mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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134 lines
4.0 KiB
Python
134 lines
4.0 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import requests
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from typing import List
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from fastdeploy.utils.hub_config import config
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class ServerConnectionError(Exception):
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def __init__(self, url: str):
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self.url = url
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def __str__(self):
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tips = 'Can\'t connect to FastDeploy Model Server: {}'.format(self.url)
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return tips
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class ModelServer(object):
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'''
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FastDeploy server source
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Args:
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url(str) : Url of the server
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timeout(int) : Request timeout
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'''
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def __init__(self, url: str, timeout: int=10):
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self._url = url
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self._timeout = timeout
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def search_model(self, name: str, format: str=None,
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version: str=None) -> List[dict]:
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'''
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Search model from model server.
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Args:
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name(str) : FastDeploy model name
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format(str): FastDeploy model format
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version(str) : FastDeploy model version
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Return:
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result(list): search results
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'''
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params = {}
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params['name'] = name
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if format:
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params['format'] = format
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if version:
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params['version'] = version
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result = self.request(path='fastdeploy_search', params=params)
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if result['status'] == 0 and len(result['data']) > 0:
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return result['data']
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return None
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def stat_model(self, name: str, format: str, version: str):
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'''
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Note a record when download a model for statistics.
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Args:
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name(str) : FastDeploy model name
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format(str): FastDeploy model format
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version(str) : FastDeploy model version
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Return:
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is_successful(bool): True if successful, False otherwise
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'''
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params = {}
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params['name'] = name
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params['format'] = format
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params['version'] = version
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params['from'] = 'fastdeploy'
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try:
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result = self.request(path='stat', params=params)
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except Exception:
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return False
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if result['status'] == 0:
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return True
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else:
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return False
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def request(self, path: str, params: dict) -> dict:
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'''Request server.'''
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api = '{}/{}'.format(self._url, path)
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try:
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result = requests.get(api, params, timeout=self._timeout)
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return result.json()
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except requests.exceptions.ConnectionError as e:
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raise ServerConnectionError(self._url)
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def get_model_list(self):
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'''
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Get all pre-trained models information in dataset.
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Return:
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result(dict): key is category name, value is a list which contains models \
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information such as name, format and version.
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'''
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api = '{}/{}'.format(self._url, 'fastdeploy_listmodels')
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try:
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result = requests.get(api, timeout=self._timeout)
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return result.json()
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except requests.exceptions.ConnectionError as e:
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raise ServerConnectionError(self._url)
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def is_connected(self):
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return self.check(self._url)
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@classmethod
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def check(cls, url: str) -> bool:
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'''
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Check if the specified url is a valid model server
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Args:
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url(str) : Url to check
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'''
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try:
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r = requests.get(url + '/search')
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return r.status_code == 200
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except:
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return False
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model_server = ModelServer(config.server)
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