mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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【FastDeploy CLI】collect-env subcommand (#4044)
* collect-env subcommand * trigger ci --------- Co-authored-by: K11OntheBoat <your_email@example.com>
This commit is contained in:
qwes5s5
783
fastdeploy/collect_env.py
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783
fastdeploy/collect_env.py
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"""
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# Copyright (c) 2025 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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"""
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# This file is modified from https://github.com/vllm-project/vllm/collect_env.py
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import datetime
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import locale
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import os
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import subprocess
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import sys
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# Unlike the rest of the PyTorch this file must be python2 compliant.
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# This script outputs relevant system environment info
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# Run it with `python collect_env.py` or `python -m torch.utils.collect_env`
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from collections import namedtuple
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import regex as re
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from fastdeploy.envs import environment_variables
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try:
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import torch
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TORCH_AVAILABLE = True
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except (ImportError, NameError, AttributeError, OSError):
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TORCH_AVAILABLE = False
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try:
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import paddle
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PADDLE_AVAILABLE = True
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except (ImportError, NameError, AttributeError, OSError):
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PADDLE_AVAILABLE = False
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# System Environment Information
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SystemEnv = namedtuple(
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"SystemEnv",
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[
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"torch_version",
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"is_debug_build",
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"cuda_compiled_version",
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"paddle_version",
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"cuda_compiled_version_paddle",
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"gcc_version",
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"clang_version",
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"cmake_version",
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"os",
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"libc_version",
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"python_version",
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"python_platform",
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"is_cuda_available",
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"cuda_runtime_version",
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"cuda_module_loading",
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"nvidia_driver_version",
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"nvidia_gpu_models",
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"cudnn_version",
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"pip_version", # 'pip' or 'pip3'
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"pip_packages",
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"conda_packages",
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"is_xnnpack_available",
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"cpu_info",
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"fastdeploy_version", # fastdploy specific field
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"fastdeploy_build_flags", # fastdploy specific field
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"gpu_topo", # fastdploy specific field
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"env_vars",
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],
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)
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DEFAULT_CONDA_PATTERNS = {
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"torch",
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"numpy",
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"cudatoolkit",
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"soumith",
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"mkl",
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"magma",
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"triton",
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"optree",
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"nccl",
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"transformers",
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"zmq",
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"nvidia",
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"pynvml",
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}
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DEFAULT_PIP_PATTERNS = {
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"torch",
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"numpy",
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"mypy",
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"flake8",
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"triton",
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"optree",
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"onnx",
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"nccl",
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"transformers",
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"zmq",
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"nvidia",
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"pynvml",
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}
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def run(command):
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"""Return (return-code, stdout, stderr)."""
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shell = True if type(command) is str else False
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try:
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p = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=shell)
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raw_output, raw_err = p.communicate()
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rc = p.returncode
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if get_platform() == "win32":
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enc = "oem"
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else:
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enc = locale.getpreferredencoding()
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output = raw_output.decode(enc)
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if command == "nvidia-smi topo -m":
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# don't remove the leading whitespace of `nvidia-smi topo -m`
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# because they are meaningful
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output = output.rstrip()
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else:
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output = output.strip()
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err = raw_err.decode(enc)
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return rc, output, err.strip()
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except FileNotFoundError:
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cmd_str = command if isinstance(command, str) else command[0]
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return 127, "", f"Command not found: {cmd_str}"
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def run_and_read_all(run_lambda, command):
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"""Run command using run_lambda; reads and returns entire output if rc is 0."""
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rc, out, _ = run_lambda(command)
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if rc != 0:
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return None
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return out
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def run_and_parse_first_match(run_lambda, command, regex):
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"""Run command using run_lambda, returns the first regex match if it exists."""
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rc, out, _ = run_lambda(command)
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if rc != 0:
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return None
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match = re.search(regex, out)
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if match is None:
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return None
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return match.group(1)
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def run_and_return_first_line(run_lambda, command):
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"""Run command using run_lambda and returns first line if output is not empty."""
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rc, out, _ = run_lambda(command)
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if rc != 0:
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return None
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return out.split("\n")[0]
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def get_conda_packages(run_lambda, patterns=None):
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if patterns is None:
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patterns = DEFAULT_CONDA_PATTERNS
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conda = os.environ.get("CONDA_EXE", "conda")
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out = run_and_read_all(run_lambda, [conda, "list"])
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if out is None:
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return out
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return "\n".join(
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line for line in out.splitlines() if not line.startswith("#") and any(name in line for name in patterns)
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)
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def get_gcc_version(run_lambda):
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return run_and_parse_first_match(run_lambda, "gcc --version", r"gcc (.*)")
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def get_clang_version(run_lambda):
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return run_and_parse_first_match(run_lambda, "clang --version", r"clang version (.*)")
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def get_cmake_version(run_lambda):
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return run_and_parse_first_match(run_lambda, "cmake --version", r"cmake (.*)")
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def get_nvidia_driver_version(run_lambda):
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if get_platform() == "darwin":
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cmd = "kextstat | grep -i cuda"
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return run_and_parse_first_match(run_lambda, cmd, r"com[.]nvidia[.]CUDA [(](.*?)[)]")
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smi = get_nvidia_smi()
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return run_and_parse_first_match(run_lambda, smi, r"Driver Version: (.*?) ")
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def get_gpu_info(run_lambda):
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if get_platform() == "darwin":
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if TORCH_AVAILABLE and torch.cuda.is_available():
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gcnArch = ""
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return torch.cuda.get_device_name(None) + gcnArch
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return None
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smi = get_nvidia_smi()
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uuid_regex = re.compile(r" \(UUID: .+?\)")
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rc, out, _ = run_lambda(smi + " -L")
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if rc != 0:
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return None
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# Anonymize GPUs by removing their UUID
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return re.sub(uuid_regex, "", out)
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def get_running_cuda_version(run_lambda):
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return run_and_parse_first_match(run_lambda, "nvcc --version", r"release .+ V(.*)")
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def get_cudnn_version(run_lambda):
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"""Return a list of libcudnn.so; it's hard to tell which one is being used."""
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if get_platform() == "win32":
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system_root = os.environ.get("SYSTEMROOT", "C:\\Windows")
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cuda_path = os.environ.get("CUDA_PATH", "%CUDA_PATH%")
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where_cmd = os.path.join(system_root, "System32", "where")
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cudnn_cmd = '{} /R "{}\\bin" cudnn*.dll'.format(where_cmd, cuda_path)
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elif get_platform() == "darwin":
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# CUDA libraries and drivers can be found in /usr/local/cuda/. See
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# https://docs.nvidia.com/cuda/cuda-installation-guide-mac-os-x/index.html#install
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# https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installmac
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# Use CUDNN_LIBRARY when cudnn library is installed elsewhere.
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cudnn_cmd = "ls /usr/local/cuda/lib/libcudnn*"
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else:
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cudnn_cmd = 'ldconfig -p | grep libcudnn | rev | cut -d" " -f1 | rev'
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rc, out, _ = run_lambda(cudnn_cmd)
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# find will return 1 if there are permission errors or if not found
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if len(out) == 0 or (rc != 1 and rc != 0):
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l = os.environ.get("CUDNN_LIBRARY")
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if l is not None and os.path.isfile(l):
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return os.path.realpath(l)
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return None
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files_set = set()
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for fn in out.split("\n"):
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fn = os.path.realpath(fn) # eliminate symbolic links
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if os.path.isfile(fn):
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files_set.add(fn)
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if not files_set:
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return None
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# Alphabetize the result because the order is non-deterministic otherwise
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files = sorted(files_set)
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if len(files) == 1:
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return files[0]
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result = "\n".join(files)
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return "Probably one of the following:\n{}".format(result)
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def get_nvidia_smi():
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# Note: nvidia-smi is currently available only on Windows and Linux
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smi = "nvidia-smi"
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if get_platform() == "win32":
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system_root = os.environ.get("SYSTEMROOT", "C:\\Windows")
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program_files_root = os.environ.get("PROGRAMFILES", "C:\\Program Files")
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legacy_path = os.path.join(program_files_root, "NVIDIA Corporation", "NVSMI", smi)
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new_path = os.path.join(system_root, "System32", smi)
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smis = [new_path, legacy_path]
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for candidate_smi in smis:
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if os.path.exists(candidate_smi):
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smi = '"{}"'.format(candidate_smi)
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break
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return smi
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def get_fastdeploy_version():
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import pkg_resources
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version = os.environ.get("FASTDEPLOY_VERSION")
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if version:
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return version
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try:
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return pkg_resources.get_distribution("fastdeploy").version
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except:
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pass
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try:
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result = subprocess.run(["pip", "show", "fastdeploy"], capture_output=True, text=True)
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if result.returncode == 0:
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for line in result.stdout.split("\n"):
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if line.startswith("Version:"):
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return line.split(":")[1].strip()
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except:
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pass
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return "unknown (could not determine version)"
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def summarize_fastdeploy_build_flags():
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# This could be a static method if the flags are constant, or dynamic if you need to check environment variables, etc.
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return "CUDA Archs: {};".format(os.getenv("FD_BUILDING_ARCS", "[]"))
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def get_gpu_topo(run_lambda):
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output = None
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if get_platform() == "linux":
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output = run_and_read_all(run_lambda, "nvidia-smi topo -m")
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if output is None:
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output = run_and_read_all(run_lambda, "rocm-smi --showtopo")
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return output
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# example outputs of CPU infos
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# * linux
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# Architecture: x86_64
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# CPU op-mode(s): 32-bit, 64-bit
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# Address sizes: 46 bits physical, 48 bits virtual
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# Byte Order: Little Endian
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# CPU(s): 128
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# On-line CPU(s) list: 0-127
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# Vendor ID: GenuineIntel
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# Model name: Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz
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# CPU family: 6
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# Model: 106
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# Thread(s) per core: 2
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# Core(s) per socket: 32
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# Socket(s): 2
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# Stepping: 6
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# BogoMIPS: 5799.78
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# Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr
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# sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl
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# xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16
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# pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand
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# hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced
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# fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap
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# avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1
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# xsaves wbnoinvd ida arat avx512vbmi pku ospke avx512_vbmi2 gfni vaes vpclmulqdq
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# avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid md_clear flush_l1d arch_capabilities
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# Virtualization features:
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# Hypervisor vendor: KVM
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# Virtualization type: full
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# Caches (sum of all):
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# L1d: 3 MiB (64 instances)
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# L1i: 2 MiB (64 instances)
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# L2: 80 MiB (64 instances)
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# L3: 108 MiB (2 instances)
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# NUMA:
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# NUMA node(s): 2
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# NUMA node0 CPU(s): 0-31,64-95
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# NUMA node1 CPU(s): 32-63,96-127
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# Vulnerabilities:
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# Itlb multihit: Not affected
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# L1tf: Not affected
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# Mds: Not affected
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# Meltdown: Not affected
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# Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
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# Retbleed: Not affected
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# Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
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# Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
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# Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
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# Srbds: Not affected
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# Tsx async abort: Not affected
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# * win32
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# Architecture=9
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# CurrentClockSpeed=2900
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# DeviceID=CPU0
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# Family=179
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# L2CacheSize=40960
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# L2CacheSpeed=
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# Manufacturer=GenuineIntel
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# MaxClockSpeed=2900
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# Name=Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz
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# ProcessorType=3
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# Revision=27142
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#
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# Architecture=9
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# CurrentClockSpeed=2900
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# DeviceID=CPU1
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# Family=179
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# L2CacheSize=40960
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# L2CacheSpeed=
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# Manufacturer=GenuineIntel
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# MaxClockSpeed=2900
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# Name=Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz
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# ProcessorType=3
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# Revision=27142
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def get_cpu_info(run_lambda):
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rc, out, err = 0, "", ""
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if get_platform() == "linux":
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rc, out, err = run_lambda("lscpu")
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elif get_platform() == "win32":
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rc, out, err = run_lambda(
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"wmic cpu get Name,Manufacturer,Family,Architecture,ProcessorType,DeviceID, \
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CurrentClockSpeed,MaxClockSpeed,L2CacheSize,L2CacheSpeed,Revision /VALUE"
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)
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elif get_platform() == "darwin":
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rc, out, err = run_lambda("sysctl -n machdep.cpu.brand_string")
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cpu_info = "None"
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if rc == 0:
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cpu_info = out
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else:
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cpu_info = err
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return cpu_info
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def get_platform():
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if sys.platform.startswith("linux"):
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return "linux"
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elif sys.platform.startswith("win32"):
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return "win32"
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elif sys.platform.startswith("cygwin"):
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return "cygwin"
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elif sys.platform.startswith("darwin"):
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return "darwin"
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else:
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return sys.platform
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def get_mac_version(run_lambda):
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return run_and_parse_first_match(run_lambda, "sw_vers -productVersion", r"(.*)")
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def get_windows_version(run_lambda):
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system_root = os.environ.get("SYSTEMROOT", "C:\\Windows")
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wmic_cmd = os.path.join(system_root, "System32", "Wbem", "wmic")
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findstr_cmd = os.path.join(system_root, "System32", "findstr")
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return run_and_read_all(run_lambda, "{} os get Caption | {} /v Caption".format(wmic_cmd, findstr_cmd))
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def get_lsb_version(run_lambda):
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return run_and_parse_first_match(run_lambda, "lsb_release -a", r"Description:\t(.*)")
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|
||||
|
||||
def check_release_file(run_lambda):
|
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return run_and_parse_first_match(run_lambda, "cat /etc/*-release", r'PRETTY_NAME="(.*)"')
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||||
|
||||
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||||
def get_os(run_lambda):
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||||
from platform import machine
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||||
|
||||
platform = get_platform()
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||||
|
||||
if platform == "win32" or platform == "cygwin":
|
||||
return get_windows_version(run_lambda)
|
||||
|
||||
if platform == "darwin":
|
||||
version = get_mac_version(run_lambda)
|
||||
if version is None:
|
||||
return None
|
||||
return "macOS {} ({})".format(version, machine())
|
||||
|
||||
if platform == "linux":
|
||||
# Ubuntu/Debian based
|
||||
desc = get_lsb_version(run_lambda)
|
||||
if desc is not None:
|
||||
return "{} ({})".format(desc, machine())
|
||||
|
||||
# Try reading /etc/*-release
|
||||
desc = check_release_file(run_lambda)
|
||||
if desc is not None:
|
||||
return "{} ({})".format(desc, machine())
|
||||
|
||||
return "{} ({})".format(platform, machine())
|
||||
|
||||
# Unknown platform
|
||||
return platform
|
||||
|
||||
|
||||
def get_python_platform():
|
||||
import platform
|
||||
|
||||
return platform.platform()
|
||||
|
||||
|
||||
def get_libc_version():
|
||||
import platform
|
||||
|
||||
if get_platform() != "linux":
|
||||
return "N/A"
|
||||
return "-".join(platform.libc_ver())
|
||||
|
||||
|
||||
def get_pip_packages(run_lambda, patterns=None):
|
||||
"""Return `pip list` output. Note: will also find conda-installed pytorch and numpy packages."""
|
||||
if patterns is None:
|
||||
patterns = DEFAULT_PIP_PATTERNS
|
||||
|
||||
def run_with_pip():
|
||||
try:
|
||||
import importlib.util
|
||||
|
||||
pip_spec = importlib.util.find_spec("pip")
|
||||
pip_available = pip_spec is not None
|
||||
except ImportError:
|
||||
pip_available = False
|
||||
|
||||
if pip_available:
|
||||
cmd = [sys.executable, "-mpip", "list", "--format=freeze"]
|
||||
elif os.environ.get("UV") is not None:
|
||||
print("uv is set")
|
||||
cmd = ["uv", "pip", "list", "--format=freeze"]
|
||||
else:
|
||||
raise RuntimeError("Could not collect pip list output (pip or uv module not available)")
|
||||
|
||||
out = run_and_read_all(run_lambda, cmd)
|
||||
return "\n".join(line for line in out.splitlines() if any(name in line for name in patterns))
|
||||
|
||||
pip_version = "pip3" if sys.version[0] == "3" else "pip"
|
||||
out = run_with_pip()
|
||||
return pip_version, out
|
||||
|
||||
|
||||
def get_cuda_module_loading_config():
|
||||
if TORCH_AVAILABLE and torch.cuda.is_available():
|
||||
torch.cuda.init()
|
||||
config = os.environ.get("CUDA_MODULE_LOADING", "")
|
||||
return config
|
||||
else:
|
||||
return "N/A"
|
||||
|
||||
|
||||
def is_xnnpack_available():
|
||||
if TORCH_AVAILABLE:
|
||||
try:
|
||||
import torch.backends.xnnpack
|
||||
|
||||
return str(torch.backends.xnnpack.enabled) # type: ignore[attr-defined]
|
||||
except:
|
||||
return "N/A"
|
||||
else:
|
||||
return "N/A"
|
||||
|
||||
|
||||
def get_env_vars():
|
||||
env_vars = ""
|
||||
secret_terms = ("secret", "token", "api", "access", "password")
|
||||
report_prefix = ("TORCH", "NCCL", "PYTORCH", "CUDA", "CUBLAS", "CUDNN", "OMP_", "MKL_", "NVIDIA")
|
||||
for k, v in os.environ.items():
|
||||
if any(term in k.lower() for term in secret_terms):
|
||||
continue
|
||||
if k in environment_variables:
|
||||
env_vars = env_vars + "{}={}".format(k, v) + "\n"
|
||||
if k.startswith(report_prefix):
|
||||
env_vars = env_vars + "{}={}".format(k, v) + "\n"
|
||||
|
||||
return env_vars
|
||||
|
||||
|
||||
def get_env_info():
|
||||
run_lambda = run
|
||||
pip_version, pip_list_output = get_pip_packages(run_lambda)
|
||||
|
||||
if TORCH_AVAILABLE:
|
||||
version_str = torch.__version__
|
||||
debug_mode_str = str(torch.version.debug)
|
||||
torch_cuda_available_str = str(torch.cuda.is_available())
|
||||
cuda_version_str = torch.version.cuda
|
||||
else:
|
||||
version_str = debug_mode_str = torch_cuda_available_str = cuda_version_str = "N/A"
|
||||
|
||||
if PADDLE_AVAILABLE:
|
||||
paddle_version_str = paddle.__version__
|
||||
paddle_cuda_available_str = str(torch.cuda.is_available())
|
||||
paddle_cuda_version_str = str(paddle.version.cuda())
|
||||
else:
|
||||
version_str = paddle_cuda_available_str = cuda_version_str = "N/A"
|
||||
|
||||
if torch_cuda_available_str == "True" or paddle_cuda_available_str == "True":
|
||||
cuda_available_str = "True"
|
||||
else:
|
||||
cuda_available_str = "False"
|
||||
|
||||
sys_version = sys.version.replace("\n", " ")
|
||||
|
||||
conda_packages = get_conda_packages(run_lambda)
|
||||
|
||||
fastdeploy_version = get_fastdeploy_version()
|
||||
fastdeploy_build_flags = summarize_fastdeploy_build_flags()
|
||||
gpu_topo = get_gpu_topo(run_lambda)
|
||||
|
||||
return SystemEnv(
|
||||
torch_version=version_str,
|
||||
is_debug_build=debug_mode_str,
|
||||
paddle_version=paddle_version_str,
|
||||
cuda_compiled_version_paddle=paddle_cuda_version_str,
|
||||
python_version="{} ({}-bit runtime)".format(sys_version, sys.maxsize.bit_length() + 1),
|
||||
python_platform=get_python_platform(),
|
||||
is_cuda_available=cuda_available_str,
|
||||
cuda_compiled_version=cuda_version_str,
|
||||
cuda_runtime_version=get_running_cuda_version(run_lambda),
|
||||
cuda_module_loading=get_cuda_module_loading_config(),
|
||||
nvidia_gpu_models=get_gpu_info(run_lambda),
|
||||
nvidia_driver_version=get_nvidia_driver_version(run_lambda),
|
||||
cudnn_version=get_cudnn_version(run_lambda),
|
||||
pip_version=pip_version,
|
||||
pip_packages=pip_list_output,
|
||||
conda_packages=conda_packages,
|
||||
os=get_os(run_lambda),
|
||||
libc_version=get_libc_version(),
|
||||
gcc_version=get_gcc_version(run_lambda),
|
||||
clang_version=get_clang_version(run_lambda),
|
||||
cmake_version=get_cmake_version(run_lambda),
|
||||
is_xnnpack_available=is_xnnpack_available(),
|
||||
cpu_info=get_cpu_info(run_lambda),
|
||||
fastdeploy_version=fastdeploy_version,
|
||||
fastdeploy_build_flags=fastdeploy_build_flags,
|
||||
gpu_topo=gpu_topo,
|
||||
env_vars=get_env_vars(),
|
||||
)
|
||||
|
||||
|
||||
env_info_fmt = """
|
||||
==============================
|
||||
System Info
|
||||
==============================
|
||||
OS : {os}
|
||||
GCC version : {gcc_version}
|
||||
Clang version : {clang_version}
|
||||
CMake version : {cmake_version}
|
||||
Libc version : {libc_version}
|
||||
|
||||
==============================
|
||||
PyTorch Info
|
||||
==============================
|
||||
PyTorch version : {torch_version}
|
||||
Is debug build : {is_debug_build}
|
||||
CUDA used to build PyTorch : {cuda_compiled_version}
|
||||
|
||||
==============================
|
||||
Paddle Info
|
||||
==============================
|
||||
Paddle version : {paddle_version}
|
||||
CUDA used to build paddle : {cuda_compiled_version_paddle}
|
||||
|
||||
==============================
|
||||
Python Environment
|
||||
==============================
|
||||
Python version : {python_version}
|
||||
Python platform : {python_platform}
|
||||
|
||||
==============================
|
||||
CUDA / GPU Info
|
||||
==============================
|
||||
Is CUDA available : {is_cuda_available}
|
||||
CUDA runtime version : {cuda_runtime_version}
|
||||
CUDA_MODULE_LOADING set to : {cuda_module_loading}
|
||||
GPU models and configuration : {nvidia_gpu_models}
|
||||
Nvidia driver version : {nvidia_driver_version}
|
||||
cuDNN version : {cudnn_version}
|
||||
Is XNNPACK available : {is_xnnpack_available}
|
||||
|
||||
==============================
|
||||
CPU Info
|
||||
==============================
|
||||
{cpu_info}
|
||||
|
||||
==============================
|
||||
Versions of relevant libraries
|
||||
==============================
|
||||
{pip_packages}
|
||||
{conda_packages}
|
||||
""".strip()
|
||||
|
||||
# both the above code and the following code use `strip()` to
|
||||
# remove leading/trailing whitespaces, so we need to add a newline
|
||||
# in between to separate the two sections
|
||||
env_info_fmt += "\n\n"
|
||||
|
||||
env_info_fmt += """
|
||||
==============================
|
||||
FastDeploy Info
|
||||
==============================
|
||||
FastDeply Version : {fastdeploy_version}
|
||||
FastDeply Build Flags:
|
||||
{fastdeploy_build_flags}
|
||||
GPU Topology:
|
||||
{gpu_topo}
|
||||
|
||||
==============================
|
||||
Environment Variables
|
||||
==============================
|
||||
{env_vars}
|
||||
""".strip()
|
||||
|
||||
|
||||
def pretty_str(envinfo):
|
||||
|
||||
def replace_nones(dct, replacement="Could not collect"):
|
||||
for key in dct.keys():
|
||||
if dct[key] is not None:
|
||||
continue
|
||||
dct[key] = replacement
|
||||
return dct
|
||||
|
||||
def replace_bools(dct, true="Yes", false="No"):
|
||||
for key in dct.keys():
|
||||
if dct[key] is True:
|
||||
dct[key] = true
|
||||
elif dct[key] is False:
|
||||
dct[key] = false
|
||||
return dct
|
||||
|
||||
def prepend(text, tag="[prepend]"):
|
||||
lines = text.split("\n")
|
||||
updated_lines = [tag + line for line in lines]
|
||||
return "\n".join(updated_lines)
|
||||
|
||||
def replace_if_empty(text, replacement="No relevant packages"):
|
||||
if text is not None and len(text) == 0:
|
||||
return replacement
|
||||
return text
|
||||
|
||||
def maybe_start_on_next_line(string):
|
||||
# If `string` is multiline, prepend a \n to it.
|
||||
if string is not None and len(string.split("\n")) > 1:
|
||||
return "\n{}\n".format(string)
|
||||
return string
|
||||
|
||||
mutable_dict = envinfo._asdict()
|
||||
|
||||
# If nvidia_gpu_models is multiline, start on the next line
|
||||
mutable_dict["nvidia_gpu_models"] = maybe_start_on_next_line(envinfo.nvidia_gpu_models)
|
||||
|
||||
# If the machine doesn't have CUDA, report some fields as 'No CUDA'
|
||||
dynamic_cuda_fields = [
|
||||
"cuda_runtime_version",
|
||||
"nvidia_gpu_models",
|
||||
"nvidia_driver_version",
|
||||
]
|
||||
all_cuda_fields = dynamic_cuda_fields + ["cudnn_version"]
|
||||
all_dynamic_cuda_fields_missing = all(mutable_dict[field] is None for field in dynamic_cuda_fields)
|
||||
if TORCH_AVAILABLE and not torch.cuda.is_available() and all_dynamic_cuda_fields_missing:
|
||||
for field in all_cuda_fields:
|
||||
mutable_dict[field] = "No CUDA"
|
||||
if envinfo.cuda_compiled_version is None:
|
||||
mutable_dict["cuda_compiled_version"] = "None"
|
||||
|
||||
# Replace True with Yes, False with No
|
||||
mutable_dict = replace_bools(mutable_dict)
|
||||
|
||||
# Replace all None objects with 'Could not collect'
|
||||
mutable_dict = replace_nones(mutable_dict)
|
||||
|
||||
# If either of these are '', replace with 'No relevant packages'
|
||||
mutable_dict["pip_packages"] = replace_if_empty(mutable_dict["pip_packages"])
|
||||
mutable_dict["conda_packages"] = replace_if_empty(mutable_dict["conda_packages"])
|
||||
|
||||
# Tag conda and pip packages with a prefix
|
||||
# If they were previously None, they'll show up as ie '[conda] Could not collect'
|
||||
if mutable_dict["pip_packages"]:
|
||||
mutable_dict["pip_packages"] = prepend(mutable_dict["pip_packages"], "[{}] ".format(envinfo.pip_version))
|
||||
if mutable_dict["conda_packages"]:
|
||||
mutable_dict["conda_packages"] = prepend(mutable_dict["conda_packages"], "[conda] ")
|
||||
mutable_dict["cpu_info"] = envinfo.cpu_info
|
||||
return env_info_fmt.format(**mutable_dict)
|
||||
|
||||
|
||||
def get_pretty_env_info():
|
||||
return pretty_str(get_env_info())
|
||||
|
||||
|
||||
def main():
|
||||
print("Collecting environment information...")
|
||||
output = get_pretty_env_info()
|
||||
print(output)
|
||||
|
||||
if TORCH_AVAILABLE and hasattr(torch, "utils") and hasattr(torch.utils, "_crash_handler"):
|
||||
minidump_dir = torch.utils._crash_handler.DEFAULT_MINIDUMP_DIR
|
||||
if sys.platform == "linux" and os.path.exists(minidump_dir):
|
||||
dumps = [os.path.join(minidump_dir, dump) for dump in os.listdir(minidump_dir)]
|
||||
latest = max(dumps, key=os.path.getctime)
|
||||
ctime = os.path.getctime(latest)
|
||||
creation_time = datetime.datetime.fromtimestamp(ctime).strftime("%Y-%m-%d %H:%M:%S")
|
||||
msg = (
|
||||
"\n*** Detected a minidump at {} created on {}, ".format(latest, creation_time)
|
||||
+ "if this is related to your bug please include it when you file a report ***"
|
||||
)
|
||||
print(msg, file=sys.stderr)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user