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| # Copyright 2020 The HuggingFace Team. All rights reserved. | |
| # | |
| # 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. | |
| import platform | |
| from argparse import ArgumentParser | |
| from .. import __version__ as version | |
| from ..file_utils import is_flax_available, is_tf_available, is_torch_available | |
| from . 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")) | |
| flax_version = "not installed" | |
| jax_version = "not installed" | |
| jaxlib_version = "not installed" | |
| jax_backend = "NA" | |
| if is_flax_available(): | |
| import flax | |
| import jax | |
| import jaxlib | |
| flax_version = flax.__version__ | |
| jax_version = jax.__version__ | |
| jaxlib_version = jaxlib.__version__ | |
| jax_backend = jax.lib.xla_bridge.get_backend().platform | |
| info = { | |
| "`transformers` version": version, | |
| "Platform": platform.platform(), | |
| "Python version": platform.python_version(), | |
| "PyTorch version (GPU?)": f"{pt_version} ({pt_cuda_available})", | |
| "Tensorflow version (GPU?)": f"{tf_version} ({tf_cuda_available})", | |
| "Flax version (CPU?/GPU?/TPU?)": f"{flax_version} ({jax_backend})", | |
| "Jax version": f"{jax_version}", | |
| "JaxLib version": f"{jaxlib_version}", | |
| "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([f"- {prop}: {val}" for prop, val in d.items()]) + "\n" | |