Installation

安装 / Installation

中文

当你要在本地机器或训练机器上准备 DomainPostTrain 环境时，使用本页。

默认依赖目标

requirements.txt 默认面向 CUDA 12.6 GPU 训练，并包含 PyTorch CUDA wheel index：

--extra-index-url https://download.pytorch.org/whl/cu126

如果你的 CUDA runtime、Python ABI、驱动栈或操作系统不同，请先安装匹配的 PyTorch，再安装其余非 torch 依赖。

Linux/macOS

python -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -r requirements.txt

验证：

python -m compileall pipeline scripts serve_inference.py

Windows PowerShell

py -3.10 -m venv .venv
& .\.venv\Scripts\python.exe -m pip install --upgrade pip
& .\.venv\Scripts\python.exe -m pip install -r requirements.txt

验证：

& .\.venv\Scripts\python.exe -m compileall pipeline scripts serve_inference.py

可选 ONNX 依赖

ONNX 导出不属于默认训练路径。只有运行 scripts/model_artifacts/export_onnx.py 时才需要安装：

python -m pip install -r requirements-onnx.txt

ONNX GPU 导出可能需要与目标机器匹配的 ONNX Runtime CUDA wheel。

离线或私有 wheelhouse

推荐顺序：

1. 先安装与机器匹配的 torch、torchvision、torchaudio wheels。
2. 再从 requirements.txt 安装其余包，必要时排除 torch 包。
3. 运行 python -m compileall pipeline scripts serve_inference.py。
4. 确认 ML 依赖可用后再运行 smoke test。

常用安装检查

python -c "import torch; print(torch.__version__)"
python -c "import transformers, datasets, peft, trl; print('training deps ok')"
python -c "import flask; print('service deps ok')"

如果任一 import 失败，请在当前激活环境中安装缺失包。常见问题见 故障排查。

如需检查 GPU、CUDA 和训练依赖状态，运行 python scripts/diagnostics/check_training_environment.py，并参考 操作手册。

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English

Use this page when setting up a local or training-machine environment for DomainPostTrain.

Default Dependency Target

requirements.txt targets CUDA 12.6 GPU training and includes a PyTorch CUDA wheel index:

--extra-index-url https://download.pytorch.org/whl/cu126

If your CUDA runtime, Python ABI, driver stack, or operating system differs, install a matching PyTorch build first, then install the remaining non-torch dependencies.

Linux/macOS

python -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -r requirements.txt

Verify:

python -m compileall pipeline scripts serve_inference.py

Windows PowerShell

py -3.10 -m venv .venv
& .\.venv\Scripts\python.exe -m pip install --upgrade pip
& .\.venv\Scripts\python.exe -m pip install -r requirements.txt

Verify:

& .\.venv\Scripts\python.exe -m compileall pipeline scripts serve_inference.py

Optional ONNX Dependencies

ONNX export is not part of the default path. Install it only when running scripts/model_artifacts/export_onnx.py:

python -m pip install -r requirements-onnx.txt

ONNX GPU export can require ONNX Runtime CUDA runtime wheels that match the target machine.

Offline or Private Wheelhouse Setup

Recommended order:

1. Install the matching torch, torchvision, and torchaudio wheels for the machine.
2. Install the remaining packages from requirements.txt, excluding torch packages if necessary.
3. Run python -m compileall pipeline scripts serve_inference.py.
4. Run the smoke test only after the ML dependencies are available.

Common Install Checks

python -c "import torch; print(torch.__version__)"
python -c "import transformers, datasets, peft, trl; print('training deps ok')"
python -c "import flask; print('service deps ok')"

If any import fails, install the missing package in the active environment. See Troubleshooting for common dependency failures.

For GPU and package diagnostics, run python scripts/diagnostics/check_training_environment.py and use Operations Runbook.