEPANEL_RESOURCE_NAMESPACE)/annotations/'
-      - name: CVAT_ONEPANEL_WORKFLOWS_LABEL
-        value: 'key=used-by,value=cvat'
-      - name: NVIDIA_VISIBLE_DEVICES
-        value: all
-      - name: NVIDIA_DRIVER_CAPABILITIES
-        value: compute,utility
-      - name: NVIDIA_REQUIRE_CUDA
-        value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
-      - name: ONEPANEL_MAIN_CONTAINER
-        value: 'true'
-    ports:
-      - containerPort: 8080
-        name: http
-    volumeMounts:
-      - name: cvat-data
-        mountPath: /cvat
-      - name: share
-        mountPath: /share
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-  - name: cvat-ui
-    image: onepanel/cvat-ui:v0.19.0_cvat.1.0.0
-    ports:
-      - containerPort: 80
-        name: http
-  - name: sys-filesyncer
-    image: onepanel/filesyncer:v1.0.0
-    imagePullPolicy: Always
-    args:
-      - server
-      - -server-prefix=/sys/filesyncer
-    volumeMounts:
-      - name: share
-        mountPath: /share
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-ports:
-  - name: cvat-ui
-    port: 80
-    protocol: TCP
-    targetPort: 80
-  - name: cvat
-    port: 8080
-    protocol: TCP
-    targetPort: 8080
-  - name: fs
-    port: 8888
-    protocol: TCP
-    targetPort: 8888
-routes:
-  - match:
-      - uri:
-          prefix: /sys/filesyncer
-    route:
-      - destination:
-          port:
-            number: 8888
-  - match:
-      - uri:
-          regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
-      - queryParams:
-          id:
-            regex: \d+.*
-    route:
-      - destination:
-          port:
-            number: 8080
-  - match:
-      - uri:
-          prefix: /
-    route:
-      - destination:
-          port:
-            number: 80
-volumeClaimTemplates:
-  - metadata:
-      name: db
-    spec:
-      accessModes: ["ReadWriteOnce"]
-      resources:
-        requests:
-          storage: 20Gi
+metadata:
+  name: CVAT
+  kind: Workspace
+  version: 20210719190719
+  action: update
+  description: "Powerful and efficient Computer Vision Annotation Tool (CVAT)"
+spec:
+  containers:
+    - name: cvat-db
+      image: postgres:10-alpine
+      env:
+        - name: POSTGRES_USER
+          value: root
+        - name: POSTGRES_DB
+          value: cvat
+        - name: POSTGRES_HOST_AUTH_METHOD
+          value: trust
+        - name: PGDATA
+          value: /var/lib/psql/data
+      ports:
+        - containerPort: 5432
+          name: tcp
+      volumeMounts:
+        - name: db
+          mountPath: /var/lib/psql
+    - name: cvat-redis
+      image: redis:4.0-alpine
+      ports:
+        - containerPort: 6379
+          name: tcp
+    - name: cvat
+      image: onepanel/cvat:v0.19.0_cvat.1.0.0
+      env:
+        - name: DJANGO_MODWSGI_EXTRA_ARGS
+          value: ""
+        - name: ALLOWED_HOSTS
+          value: '*'
+        - name: CVAT_REDIS_HOST
+          value: localhost
+        - name: CVAT_POSTGRES_HOST
+          value: localhost
+        - name: CVAT_SHARE_URL
+          value: /cvat/data
+        - name: CVAT_SHARE_DIR
+          value: /share
+        - name: CVAT_DATA_DIR
+          value: /cvat/data
+        - name: CVAT_MEDIA_DATA_DIR
+          value: /cvat/data/data
+        - name: CVAT_KEYS_DIR
+          value: /cvat/data/keys
+        - name: CVAT_MODELS_DIR
+          value: /cvat/data/models
+        - name: CVAT_LOGS_DIR
+          value: /cvat/logs
+        - name: CVAT_ANNOTATIONS_OBJECT_STORAGE_PREFIX
+          value: 'artifacts/$(ONEPANEL_RESOURCE_NAMESPACE)/annotations/'
+        - name: CVAT_ONEPANEL_WORKFLOWS_LABEL
+          value: 'key=used-by,value=cvat'
+        - name: NVIDIA_VISIBLE_DEVICES
+          value: all
+        - name: NVIDIA_DRIVER_CAPABILITIES
+          value: compute,utility
+        - name: NVIDIA_REQUIRE_CUDA
+          value: "cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411"
+        - name: ONEPANEL_MAIN_CONTAINER
+          value: 'true'
+      ports:
+        - containerPort: 8080
+          name: http
+      volumeMounts:
+        - name: cvat-data
+          mountPath: /cvat
+        - name: share
+          mountPath: /share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+    - name: cvat-ui
+      image: onepanel/cvat-ui:v0.19.0_cvat.1.0.0
+      ports:
+        - containerPort: 80
+          name: http
+    - name: sys-filesyncer
+      image: onepanel/filesyncer:v1.0.0
+      imagePullPolicy: Always
+      args:
+        - server
+        - -server-prefix=/sys/filesyncer
+      volumeMounts:
+        - name: share
+          mountPath: /share
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: cvat-ui
+      port: 80
+      protocol: TCP
+      targetPort: 80
+    - name: cvat
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+    - name: fs
+      port: 8888
+      protocol: TCP
+      targetPort: 8888
+  routes:
+    - match:
+        - uri:
+            prefix: /sys/filesyncer
+      route:
+        - destination:
+            port:
+              number: 8888
+    - match:
+        - uri:
+            regex: /api/.*|/git/.*|/tensorflow/.*|/onepanelio/.*|/tracking/.*|/auto_annotation/.*|/analytics/.*|/static/.*|/admin/.*|/documentation/.*|/dextr/.*|/reid/.*
+        - queryParams:
+            id:
+              regex: \d+.*
+      route:
+        - destination:
+            port:
+              number: 8080
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80
+  volumeClaimTemplates:
+    - metadata:
+        name: db
+      spec:
+        accessModes: ["ReadWriteOnce"]
+        resources:
+          requests:
+            storage: 20Gi

db/yaml/workspaces/jupyterlab/20200525160514.yaml

@@ -0,0 +1,64 @@
+metadata:
+  name: JupyterLab
+  kind: Workspace
+  version: 20200525160514
+  action: create
+  description: "Interactive development environment for notebooks"
+spec:
+  # Docker containers that are part of the Workspace
+  containers:
+    - name: jupyterlab-tensorflow
+      image: jupyter/tensorflow-notebook
+      command: [start.sh, jupyter]
+      env:
+        - name: tornado
+          value: "{ 'headers': { 'Content-Security-Policy': \"frame-ancestors * 'self'\" }  }"
+      args:
+        - lab
+        - --LabApp.token=''
+        - --LabApp.allow_remote_access=True
+        - --LabApp.allow_origin="*"
+        - --LabApp.disable_check_xsrf=True
+        - --LabApp.trust_xheaders=True
+        - --LabApp.tornado_settings=$(tornado)
+        - --notebook-dir='/data'
+      ports:
+        - containerPort: 8888
+          name: jupyterlab
+      # Volumes to be mounted in this container
+      # Onepanel will automatically create these volumes and mount them to the container
+      volumeMounts:
+        - name: data
+          mountPath: /data
+  # Ports that need to be exposed
+  ports:
+    - name: jupyterlab
+      port: 80
+      protocol: TCP
+      targetPort: 8888
+  # Routes that will map to ports
+  routes:
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80
+  # DAG Workflow to be executed once a Workspace action completes
+  # postExecutionWorkflow:
+  #   entrypoint: main
+  #   templates:
+  #   - name: main
+  #     dag:
+  #        tasks:
+  #        - name: slack-notify
+  #          template: slack-notify
+  #   - name: slack-notify
+  #     container:
+  #       image: technosophos/slack-notify
+  #       args:
+  #       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #       command:
+  #       - sh
+  #       - -c

db/yaml/workspaces/jupyterlab/20200821162630.yaml

@@ -0,0 +1,65 @@
+metadata:
+  name: JupyterLab
+  kind: Workspace
+  version: 20200821162630
+  action: update
+  description: "Interactive development environment for notebooks"
+spec:
+  # Docker containers that are part of the Workspace
+  containers:
+    - name: jupyterlab-tensorflow
+      image: onepanel/jupyterlab:1.0.1
+      command: ["/bin/bash", "-c", "start.sh jupyter lab --LabApp.token='' --LabApp.allow_remote_access=True --LabApp.allow_origin=\"*\" --LabApp.disable_check_xsrf=True --LabApp.trust_xheaders=True --LabApp.base_url=/ --LabApp.tornado_settings='{\"headers\":{\"Content-Security-Policy\":\"frame-ancestors * \'self\'\"}}' --notebook-dir='/data' --allow-root"]
+      env:
+        - name: tornado
+          value: "'{'headers':{'Content-Security-Policy':\"frame-ancestors\ *\ \'self'\"}}'"
+      args:
+      ports:
+        - containerPort: 8888
+          name: jupyterlab
+        - containerPort: 6006
+          name: tensorboard
+      volumeMounts:
+        - name: data
+          mountPath: /data
+  ports:
+    - name: jupyterlab
+      port: 80
+      protocol: TCP
+      targetPort: 8888
+    - name: tensorboard
+      port: 6006
+      protocol: TCP
+      targetPort: 6006
+  routes:
+    - match:
+        - uri:
+            prefix: /tensorboard
+      route:
+        - destination:
+            port:
+              number: 6006
+    - match:
+        - uri:
+            prefix: / #jupyter runs at the default route
+      route:
+        - destination:
+            port:
+              number: 80
+  # DAG Workflow to be executed once a Workspace action completes (optional)
+  #postExecutionWorkflow:
+  #  entrypoint: main
+  #  templates:
+  #  - name: main
+  #    dag:
+  #       tasks:
+  #       - name: slack-notify
+  #         template: slack-notify
+  #  - name: slack-notify
+  #    container:
+  #      image: technosophos/slack-notify
+  #      args:
+  #      - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #      command:
+  #      - sh
+  #      - -c

db/yaml/workspaces/jupyterlab/20200929153931.yaml

@@ -1,58 +1,65 @@
-# Docker containers that are part of the Workspace
-containers:
-  - name: jupyterlab-tensorflow
-    image: onepanel/jupyterlab:1.0.1
-    command: ["/bin/bash", "-c", "pip install onepanel-sdk && start.sh jupyter lab --LabApp.token='' --LabApp.allow_remote_access=True --LabApp.allow_origin=\"*\" --LabApp.disable_check_xsrf=True --LabApp.trust_xheaders=True --LabApp.base_url=/ --LabApp.tornado_settings='{\"headers\":{\"Content-Security-Policy\":\"frame-ancestors * \'self\'\"}}' --notebook-dir='/data' --allow-root"]
-    env:
-      - name: tornado
-        value: "'{'headers':{'Content-Security-Policy':\"frame-ancestors\ *\ \'self'\"}}'"
-    args:
-    ports:
-      - containerPort: 8888
-        name: jupyterlab
-      - containerPort: 6006
-        name: tensorboard
-    volumeMounts:
-      - name: data
-        mountPath: /data
-ports:
-  - name: jupyterlab
-    port: 80
-    protocol: TCP
-    targetPort: 8888
-  - name: tensorboard
-    port: 6006
-    protocol: TCP
-    targetPort: 6006
-routes:
-  - match:
-      - uri:
-          prefix: /tensorboard
-    route:
-      - destination:
-          port:
-            number: 6006
-  - match:
-      - uri:
-          prefix: / #jupyter runs at the default route
-    route:
-      - destination:
-          port:
-            number: 80
-# DAG Workflow to be executed once a Workspace action completes (optional)
-#postExecutionWorkflow:
-#  entrypoint: main
-#  templates:
-#  - name: main
-#    dag:
-#       tasks:
-#       - name: slack-notify
-#         template: slack-notify
-#  - name: slack-notify
-#    container:
-#      image: technosophos/slack-notify
-#      args:
-#      - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
-#      command:
-#      - sh
-#      - -c
+metadata:
+  name: JupyterLab
+  kind: Workspace
+  version: 20200929153931
+  action: update
+  description: "Interactive development environment for notebooks"
+spec:
+  # Docker containers that are part of the Workspace
+  containers:
+    - name: jupyterlab-tensorflow
+      image: onepanel/jupyterlab:1.0.1
+      command: ["/bin/bash", "-c", "pip install onepanel-sdk && start.sh jupyter lab --LabApp.token='' --LabApp.allow_remote_access=True --LabApp.allow_origin=\"*\" --LabApp.disable_check_xsrf=True --LabApp.trust_xheaders=True --LabApp.base_url=/ --LabApp.tornado_settings='{\"headers\":{\"Content-Security-Policy\":\"frame-ancestors * \'self\'\"}}' --notebook-dir='/data' --allow-root"]
+      env:
+        - name: tornado
+          value: "'{'headers':{'Content-Security-Policy':\"frame-ancestors\ *\ \'self'\"}}'"
+      args:
+      ports:
+        - containerPort: 8888
+          name: jupyterlab
+        - containerPort: 6006
+          name: tensorboard
+      volumeMounts:
+        - name: data
+          mountPath: /data
+  ports:
+    - name: jupyterlab
+      port: 80
+      protocol: TCP
+      targetPort: 8888
+    - name: tensorboard
+      port: 6006
+      protocol: TCP
+      targetPort: 6006
+  routes:
+    - match:
+        - uri:
+            prefix: /tensorboard
+      route:
+        - destination:
+            port:
+              number: 6006
+    - match:
+        - uri:
+            prefix: / #jupyter runs at the default route
+      route:
+        - destination:
+            port:
+              number: 80
+  # DAG Workflow to be executed once a Workspace action completes (optional)
+  #postExecutionWorkflow:
+  #  entrypoint: main
+  #  templates:
+  #  - name: main
+  #    dag:
+  #       tasks:
+  #       - name: slack-notify
+  #         template: slack-notify
+  #  - name: slack-notify
+  #    container:
+  #      image: technosophos/slack-notify
+  #      args:
+  #      - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #      command:
+  #      - sh
+  #      - -c

db/yaml/workspaces/jupyterlab/20201028145442.yaml

@@ -1,77 +1,84 @@
-# Docker containers that are part of the Workspace
-containers:
-  - name: jupyterlab
-    image: onepanel/jupyterlab:1.0.1
-    command: ["/bin/bash", "-c", "pip install onepanel-sdk && start.sh jupyter lab --LabApp.token='' --LabApp.allow_remote_access=True --LabApp.allow_origin=\"*\" --LabApp.disable_check_xsrf=True --LabApp.trust_xheaders=True --LabApp.base_url=/ --LabApp.tornado_settings='{\"headers\":{\"Content-Security-Policy\":\"frame-ancestors * \'self\'\"}}' --notebook-dir='/data' --allow-root"]
-    env:
-      - name: tornado
-        value: "'{'headers':{'Content-Security-Policy':\"frame-ancestors\ *\ \'self'\"}}'"
-    ports:
-      - containerPort: 8888
-        name: jupyterlab
-      - containerPort: 6006
-        name: tensorboard
-    volumeMounts:
-      - name: data
-        mountPath: /data
-    lifecycle:
-      postStart:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              condayml="/data/.environment.yml";
-              jupytertxt="/data/.jupexported.txt";
-              if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
-              if [ -f "$jupytertxt" ]; then cat $jupytertxt | xargs -n 1 jupyter labextension install --no-build && jupyter lab build --minimize=False; fi;
-      preStop:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              conda env export > /data/.environment.yml -n base;
-              jupyter labextension list 1>/dev/null 2> /data/.jup.txt;
-              cat /data/.jup.txt | sed -n '2,$p' | awk 'sub(/v/,"@", $2){print $1$2}' > /data/.jupexported.txt;
-ports:
-  - name: jupyterlab
-    port: 80
-    protocol: TCP
-    targetPort: 8888
-  - name: tensorboard
-    port: 6006
-    protocol: TCP
-    targetPort: 6006
-routes:
-  - match:
-      - uri:
-          prefix: /tensorboard
-    route:
-      - destination:
-          port:
-            number: 6006
-  - match:
-      - uri:
-          prefix: / #jupyter runs at the default route
-    route:
-      - destination:
-          port:
-            number: 80
-# DAG Workflow to be executed once a Workspace action completes (optional)
-#postExecutionWorkflow:
-#  entrypoint: main
-#  templates:
-#  - name: main
-#    dag:
-#       tasks:
-#       - name: slack-notify
-#         template: slack-notify
-#  - name: slack-notify
-#    container:
-#      image: technosophos/slack-notify
-#      args:
-#      - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
-#      command:
-#      - sh
-#      - -c
+metadata:
+  name: JupyterLab
+  kind: Workspace
+  version: 20201028145442
+  action: update
+  description: "Interactive development environment for notebooks"
+spec:
+  # Docker containers that are part of the Workspace
+  containers:
+    - name: jupyterlab
+      image: onepanel/jupyterlab:1.0.1
+      command: ["/bin/bash", "-c", "pip install onepanel-sdk && start.sh jupyter lab --LabApp.token='' --LabApp.allow_remote_access=True --LabApp.allow_origin=\"*\" --LabApp.disable_check_xsrf=True --LabApp.trust_xheaders=True --LabApp.base_url=/ --LabApp.tornado_settings='{\"headers\":{\"Content-Security-Policy\":\"frame-ancestors * \'self\'\"}}' --notebook-dir='/data' --allow-root"]
+      env:
+        - name: tornado
+          value: "'{'headers':{'Content-Security-Policy':\"frame-ancestors\ *\ \'self'\"}}'"
+      ports:
+        - containerPort: 8888
+          name: jupyterlab
+        - containerPort: 6006
+          name: tensorboard
+      volumeMounts:
+        - name: data
+          mountPath: /data
+      lifecycle:
+        postStart:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                condayml="/data/.environment.yml";
+                jupytertxt="/data/.jupexported.txt";
+                if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
+                if [ -f "$jupytertxt" ]; then cat $jupytertxt | xargs -n 1 jupyter labextension install --no-build && jupyter lab build --minimize=False; fi;
+        preStop:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                conda env export > /data/.environment.yml -n base;
+                jupyter labextension list 1>/dev/null 2> /data/.jup.txt;
+                cat /data/.jup.txt | sed -n '2,$p' | awk 'sub(/v/,"@", $2){print $1$2}' > /data/.jupexported.txt;
+  ports:
+    - name: jupyterlab
+      port: 80
+      protocol: TCP
+      targetPort: 8888
+    - name: tensorboard
+      port: 6006
+      protocol: TCP
+      targetPort: 6006
+  routes:
+    - match:
+        - uri:
+            prefix: /tensorboard
+      route:
+        - destination:
+            port:
+              number: 6006
+    - match:
+        - uri:
+            prefix: / #jupyter runs at the default route
+      route:
+        - destination:
+            port:
+              number: 80
+  # DAG Workflow to be executed once a Workspace action completes (optional)
+  #postExecutionWorkflow:
+  #  entrypoint: main
+  #  templates:
+  #  - name: main
+  #    dag:
+  #       tasks:
+  #       - name: slack-notify
+  #         template: slack-notify
+  #  - name: slack-notify
+  #    container:
+  #      image: technosophos/slack-notify
+  #      args:
+  #      - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #      command:
+  #      - sh
+  #      - -c

db/yaml/workspaces/jupyterlab/20201031165106.yaml

@@ -1,79 +1,86 @@
-# Docker containers that are part of the Workspace
-containers:
-  - name: jupyterlab
-    image: onepanel/jupyterlab:1.0.1
-    command: ["/bin/bash", "-c", "pip install onepanel-sdk && start.sh jupyter lab --LabApp.token='' --LabApp.allow_remote_access=True --LabApp.allow_origin=\"*\" --LabApp.disable_check_xsrf=True --LabApp.trust_xheaders=True --LabApp.base_url=/ --LabApp.tornado_settings='{\"headers\":{\"Content-Security-Policy\":\"frame-ancestors * \'self\'\"}}' --notebook-dir='/data' --allow-root"]
-    env:
-      - name: tornado
-        value: "'{'headers':{'Content-Security-Policy':\"frame-ancestors\ *\ \'self'\"}}'"
-      - name: TENSORBOARD_PROXY_URL
-        value: '//$(ONEPANEL_RESOURCE_UID)--$(ONEPANEL_RESOURCE_NAMESPACE).$(ONEPANEL_DOMAIN)/tensorboard'
-    ports:
-      - containerPort: 8888
-        name: jupyterlab
-      - containerPort: 6006
-        name: tensorboard
-    volumeMounts:
-      - name: data
-        mountPath: /data
-    lifecycle:
-      postStart:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              condayml="/data/.environment.yml";
-              jupytertxt="/data/.jupexported.txt";
-              if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
-              if [ -f "$jupytertxt" ]; then cat $jupytertxt | xargs -n 1 jupyter labextension install --no-build && jupyter lab build --minimize=False; fi;
-      preStop:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              conda env export > /data/.environment.yml -n base;
-              jupyter labextension list 1>/dev/null 2> /data/.jup.txt;
-              cat /data/.jup.txt | sed -n '2,$p' | awk 'sub(/v/,"@", $2){print $1$2}' > /data/.jupexported.txt;
-ports:
-  - name: jupyterlab
-    port: 80
-    protocol: TCP
-    targetPort: 8888
-  - name: tensorboard
-    port: 6006
-    protocol: TCP
-    targetPort: 6006
-routes:
-  - match:
-      - uri:
-          prefix: /tensorboard
-    route:
-      - destination:
-          port:
-            number: 6006
-  - match:
-      - uri:
-          prefix: / #jupyter runs at the default route
-    route:
-      - destination:
-          port:
-            number: 80
-# DAG Workflow to be executed once a Workspace action completes (optional)
-#postExecutionWorkflow:
-#  entrypoint: main
-#  templates:
-#  - name: main
-#    dag:
-#       tasks:
-#       - name: slack-notify
-#         template: slack-notify
-#  - name: slack-notify
-#    container:
-#      image: technosophos/slack-notify
-#      args:
-#      - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
-#      command:
-#      - sh
-#      - -c
+metadata:
+  name: JupyterLab
+  kind: Workspace
+  version: 20201031165106
+  action: update
+  description: "Interactive development environment for notebooks"
+spec:
+  # Docker containers that are part of the Workspace
+  containers:
+    - name: jupyterlab
+      image: onepanel/jupyterlab:1.0.1
+      command: ["/bin/bash", "-c", "pip install onepanel-sdk && start.sh jupyter lab --LabApp.token='' --LabApp.allow_remote_access=True --LabApp.allow_origin=\"*\" --LabApp.disable_check_xsrf=True --LabApp.trust_xheaders=True --LabApp.base_url=/ --LabApp.tornado_settings='{\"headers\":{\"Content-Security-Policy\":\"frame-ancestors * \'self\'\"}}' --notebook-dir='/data' --allow-root"]
+      env:
+        - name: tornado
+          value: "'{'headers':{'Content-Security-Policy':\"frame-ancestors\ *\ \'self'\"}}'"
+        - name: TENSORBOARD_PROXY_URL
+          value: '//$(ONEPANEL_RESOURCE_UID)--$(ONEPANEL_RESOURCE_NAMESPACE).$(ONEPANEL_DOMAIN)/tensorboard'
+      ports:
+        - containerPort: 8888
+          name: jupyterlab
+        - containerPort: 6006
+          name: tensorboard
+      volumeMounts:
+        - name: data
+          mountPath: /data
+      lifecycle:
+        postStart:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                condayml="/data/.environment.yml";
+                jupytertxt="/data/.jupexported.txt";
+                if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
+                if [ -f "$jupytertxt" ]; then cat $jupytertxt | xargs -n 1 jupyter labextension install --no-build && jupyter lab build --minimize=False; fi;
+        preStop:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                conda env export > /data/.environment.yml -n base;
+                jupyter labextension list 1>/dev/null 2> /data/.jup.txt;
+                cat /data/.jup.txt | sed -n '2,$p' | awk 'sub(/v/,"@", $2){print $1$2}' > /data/.jupexported.txt;
+  ports:
+    - name: jupyterlab
+      port: 80
+      protocol: TCP
+      targetPort: 8888
+    - name: tensorboard
+      port: 6006
+      protocol: TCP
+      targetPort: 6006
+  routes:
+    - match:
+        - uri:
+            prefix: /tensorboard
+      route:
+        - destination:
+            port:
+              number: 6006
+    - match:
+        - uri:
+            prefix: / #jupyter runs at the default route
+      route:
+        - destination:
+            port:
+              number: 80
+  # DAG Workflow to be executed once a Workspace action completes (optional)
+  #postExecutionWorkflow:
+  #  entrypoint: main
+  #  templates:
+  #  - name: main
+  #    dag:
+  #       tasks:
+  #       - name: slack-notify
+  #         template: slack-notify
+  #  - name: slack-notify
+  #    container:
+  #      image: technosophos/slack-notify
+  #      args:
+  #      - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #      command:
+  #      - sh
+  #      - -c

db/yaml/workspaces/jupyterlab/20201214133458.yaml

@@ -1,80 +1,87 @@
-# Docker containers that are part of the Workspace
-containers:
-  - name: jupyterlab
-    image: onepanel/jupyterlab:1.0.1
-    command: ["/bin/bash", "-c", "pip install onepanel-sdk && start.sh LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64 jupyter lab --LabApp.token='' --LabApp.allow_remote_access=True --LabApp.allow_origin=\"*\" --LabApp.disable_check_xsrf=True --LabApp.trust_xheaders=True --LabApp.base_url=/ --LabApp.tornado_settings='{\"headers\":{\"Content-Security-Policy\":\"frame-ancestors * \'self\'\"}}' --notebook-dir='/data' --allow-root"]
-    workingDir: /data
-    env:
-      - name: tornado
-        value: "'{'headers':{'Content-Security-Policy':\"frame-ancestors\ *\ \'self'\"}}'"
-      - name: TENSORBOARD_PROXY_URL
-        value: '//$(ONEPANEL_RESOURCE_UID)--$(ONEPANEL_RESOURCE_NAMESPACE).$(ONEPANEL_DOMAIN)/tensorboard'
-    ports:
-      - containerPort: 8888
-        name: jupyterlab
-      - containerPort: 6006
-        name: tensorboard
-    volumeMounts:
-      - name: data
-        mountPath: /data
-    lifecycle:
-      postStart:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              condayml="/data/.environment.yml";
-              jupytertxt="/data/.jupexported.txt";
-              if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
-              if [ -f "$jupytertxt" ]; then cat $jupytertxt | xargs -n 1 jupyter labextension install --no-build && jupyter lab build --minimize=False; fi;
-      preStop:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              conda env export > /data/.environment.yml -n base;
-              jupyter labextension list 1>/dev/null 2> /data/.jup.txt;
-              cat /data/.jup.txt | sed -n '2,$p' | awk 'sub(/v/,"@", $2){print $1$2}' > /data/.jupexported.txt;
-ports:
-  - name: jupyterlab
-    port: 80
-    protocol: TCP
-    targetPort: 8888
-  - name: tensorboard
-    port: 6006
-    protocol: TCP
-    targetPort: 6006
-routes:
-  - match:
-      - uri:
-          prefix: /tensorboard
-    route:
-      - destination:
-          port:
-            number: 6006
-  - match:
-      - uri:
-          prefix: / #jupyter runs at the default route
-    route:
-      - destination:
-          port:
-            number: 80
-# DAG Workflow to be executed once a Workspace action completes (optional)
-#postExecutionWorkflow:
-#  entrypoint: main
-#  templates:
-#  - name: main
-#    dag:
-#       tasks:
-#       - name: slack-notify
-#         template: slack-notify
-#  - name: slack-notify
-#    container:
-#      image: technosophos/slack-notify
-#      args:
-#      - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
-#      command:
-#      - sh
-#      - -c
+metadata:
+  name: JupyterLab
+  kind: Workspace
+  version: 20201214133458
+  action: update
+  description: "Interactive development environment for notebooks"
+spec:
+  # Docker containers that are part of the Workspace
+  containers:
+    - name: jupyterlab
+      image: onepanel/jupyterlab:1.0.1
+      command: ["/bin/bash", "-c", "pip install onepanel-sdk && start.sh LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64 jupyter lab --LabApp.token='' --LabApp.allow_remote_access=True --LabApp.allow_origin=\"*\" --LabApp.disable_check_xsrf=True --LabApp.trust_xheaders=True --LabApp.base_url=/ --LabApp.tornado_settings='{\"headers\":{\"Content-Security-Policy\":\"frame-ancestors * \'self\'\"}}' --notebook-dir='/data' --allow-root"]
+      workingDir: /data
+      env:
+        - name: tornado
+          value: "'{'headers':{'Content-Security-Policy':\"frame-ancestors\ *\ \'self'\"}}'"
+        - name: TENSORBOARD_PROXY_URL
+          value: '//$(ONEPANEL_RESOURCE_UID)--$(ONEPANEL_RESOURCE_NAMESPACE).$(ONEPANEL_DOMAIN)/tensorboard'
+      ports:
+        - containerPort: 8888
+          name: jupyterlab
+        - containerPort: 6006
+          name: tensorboard
+      volumeMounts:
+        - name: data
+          mountPath: /data
+      lifecycle:
+        postStart:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                condayml="/data/.environment.yml";
+                jupytertxt="/data/.jupexported.txt";
+                if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
+                if [ -f "$jupytertxt" ]; then cat $jupytertxt | xargs -n 1 jupyter labextension install --no-build && jupyter lab build --minimize=False; fi;
+        preStop:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                conda env export > /data/.environment.yml -n base;
+                jupyter labextension list 1>/dev/null 2> /data/.jup.txt;
+                cat /data/.jup.txt | sed -n '2,$p' | awk 'sub(/v/,"@", $2){print $1$2}' > /data/.jupexported.txt;
+  ports:
+    - name: jupyterlab
+      port: 80
+      protocol: TCP
+      targetPort: 8888
+    - name: tensorboard
+      port: 6006
+      protocol: TCP
+      targetPort: 6006
+  routes:
+    - match:
+        - uri:
+            prefix: /tensorboard
+      route:
+        - destination:
+            port:
+              number: 6006
+    - match:
+        - uri:
+            prefix: / #jupyter runs at the default route
+      route:
+        - destination:
+            port:
+              number: 80
+  # DAG Workflow to be executed once a Workspace action completes (optional)
+  #postExecutionWorkflow:
+  #  entrypoint: main
+  #  templates:
+  #  - name: main
+  #    dag:
+  #       tasks:
+  #       - name: slack-notify
+  #         template: slack-notify
+  #  - name: slack-notify
+  #    container:
+  #      image: technosophos/slack-notify
+  #      args:
+  #      - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #      command:
+  #      - sh
+  #      - -c

db/yaml/workspaces/jupyterlab/20201229205644.yaml

@@ -1,93 +1,100 @@
-# Docker containers that are part of the Workspace
-containers:
-  - name: jupyterlab
-    image: onepanel/dl:0.17.0
-    command: ["/bin/bash", "-c", "pip install onepanel-sdk && start.sh LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64 jupyter lab --LabApp.token='' --LabApp.allow_remote_access=True --LabApp.allow_origin=\"*\" --LabApp.disable_check_xsrf=True --LabApp.trust_xheaders=True --LabApp.base_url=/ --LabApp.tornado_settings='{\"headers\":{\"Content-Security-Policy\":\"frame-ancestors * 'self'\"}}' --notebook-dir='/data' --allow-root"]
-    workingDir: /data
-    env:
-      - name: tornado
-        value: "'{'headers':{'Content-Security-Policy':\"frame-ancestors\ *\ 'self'\"}}'"
-      - name: TENSORBOARD_PROXY_URL
-        value: '//$(ONEPANEL_RESOURCE_UID)--$(ONEPANEL_RESOURCE_NAMESPACE).$(ONEPANEL_DOMAIN)/tensorboard'
-    ports:
-      - containerPort: 8888
-        name: jupyterlab
-      - containerPort: 6006
-        name: tensorboard
-      - containerPort: 8080
-        name: nni
-    volumeMounts:
-      - name: data
-        mountPath: /data
-    lifecycle:
-      postStart:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              condayml="/data/.environment.yml";
-              jupytertxt="/data/.jupexported.txt";
-              if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
-              if [ -f "$jupytertxt" ]; then cat $jupytertxt | xargs -n 1 jupyter labextension install --no-build && jupyter lab build --minimize=False; fi;
-      preStop:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              conda env export > /data/.environment.yml -n base;
-              jupyter labextension list 1>/dev/null 2> /data/.jup.txt;
-              cat /data/.jup.txt | sed -n '2,$p' | awk 'sub(/v/,"@", $2){print $1$2}' > /data/.jupexported.txt;
-ports:
-  - name: jupyterlab
-    port: 80
-    protocol: TCP
-    targetPort: 8888
-  - name: tensorboard
-    port: 6006
-    protocol: TCP
-    targetPort: 6006
-  - name: nni
-    port: 8080
-    protocol: TCP
-    targetPort: 8080
-routes:
-  - match:
-      - uri:
-          prefix: /tensorboard
-    route:
-      - destination:
-          port:
-            number: 6006
-  - match:
-      - uri:
-          prefix: /nni
-    route:
-      - destination:
-          port:
-            number: 8080
-  - match:
-      - uri:
-          prefix: / #jupyter runs at the default route
-    route:
-      - destination:
-          port:
-            number: 80
-# DAG Workflow to be executed once a Workspace action completes (optional)
-#postExecutionWorkflow:
-#  entrypoint: main
-#  templates:
-#  - name: main
-#    dag:
-#       tasks:
-#       - name: slack-notify
-#         template: slack-notify
-#  - name: slack-notify
-#    container:
-#      image: technosophos/slack-notify
-#      args:
-#      - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
-#      command:
-#      - sh
-#      - -c
+metadata:
+  name: JupyterLab
+  kind: Workspace
+  version: 20201229205644
+  action: update
+  description: "Interactive development environment for notebooks"
+spec:
+  # Docker containers that are part of the Workspace
+  containers:
+    - name: jupyterlab
+      image: onepanel/dl:0.17.0
+      command: ["/bin/bash", "-c", "pip install onepanel-sdk && start.sh LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64 jupyter lab --LabApp.token='' --LabApp.allow_remote_access=True --LabApp.allow_origin=\"*\" --LabApp.disable_check_xsrf=True --LabApp.trust_xheaders=True --LabApp.base_url=/ --LabApp.tornado_settings='{\"headers\":{\"Content-Security-Policy\":\"frame-ancestors * 'self'\"}}' --notebook-dir='/data' --allow-root"]
+      workingDir: /data
+      env:
+        - name: tornado
+          value: "'{'headers':{'Content-Security-Policy':\"frame-ancestors\ *\ 'self'\"}}'"
+        - name: TENSORBOARD_PROXY_URL
+          value: '//$(ONEPANEL_RESOURCE_UID)--$(ONEPANEL_RESOURCE_NAMESPACE).$(ONEPANEL_DOMAIN)/tensorboard'
+      ports:
+        - containerPort: 8888
+          name: jupyterlab
+        - containerPort: 6006
+          name: tensorboard
+        - containerPort: 8080
+          name: nni
+      volumeMounts:
+        - name: data
+          mountPath: /data
+      lifecycle:
+        postStart:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                condayml="/data/.environment.yml";
+                jupytertxt="/data/.jupexported.txt";
+                if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
+                if [ -f "$jupytertxt" ]; then cat $jupytertxt | xargs -n 1 jupyter labextension install --no-build && jupyter lab build --minimize=False; fi;
+        preStop:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                conda env export > /data/.environment.yml -n base;
+                jupyter labextension list 1>/dev/null 2> /data/.jup.txt;
+                cat /data/.jup.txt | sed -n '2,$p' | awk 'sub(/v/,"@", $2){print $1$2}' > /data/.jupexported.txt;
+  ports:
+    - name: jupyterlab
+      port: 80
+      protocol: TCP
+      targetPort: 8888
+    - name: tensorboard
+      port: 6006
+      protocol: TCP
+      targetPort: 6006
+    - name: nni
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+  routes:
+    - match:
+        - uri:
+            prefix: /tensorboard
+      route:
+        - destination:
+            port:
+              number: 6006
+    - match:
+        - uri:
+            prefix: /nni
+      route:
+        - destination:
+            port:
+              number: 8080
+    - match:
+        - uri:
+            prefix: / #jupyter runs at the default route
+      route:
+        - destination:
+            port:
+              number: 80
+  # DAG Workflow to be executed once a Workspace action completes (optional)
+  #postExecutionWorkflow:
+  #  entrypoint: main
+  #  templates:
+  #  - name: main
+  #    dag:
+  #       tasks:
+  #       - name: slack-notify
+  #         template: slack-notify
+  #  - name: slack-notify
+  #    container:
+  #      image: technosophos/slack-notify
+  #      args:
+  #      - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #      command:
+  #      - sh
+  #      - -c

db/yaml/workspaces/jupyterlab/20210129142057.yaml

@@ -1,101 +1,108 @@
-containers:
-  - name: jupyterlab
-    image: onepanel/dl:0.17.0
-    command: ["/bin/bash", "-c", "pip install onepanel-sdk && start.sh LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64 jupyter lab --LabApp.token='' --LabApp.allow_remote_access=True --LabApp.allow_origin=\"*\" --LabApp.disable_check_xsrf=True --LabApp.trust_xheaders=True --LabApp.base_url=/ --LabApp.tornado_settings='{\"headers\":{\"Content-Security-Policy\":\"frame-ancestors * 'self'\"}}' --notebook-dir='/data' --allow-root"]
-    workingDir: /data
-    env:
-      - name: tornado
-        value: "'{'headers':{'Content-Security-Policy':\"frame-ancestors\ *\ 'self'\"}}'"
-      - name: TENSORBOARD_PROXY_URL
-        value: '//$(ONEPANEL_RESOURCE_UID)--$(ONEPANEL_RESOURCE_NAMESPACE).$(ONEPANEL_DOMAIN)/tensorboard'
-      - name: ONEPANEL_MAIN_CONTAINER
-        value: 'true'
-    ports:
-      - containerPort: 8888
-        name: jupyterlab
-      - containerPort: 6006
-        name: tensorboard
-      - containerPort: 8080
-        name: nni
-    volumeMounts:
-      - name: data
-        mountPath: /data
-    lifecycle:
-      postStart:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              condayml="/data/.environment.yml";
-              jupytertxt="/data/.jupexported.txt";
-              if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
-              if [ -f "$jupytertxt" ]; then cat $jupytertxt | xargs -n 1 jupyter labextension install --no-build && jupyter lab build --minimize=False; fi;
-      preStop:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              conda env export > /data/.environment.yml -n base;
-              jupyter labextension list 1>/dev/null 2> /data/.jup.txt;
-              cat /data/.jup.txt | sed -n '2,$p' | awk 'sub(/v/,"@", $2){print $1$2}' > /data/.jupexported.txt;
-  - name: sys-filesyncer
-    image: onepanel/filesyncer:v0.18.0
-    imagePullPolicy: Always
-    args:
-      - server
-      - -host=localhost:8889
-      - -server-prefix=/sys/filesyncer
-    volumeMounts:
-      - name: data
-        mountPath: /data
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-ports:
-  - name: jupyterlab
-    port: 80
-    protocol: TCP
-    targetPort: 8888
-  - name: tensorboard
-    port: 6006
-    protocol: TCP
-    targetPort: 6006
-  - name: nni
-    port: 8080
-    protocol: TCP
-    targetPort: 8080
-  - name: fs
-    port: 8889
-    protocol: TCP
-    targetPort: 8889
-routes:
-  - match:
-      - uri:
-          prefix: /sys/filesyncer
-    route:
-      - destination:
-          port:
-            number: 8889
-  - match:
-      - uri:
-          prefix: /tensorboard
-    route:
-      - destination:
-          port:
-            number: 6006
-  - match:
-      - uri:
-          prefix: /nni
-    route:
-      - destination:
-          port:
-            number: 8080
-  - match:
-      - uri:
-          prefix: /
-    route:
-      - destination:
-          port:
-            number: 80
+metadata:
+  name: JupyterLab
+  kind: Workspace
+  version: 20210129142057
+  action: update
+  description: "Interactive development environment for notebooks"
+spec:
+  containers:
+    - name: jupyterlab
+      image: onepanel/dl:0.17.0
+      command: ["/bin/bash", "-c", "pip install onepanel-sdk && start.sh LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64 jupyter lab --LabApp.token='' --LabApp.allow_remote_access=True --LabApp.allow_origin=\"*\" --LabApp.disable_check_xsrf=True --LabApp.trust_xheaders=True --LabApp.base_url=/ --LabApp.tornado_settings='{\"headers\":{\"Content-Security-Policy\":\"frame-ancestors * 'self'\"}}' --notebook-dir='/data' --allow-root"]
+      workingDir: /data
+      env:
+        - name: tornado
+          value: "'{'headers':{'Content-Security-Policy':\"frame-ancestors\ *\ 'self'\"}}'"
+        - name: TENSORBOARD_PROXY_URL
+          value: '//$(ONEPANEL_RESOURCE_UID)--$(ONEPANEL_RESOURCE_NAMESPACE).$(ONEPANEL_DOMAIN)/tensorboard'
+        - name: ONEPANEL_MAIN_CONTAINER
+          value: 'true'
+      ports:
+        - containerPort: 8888
+          name: jupyterlab
+        - containerPort: 6006
+          name: tensorboard
+        - containerPort: 8080
+          name: nni
+      volumeMounts:
+        - name: data
+          mountPath: /data
+      lifecycle:
+        postStart:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                condayml="/data/.environment.yml";
+                jupytertxt="/data/.jupexported.txt";
+                if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
+                if [ -f "$jupytertxt" ]; then cat $jupytertxt | xargs -n 1 jupyter labextension install --no-build && jupyter lab build --minimize=False; fi;
+        preStop:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                conda env export > /data/.environment.yml -n base;
+                jupyter labextension list 1>/dev/null 2> /data/.jup.txt;
+                cat /data/.jup.txt | sed -n '2,$p' | awk 'sub(/v/,"@", $2){print $1$2}' > /data/.jupexported.txt;
+    - name: sys-filesyncer
+      image: onepanel/filesyncer:v0.18.0
+      imagePullPolicy: Always
+      args:
+        - server
+        - -host=localhost:8889
+        - -server-prefix=/sys/filesyncer
+      volumeMounts:
+        - name: data
+          mountPath: /data
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: jupyterlab
+      port: 80
+      protocol: TCP
+      targetPort: 8888
+    - name: tensorboard
+      port: 6006
+      protocol: TCP
+      targetPort: 6006
+    - name: nni
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+    - name: fs
+      port: 8889
+      protocol: TCP
+      targetPort: 8889
+  routes:
+    - match:
+        - uri:
+            prefix: /sys/filesyncer
+      route:
+        - destination:
+            port:
+              number: 8889
+    - match:
+        - uri:
+            prefix: /tensorboard
+      route:
+        - destination:
+            port:
+              number: 6006
+    - match:
+        - uri:
+            prefix: /nni
+      route:
+        - destination:
+            port:
+              number: 8080
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80

db/yaml/workspaces/jupyterlab/20210224180017.yaml

@@ -1,101 +1,108 @@
-containers:
-  - name: jupyterlab
-    image: onepanel/dl:0.17.0
-    command: ["/bin/bash", "-c", "pip install onepanel-sdk && start.sh LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64 jupyter lab --LabApp.token='' --LabApp.allow_remote_access=True --LabApp.allow_origin=\"*\" --LabApp.disable_check_xsrf=True --LabApp.trust_xheaders=True --LabApp.base_url=/ --LabApp.tornado_settings='{\"headers\":{\"Content-Security-Policy\":\"frame-ancestors * 'self'\"}}' --notebook-dir='/data' --allow-root"]
-    workingDir: /data
-    env:
-      - name: tornado
-        value: "'{'headers':{'Content-Security-Policy':\"frame-ancestors\ *\ 'self'\"}}'"
-      - name: TENSORBOARD_PROXY_URL
-        value: '//$(ONEPANEL_RESOURCE_UID)--$(ONEPANEL_RESOURCE_NAMESPACE).$(ONEPANEL_DOMAIN)/tensorboard'
-      - name: ONEPANEL_MAIN_CONTAINER
-        value: 'true'
-    ports:
-      - containerPort: 8888
-        name: jupyterlab
-      - containerPort: 6006
-        name: tensorboard
-      - containerPort: 8080
-        name: nni
-    volumeMounts:
-      - name: data
-        mountPath: /data
-    lifecycle:
-      postStart:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              condayml="/data/.environment.yml";
-              jupytertxt="/data/.jupexported.txt";
-              if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
-              if [ -f "$jupytertxt" ]; then cat $jupytertxt | xargs -n 1 jupyter labextension install --no-build && jupyter lab build --minimize=False; fi;
-      preStop:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              conda env export > /data/.environment.yml -n base;
-              jupyter labextension list 1>/dev/null 2> /data/.jup.txt;
-              cat /data/.jup.txt | sed -n '2,$p' | awk 'sub(/v/,"@", $2){print $1$2}' > /data/.jupexported.txt;
-  - name: sys-filesyncer
-    image: onepanel/filesyncer:v0.19.0
-    imagePullPolicy: Always
-    args:
-      - server
-      - -host=localhost:8889
-      - -server-prefix=/sys/filesyncer
-    volumeMounts:
-      - name: data
-        mountPath: /data
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-ports:
-  - name: jupyterlab
-    port: 80
-    protocol: TCP
-    targetPort: 8888
-  - name: tensorboard
-    port: 6006
-    protocol: TCP
-    targetPort: 6006
-  - name: nni
-    port: 8080
-    protocol: TCP
-    targetPort: 8080
-  - name: fs
-    port: 8889
-    protocol: TCP
-    targetPort: 8889
-routes:
-  - match:
-      - uri:
-          prefix: /sys/filesyncer
-    route:
-      - destination:
-          port:
-            number: 8889
-  - match:
-      - uri:
-          prefix: /tensorboard
-    route:
-      - destination:
-          port:
-            number: 6006
-  - match:
-      - uri:
-          prefix: /nni
-    route:
-      - destination:
-          port:
-            number: 8080
-  - match:
-      - uri:
-          prefix: /
-    route:
-      - destination:
-          port:
-            number: 80
+metadata:
+  name: JupyterLab
+  kind: Workspace
+  version: 20210224180017
+  action: update
+  description: "Interactive development environment for notebooks"
+spec:
+  containers:
+    - name: jupyterlab
+      image: onepanel/dl:0.17.0
+      command: ["/bin/bash", "-c", "pip install onepanel-sdk && start.sh LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64 jupyter lab --LabApp.token='' --LabApp.allow_remote_access=True --LabApp.allow_origin=\"*\" --LabApp.disable_check_xsrf=True --LabApp.trust_xheaders=True --LabApp.base_url=/ --LabApp.tornado_settings='{\"headers\":{\"Content-Security-Policy\":\"frame-ancestors * 'self'\"}}' --notebook-dir='/data' --allow-root"]
+      workingDir: /data
+      env:
+        - name: tornado
+          value: "'{'headers':{'Content-Security-Policy':\"frame-ancestors\ *\ 'self'\"}}'"
+        - name: TENSORBOARD_PROXY_URL
+          value: '//$(ONEPANEL_RESOURCE_UID)--$(ONEPANEL_RESOURCE_NAMESPACE).$(ONEPANEL_DOMAIN)/tensorboard'
+        - name: ONEPANEL_MAIN_CONTAINER
+          value: 'true'
+      ports:
+        - containerPort: 8888
+          name: jupyterlab
+        - containerPort: 6006
+          name: tensorboard
+        - containerPort: 8080
+          name: nni
+      volumeMounts:
+        - name: data
+          mountPath: /data
+      lifecycle:
+        postStart:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                condayml="/data/.environment.yml";
+                jupytertxt="/data/.jupexported.txt";
+                if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
+                if [ -f "$jupytertxt" ]; then cat $jupytertxt | xargs -n 1 jupyter labextension install --no-build && jupyter lab build --minimize=False; fi;
+        preStop:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                conda env export > /data/.environment.yml -n base;
+                jupyter labextension list 1>/dev/null 2> /data/.jup.txt;
+                cat /data/.jup.txt | sed -n '2,$p' | awk 'sub(/v/,"@", $2){print $1$2}' > /data/.jupexported.txt;
+    - name: sys-filesyncer
+      image: onepanel/filesyncer:v0.19.0
+      imagePullPolicy: Always
+      args:
+        - server
+        - -host=localhost:8889
+        - -server-prefix=/sys/filesyncer
+      volumeMounts:
+        - name: data
+          mountPath: /data
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: jupyterlab
+      port: 80
+      protocol: TCP
+      targetPort: 8888
+    - name: tensorboard
+      port: 6006
+      protocol: TCP
+      targetPort: 6006
+    - name: nni
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+    - name: fs
+      port: 8889
+      protocol: TCP
+      targetPort: 8889
+  routes:
+    - match:
+        - uri:
+            prefix: /sys/filesyncer
+      route:
+        - destination:
+            port:
+              number: 8889
+    - match:
+        - uri:
+            prefix: /tensorboard
+      route:
+        - destination:
+            port:
+              number: 6006
+    - match:
+        - uri:
+            prefix: /nni
+      route:
+        - destination:
+            port:
+              number: 8080
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80

db/yaml/workspaces/jupyterlab/20210323175655.yaml

@@ -1,101 +1,108 @@
-containers:
-  - name: jupyterlab
-    image: onepanel/dl:v0.20.0
-    command: ["/bin/bash", "-c", "pip install onepanel-sdk && start.sh LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64 jupyter lab --LabApp.token='' --LabApp.allow_remote_access=True --LabApp.allow_origin=\"*\" --LabApp.disable_check_xsrf=True --LabApp.trust_xheaders=True --LabApp.base_url=/ --LabApp.tornado_settings='{\"headers\":{\"Content-Security-Policy\":\"frame-ancestors * 'self'\"}}' --notebook-dir='/data' --allow-root"]
-    workingDir: /data
-    env:
-      - name: tornado
-        value: "'{'headers':{'Content-Security-Policy':\"frame-ancestors\ *\ 'self'\"}}'"
-      - name: TENSORBOARD_PROXY_URL
-        value: '//$(ONEPANEL_RESOURCE_UID)--$(ONEPANEL_RESOURCE_NAMESPACE).$(ONEPANEL_DOMAIN)/tensorboard'
-      - name: ONEPANEL_MAIN_CONTAINER
-        value: 'true'
-    ports:
-      - containerPort: 8888
-        name: jupyterlab
-      - containerPort: 6006
-        name: tensorboard
-      - containerPort: 8080
-        name: nni
-    volumeMounts:
-      - name: data
-        mountPath: /data
-    lifecycle:
-      postStart:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              condayml="/data/.environment.yml";
-              jupytertxt="/data/.jupexported.txt";
-              if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
-              if [ -f "$jupytertxt" ]; then cat $jupytertxt | xargs -n 1 jupyter labextension install --no-build && jupyter lab build --minimize=False; fi;
-      preStop:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              conda env export > /data/.environment.yml -n base;
-              jupyter labextension list 1>/dev/null 2> /data/.jup.txt;
-              cat /data/.jup.txt | sed -n '2,$p' | awk 'sub(/v/,"@", $2){print $1$2}' > /data/.jupexported.txt;
-  - name: sys-filesyncer
-    image: onepanel/filesyncer:v0.20.0
-    imagePullPolicy: Always
-    args:
-      - server
-      - -host=localhost:8889
-      - -server-prefix=/sys/filesyncer
-    volumeMounts:
-      - name: data
-        mountPath: /data
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-ports:
-  - name: jupyterlab
-    port: 80
-    protocol: TCP
-    targetPort: 8888
-  - name: tensorboard
-    port: 6006
-    protocol: TCP
-    targetPort: 6006
-  - name: nni
-    port: 8080
-    protocol: TCP
-    targetPort: 8080
-  - name: fs
-    port: 8889
-    protocol: TCP
-    targetPort: 8889
-routes:
-  - match:
-      - uri:
-          prefix: /sys/filesyncer
-    route:
-      - destination:
-          port:
-            number: 8889
-  - match:
-      - uri:
-          prefix: /tensorboard
-    route:
-      - destination:
-          port:
-            number: 6006
-  - match:
-      - uri:
-          prefix: /nni
-    route:
-      - destination:
-          port:
-            number: 8080
-  - match:
-      - uri:
-          prefix: /
-    route:
-      - destination:
-          port:
-            number: 80
+metadata:
+  name: JupyterLab
+  kind: Workspace
+  version: 20210323175655
+  action: update
+  description: "Interactive development environment for notebooks"
+spec:
+  containers:
+    - name: jupyterlab
+      image: onepanel/dl:v0.20.0
+      command: ["/bin/bash", "-c", "pip install onepanel-sdk && start.sh LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64 jupyter lab --LabApp.token='' --LabApp.allow_remote_access=True --LabApp.allow_origin=\"*\" --LabApp.disable_check_xsrf=True --LabApp.trust_xheaders=True --LabApp.base_url=/ --LabApp.tornado_settings='{\"headers\":{\"Content-Security-Policy\":\"frame-ancestors * 'self'\"}}' --notebook-dir='/data' --allow-root"]
+      workingDir: /data
+      env:
+        - name: tornado
+          value: "'{'headers':{'Content-Security-Policy':\"frame-ancestors\ *\ 'self'\"}}'"
+        - name: TENSORBOARD_PROXY_URL
+          value: '//$(ONEPANEL_RESOURCE_UID)--$(ONEPANEL_RESOURCE_NAMESPACE).$(ONEPANEL_DOMAIN)/tensorboard'
+        - name: ONEPANEL_MAIN_CONTAINER
+          value: 'true'
+      ports:
+        - containerPort: 8888
+          name: jupyterlab
+        - containerPort: 6006
+          name: tensorboard
+        - containerPort: 8080
+          name: nni
+      volumeMounts:
+        - name: data
+          mountPath: /data
+      lifecycle:
+        postStart:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                condayml="/data/.environment.yml";
+                jupytertxt="/data/.jupexported.txt";
+                if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
+                if [ -f "$jupytertxt" ]; then cat $jupytertxt | xargs -n 1 jupyter labextension install --no-build && jupyter lab build --minimize=False; fi;
+        preStop:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                conda env export > /data/.environment.yml -n base;
+                jupyter labextension list 1>/dev/null 2> /data/.jup.txt;
+                cat /data/.jup.txt | sed -n '2,$p' | awk 'sub(/v/,"@", $2){print $1$2}' > /data/.jupexported.txt;
+    - name: sys-filesyncer
+      image: onepanel/filesyncer:v0.20.0
+      imagePullPolicy: Always
+      args:
+        - server
+        - -host=localhost:8889
+        - -server-prefix=/sys/filesyncer
+      volumeMounts:
+        - name: data
+          mountPath: /data
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: jupyterlab
+      port: 80
+      protocol: TCP
+      targetPort: 8888
+    - name: tensorboard
+      port: 6006
+      protocol: TCP
+      targetPort: 6006
+    - name: nni
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+    - name: fs
+      port: 8889
+      protocol: TCP
+      targetPort: 8889
+  routes:
+    - match:
+        - uri:
+            prefix: /sys/filesyncer
+      route:
+        - destination:
+            port:
+              number: 8889
+    - match:
+        - uri:
+            prefix: /tensorboard
+      route:
+        - destination:
+            port:
+              number: 6006
+    - match:
+        - uri:
+            prefix: /nni
+      route:
+        - destination:
+            port:
+              number: 8080
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80

db/yaml/workspaces/jupyterlab/20210719190719.yaml

@@ -1,101 +1,108 @@
-containers:
-  - name: jupyterlab
-    image: onepanel/dl:v0.20.0
-    command: ["/bin/bash", "-c", "pip install onepanel-sdk && start.sh LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64 jupyter lab --LabApp.token='' --LabApp.allow_remote_access=True --LabApp.allow_origin=\"*\" --LabApp.disable_check_xsrf=True --LabApp.trust_xheaders=True --LabApp.base_url=/ --LabApp.tornado_settings='{\"headers\":{\"Content-Security-Policy\":\"frame-ancestors * 'self'\"}}' --notebook-dir='/data' --allow-root"]
-    workingDir: /data
-    env:
-      - name: tornado
-        value: "'{'headers':{'Content-Security-Policy':\"frame-ancestors\ *\ 'self'\"}}'"
-      - name: TENSORBOARD_PROXY_URL
-        value: '//$(ONEPANEL_RESOURCE_UID)--$(ONEPANEL_RESOURCE_NAMESPACE).$(ONEPANEL_DOMAIN)/tensorboard'
-      - name: ONEPANEL_MAIN_CONTAINER
-        value: 'true'
-    ports:
-      - containerPort: 8888
-        name: jupyterlab
-      - containerPort: 6006
-        name: tensorboard
-      - containerPort: 8080
-        name: nni
-    volumeMounts:
-      - name: data
-        mountPath: /data
-    lifecycle:
-      postStart:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              condayml="/data/.environment.yml";
-              jupytertxt="/data/.jupexported.txt";
-              if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
-              if [ -f "$jupytertxt" ]; then cat $jupytertxt | xargs -n 1 jupyter labextension install --no-build && jupyter lab build --minimize=False; fi;
-      preStop:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              conda env export > /data/.environment.yml -n base;
-              jupyter labextension list 1>/dev/null 2> /data/.jup.txt;
-              cat /data/.jup.txt | sed -n '2,$p' | awk 'sub(/v/,"@", $2){print $1$2}' > /data/.jupexported.txt;
-  - name: sys-filesyncer
-    image: onepanel/filesyncer:v1.0.0
-    imagePullPolicy: Always
-    args:
-      - server
-      - -host=localhost:8889
-      - -server-prefix=/sys/filesyncer
-    volumeMounts:
-      - name: data
-        mountPath: /data
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-ports:
-  - name: jupyterlab
-    port: 80
-    protocol: TCP
-    targetPort: 8888
-  - name: tensorboard
-    port: 6006
-    protocol: TCP
-    targetPort: 6006
-  - name: nni
-    port: 8080
-    protocol: TCP
-    targetPort: 8080
-  - name: fs
-    port: 8889
-    protocol: TCP
-    targetPort: 8889
-routes:
-  - match:
-      - uri:
-          prefix: /sys/filesyncer
-    route:
-      - destination:
-          port:
-            number: 8889
-  - match:
-      - uri:
-          prefix: /tensorboard
-    route:
-      - destination:
-          port:
-            number: 6006
-  - match:
-      - uri:
-          prefix: /nni
-    route:
-      - destination:
-          port:
-            number: 8080
-  - match:
-      - uri:
-          prefix: /
-    route:
-      - destination:
-          port:
-            number: 80
+metadata:
+  name: JupyterLab
+  kind: Workspace
+  version: 20210719190719
+  action: update
+  description: "Interactive development environment for notebooks"
+spec:
+  containers:
+    - name: jupyterlab
+      image: onepanel/dl:v0.20.0
+      command: ["/bin/bash", "-c", "pip install onepanel-sdk && start.sh LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64 jupyter lab --LabApp.token='' --LabApp.allow_remote_access=True --LabApp.allow_origin=\"*\" --LabApp.disable_check_xsrf=True --LabApp.trust_xheaders=True --LabApp.base_url=/ --LabApp.tornado_settings='{\"headers\":{\"Content-Security-Policy\":\"frame-ancestors * 'self'\"}}' --notebook-dir='/data' --allow-root"]
+      workingDir: /data
+      env:
+        - name: tornado
+          value: "'{'headers':{'Content-Security-Policy':\"frame-ancestors\ *\ 'self'\"}}'"
+        - name: TENSORBOARD_PROXY_URL
+          value: '//$(ONEPANEL_RESOURCE_UID)--$(ONEPANEL_RESOURCE_NAMESPACE).$(ONEPANEL_DOMAIN)/tensorboard'
+        - name: ONEPANEL_MAIN_CONTAINER
+          value: 'true'
+      ports:
+        - containerPort: 8888
+          name: jupyterlab
+        - containerPort: 6006
+          name: tensorboard
+        - containerPort: 8080
+          name: nni
+      volumeMounts:
+        - name: data
+          mountPath: /data
+      lifecycle:
+        postStart:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                condayml="/data/.environment.yml";
+                jupytertxt="/data/.jupexported.txt";
+                if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
+                if [ -f "$jupytertxt" ]; then cat $jupytertxt | xargs -n 1 jupyter labextension install --no-build && jupyter lab build --minimize=False; fi;
+        preStop:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                conda env export > /data/.environment.yml -n base;
+                jupyter labextension list 1>/dev/null 2> /data/.jup.txt;
+                cat /data/.jup.txt | sed -n '2,$p' | awk 'sub(/v/,"@", $2){print $1$2}' > /data/.jupexported.txt;
+    - name: sys-filesyncer
+      image: onepanel/filesyncer:v1.0.0
+      imagePullPolicy: Always
+      args:
+        - server
+        - -host=localhost:8889
+        - -server-prefix=/sys/filesyncer
+      volumeMounts:
+        - name: data
+          mountPath: /data
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: jupyterlab
+      port: 80
+      protocol: TCP
+      targetPort: 8888
+    - name: tensorboard
+      port: 6006
+      protocol: TCP
+      targetPort: 6006
+    - name: nni
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+    - name: fs
+      port: 8889
+      protocol: TCP
+      targetPort: 8889
+  routes:
+    - match:
+        - uri:
+            prefix: /sys/filesyncer
+      route:
+        - destination:
+            port:
+              number: 8889
+    - match:
+        - uri:
+            prefix: /tensorboard
+      route:
+        - destination:
+            port:
+              number: 6006
+    - match:
+        - uri:
+            prefix: /nni
+      route:
+        - destination:
+            port:
+              number: 8080
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80

db/yaml/workspaces/vnc/20210414165510.yaml

@@ -1,57 +1,64 @@
-arguments:
-  parameters:
-    # parameter screen-resolution allows users to select screen resolution
-    - name: screen-resolution
-      value: 1680x1050
-      type: select.select
-      displayName: Screen Resolution
-      options:
-        - name: 1280x1024
-          value: 1280x1024
-        - name: 1680x1050
-          value: 1680x1050
-        - name: 2880x1800
-          value: 2880x1800
-containers:
-  - name: ubuntu
-    image: onepanel/vnc:dl-vnc
-    env:
-      - name: VNC_PASSWORDLESS
-        value: true
-      - name: VNC_RESOLUTION
-        value: '{{workflow.parameters.screen-resolution}}'
-    ports:
-      - containerPort: 6901
-        name: vnc
-    volumeMounts:
-      - name: data
-        mountPath: /data
-ports:
-  - name: vnc
-    port: 80
-    protocol: TCP
-    targetPort: 6901
-routes:
-  - match:
-      - uri:
-          prefix: /
-    route:
-      - destination:
-          port:
-            number: 80
-# DAG Workflow to be executed once a Workspace action completes (optional)
-#postExecutionWorkflow:
-#  entrypoint: main
-#  templates:
-#  - name: main
-#    dag:
-#       tasks:
-#       - name: slack-notify
-#         template: slack-notify
-#  -  name: slack-notify
-#     container:
-#       image: technosophos/slack-notify
-#       args:
-#       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
-#       command:
-#       - sh
+metadata:
+  name: "Deep Learning Desktop"
+  kind: Workspace
+  version: 20210414165510
+  action: create
+  description: "Deep learning desktop with VNC"
+spec:
+  arguments:
+    parameters:
+      # parameter screen-resolution allows users to select screen resolution
+      - name: screen-resolution
+        value: 1680x1050
+        type: select.select
+        displayName: Screen Resolution
+        options:
+          - name: 1280x1024
+            value: 1280x1024
+          - name: 1680x1050
+            value: 1680x1050
+          - name: 2880x1800
+            value: 2880x1800
+  containers:
+    - name: ubuntu
+      image: onepanel/vnc:dl-vnc
+      env:
+        - name: VNC_PASSWORDLESS
+          value: true
+        - name: VNC_RESOLUTION
+          value: '{{workflow.parameters.screen-resolution}}'
+      ports:
+        - containerPort: 6901
+          name: vnc
+      volumeMounts:
+        - name: data
+          mountPath: /data
+  ports:
+    - name: vnc
+      port: 80
+      protocol: TCP
+      targetPort: 6901
+  routes:
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80
+  # DAG Workflow to be executed once a Workspace action completes (optional)
+  #postExecutionWorkflow:
+  #  entrypoint: main
+  #  templates:
+  #  - name: main
+  #    dag:
+  #       tasks:
+  #       - name: slack-notify
+  #         template: slack-notify
+  #  -  name: slack-notify
+  #     container:
+  #       image: technosophos/slack-notify
+  #       args:
+  #       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #       command:
+  #       - sh

db/yaml/workspaces/vnc/20210719190719.yaml

@@ -1,81 +1,88 @@
-arguments:
-  parameters:
-    # parameter screen-resolution allows users to select screen resolution
-    - name: screen-resolution
-      value: 1680x1050
-      type: select.select
-      displayName: Screen Resolution
-      options:
-        - name: 1280x1024
-          value: 1280x1024
-        - name: 1680x1050
-          value: 1680x1050
-        - name: 2880x1800
-          value: 2880x1800
-containers:
-  - name: ubuntu
-    image: onepanel/vnc:dl-vnc
-    env:
-      - name: VNC_PASSWORDLESS
-        value: true
-      - name: VNC_RESOLUTION
-        value: '{{workflow.parameters.screen-resolution}}'
-    ports:
-      - containerPort: 6901
-        name: vnc
-    volumeMounts:
-      - name: data
-        mountPath: /data
-  - name: sys-filesyncer
-    image: onepanel/filesyncer:v1.0.0
-    imagePullPolicy: Always
-    args:
-      - server
-      - -host=localhost:8889
-      - -server-prefix=/sys/filesyncer
-    volumeMounts:
-      - name: data
-        mountPath: /data
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-ports:
-  - name: vnc
-    port: 80
-    protocol: TCP
-    targetPort: 6901
-  - name: fs
-    port: 8889
-    protocol: TCP
-    targetPort: 8889
-routes:
-  - match:
-      - uri:
-          prefix: /sys/filesyncer
-    route:
-      - destination:
-          port:
-            number: 8889
-  - match:
-      - uri:
-          prefix: /
-    route:
-      - destination:
-          port:
-            number: 80
-# DAG Workflow to be executed once a Workspace action completes (optional)
-#postExecutionWorkflow:
-#  entrypoint: main
-#  templates:
-#  - name: main
-#    dag:
-#       tasks:
-#       - name: slack-notify
-#         template: slack-notify
-#  -  name: slack-notify
-#     container:
-#       image: technosophos/slack-notify
-#       args:
-#       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
-#       command:
-#       - sh
+metadata:
+  name: "Deep Learning Desktop"
+  kind: Workspace
+  version: 20210719190719
+  action: update
+  description: "Deep learning desktop with VNC"
+spec:
+  arguments:
+    parameters:
+      # parameter screen-resolution allows users to select screen resolution
+      - name: screen-resolution
+        value: 1680x1050
+        type: select.select
+        displayName: Screen Resolution
+        options:
+          - name: 1280x1024
+            value: 1280x1024
+          - name: 1680x1050
+            value: 1680x1050
+          - name: 2880x1800
+            value: 2880x1800
+  containers:
+    - name: ubuntu
+      image: onepanel/vnc:dl-vnc
+      env:
+        - name: VNC_PASSWORDLESS
+          value: true
+        - name: VNC_RESOLUTION
+          value: '{{workflow.parameters.screen-resolution}}'
+      ports:
+        - containerPort: 6901
+          name: vnc
+      volumeMounts:
+        - name: data
+          mountPath: /data
+    - name: sys-filesyncer
+      image: onepanel/filesyncer:v1.0.0
+      imagePullPolicy: Always
+      args:
+        - server
+        - -host=localhost:8889
+        - -server-prefix=/sys/filesyncer
+      volumeMounts:
+        - name: data
+          mountPath: /data
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: vnc
+      port: 80
+      protocol: TCP
+      targetPort: 6901
+    - name: fs
+      port: 8889
+      protocol: TCP
+      targetPort: 8889
+  routes:
+    - match:
+        - uri:
+            prefix: /sys/filesyncer
+      route:
+        - destination:
+            port:
+              number: 8889
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 80
+  # DAG Workflow to be executed once a Workspace action completes (optional)
+  #postExecutionWorkflow:
+  #  entrypoint: main
+  #  templates:
+  #  - name: main
+  #    dag:
+  #       tasks:
+  #       - name: slack-notify
+  #         template: slack-notify
+  #  -  name: slack-notify
+  #     container:
+  #       image: technosophos/slack-notify
+  #       args:
+  #       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #       command:
+  #       - sh

db/yaml/workspaces/vscode/20200929144301.yaml

@@ -1,41 +1,48 @@
-# Docker containers that are part of the Workspace
-containers:
-  - name: vscode
-    image: onepanel/vscode:1.0.0
-    command: ["/bin/bash", "-c", "pip install onepanel-sdk && /usr/bin/entrypoint.sh --bind-addr 0.0.0.0:8080 --auth none ."]
-    ports:
-      - containerPort: 8080
-        name: vscode
-    volumeMounts:
-      - name: data
-        mountPath: /data
-ports:
-  - name: vscode
-    port: 8080
-    protocol: TCP
-    targetPort: 8080
-routes:
-  - match:
-      - uri:
-          prefix: / #vscode runs at the default route
-    route:
-      - destination:
-          port:
-            number: 8080
-# DAG Workflow to be executed once a Workspace action completes (optional)
-#postExecutionWorkflow:
-#  entrypoint: main
-#  templates:
-#  - name: main
-#    dag:
-#       tasks:
-#       - name: slack-notify
-#         template: slack-notify
-#  -  name: slack-notify
-#     container:
-#       image: technosophos/slack-notify
-#       args:
-#       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
-#       command:
-#       - sh
-#       - -c
+metadata:
+  name: "Visual Studio Code"
+  kind: Workspace
+  version: 20200929144301
+  action: create
+  description: "Open source code editor"
+spec:
+  # Docker containers that are part of the Workspace
+  containers:
+    - name: vscode
+      image: onepanel/vscode:1.0.0
+      command: ["/bin/bash", "-c", "pip install onepanel-sdk && /usr/bin/entrypoint.sh --bind-addr 0.0.0.0:8080 --auth none ."]
+      ports:
+        - containerPort: 8080
+          name: vscode
+      volumeMounts:
+        - name: data
+          mountPath: /data
+  ports:
+    - name: vscode
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+  routes:
+    - match:
+        - uri:
+            prefix: / #vscode runs at the default route
+      route:
+        - destination:
+            port:
+              number: 8080
+  # DAG Workflow to be executed once a Workspace action completes (optional)
+  #postExecutionWorkflow:
+  #  entrypoint: main
+  #  templates:
+  #  - name: main
+  #    dag:
+  #       tasks:
+  #       - name: slack-notify
+  #         template: slack-notify
+  #  -  name: slack-notify
+  #     container:
+  #       image: technosophos/slack-notify
+  #       args:
+  #       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #       command:
+  #       - sh
+  #       - -c

db/yaml/workspaces/vscode/20201028145443.yaml

@@ -1,60 +1,66 @@
-# Docker containers that are part of the Workspace
-containers:
-  - name: vscode
-    image: onepanel/vscode:1.0.0
-    command: ["/bin/bash", "-c", "pip install onepanel-sdk && /usr/bin/entrypoint.sh --bind-addr 0.0.0.0:8080 --auth none ."]
-    ports:
-      - containerPort: 8080
-        name: vscode
-    volumeMounts:
-      - name: data
-        mountPath: /data
-    lifecycle:
-      postStart:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              condayml="/data/.environment.yml";
-              vscodetxt="/data/.vscode-extensions.txt";
-              if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
-              if [ -f "$vscodetxt" ]; then cat $vscodetxt | xargs -n 1 code-server --install-extension; fi;
-      preStop:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              conda env export > /data/.environment.yml -n base;
-              code-server --list-extensions | tail -n +2 > /data/.vscode-extensions.txt;
-ports:
-  - name: vscode
-    port: 8080
-    protocol: TCP
-    targetPort: 8080
-routes:
-  - match:
-      - uri:
-          prefix: / #vscode runs at the default route
-    route:
-      - destination:
-          port:
-            number: 8080
-# DAG Workflow to be executed once a Workspace action completes (optional)
-#postExecutionWorkflow:
-#  entrypoint: main
-#  templates:
-#  - name: main
-#    dag:
-#       tasks:
-#       - name: slack-notify
-#         template: slack-notify
-#  -  name: slack-notify
-#     container:
-#       image: technosophos/slack-notify
-#       args:
-#       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
-#       command:
-#       - sh
-#       - -c
+metadata:
+  name: "Visual Studio Code"
+  kind: Workspace
+  version: 20201028145443
+  action: update
+spec:
+  # Docker containers that are part of the Workspace
+  containers:
+    - name: vscode
+      image: onepanel/vscode:1.0.0
+      command: ["/bin/bash", "-c", "pip install onepanel-sdk && /usr/bin/entrypoint.sh --bind-addr 0.0.0.0:8080 --auth none ."]
+      ports:
+        - containerPort: 8080
+          name: vscode
+      volumeMounts:
+        - name: data
+          mountPath: /data
+      lifecycle:
+        postStart:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                condayml="/data/.environment.yml";
+                vscodetxt="/data/.vscode-extensions.txt";
+                if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
+                if [ -f "$vscodetxt" ]; then cat $vscodetxt | xargs -n 1 code-server --install-extension; fi;
+        preStop:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                conda env export > /data/.environment.yml -n base;
+                code-server --list-extensions | tail -n +2 > /data/.vscode-extensions.txt;
+  ports:
+    - name: vscode
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+  routes:
+    - match:
+        - uri:
+            prefix: / #vscode runs at the default route
+      route:
+        - destination:
+            port:
+              number: 8080
+  # DAG Workflow to be executed once a Workspace action completes (optional)
+  #postExecutionWorkflow:
+  #  entrypoint: main
+  #  templates:
+  #  - name: main
+  #    dag:
+  #       tasks:
+  #       - name: slack-notify
+  #         template: slack-notify
+  #  -  name: slack-notify
+  #     container:
+  #       image: technosophos/slack-notify
+  #       args:
+  #       - SLACK_USERNAME=onepanel SLACK_TITLE="Your workspace is ready" SLACK_ICON=https://www.gravatar.com/avatar/5c4478592fe00878f62f0027be59c1bd SLACK_MESSAGE="Your workspace is now running" ./slack-notify
+  #       command:
+  #       - sh
+  #       - -c

db/yaml/workspaces/vscode/20210129152427.yaml

@@ -1,68 +1,74 @@
-containers:
-  - name: vscode
-    image: onepanel/vscode:1.0.0
-    command: ["/bin/bash", "-c", "pip install onepanel-sdk && /usr/bin/entrypoint.sh --bind-addr 0.0.0.0:8080 --auth none ."]
-    env:
-      - name: ONEPANEL_MAIN_CONTAINER
-        value: 'true'
-    ports:
-      - containerPort: 8080
-        name: vscode
-    volumeMounts:
-      - name: data
-        mountPath: /data
-    lifecycle:
-      postStart:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              condayml="/data/.environment.yml";
-              vscodetxt="/data/.vscode-extensions.txt";
-              if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
-              if [ -f "$vscodetxt" ]; then cat $vscodetxt | xargs -n 1 code-server --install-extension; fi;
-      preStop:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              conda env export > /data/.environment.yml -n base;
-              code-server --list-extensions | tail -n +2 > /data/.vscode-extensions.txt;
-  - name: sys-filesyncer
-    image: onepanel/filesyncer:v0.18.0
-    imagePullPolicy: Always
-    args:
-      - server
-      - -server-prefix=/sys/filesyncer
-    volumeMounts:
-      - name: data
-        mountPath: /data
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-ports:
-  - name: vscode
-    port: 8080
-    protocol: TCP
-    targetPort: 8080
-  - name: fs
-    port: 8888
-    protocol: TCP
-    targetPort: 8888
-routes:
-  - match:
-      - uri:
-          prefix: /sys/filesyncer
-    route:
-      - destination:
-          port:
-            number: 8888
-  - match:
-      - uri:
-          prefix: /
-    route:
-      - destination:
-          port:
-            number: 8080
+metadata:
+  name: "Visual Studio Code"
+  kind: Workspace
+  version: 20210129152427
+  action: update
+spec:
+  containers:
+    - name: vscode
+      image: onepanel/vscode:1.0.0
+      command: ["/bin/bash", "-c", "pip install onepanel-sdk && /usr/bin/entrypoint.sh --bind-addr 0.0.0.0:8080 --auth none ."]
+      env:
+        - name: ONEPANEL_MAIN_CONTAINER
+          value: 'true'
+      ports:
+        - containerPort: 8080
+          name: vscode
+      volumeMounts:
+        - name: data
+          mountPath: /data
+      lifecycle:
+        postStart:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                condayml="/data/.environment.yml";
+                vscodetxt="/data/.vscode-extensions.txt";
+                if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
+                if [ -f "$vscodetxt" ]; then cat $vscodetxt | xargs -n 1 code-server --install-extension; fi;
+        preStop:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                conda env export > /data/.environment.yml -n base;
+                code-server --list-extensions | tail -n +2 > /data/.vscode-extensions.txt;
+    - name: sys-filesyncer
+      image: onepanel/filesyncer:v0.18.0
+      imagePullPolicy: Always
+      args:
+        - server
+        - -server-prefix=/sys/filesyncer
+      volumeMounts:
+        - name: data
+          mountPath: /data
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: vscode
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+    - name: fs
+      port: 8888
+      protocol: TCP
+      targetPort: 8888
+  routes:
+    - match:
+        - uri:
+            prefix: /sys/filesyncer
+      route:
+        - destination:
+            port:
+              number: 8888
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 8080

db/yaml/workspaces/vscode/20210224180017.yaml

@@ -1,68 +1,74 @@
-containers:
-  - name: vscode
-    image: onepanel/vscode:1.0.0
-    command: ["/bin/bash", "-c", "pip install onepanel-sdk && /usr/bin/entrypoint.sh --bind-addr 0.0.0.0:8080 --auth none ."]
-    env:
-      - name: ONEPANEL_MAIN_CONTAINER
-        value: 'true'
-    ports:
-      - containerPort: 8080
-        name: vscode
-    volumeMounts:
-      - name: data
-        mountPath: /data
-    lifecycle:
-      postStart:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              condayml="/data/.environment.yml";
-              vscodetxt="/data/.vscode-extensions.txt";
-              if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
-              if [ -f "$vscodetxt" ]; then cat $vscodetxt | xargs -n 1 code-server --install-extension; fi;
-      preStop:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              conda env export > /data/.environment.yml -n base;
-              code-server --list-extensions | tail -n +2 > /data/.vscode-extensions.txt;
-  - name: sys-filesyncer
-    image: onepanel/filesyncer:v0.19.0
-    imagePullPolicy: Always
-    args:
-      - server
-      - -server-prefix=/sys/filesyncer
-    volumeMounts:
-      - name: data
-        mountPath: /data
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-ports:
-  - name: vscode
-    port: 8080
-    protocol: TCP
-    targetPort: 8080
-  - name: fs
-    port: 8888
-    protocol: TCP
-    targetPort: 8888
-routes:
-  - match:
-      - uri:
-          prefix: /sys/filesyncer
-    route:
-      - destination:
-          port:
-            number: 8888
-  - match:
-      - uri:
-          prefix: /
-    route:
-      - destination:
-          port:
-            number: 8080
+metadata:
+  name: "Visual Studio Code"
+  kind: Workspace
+  version: 20210224180017
+  action: update
+spec:
+  containers:
+    - name: vscode
+      image: onepanel/vscode:1.0.0
+      command: ["/bin/bash", "-c", "pip install onepanel-sdk && /usr/bin/entrypoint.sh --bind-addr 0.0.0.0:8080 --auth none ."]
+      env:
+        - name: ONEPANEL_MAIN_CONTAINER
+          value: 'true'
+      ports:
+        - containerPort: 8080
+          name: vscode
+      volumeMounts:
+        - name: data
+          mountPath: /data
+      lifecycle:
+        postStart:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                condayml="/data/.environment.yml";
+                vscodetxt="/data/.vscode-extensions.txt";
+                if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
+                if [ -f "$vscodetxt" ]; then cat $vscodetxt | xargs -n 1 code-server --install-extension; fi;
+        preStop:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                conda env export > /data/.environment.yml -n base;
+                code-server --list-extensions | tail -n +2 > /data/.vscode-extensions.txt;
+    - name: sys-filesyncer
+      image: onepanel/filesyncer:v0.19.0
+      imagePullPolicy: Always
+      args:
+        - server
+        - -server-prefix=/sys/filesyncer
+      volumeMounts:
+        - name: data
+          mountPath: /data
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: vscode
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+    - name: fs
+      port: 8888
+      protocol: TCP
+      targetPort: 8888
+  routes:
+    - match:
+        - uri:
+            prefix: /sys/filesyncer
+      route:
+        - destination:
+            port:
+              number: 8888
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 8080

db/yaml/workspaces/vscode/20210323175655.yaml

@@ -1,68 +1,74 @@
-containers:
-  - name: vscode
-    image: onepanel/vscode:v0.20.0_code-server.3.9.1
-    command: ["/bin/bash", "-c", "pip install onepanel-sdk && /usr/bin/entrypoint.sh --bind-addr 0.0.0.0:8080 --auth none ."]
-    env:
-      - name: ONEPANEL_MAIN_CONTAINER
-        value: 'true'
-    ports:
-      - containerPort: 8080
-        name: vscode
-    volumeMounts:
-      - name: data
-        mountPath: /data
-    lifecycle:
-      postStart:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              condayml="/data/.environment.yml";
-              vscodetxt="/data/.vscode-extensions.txt";
-              if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
-              if [ -f "$vscodetxt" ]; then cat $vscodetxt | xargs -n 1 code-server --install-extension; fi;
-      preStop:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              conda env export > /data/.environment.yml -n base;
-              code-server --list-extensions | tail -n +2 > /data/.vscode-extensions.txt;
-  - name: sys-filesyncer
-    image: onepanel/filesyncer:v0.20.0
-    imagePullPolicy: Always
-    args:
-      - server
-      - -server-prefix=/sys/filesyncer
-    volumeMounts:
-      - name: data
-        mountPath: /data
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-ports:
-  - name: vscode
-    port: 8080
-    protocol: TCP
-    targetPort: 8080
-  - name: fs
-    port: 8888
-    protocol: TCP
-    targetPort: 8888
-routes:
-  - match:
-      - uri:
-          prefix: /sys/filesyncer
-    route:
-      - destination:
-          port:
-            number: 8888
-  - match:
-      - uri:
-          prefix: /
-    route:
-      - destination:
-          port:
-            number: 8080
+metadata:
+  name: "Visual Studio Code"
+  kind: Workspace
+  version: 20210323175655
+  action: update
+spec:
+  containers:
+    - name: vscode
+      image: onepanel/vscode:v0.20.0_code-server.3.9.1
+      command: ["/bin/bash", "-c", "pip install onepanel-sdk && /usr/bin/entrypoint.sh --bind-addr 0.0.0.0:8080 --auth none ."]
+      env:
+        - name: ONEPANEL_MAIN_CONTAINER
+          value: 'true'
+      ports:
+        - containerPort: 8080
+          name: vscode
+      volumeMounts:
+        - name: data
+          mountPath: /data
+      lifecycle:
+        postStart:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                condayml="/data/.environment.yml";
+                vscodetxt="/data/.vscode-extensions.txt";
+                if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
+                if [ -f "$vscodetxt" ]; then cat $vscodetxt | xargs -n 1 code-server --install-extension; fi;
+        preStop:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                conda env export > /data/.environment.yml -n base;
+                code-server --list-extensions | tail -n +2 > /data/.vscode-extensions.txt;
+    - name: sys-filesyncer
+      image: onepanel/filesyncer:v0.20.0
+      imagePullPolicy: Always
+      args:
+        - server
+        - -server-prefix=/sys/filesyncer
+      volumeMounts:
+        - name: data
+          mountPath: /data
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: vscode
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+    - name: fs
+      port: 8888
+      protocol: TCP
+      targetPort: 8888
+  routes:
+    - match:
+        - uri:
+            prefix: /sys/filesyncer
+      route:
+        - destination:
+            port:
+              number: 8888
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 8080

db/yaml/workspaces/vscode/20210719190719.yaml

@@ -1,68 +1,74 @@
-containers:
-  - name: vscode
-    image: onepanel/vscode:v0.20.0_code-server.3.9.1
-    command: ["/bin/bash", "-c", "pip install onepanel-sdk && /usr/bin/entrypoint.sh --bind-addr 0.0.0.0:8080 --auth none ."]
-    env:
-      - name: ONEPANEL_MAIN_CONTAINER
-        value: 'true'
-    ports:
-      - containerPort: 8080
-        name: vscode
-    volumeMounts:
-      - name: data
-        mountPath: /data
-    lifecycle:
-      postStart:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              condayml="/data/.environment.yml";
-              vscodetxt="/data/.vscode-extensions.txt";
-              if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
-              if [ -f "$vscodetxt" ]; then cat $vscodetxt | xargs -n 1 code-server --install-extension; fi;
-      preStop:
-        exec:
-          command:
-            - /bin/sh
-            - -c
-            - >
-              conda env export > /data/.environment.yml -n base;
-              code-server --list-extensions | tail -n +2 > /data/.vscode-extensions.txt;
-  - name: sys-filesyncer
-    image: onepanel/filesyncer:v1.0.0
-    imagePullPolicy: Always
-    args:
-      - server
-      - -server-prefix=/sys/filesyncer
-    volumeMounts:
-      - name: data
-        mountPath: /data
-      - name: sys-namespace-config
-        mountPath: /etc/onepanel
-        readOnly: true
-ports:
-  - name: vscode
-    port: 8080
-    protocol: TCP
-    targetPort: 8080
-  - name: fs
-    port: 8888
-    protocol: TCP
-    targetPort: 8888
-routes:
-  - match:
-      - uri:
-          prefix: /sys/filesyncer
-    route:
-      - destination:
-          port:
-            number: 8888
-  - match:
-      - uri:
-          prefix: /
-    route:
-      - destination:
-          port:
-            number: 8080
+metadata:
+  name: "Visual Studio Code"
+  kind: Workspace
+  version: 20210719190719
+  action: update
+spec:
+  containers:
+    - name: vscode
+      image: onepanel/vscode:v0.20.0_code-server.3.9.1
+      command: ["/bin/bash", "-c", "pip install onepanel-sdk && /usr/bin/entrypoint.sh --bind-addr 0.0.0.0:8080 --auth none ."]
+      env:
+        - name: ONEPANEL_MAIN_CONTAINER
+          value: 'true'
+      ports:
+        - containerPort: 8080
+          name: vscode
+      volumeMounts:
+        - name: data
+          mountPath: /data
+      lifecycle:
+        postStart:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                condayml="/data/.environment.yml";
+                vscodetxt="/data/.vscode-extensions.txt";
+                if [ -f "$condayml" ]; then conda env update -f $condayml; fi;
+                if [ -f "$vscodetxt" ]; then cat $vscodetxt | xargs -n 1 code-server --install-extension; fi;
+        preStop:
+          exec:
+            command:
+              - /bin/sh
+              - -c
+              - >
+                conda env export > /data/.environment.yml -n base;
+                code-server --list-extensions | tail -n +2 > /data/.vscode-extensions.txt;
+    - name: sys-filesyncer
+      image: onepanel/filesyncer:v1.0.0
+      imagePullPolicy: Always
+      args:
+        - server
+        - -server-prefix=/sys/filesyncer
+      volumeMounts:
+        - name: data
+          mountPath: /data
+        - name: sys-namespace-config
+          mountPath: /etc/onepanel
+          readOnly: true
+  ports:
+    - name: vscode
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+    - name: fs
+      port: 8888
+      protocol: TCP
+      targetPort: 8888
+  routes:
+    - match:
+        - uri:
+            prefix: /sys/filesyncer
+      route:
+        - destination:
+            port:
+              number: 8888
+    - match:
+        - uri:
+            prefix: /
+      route:
+        - destination:
+            port:
+              number: 8080

pkg/data.go

@@ -0,0 +1,135 @@
+package v1
+
+import (
+	"github.com/onepanelio/core/pkg/util/data"
+	"github.com/onepanelio/core/pkg/util/extensions"
+)
+
+// createWorkspaceTemplateFromGenericFile will create the workspace template given by {{templateName}} with the contents
+// given by {{filename}} for the input {{namespace}}
+func (c *Client) createWorkspaceTemplateFromGenericManifest(namespace string, manifestFile *data.ManifestFile) (err error) {
+	manifest, err := manifestFile.SpecString()
+	if err != nil {
+		return err
+	}
+	templateName := manifestFile.Metadata.Name
+	description := manifestFile.Metadata.Description
+
+	artifactRepositoryType, err := c.GetArtifactRepositoryType(namespace)
+	if err != nil {
+		return err
+	}
+
+	replaceMap := map[string]string{
+		"{{.ArtifactRepositoryType}}": artifactRepositoryType,
+	}
+	manifest = extensions.ReplaceMapValues(manifest, replaceMap)
+
+	workspaceTemplate, err := CreateWorkspaceTemplate(templateName)
+	if err != nil {
+		return err
+	}
+	workspaceTemplate.Manifest = manifest
+
+	if description != nil {
+		workspaceTemplate.Description = *description
+	}
+
+	_, err = c.CreateWorkspaceTemplate(namespace, workspaceTemplate)
+
+	return
+}
+
+// updateWorkspaceTemplateManifest will update the workspace template given by {{templateName}} with the contents
+// given by {{filename}}
+func (c *Client) updateWorkspaceTemplateManifest(namespace string, manifestFile *data.ManifestFile) (err error) {
+	manifest, err := manifestFile.SpecString()
+	if err != nil {
+		return err
+	}
+	templateName := manifestFile.Metadata.Name
+
+	artifactRepositoryType, err := c.GetArtifactRepositoryType(namespace)
+	if err != nil {
+		return err
+	}
+
+	replaceMap := map[string]string{
+		"{{.ArtifactRepositoryType}}": artifactRepositoryType,
+	}
+	manifest = extensions.ReplaceMapValues(manifest, replaceMap)
+
+	workspaceTemplate, err := CreateWorkspaceTemplate(templateName)
+	if err != nil {
+		return err
+	}
+	workspaceTemplate.Manifest = manifest
+
+	_, err = c.UpdateWorkspaceTemplateManifest(namespace, workspaceTemplate.UID, workspaceTemplate.Manifest)
+
+	return
+}
+
+// createWorkflowTemplate will create the workflow template given by {{templateName}} with the contents
+// given by {{filename}}
+func (c *Client) createWorkflowTemplateFromGenericManifest(namespace string, manifestFile *data.ManifestFile) (err error) {
+	manifest, err := manifestFile.SpecString()
+	if err != nil {
+		return err
+	}
+	templateName := manifestFile.Metadata.Name
+	labels := manifestFile.Metadata.Labels
+
+	artifactRepositoryType, err := c.GetArtifactRepositoryType(namespace)
+	if err != nil {
+		return err
+	}
+
+	replaceMap := map[string]string{
+		"{{.ArtifactRepositoryType}}": artifactRepositoryType,
+	}
+	manifest = extensions.ReplaceMapValues(manifest, replaceMap)
+
+	workflowTemplate, err := CreateWorkflowTemplate(templateName)
+	if err != nil {
+		return
+	}
+	workflowTemplate.Manifest = manifest
+	workflowTemplate.Labels = labels
+
+	_, err = c.CreateWorkflowTemplate(namespace, workflowTemplate)
+
+	return
+}
+
+// updateWorkflowTemplateManifest will update the workflow template given by {{templateName}} with the contents
+// given by {{filename}}
+func (c *Client) updateWorkflowTemplateManifest(namespace string, manifestFile *data.ManifestFile) (err error) {
+	manifest, err := manifestFile.SpecString()
+	if err != nil {
+		return err
+	}
+	templateName := manifestFile.Metadata.Name
+	labels := manifestFile.Metadata.Labels
+
+	artifactRepositoryType, err := c.GetArtifactRepositoryType(namespace)
+	if err != nil {
+		return err
+	}
+
+	replaceMap := map[string]string{
+		"{{.ArtifactRepositoryType}}": artifactRepositoryType,
+	}
+	manifest = extensions.ReplaceMapValues(manifest, replaceMap)
+
+	workflowTemplate, err := CreateWorkflowTemplate(templateName)
+	if err != nil {
+		return
+	}
+	workflowTemplate.Manifest = manifest
+	workflowTemplate.Labels = labels
+
+	_, err = c.CreateWorkflowTemplateVersion(namespace, workflowTemplate)
+
+	return
+}

pkg/util/data/migration.go

@@ -0,0 +1,49 @@
+package data
+
+import (
+	"gopkg.in/yaml.v3"
+	"io/ioutil"
+)
+
+// ManifestFile represents a file that contains information about a workflow or workspace template
+type ManifestFile struct {
+	Metadata ManifestFileMetadata `yaml:"metadata"`
+	Spec     interface{}          `yaml:"spec"`
+}
+
+// ManifestFileMetadata represents information about the tempalte we are working with
+type ManifestFileMetadata struct {
+	Name        string
+	Kind        string // {Workflow, Workspace}
+	Version     uint64
+	Action      string // {create,update}
+	Description *string
+	Labels      map[string]string
+	Deprecated  *bool
+	Source      *string
+}
+
+// SpecString returns the spec of a manifest file as a string
+func (m *ManifestFile) SpecString() (string, error) {
+	data, err := yaml.Marshal(m.Spec)
+	if err != nil {
+		return "", err
+	}
+
+	return string(data), err
+}
+
+// ManifestFileFromFile loads a manifest from a yaml file.
+func ManifestFileFromFile(path string) (*ManifestFile, error) {
+	fileData, err := ioutil.ReadFile(path)
+	if err != nil {
+		return nil, err
+	}
+
+	manifest := &ManifestFile{}
+	if err := yaml.Unmarshal(fileData, manifest); err != nil {
+		return nil, err
+	}
+
+	return manifest, nil
+}

pkg/util/extensions/extensions.go

@@ -213,3 +213,17 @@ func DeleteNode(node *yaml.Node, key *YamlIndex) error {
 
 	return nil
 }
+
+// ReplaceMapValues will replace strings that are keys in the input map with their values
+// the result is returned
+func ReplaceMapValues(value string, replaceMap map[string]string) string {
+	replacePairs := make([]string, 0)
+
+	for key, value := range replaceMap {
+		replacePairs = append(replacePairs, key)
+		replacePairs = append(replacePairs, value)
+	}
+
+	return strings.NewReplacer(replacePairs...).
+		Replace(value)
+}

pkg/workflow_template_types.go

@@ -55,6 +55,22 @@ func (wt *WorkflowTemplate) GenerateUID(name string) error {
 	return nil
 }
 
+// CreateWorkflowTemplate creates a new workflow template with the given name.
+// All fields that can be generated in memory without external requests are filled out, such as the UID.
+func CreateWorkflowTemplate(name string) (*WorkflowTemplate, error) {
+	nameUID, err := uid2.GenerateUID(name, 30)
+	if err != nil {
+		return nil, err
+	}
+
+	workflowTemplate := &WorkflowTemplate{
+		Name: name,
+		UID:  nameUID,
+	}
+
+	return workflowTemplate, nil
+}
+
 // GetManifestBytes returns the manifest as []byte
 func (wt *WorkflowTemplate) GetManifestBytes() []byte {
 	return []byte(wt.Manifest)

pkg/workspace_template_types.go

@@ -51,6 +51,22 @@ func (wt *WorkspaceTemplate) GenerateUID(name string) error {
 	return nil
 }
 
+// CreateWorkspaceTemplate creates a new workspace template with the given name.
+// All fields that can be generated in memory without external requests are filled out, such as the UID.
+func CreateWorkspaceTemplate(name string) (*WorkspaceTemplate, error) {
+	nameUID, err := uid2.GenerateUID(name, 30)
+	if err != nil {
+		return nil, err
+	}
+
+	workspaceTemplate := &WorkspaceTemplate{
+		Name: name,
+		UID:  nameUID,
+	}
+
+	return workspaceTemplate, nil
+}
+
 // InjectRuntimeParameters will inject all runtime variables into the WorkflowTemplate's manifest.
 func (wt *WorkspaceTemplate) InjectRuntimeParameters(config SystemConfig) error {
 	if wt.WorkflowTemplate == nil {