| .. _distributed-training: | |
| Distributed Training | |
| ==================== | |
| mjlab supports multi-GPU distributed training using | |
| `torchrunx <https://github.com/apoorvkh/torchrunx>`_. Distributed training | |
| parallelizes RL workloads across multiple GPUs by running independent rollouts | |
| on each device and synchronizing gradients during policy updates. Throughput | |
| scales nearly linearly with GPU count. | |
| TL;DR | |
| ----- | |
| **Single GPU (default):** | |
| .. code-block:: bash | |
| uv run train <task-name> <task-specific CLI args> | |
| # or explicitly: --gpu-ids 0 | |
| **Multi-GPU:** | |
| .. code-block:: bash | |
| uv run train <task-name> \ | |
| --gpu-ids 0 1 \ | |
| <task-specific CLI args> | |
| **All GPUs:** | |
| .. code-block:: bash | |
| uv run train <task-name> \ | |
| --gpu-ids all \ | |
| <task-specific CLI args> | |
| **CPU mode:** | |
| .. code-block:: bash | |
| uv run train <task-name> \ | |
| --gpu-ids None \ | |
| <task-specific CLI args> | |
| # or: CUDA_VISIBLE_DEVICES="" uv run train <task-name> ... | |
| **Key points:** | |
| - ``--gpu-ids`` specifies GPU indices (e.g., ``--gpu-ids 0 1`` for 2 GPUs) | |
| - GPU indices are relative to ``CUDA_VISIBLE_DEVICES`` if set | |
| - ``CUDA_VISIBLE_DEVICES=2,3 uv run train ... --gpu-ids 0 1`` uses physical GPUs 2 and 3 | |
| - Each GPU runs the full ``num-envs`` count (e.g., 2 GPUs × 4096 envs = 8192 total) | |
| - Single-GPU and CPU modes run directly; multi-GPU uses ``torchrunx`` for process | |
| spawning | |
| Configuration | |
| ------------- | |
| **torchrunx Logging:** | |
| By default, torchrunx process logs are saved to ``{log_dir}/torchrunx/``. You can | |
| customize this: | |
| .. code-block:: bash | |
| # Disable torchrunx file logging. | |
| uv run train <task-name> --gpu-ids 0 1 --torchrunx-log-dir "" | |
| # Custom log directory. | |
| uv run train <task-name> --gpu-ids 0 1 --torchrunx-log-dir /path/to/logs | |
| # Or use environment variable (takes precedence over flag). | |
| TORCHRUNX_LOG_DIR=/tmp/logs uv run train <task-name> --gpu-ids 0 1 | |
| The priority is ``TORCHRUNX_LOG_DIR`` env var, ``--torchrunx-log-dir`` flag, default | |
| ``{log_dir}/torchrunx``. | |
| **Single-Writer Operations:** | |
| Only rank 0 performs file I/O operations (config files, videos, wandb logging) | |
| to avoid race conditions. All workers participate in training, but logging | |
| artifacts are written once by the main process. | |
| How It Works | |
| ------------ | |
| mjlab's role is simple: **isolate mjwarp simulations on each GPU** using | |
| ``wp.ScopedDevice``. This ensures each process's environments stay on their | |
| assigned device. ``torchrunx`` handles the rest. | |
| **Process spawning.** Multi-GPU training uses ``torchrunx.Launcher(...).run(...)`` | |
| to spawn N independent processes (one per GPU) and sets environment variables | |
| (``RANK``, ``LOCAL_RANK``, ``WORLD_SIZE``) to coordinate them. Each process executes | |
| the training function with its assigned GPU. | |
| **Independent rollouts.** Each process maintains its own: | |
| - Environment instances (with ``num-envs`` parallel environments), isolated on | |
| its assigned GPU via ``wp.ScopedDevice`` | |
| - Policy network copy | |
| - Experience buffer (sized ``num_steps_per_env × num-envs``) | |
| Each process uses ``seed = cfg.seed + local_rank`` to ensure different random | |
| experiences across GPUs, increasing sample diversity. | |
| **Gradient synchronization.** During the update phase, ``rsl_rl`` synchronizes | |
| gradients after each mini-batch through its ``reduce_parameters()`` method: | |
| 1. Each process computes gradients independently on its local mini-batch | |
| 2. All policy gradients are flattened into a single tensor | |
| 3. ``torch.distributed.all_reduce`` averages gradients across all GPUs | |
| 4. Averaged gradients are copied back to each parameter, keeping policies | |
| synchronized | |