Source code for oumi.cli.env

# Copyright 2025 - Oumi
#
# 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 importlib.metadata
import importlib.util
import os
import platform

from rich.table import Table

import oumi.cli.cli_utils as cli_utils


def _get_package_version(package_name: str, version_fallback: str) -> str:
    """Gets the version of the specified package.

    Args:
        package_name: The name of the package.
        version_fallback: The fallback version string.

    Returns:
        str: The version of the package, or a fallback string if the package is not
            installed.
    """
    try:
        return importlib.metadata.version(package_name)
    except importlib.metadata.PackageNotFoundError:
        return version_fallback


[docs] def env(): """Prints information about the current environment.""" # Delayed imports from oumi.utils.torch_utils import format_cudnn_version # End imports version_fallback = "<not installed>" env_var_fallback = "<not set>" # All relevant environment vars. env_vars = sorted( [ "ACCELERATE_DYNAMO_BACKEND", "ACCELERATE_DYNAMO_MODE", "ACCELERATE_DYNAMO_USE_FULLGRAPH", "ACCELERATE_DYNAMO_USE_DYNAMIC", "ACCELERATE_USE_FSDP", "CUDA_VISIBLE_DEVICES", "LOCAL_RANK", "LOCAL_WORLD_SIZE", "OUMI_EXTRA_DEPS_FILE", "OUMI_SLURM_CONNECTIONS", "OUMI_FORCE_EDITABLE_INSTALL", "OUMI_USE_SPOT_VM", "RANK", "WORLD_SIZE", ] ) # All deps, excluding dev, docs, and gcp. core_packages = sorted( [ "accelerate", "aiohttp", "bitsandbytes", "datasets", "diffusers", "einops", "jsonlines", "llama-cpp-python", "liger-kernel", "lm-eval", "numpy", "nvidia-ml-py", "omegaconf", "open_clip_torch", "pandas", "peft", "pexpect", "pillow", "pydantic", "responses", "sglang", "skypilot", "tensorboard", "timm", "torch", "torchdata", "torchvision", "tqdm", "transformers", "trl", "typer", "vllm", "wandb", "mlflow", ] ) package_versions = { package: _get_package_version(package, version_fallback) for package in core_packages } env_values = {env_var: os.getenv(env_var, env_var_fallback) for env_var in env_vars} cli_utils.section_header("Oumi environment information:") env_table = Table(show_header=False, show_lines=False) env_table.add_row("Oumi version", _get_package_version("oumi", version_fallback)) env_table.add_row("Python version", platform.python_version()) env_table.add_row("Platform", platform.platform()) cli_utils.CONSOLE.print(env_table) cli_utils.section_header("Installed dependencies:") deps_table = Table(show_header=True, show_lines=False) deps_table.add_column("PACKAGE", justify="left") deps_table.add_column("VERSION", justify="left") for package, version in package_versions.items(): deps_table.add_row(package, version) cli_utils.CONSOLE.print(deps_table) if env_vars: cli_utils.section_header("Environment variables:") env_var_table = Table(show_header=True, show_lines=False) env_var_table.add_column("VARIABLE", justify="left") env_var_table.add_column("VALUE", justify="left") for var in env_vars: env_var_table.add_row(var, env_values[var]) cli_utils.CONSOLE.print(env_var_table) if importlib.util.find_spec("torch") is not None: torch = importlib.import_module("torch") cli_utils.section_header("PyTorch information:") cuda_table = Table(show_header=False, show_lines=False) cuda_table.add_row("CUDA available", str(torch.cuda.is_available())) if torch.cuda.is_available(): cuda_table.add_row("CUDA version", str(torch.version.cuda)) cuda_table.add_row( "cuDNN version", format_cudnn_version(torch.backends.cudnn.version()) ) cuda_table.add_row("Number of GPUs", str(torch.cuda.device_count())) cuda_table.add_row("GPU type", torch.cuda.get_device_name()) total_memory_gb = float(torch.cuda.mem_get_info()[1]) / float( 1024 * 1024 * 1024 ) cuda_table.add_row("GPU memory", f"{total_memory_gb:.1f}GB") cli_utils.CONSOLE.print(cuda_table)