Download src/transformers/commands/env.py from Deku21/RegFM: direct link, hf CLI and curl.
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2.03 kB
| import platform | |
| from argparse import ArgumentParser | |
| from transformers import __version__ as version | |
| from transformers import is_tf_available, is_torch_available | |
| from transformers.commands import BaseTransformersCLICommand | |
| def info_command_factory(_): | |
| return EnvironmentCommand() | |
| class EnvironmentCommand(BaseTransformersCLICommand): | |
| def register_subcommand(parser: ArgumentParser): | |
| download_parser = parser.add_parser("env") | |
| download_parser.set_defaults(func=info_command_factory) | |
| def run(self): | |
| pt_version = "not installed" | |
| pt_cuda_available = "NA" | |
| if is_torch_available(): | |
| import torch | |
| pt_version = torch.__version__ | |
| pt_cuda_available = torch.cuda.is_available() | |
| tf_version = "not installed" | |
| tf_cuda_available = "NA" | |
| if is_tf_available(): | |
| import tensorflow as tf | |
| tf_version = tf.__version__ | |
| try: | |
| # deprecated in v2.1 | |
| tf_cuda_available = tf.test.is_gpu_available() | |
| except AttributeError: | |
| # returns list of devices, convert to bool | |
| tf_cuda_available = bool(tf.config.list_physical_devices("GPU")) | |
| info = { | |
| "`transformers` version": version, | |
| "Platform": platform.platform(), | |
| "Python version": platform.python_version(), | |
| "PyTorch version (GPU?)": "{} ({})".format(pt_version, pt_cuda_available), | |
| "Tensorflow version (GPU?)": "{} ({})".format(tf_version, tf_cuda_available), | |
| "Using GPU in script?": "<fill in>", | |
| "Using distributed or parallel set-up in script?": "<fill in>", | |
| } | |
| print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n") | |
| print(self.format_dict(info)) | |
| return info | |
| def format_dict(d): | |
| return "\n".join(["- {}: {}".format(prop, val) for prop, val in d.items()]) + "\n" | |