feat(train): add accelerate for multi gpu training (#2154)

* Enhance training and logging functionality with accelerator support

- Added support for multi-GPU training by introducing an `accelerator` parameter in training functions.
- Updated `update_policy` to handle gradient updates based on the presence of an accelerator.
- Modified logging to prevent duplicate messages in non-main processes.
- Enhanced `set_seed` and `get_safe_torch_device` functions to accommodate accelerator usage.
- Updated `MetricsTracker` to account for the number of processes when calculating metrics.
- Introduced a new feature in `pyproject.toml` for the `accelerate` library dependency.

* Initialize logging in training script for both main and non-main processes

- Added `init_logging` calls to ensure proper logging setup when using the accelerator and in standard training mode.
- This change enhances the clarity and consistency of logging during training sessions.

* add docs and only push model once

* Place  logging under accelerate and update docs

* fix pre commit

* only log in main process

* main logging

* try with local rank

* add tests

* change runner

* fix test

* dont push to hub in multi gpu tests

* pre download dataset in tests

* small fixes

* fix path optimizer state

* update docs, and small improvements in train

* simplify accelerate main process detection

* small improvements in train

* fix OOM bug

* change accelerate detection

* add some debugging

* always use accelerate

* cleanup update method

* cleanup

* fix bug

* scale lr decay if we reduce steps

* cleanup logging

* fix formatting

* encorperate feedback pr

* add min memory to cpu tests

* use accelerate to determin logging

* fix precommit and fix tests

* chore: minor details

---------

Co-authored-by: AdilZouitine <adilzouitinegm@gmail.com>
Co-authored-by: Steven Palma <steven.palma@huggingface.co>

src/lerobot/utils/random_utils.py

@@ -14,7 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import random
-from collections.abc import Generator
+from collections.abc import Callable, Generator
 from contextlib import contextmanager
 from pathlib import Path
 from typing import Any
@@ -164,14 +164,20 @@ def set_rng_state(random_state_dict: dict[str, Any]):
         torch.cuda.random.set_rng_state(random_state_dict["torch_cuda_random_state"])
 
 
-def set_seed(seed) -> None:
+def set_seed(seed, accelerator: Callable | None = None) -> None:
     """Set seed for reproducibility."""
     random.seed(seed)
     np.random.seed(seed)
     torch.manual_seed(seed)
+
     if torch.cuda.is_available():
         torch.cuda.manual_seed_all(seed)
 
+    if accelerator:
+        from accelerate.utils import set_seed as _accelerate_set_seed
+
+        _accelerate_set_seed(seed)
+
 
 @contextmanager
 def seeded_context(seed: int) -> Generator[None, None, None]: