From 328fb61b831c838c876a04449ccfc0361bb4aafb Mon Sep 17 00:00:00 2001 From: Steven Palma Date: Thu, 9 Apr 2026 13:48:57 +0200 Subject: [PATCH] chore(config): change defaults configs values --- src/lerobot/configs/default.py | 4 ++-- src/lerobot/configs/eval.py | 2 +- src/lerobot/configs/policies.py | 8 ++++---- src/lerobot/configs/train.py | 8 ++++---- 4 files changed, 11 insertions(+), 11 deletions(-) diff --git a/src/lerobot/configs/default.py b/src/lerobot/configs/default.py index d6ad665bf..38abe2dad 100644 --- a/src/lerobot/configs/default.py +++ b/src/lerobot/configs/default.py @@ -33,9 +33,9 @@ class DatasetConfig: episodes: list[int] | None = None image_transforms: ImageTransformsConfig = field(default_factory=ImageTransformsConfig) revision: str | None = None - use_imagenet_stats: bool = True + use_imagenet_stats: bool = False video_backend: str = field(default_factory=get_safe_default_codec) - streaming: bool = False + streaming: bool = True def __post_init__(self) -> None: if self.episodes is not None: diff --git a/src/lerobot/configs/eval.py b/src/lerobot/configs/eval.py index da8bee6b2..f16208315 100644 --- a/src/lerobot/configs/eval.py +++ b/src/lerobot/configs/eval.py @@ -39,7 +39,7 @@ class EvalPipelineConfig: # Rename map for the observation to override the image and state keys rename_map: dict[str, str] = field(default_factory=dict) # Explicit consent to execute remote code from the Hub (required for hub environments). - trust_remote_code: bool = False + trust_remote_code: bool = True def __post_init__(self) -> None: # HACK: We parse again the cli args here to get the pretrained path if there was one. diff --git a/src/lerobot/configs/policies.py b/src/lerobot/configs/policies.py index ce567b8f5..11c8eaada 100644 --- a/src/lerobot/configs/policies.py +++ b/src/lerobot/configs/policies.py @@ -62,16 +62,16 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC): # type: igno device: str | None = None # e.g. "cuda", "cuda:0", "cpu", or "mps" # `use_amp` determines whether to use Automatic Mixed Precision (AMP) for training and evaluation. With AMP, # automatic gradient scaling is used. - use_amp: bool = False + use_amp: bool = True # Whether the policy employed PEFT for training. - use_peft: bool = False + use_peft: bool = True - push_to_hub: bool = True # type: ignore[assignment] # TODO: use a different name to avoid override + push_to_hub: bool = False # type: ignore[assignment] # TODO: use a different name to avoid override repo_id: str | None = None # Upload on private repository on the Hugging Face hub. - private: bool | None = None + private: bool | None = True # Add tags to your policy on the hub. tags: list[str] | None = None # Add tags to your policy on the hub. diff --git a/src/lerobot/configs/train.py b/src/lerobot/configs/train.py index 8b8aedb26..a753a8ff9 100644 --- a/src/lerobot/configs/train.py +++ b/src/lerobot/configs/train.py @@ -46,13 +46,13 @@ class TrainPipelineConfig(HubMixin): # `dir` is the directory of an existing run with at least one checkpoint in it. # Note that when resuming a run, the default behavior is to use the configuration from the checkpoint, # regardless of what's provided with the training command at the time of resumption. - resume: bool = False + resume: bool = True # `seed` is used for training (eg: model initialization, dataset shuffling) # AND for the evaluation environments. seed: int | None = 1000 # Set to True to use deterministic cuDNN algorithms for reproducibility. # This disables cudnn.benchmark and may reduce training speed by ~10-20 percent. - cudnn_deterministic: bool = False + cudnn_deterministic: bool = True # Number of workers for the dataloader. num_workers: int = 4 batch_size: int = 8 @@ -60,10 +60,10 @@ class TrainPipelineConfig(HubMixin): eval_freq: int = 20_000 log_freq: int = 200 tolerance_s: float = 1e-4 - save_checkpoint: bool = True + save_checkpoint: bool = False # Checkpoint is saved every `save_freq` training iterations and after the last training step. save_freq: int = 20_000 - use_policy_training_preset: bool = True + use_policy_training_preset: bool = False optimizer: OptimizerConfig | None = None scheduler: LRSchedulerConfig | None = None eval: EvalConfig = field(default_factory=EvalConfig)