# Cursor Chat: ai-agent-book

## Metadata
- **Project**: ai-agent-book
- **Path**: `/Users/boj`
- **Date**: 2025-10-06 16:58:53
- **Session ID**: `ceb3c260-0df6-4fe4-bb11-97077d6c5610`

## Conversation

### 👤 You

in verl, how to specify starting step

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### 👤 You

in verl, how to specify starting step

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### 👤 You

in verl, how to specify starting step

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### 👤 You

in verl, how to specify starting step number

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### 🤖 Cursor Assistant

I'll help you find how to specify the starting step number in verl. Let me search through the codebase to understand this.

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### 🤖 Cursor Assistant

Based on the verl codebase, here's how to specify the starting step number:

## Resume Configuration

In verl, you control the starting step through checkpoint resumption settings in your trainer configuration YAML file. There are two key parameters:

### 1. `resume_mode`
Three options available:
- **`auto`** (default): Automatically resumes from the latest checkpoint in `default_local_dir`
- **`disable`**: Always starts from step 0 (no resumption)
- **`resume_path`**: Resumes from a specific checkpoint path

### 2. `resume_from_path`
The path to a specific checkpoint directory (must contain `global_step_` in the path name)

## Example Configuration

```yaml
trainer:
  default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name}
  
  # Option 1: Auto-resume from latest checkpoint
  resume_mode: auto
  resume_from_path: null
  
  # Option 2: Resume from specific step
  resume_mode: resume_path
  resume_from_path: checkpoints/my_project/my_experiment/global_step_1000
  
  # Option 3: Start from scratch
  resume_mode: disable
  resume_from_path: null
```

## How It Works

When the trainer loads a checkpoint:
1. It extracts the step number from the checkpoint directory name (e.g., `global_step_1000` → step 1000)
2. Sets `self.global_steps` or `self.resume_global_step` to that value
3. Training continues from that step

The checkpoint path must follow the naming pattern: `.../global_step_{NUMBER}/...`

You can see this in action at:
- ```593:625:projects/week7/verl/verl/trainer/fsdp_sft_trainer.py``` for SFT training
- ```825:859:projects/week7/verl/verl/trainer/ppo/ray_trainer.py``` for PPO training

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### 👤 You

在 readme 中，加入 resume 有关的说明，例如，54 步时中断：

    trainer.resume_mode=resume_path \
    trainer.resume_from_path=recipe/retool/checkpoint/qwen2.5-32b_dapo_with_tool/global_step_50 \

或者直接用 auto。

还需要加入训练完成后，或者训练进行到一半，evaluate 一个 checkpoint 的配置：首先需要合并模型

python3 -m verl.model_merger merge --backend fsdp --local_dir qwen2.5-32b_dapo_with_tool/global_step_40/actor/ --target_dir qwen2.5-32b_dapo_with_tool/global_step_40/actor/huggingface

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### 🤖 Cursor Assistant

I'll read the current README to understand its structure and add the resume and evaluation documentation.

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### 🤖 Cursor Assistant

I'll add detailed documentation about resuming training and evaluating checkpoints to the README.

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### 🤖 Cursor Assistant

Perfect! I've added comprehensive documentation about resuming training and evaluating checkpoints to the README. Let me verify there are no issues with the updated file.

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### 🤖 Cursor Assistant

完成！我已经在 README 中添加了关于 resume 和 checkpoint 评估的详细说明：

## 主要更新

### 1. **训练中断恢复**（扩展了"训练中断如何恢复？"章节）

添加了两种恢复模式的详细说明：

- **方式一：指定具体检查点** - 使用 `resume_mode=resume_path` 配合具体的检查点路径
  - 示例：在第 54 步中断后，从第 50 步恢复
  - 提供了完整的命令示例

- **方式二：自动恢复** - 使用 `resume_mode=auto` 自动找到最新检查点
  - 系统会自动搜索 `default_local_dir` 目录
  - 如果找不到检查点则从头开始

### 2. **检查点评估**（扩展了"如何评估模型性能？"章节）

添加了完整的两步评估流程：

- **步骤一：合并模型检查点**
  - 提供了 `verl.model_merger` 命令的完整示例
  - 详细说明了各个参数的含义（`--backend`, `--local_dir`, `--target_dir`）
  - 解释了为什么需要合并（FSDP 分片格式转换为 Hugging Face 格式）

- **步骤二：运行评估**
  - 说明可以在训练任意阶段评估检查点
  - 便于跟踪不同训练阶段的性能

这些更新让用户能够更方便地：
- 从训练中断处恢复
- 评估训练过程中的任意检查点
- 理解整个 checkpoint 管理流程

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*Exported from [Cursor View](https://github.com/saharmor/cursor-view)*