forked from tangger/lerobot
e82e7a02e9
* 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>