File size: 2,046 Bytes
aad9f16 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | # OPD-only ChartQA：8×H GPU 部署
在现有 [`config/config_opd_only_7b_chartqa.yaml`](../config/config_opd_only_7b_chartqa.yaml) 中直接修改：
```yaml
paths:
# 唯一的项目内路径基目录；project://... 会由 loader 拼接到这里
project_root: /workspace/agentic-rl-main
model:
# SFT 完成后的本地完整 Hugging Face checkpoint（必须含 config.json 与模型权重）
pretrained_model_path: /mnt/models/chartqa-sft/final_checkpoint
# 本地冻结 7B teacher
teacher_model_path: /mnt/models/llava-onevision-qwen2-7b-ov
teacher_device_map: same
training:
stage: opd_only
num_gpus: 8
num_client: 8
dyme_args:
output_dir: /mnt/experiments/opd-only-chartqa-8h
per_device_train_batch_size: 2
gradient_accumulation_steps: 16
dataset:
train_dataset: project://data/chartqa/train_new_prerefine_vf_full_real_deplot_fp32_qwen25.json
```
项目内的 `project://outputs/...`、`project://data/...` 会自动使用该基目录，无需逐项修改。若显存不足，设 `per_device_train_batch_size: 1`、`gradient_accumulation_steps: 32`。
## 8 卡启动
```bash
cd /workspace/agentic-rl-main
mkdir -p logs
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
accelerate launch \
--config_file default_config_8gpu.yaml \
--num_processes 8 \
--main_process_port 29531 \
main.py \
--config config/config_opd_only_7b_chartqa.yaml \
--mode rl \
2>&1 | tee logs/opd_only_chartqa_8h.log
```
不要用 `DYME_*` 环境变量覆盖训练配置。
## 运行后检查
```bash
rg -n "training_stage=opd_only|opd_route_count|grpo_route_count|sft_route_count|loss/opsd|teacher_trajectory|teacher_probe|visual" \
logs/opd_only_chartqa_8h.log
```
应确认：
- `training_stage=opd_only`
- `opd_route_count` 覆盖全部 completion
- `grpo_route_count=0`、`sft_route_count=0`
- `loss/opsd` 有限且非零
- teacher probe、trajectory、visual checker、refiner 都有输出
运行快照位于 `training.dyme_args.output_dir`，包括 `resolved_config.yaml`、`run_env.json` 与 `logs/`。
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