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# Multi-GPU Training
This guide shows you how to train policies on multiple GPUs using [Hugging Face Accelerate](https://huggingface.co/docs/accelerate).
## Installation
First, ensure you have accelerate installed:
```bash
pip install accelerate
```
## Training with Multiple GPUs
You can launch training in two ways:
### Option 1: Without config (specify parameters directly)
You can specify all parameters directly in the command without running `accelerate config`:
```bash
accelerate launch \
--multi_gpu \
--num_processes=2 \
$(which lerobot-train) \
--dataset.repo_id=${HF_USER}/my_dataset \
--policy.type=act \
--policy.repo_id=${HF_USER}/my_trained_policy \
--output_dir=outputs/train/act_multi_gpu \
--job_name=act_multi_gpu \
--wandb.enable=true
```
**Key accelerate parameters:**
- `--multi_gpu`: Enable multi-GPU training
- `--num_processes=2`: Number of GPUs to use
- `--mixed_precision=fp16`: Use fp16 mixed precision (or `bf16` if supported)
### Option 2: Using accelerate config
If you prefer to save your configuration, you can optionally configure accelerate for your hardware setup by running:
```bash
accelerate config
```
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:
- Compute environment: This machine
- Number of machines: 1
- Number of processes: (number of GPUs you want to use)
- GPU ids to use: (leave empty to use all)
- Mixed precision: fp16 or bf16 (recommended for faster training)
Then launch training with:
```bash
accelerate launch $(which lerobot-train) \
--dataset.repo_id=${HF_USER}/my_dataset \
--policy.type=act \
--policy.repo_id=${HF_USER}/my_trained_policy \
--output_dir=outputs/train/act_multi_gpu \
--job_name=act_multi_gpu \
--wandb.enable=true
```
## How It Works
When you launch training with accelerate:
1. **Automatic detection**: LeRobot automatically detects if it's running under accelerate
2. **Data distribution**: Your batch is automatically split across GPUs
3. **Gradient synchronization**: Gradients are synchronized across GPUs during backpropagation
4. **Single process logging**: Only the main process logs to wandb and saves checkpoints
## Learning Rate and Training Steps Scaling
**Important:** LeRobot does **NOT** automatically scale learning rates or training steps based on the number of GPUs. This gives you full control over your training hyperparameters.
### Why No Automatic Scaling?
Many distributed training frameworks automatically scale the learning rate by the number of GPUs (e.g., `lr = base_lr × num_gpus`).
However, LeRobot keeps the learning rate exactly as you specify it.
### When and How to Scale
If you want to scale your hyperparameters when using multiple GPUs, you should do it manually:
**Learning Rate Scaling:**
```bash
# Example: 2 GPUs with linear LR scaling
# Base LR: 1e-4, with 2 GPUs -> 2e-4
accelerate launch --num_processes=2 $(which lerobot-train) \
--optimizer.lr=2e-4 \
--dataset.repo_id=lerobot/pusht \
--policy=act
```
**Training Steps Scaling:**
Since the effective batch size `bs` increases with multiple GPUs (batch_size × num_gpus), you may want to reduce the number of training steps proportionally:
```bash
# Example: 2 GPUs with effective batch size 2x larger
# Original: batch_size=8, steps=100000
# With 2 GPUs: batch_size=8 (16 in total), steps=50000
accelerate launch --num_processes=2 $(which lerobot-train) \
--batch_size=8 \
--steps=50000 \
--dataset.repo_id=lerobot/pusht \
--policy=act
```
## Notes
- 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.
- 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.
- The effective batch size is `batch_size × num_gpus`. If you use 4 GPUs with `--batch_size=8`, your effective batch size is 32.
- 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.
- When saving or pushing models, LeRobot automatically unwraps the model from accelerate's distributed wrapper to ensure compatibility.
- WandB integration automatically initializes only on the main process, preventing multiple runs from being created.
For more advanced configurations and troubleshooting, see the [Accelerate documentation](https://huggingface.co/docs/accelerate). If you want to learn more about how to train on a large number of GPUs, checkout this awesome guide: [Ultrascale Playbook](https://huggingface.co/spaces/nanotron/ultrascale-playbook).
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