67 lines
2.7 KiB
Markdown
67 lines
2.7 KiB
Markdown
# Training Configuration Guide
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This document explains the key configuration parameters that can be modified for Wall-X training.
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## Quick Start Checklist
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1. **Update run.sh**: Set `code_dir` and `config_path` to your actual paths
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2. **Configure GPUs**: Set `CUDA_VISIBLE_DEVICES` for your available GPUs
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3. **Update config paths**: Replace all `/path/to/` placeholders in config.yml with actual paths
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4. **Configure robot**: Set `dof_config` and `agent_pos_config` for your robot
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5. **Set dataset**: Choose appropriate `repo_id` for your dataset
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6. **Adjust batch size**: Set `batch_size_per_gpu` based on GPU memory
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7. **Run training**: Execute `bash ./workspace/lerobot_example/run.sh`
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## Required Paths (Must Modify)
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```yaml
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processor_path: "/path/to/model/" # Path to model processor
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pretrained_qwen_vl_path: "/path/to/qwen_vl_model/" # Path to pretrained Qwen VL model
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qwen_vl_act_config_path: "/path/to/config.json" # Path to model config file
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action_tokenizer_path: "/path/to/fast/" # Path to action tokenizer
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save_path: "/path/to/workspace/" # Path to save training outputs
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```
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## Training Parameters (Commonly Modified)
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### Learning Rate Settings
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- `learning_rate`: Initial learning rate (default: 0.00009)
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- `min_lr`: Minimum learning rate for scheduler (default: 0.00005)
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- `num_warmup_steps`: Number of warmup steps (default: 100)
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### Batch Size and Memory
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- `batch_size_per_gpu`: Batch size per GPU - adjust based on GPU memory
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- `gradient_accumulation_steps`: Gradient accumulation steps
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- `num_training_steps`: Total training steps
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- `num_epoch`: Number of training epochs
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## Robot Configuration (Modify for Your Robot)
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### DOF Configuration
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Modify `dof_config` to match your robot's action space:
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- Add/remove action keys based on your robot's capabilities
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- Ensure DOF numbers match your robot's action dimensions
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### Agent Position Configuration
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Keep `agent_pos_config` consistent with `dof_config`.
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### Action Keys
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- `obs_action_keys`: Actions used as observation context
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- `predict_action_keys`: Actions to predict/control
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## Data Configuration
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### Dataset
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- `repo_id`: LeRobot dataset identifier
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- `train_test_split`: Training/validation split ratio (default: 0.95)
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- `action_horizon`: Number of future actions to predict (default: 32)
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### Image Settings
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- `resolution`: Image resolution for different camera views
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- `download_videos`: Whether to download video files (true/false)
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## Resume Training (Optional)
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- `resume.ckpt`: Path to checkpoint for resuming training
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- `resume.load_ckpt_only`: Only load model weights, not optimizer state
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## Performance Settings (Optional)
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- `profile`: Enable PyTorch profiling (true/false)
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- `padding_side`: Token padding side (left/right) |