Check Environment Script #9038
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| # Copyright (c) MONAI Consortium | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
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| if __name__ == "__main__": | ||
| import monai | ||
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| monai.config.print_debug_info() # type: ignore |
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| #! /usr/bin/env python | ||
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| # Copyright (c) MONAI Consortium | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
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| """ | ||
| Script for checking various elements of the runtime environment and printing a large amount of diagnostic information. | ||
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| This is meant to be used for debugging environments used with MONAI, but doesn't directly need MONAI itself. It will | ||
| print information about the environment, including trying to get installed packages, test PyTorch with CUDA, and then | ||
| have MONAI print its debugging information if no errors encountered. If MONAI is not installed this script should still | ||
| work and produce useful information. Only standard libraries are needed in case a bare environment is being used. | ||
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| This can be run as a program with the following options to see all outputs: | ||
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| python check_env.py --env --envvars --monai | ||
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| With no options at all this can be used to test that PyTorch is installed and can move a tensor to a device. This is | ||
| useful when creating a fresh test environment and MONAI isn't present yet but it's good practice to valid PyTorch. | ||
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| This can also be used remotely with only Python installed to get current environment diagnostic info: | ||
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| curl https://raw.githubusercontent.com/Project-MONAI/MONAI/refs/heads/dev/monai/config/check_env.py | python | ||
| """ | ||
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| from __future__ import annotations | ||
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| import argparse | ||
| import getpass | ||
| import multiprocessing | ||
| import os | ||
| import platform | ||
| import shutil | ||
| import subprocess | ||
| import sys | ||
| from functools import partial | ||
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| DESC = """ | ||
| Script for checking various elements of the runtime environment and printing a large amount of diagnostic information. | ||
| This is used for debugging your environment by printing out various system statistics and diagnostic information. It | ||
| checks PyTorch and MONAI are installed and functioning. A typical use case is with the `--env` and `--monai` options. | ||
| """ | ||
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| USER = getpass.getuser() | ||
| HOST = platform.node() | ||
| efprint = partial(print, flush=True, file=sys.stderr) | ||
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| def fprint(*args, **kwargs): | ||
| """ | ||
| Print with flushing, replacing the username and hostname values with placeholders for better anonymization. | ||
| """ | ||
| kwargs["flush"] = True | ||
| content = " ".join(map(str, args)) | ||
| content = content.replace(USER, "<user>").replace(HOST, "<host>") | ||
| print(content, **kwargs) | ||
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| def print_platform(): | ||
| """ | ||
| Print basic platform information. | ||
| """ | ||
| fprint(platform.platform()) | ||
| fprint("uname:", list(platform.uname())) | ||
| fprint("CPU:", platform.processor(), "Count:", multiprocessing.cpu_count()) | ||
| fprint("Python:", sys.executable, platform.python_implementation(), platform.python_version()) | ||
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| def print_environment_vars(): | ||
| """ | ||
| Print all environment variables other than a few known pointless ones. | ||
| """ | ||
| fprint("Environment:") | ||
| for k, v in os.environ.items(): | ||
| if k not in ("LS_COLORS", "PS1", "PS2"): | ||
| fprint(f" {k}:", v) | ||
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| def print_environment(): | ||
| """ | ||
| Print the installed environment using `conda` or `pip`, fail if neither are present. | ||
| """ | ||
| try: | ||
| cmd = ("conda", "env", "export") if shutil.which("conda") else ("pip", "list") | ||
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| result = subprocess.check_output(cmd, stderr=subprocess.STDOUT) | ||
| fprint(result.decode()) | ||
| return True | ||
| except Exception as e: | ||
| efprint(f"Exception encountered getting environment with conda/pip: {e}") | ||
| return False | ||
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| def check_torch(): | ||
| """ | ||
| Check PyTorch is installed and a tensor can be created. | ||
| """ | ||
| try: | ||
| import torch | ||
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| t = torch.rand(2, 3) * 5 | ||
| fprint("PyTorch:", torch.__version__, torch.__path__) | ||
| fprint("Test tensor:", t.flatten()) | ||
| return True | ||
| except ImportError: | ||
| efprint("PyTorch not installed") | ||
| return False | ||
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| def check_torch_cuda(): | ||
| """ | ||
| Check CUDA capability in PyTorch by moving a tensor to each available device. | ||
| """ | ||
| import torch | ||
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| dcount = torch.cuda.device_count() | ||
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Collaborator
There was a problem hiding this comment. dcount = torch.cuda.device_count() is called before the try block starts. If this call itself raises (e.g. a broken or mismatched CUDA driver -- exactly the kind of environment problem this script exists to diagnose), the script will crash with a raw traceback instead of producing the intended graceful "PyTorch encountered CUDA error" message. Consider moving the device_count() call inside the try block below it. |
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| fprint("CUDA version:", torch.version.cuda) | ||
| fprint("PyTorch GPU Count:", dcount) | ||
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| try: | ||
| for d in range(dcount): | ||
| fprint(f" {torch.cuda.get_device_properties(d)}") | ||
| t = torch.rand(2, 3).to(torch.device(f"cuda:{d}")) * 5 | ||
| fprint("Test tensor:", t.flatten()) | ||
| return True | ||
| except Exception as e: | ||
| efprint(f"PyTorch encountered exception creating GPU tensor on device {d}: {e}") | ||
| return False | ||
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| def check_monai(): | ||
| """ | ||
| Check MONAI by importing it then printing its debug info. | ||
| """ | ||
| try: | ||
| import monai | ||
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| monai.config.print_debug_info() # type: ignore | ||
| return True | ||
| except ImportError: | ||
| efprint("MONAI not installed") | ||
| return False | ||
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coderabbitai[bot] marked this conversation as resolved.
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| if __name__ == "__main__": | ||
| parser = argparse.ArgumentParser(prog="check_env.py", description=DESC.strip()) | ||
| parser.add_argument("--env", default=False, action="store_true", help="Print environment info") | ||
| parser.add_argument("--envvars", default=False, action="store_true", help="Include environment variables") | ||
| parser.add_argument("--monai", default=False, action="store_true", help="Print MONAI info") | ||
| args = parser.parse_args() | ||
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| fprint("=" * 10, "Platform Info", "=" * 10) | ||
| print_platform() | ||
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| if args.env: | ||
| fprint("=" * 10, "Checking Environment", "=" * 10) | ||
| if args.envvars: | ||
| print_environment_vars() | ||
| print_environment() | ||
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ericspod marked this conversation as resolved.
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| fprint("=" * 10, "Checking PyTorch", "=" * 10) | ||
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| if not check_torch(): | ||
| efprint("Exiting early, no valid PyTorch install found.") | ||
| sys.exit(1) | ||
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| if not check_torch_cuda(): | ||
| efprint("Exiting early, PyTorch encountered CUDA error.") | ||
| sys.exit(1) | ||
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| if args.monai: | ||
| fprint("=" * 10, "Checking MONAI", "=" * 10) | ||
| check_monai() | ||
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This prints every environment variable except LS_COLORS/PS1/PS2. Since the module docstring recommends "python check_env.py --env --envvars --monai" as the full-diagnostics command and the script is meant to be run and shared for remote troubleshooting (including the curl-pipe-to-python usage), this risks a user pasting secrets (API keys, tokens, credentials) that happen to be set in their shell into a shared diagnostic dump. Consider redacting values for keys matching common secret patterns (TOKEN, KEY, SECRET, PASSWORD, AUTH, etc.) before printing, or adding a warning when --envvars is used.