Quick Start

快速开始 / Quick Start

中文

当你想从一个新 checkout 快速验证 DomainPostTrain 是否能跑通时，使用本页。

快速自检只验证链路是否可用，不代表已经完成真实模型训练。真实训练仍需要替换数据、配置基础模型、准备 GPU 和检查输出质量。

前置条件

• 与所选 PyTorch 构建兼容的 Python 环境。
• 主仓库已经 clone 到本地。
• 如果运行 smoke test 或训练，需要为 outputs/ 预留磁盘空间。
• 真实训练需要 CUDA GPU。CPU 只适合做链路 smoke test。

1. 创建环境

Linux/macOS:

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

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

预期结果：环境中包含 PyTorch、Transformers、Datasets、PEFT、TRL、Flask 和默认流水线依赖。

2. 复制默认配置

cp configs/domain_post_training.yaml configs/my_domain.yaml

Windows PowerShell:

Copy-Item configs/domain_post_training.yaml configs/my_domain.yaml

优先编辑复制出来的文件。保留 configs/domain_post_training.yaml 作为基线示例。

3. 运行 CPU smoke test

python scripts/training/train_pipeline.py --config configs/domain_post_training.yaml --smoke_test --device cpu

预期结果：

• 在 outputs/smoke/base_model 下创建极小 smoke model。
• 覆盖数据准备、PEFT adapter 保存、adapter merge 和报告生成链路。
• 不下载默认基础模型。

4. ML 依赖不可用时的静态检查

python -m compileall pipeline scripts serve_inference.py

预期结果：Python 文件没有语法错误。这个检查不证明 GPU 训练依赖已经安装完成。

5. 下一步

• 替换 mock 数据： 数据契约
• 检查训练环境和基础模型： 操作手册
• 修改路径或训练设置： 配置
• 跑完整训练流程： 训练流水线
• 排查失败： 故障排查

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English

Use this page when you want the shortest path from a fresh checkout to a verified DomainPostTrain setup.

The quick smoke path verifies wiring only. It does not mean a real model has been trained. Real training still requires replacing data, configuring the base model, preparing GPU resources, and checking output quality.

Prerequisites

• A Python environment compatible with the chosen PyTorch build.
• A local checkout of the main repository.
• Enough disk space for outputs/ if you run smoke tests or training.
• A CUDA GPU for real training. CPU is only practical for smoke wiring.

1. Create an Environment

Linux/macOS:

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

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

Expected result: the environment contains PyTorch, Transformers, Datasets, PEFT, TRL, Flask, and the other default pipeline dependencies.

2. Copy the Default Config

cp configs/domain_post_training.yaml configs/my_domain.yaml

Windows PowerShell:

Copy-Item configs/domain_post_training.yaml configs/my_domain.yaml

Edit the copy first. Keep configs/domain_post_training.yaml as the baseline example.

3. Run the CPU Smoke Test

python scripts/training/train_pipeline.py --config configs/domain_post_training.yaml --smoke_test --device cpu

Expected result:

• A tiny smoke model is created under outputs/smoke/base_model.
• Dataset preparation, PEFT adapter saving, adapter merge, and report generation are exercised.
• The command does not download the default base model.

4. Static Check When ML Dependencies Are Unavailable

python -m compileall pipeline scripts serve_inference.py

Expected result: Python files compile without syntax errors. This does not prove that GPU training dependencies are installed.

5. Next Pages

• Replace the mock data: Data Contracts
• Check the training environment and base model: Operations Runbook
• Change paths or training settings: Configuration
• Run the full training workflow: Training Pipeline
• Diagnose failures: Troubleshooting