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Pepijn
102 lines
3.9 KiB
Plaintext
102 lines
3.9 KiB
Plaintext
# Multi-GPU Training
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This guide shows you how to train policies on multiple GPUs using [Hugging Face Accelerate](https://huggingface.co/docs/accelerate).
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## Installation
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First, ensure you have accelerate installed:
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```bash
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pip install accelerate
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```
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Or install it with the LeRobot accelerate extra:
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```bash
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pip install lerobot[accelerate]
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```
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## Configuration (Optional)
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You can optionally configure accelerate for your hardware setup by running:
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```bash
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accelerate config
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```
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This interactive setup will ask you questions about your training environment (number of GPUs, mixed precision settings, etc.) and saves the configuration for future use. For a simple multi-GPU setup on a single machine, you can use these recommended settings:
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- Compute environment: This machine
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- Number of machines: 1
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- Number of processes: (number of GPUs you want to use)
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- GPU ids to use: (leave empty to use all)
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- Mixed precision: fp16 or bf16 (recommended for faster training)
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**Note:** You can skip this step and specify parameters directly in the launch command (see Option 1 below).
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## Training with Multiple GPUs
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You can launch training in two ways:
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### Option 1: Without config (specify parameters directly)
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You can specify all parameters directly in the command without running `accelerate config`:
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```bash
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accelerate launch \
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--multi_gpu \
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--num_processes=2 \
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--mixed_precision=fp16 \
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$(which lerobot-train) \
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--dataset.repo_id=${HF_USER}/my_dataset \
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--policy.type=act \
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--policy.repo_id=${HF_USER}/my_trained_policy \
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--output_dir=outputs/train/act_multi_gpu \
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--job_name=act_multi_gpu \
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--wandb.enable=true
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```
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**Key accelerate parameters:**
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- `--multi_gpu`: Enable multi-GPU training
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- `--num_processes=2`: Number of GPUs to use
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- `--mixed_precision=fp16`: Use fp16 mixed precision (or `bf16` if supported)
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### Option 2: Using accelerate config
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If you prefer to save your configuration, run `accelerate config` once and then simply launch with:
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```bash
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accelerate launch $(which lerobot-train) \
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--dataset.repo_id=${HF_USER}/my_dataset \
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--policy.type=act \
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--policy.repo_id=${HF_USER}/my_trained_policy \
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--output_dir=outputs/train/act_multi_gpu \
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--job_name=act_multi_gpu \
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--wandb.enable=true
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```
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## How It Works
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When you launch training with accelerate:
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1. **Automatic detection**: LeRobot automatically detects if it's running under accelerate
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2. **Data distribution**: Your batch is automatically split across GPUs
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3. **Gradient synchronization**: Gradients are synchronized across GPUs during backpropagation
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4. **Single process logging**: Only the main process logs to wandb and saves checkpoints
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## Mixed Precision Training
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For faster training, you can enable mixed precision (fp16 or bf16). This is configured during `accelerate config` or by passing `--mixed_precision=fp16` to `accelerate launch`. LeRobot's `use_amp` setting is automatically handled when using accelerate.
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## Notes
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- The `--policy.use_amp` flag in `lerobot-train` is only used when **not** running with accelerate. When using accelerate, mixed precision is controlled by accelerate's configuration.
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- Training logs, checkpoints, and hub uploads are only done by the main process to avoid conflicts. Non-main processes have console logging disabled to prevent duplicate output.
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- The effective batch size is `batch_size × num_gpus`. If you use 4 GPUs with `--batch_size=8`, your effective batch size is 32.
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- Learning rate scheduling is handled correctly across multiple processes—LeRobot sets `step_scheduler_with_optimizer=False` to prevent accelerate from adjusting scheduler steps based on the number of processes.
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- When saving or pushing models, LeRobot automatically unwraps the model from accelerate's distributed wrapper to ensure compatibility.
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- WandB integration automatically initializes only on the main process, preventing multiple runs from being created.
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For more advanced configurations and troubleshooting, see the [Accelerate documentation](https://huggingface.co/docs/accelerate).
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