save

Co-authored-by: Remi <re.cadene@gmail.com>

Makefile

@@ -20,6 +20,8 @@ build-gpu:
 test-end-to-end:
 	${MAKE} test-act-ete-train
 	${MAKE} test-act-ete-eval
+	${MAKE} test-act-ete-train-amp
+	${MAKE} test-act-ete-eval-amp
 	${MAKE} test-diffusion-ete-train
 	${MAKE} test-diffusion-ete-eval
 	${MAKE} test-tdmpc-ete-train
@@ -29,6 +31,7 @@ test-end-to-end:
 test-act-ete-train:
 	python lerobot/scripts/train.py \
 		policy=act \
+		policy.dim_model=64 \
 		env=aloha \
 		wandb.enable=False \
 		training.offline_steps=2 \
@@ -51,9 +54,40 @@ test-act-ete-eval:
 		env.episode_length=8 \
 		device=cpu \
 
+test-act-ete-train-amp:
+	python lerobot/scripts/train.py \
+		policy=act \
+		policy.dim_model=64 \
+		env=aloha \
+		wandb.enable=False \
+		training.offline_steps=2 \
+		training.online_steps=0 \
+		eval.n_episodes=1 \
+		eval.batch_size=1 \
+		device=cpu \
+		training.save_model=true \
+		training.save_freq=2 \
+		policy.n_action_steps=20 \
+		policy.chunk_size=20 \
+		training.batch_size=2 \
+		hydra.run.dir=tests/outputs/act/ \
+		use_amp=true
+
+test-act-ete-eval-amp:
+	python lerobot/scripts/eval.py \
+		-p tests/outputs/act/checkpoints/000002 \
+		eval.n_episodes=1 \
+		eval.batch_size=1 \
+		env.episode_length=8 \
+		device=cpu \
+		use_amp=true
+
 test-diffusion-ete-train:
 	python lerobot/scripts/train.py \
 		policy=diffusion \
+		policy.down_dims=\[64,128,256\] \
+		policy.diffusion_step_embed_dim=32 \
+		policy.num_inference_steps=10 \
 		env=pusht \
 		wandb.enable=False \
 		training.offline_steps=2 \
@@ -74,6 +108,7 @@ test-diffusion-ete-eval:
 		env.episode_length=8 \
 		device=cpu \
 
+# TODO(alexander-soare): Restore online_steps to 2 when it is reinstated.
 test-tdmpc-ete-train:
 	python lerobot/scripts/train.py \
 		policy=tdmpc \
@@ -82,7 +117,7 @@ test-tdmpc-ete-train:
 		dataset_repo_id=lerobot/xarm_lift_medium \
 		wandb.enable=False \
 		training.offline_steps=2 \
-		training.online_steps=2 \
+		training.online_steps=0 \
 		eval.n_episodes=1 \
 		eval.batch_size=1 \
 		env.episode_length=2 \
@@ -100,7 +135,6 @@ test-tdmpc-ete-eval:
 		env.episode_length=8 \
 		device=cpu \
 
-
 test-default-ete-eval:
 	python lerobot/scripts/eval.py \
 		--config lerobot/configs/default.yaml \

gym_dora/README.md

@@ -1 +0,0 @@
-# gym_dora

gym_dora/example.py

@@ -1,17 +0,0 @@
-import gymnasium as gym
-
-import gym_dora  # noqa: F401
-
-env = gym.make("gym_dora/DoraAloha-v0", disable_env_checker=True)
-obs = env.reset()
-
-policy = ...  # make_policy
-
-done = False
-while not done:
-    actions = policy.select_action(obs)
-    observation, reward, terminated, truncated, info = env.step(actions)
-
-    done = terminated | truncated | done
-
-env.close()

gym_dora/gym_dora/__init__.py

@@ -1,17 +0,0 @@
-from gymnasium.envs.registration import register
-
-register(
-    id="gym_dora/DoraAloha-v0",
-    entry_point="gym_dora.env:DoraEnv",
-    max_episode_steps=300,
-    nondeterministic=True,
-    kwargs={"model": "aloha"},
-)
-
-register(
-    id="gym_dora/DoraKoch-v0",
-    entry_point="gym_dora.env:DoraEnv",
-    max_episode_steps=300,
-    nondeterministic=True,
-    kwargs={"model": "koch"},
-)

gym_dora/gym_dora/env.py

@@ -1,199 +0,0 @@
-import os
-
-import gymnasium as gym
-import numpy as np
-import pyarrow as pa
-from dora import Node
-from gymnasium import spaces
-
-FPS = int(os.getenv("FPS", "30"))
-IMAGE_WIDTH = int(os.getenv("IMAGE_WIDTH", "640"))
-IMAGE_HEIGHT = int(os.getenv("IMAGE_HEIGHT", "480"))
-
-ALOHA_JOINTS = [
-    # absolute joint position
-    "left_arm_waist",
-    "left_arm_shoulder",
-    "left_arm_elbow",
-    "left_arm_forearm_roll",
-    "left_arm_wrist_angle",
-    "left_arm_wrist_rotate",
-    # normalized gripper position 0: close, 1: open
-    "left_arm_gripper",
-    # absolute joint position
-    "right_arm_waist",
-    "right_arm_shoulder",
-    "right_arm_elbow",
-    "right_arm_forearm_roll",
-    "right_arm_wrist_angle",
-    "right_arm_wrist_rotate",
-    # normalized gripper position 0: close, 1: open
-    "right_arm_gripper",
-]
-ALOHA_ACTIONS = [
-    # position and quaternion for end effector
-    "left_arm_waist",
-    "left_arm_shoulder",
-    "left_arm_elbow",
-    "left_arm_forearm_roll",
-    "left_arm_wrist_angle",
-    "left_arm_wrist_rotate",
-    # normalized gripper position (0: close, 1: open)
-    "left_arm_gripper",
-    "right_arm_waist",
-    "right_arm_shoulder",
-    "right_arm_elbow",
-    "right_arm_forearm_roll",
-    "right_arm_wrist_angle",
-    "right_arm_wrist_rotate",
-    # normalized gripper position (0: close, 1: open)
-    "right_arm_gripper",
-]
-
-
-class DoraEnv(gym.Env):
-    metadata = {"render_modes": ["rgb_array"], "render_fps": FPS}
-
-    def __init__(
-        self,
-        model="aloha",
-        observation_width=IMAGE_WIDTH,
-        observation_height=IMAGE_HEIGHT,
-        cameras_names=None,
-        num_joints=None,
-        num_actions=None,
-    ):
-        """Initializes the Dora environment.
-
-        Args:
-            model (str): The model to use. Either 'aloha' or 'custom'.
-            observation_width (int): The width of the observation image.
-            observation_height (int): The height of the observation image.
-            cameras_names (list): A list of camera names to use. If not provided, the default is ['cam_high', 'cam_low', 'cam_left_wrist', 'cam_right_wrist'].
-            num_joints (int): The number of joints in the model. If not provided, the default is 14 for 'aloha' and 6 for 'fivedof'.
-            num_actions (int): The number of actions in the model. If not provided, the default is 14 for 'aloha' and 6 for 'fivedof'.
-        """
-        super().__init__()
-
-        # Initialize a new node
-        self.node = Node() if os.environ.get("DORA_NODE_CONFIG", None) is not None else None
-        self.observation = {"pixels": {}, "agent_pos": None}
-        self.terminated = False
-
-        self.observation_height = observation_height
-        self.observation_width = observation_width
-
-        # Observation space
-        if model == "aloha":
-            self.observation_space = spaces.Dict(
-                {
-                    "pixels": spaces.Dict(
-                        {
-                            "cam_high": spaces.Box(
-                                low=0,
-                                high=255,
-                                shape=(self.observation_height, self.observation_width, 3),
-                                dtype=np.uint8,
-                            ),
-                            "cam_low": spaces.Box(
-                                low=0,
-                                high=255,
-                                shape=(self.observation_height, self.observation_width, 3),
-                                dtype=np.uint8,
-                            ),
-                            "cam_left_wrist": spaces.Box(
-                                low=0,
-                                high=255,
-                                shape=(self.observation_height, self.observation_width, 3),
-                                dtype=np.uint8,
-                            ),
-                            "cam_right_wrist": spaces.Box(
-                                low=0,
-                                high=255,
-                                shape=(self.observation_height, self.observation_width, 3),
-                                dtype=np.uint8,
-                            ),
-                        }
-                    ),
-                    "agent_pos": spaces.Box(
-                        low=-1000.0,
-                        high=1000.0,
-                        shape=(len(ALOHA_JOINTS),),
-                        dtype=np.float64,
-                    ),
-                }
-            )
-        elif model == "custom":
-            pixel_dict = {}
-            for camera in cameras_names:
-                assert camera.startswith("cam"), "Camera names must start with 'cam'"
-                pixel_dict[camera] = spaces.Box(
-                    low=0,
-                    high=255,
-                    shape=(self.observation_height, self.observation_width, 3),
-                    dtype=np.uint8,
-                )
-            self.observation_space = spaces.Dict(
-                {
-                    "pixels": spaces.Dict(pixel_dict),
-                    "agent_pos": spaces.Box(
-                        low=-1000.0,
-                        high=1000.0,
-                        shape=(num_joints,),
-                        dtype=np.float64,
-                    ),
-                }
-            )
-        else:
-            raise ValueError("Model must be either 'aloha' or 'custom'.")
-
-        # Action space
-        if model == "aloha":
-            self.action_space = spaces.Box(low=-1, high=1, shape=(len(ALOHA_ACTIONS),), dtype=np.float32)
-        elif model == "custom":
-            self.action_space = spaces.Box(low=-1, high=1, shape=(num_actions,), dtype=np.float32)
-
-    def _get_obs(self):
-        while True:
-            event = self.node.next(timeout=0.001)
-
-            ## If event is None, the node event stream is closed and we should terminate the env
-            if event is None:
-                self.terminated = True
-                break
-
-            if event["type"] == "INPUT":
-                # Map Image input into pixels key within Aloha environment
-                if "cam" in event["id"]:
-                    self.observation["pixels"][event["id"]] = (
-                        event["value"].to_numpy().reshape(self.observation_height, self.observation_width, 3)
-                    )
-                else:
-                    # Map other inputs into the observation dictionary using the event id as key
-                    self.observation[event["id"]] = event["value"].to_numpy()
-
-            # If the event is a timeout error break the update loop.
-            elif event["type"] == "ERROR":
-                break
-
-    def reset(self, seed: int | None = None):
-        self.node.send_output("reset")
-        self._get_obs()
-        self.terminated = False
-        info = {}
-        return self.observation, info
-
-    def step(self, action: np.ndarray):
-        # Send the action to the dataflow as action key.
-        self.node.send_output("action", pa.array(action))
-        self._get_obs()
-        reward = 0
-        terminated = truncated = self.terminated
-        info = {}
-        return self.observation, reward, terminated, truncated, info
-
-    def render(self): ...
-
-    def close(self):
-        # Drop the node
-        del self.node

gym_dora/poetry.lock

@@ -1,182 +0,0 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
-
-[[package]]
-name = "cloudpickle"
-version = "3.0.0"
-description = "Pickler class to extend the standard pickle.Pickler functionality"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "cloudpickle-3.0.0-py3-none-any.whl", hash = "sha256:246ee7d0c295602a036e86369c77fecda4ab17b506496730f2f576d9016fd9c7"},
-    {file = "cloudpickle-3.0.0.tar.gz", hash = "sha256:996d9a482c6fb4f33c1a35335cf8afd065d2a56e973270364840712d9131a882"},
-]
-
-[[package]]
-name = "dora-rs"
-version = "0.3.4"
-description = "`dora` goal is to be a low latency, composable, and distributed data flow."
-optional = false
-python-versions = "*"
-files = [
-    {file = "dora_rs-0.3.4-cp37-abi3-macosx_10_12_x86_64.whl", hash = "sha256:d1b738eea5a4966d731c26c6b6a0a50a491a24f7e9e335475f983cfc6f0da19e"},
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-    {file = "dora_rs-0.3.4-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:37d915fbbca282446235c98a9ca08389aa3ef3155d4e88c6c136326e9a830042"},
-    {file = "dora_rs-0.3.4-cp37-abi3-win32.whl", hash = "sha256:c9f7f22f65c884ec9bee0245ce98d0c7fad25dec0f982e566f844b5e8e58818f"},
-    {file = "dora_rs-0.3.4-cp37-abi3-win_amd64.whl", hash = "sha256:0a6a37f96a9f6e13b58b02a6ea75af192af5fbe4f456f6a67b1f239c3cee3276"},
-    {file = "dora_rs-0.3.4.tar.gz", hash = "sha256:05c5d0db0d23d7c4669995ae34db11cd636dbf91f5705d832669bd04e7452903"},
-]
-
-[package.dependencies]
-pyarrow = "*"
-
-[[package]]
-name = "farama-notifications"
-version = "0.0.4"
-description = "Notifications for all Farama Foundation maintained libraries."
-optional = false
-python-versions = "*"
-files = [
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-
-[[package]]
-name = "gymnasium"
-version = "0.29.1"
-description = "A standard API for reinforcement learning and a diverse set of reference environments (formerly Gym)."
-optional = false
-python-versions = ">=3.8"
-files = [
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-
-[package.dependencies]
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-farama-notifications = ">=0.0.1"
-numpy = ">=1.21.0"
-typing-extensions = ">=4.3.0"
-
-[package.extras]
-accept-rom-license = ["autorom[accept-rom-license] (>=0.4.2,<0.5.0)"]
-all = ["box2d-py (==2.3.5)", "cython (<3)", "imageio (>=2.14.1)", "jax (>=0.4.0)", "jaxlib (>=0.4.0)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "mujoco (>=2.3.3)", "mujoco-py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (>=2.1.3)", "shimmy[atari] (>=0.1.0,<1.0)", "swig (==4.*)", "torch (>=1.0.0)"]
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-
-[[package]]
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-
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-numpy = ">=1.16.6"
-
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-name = "typing-extensions"
-version = "4.11.0"
-description = "Backported and Experimental Type Hints for Python 3.8+"
-optional = false
-python-versions = ">=3.8"
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gym_dora/pyproject.toml

@@ -1,17 +0,0 @@
-[tool.poetry]
-name = "gym-dora"
-version = "0.1.0"
-description = ""
-authors = ["Simon Alibert <alibert.sim@gmail.com>"]
-readme = "README.md"
-packages = [{ include = "gym_dora" }]
-
-[tool.poetry.dependencies]
-python = "^3.10"
-gymnasium = ">=0.29.1"
-dora-rs = ">=0.3.4"
-pyarrow = ">=12.0.0"
-
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"

lerobot/common/datasets/push_dataset_to_hub/aloha_dora_format.py

@@ -1,200 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Contains utilities to process raw data format from dora-record
-"""
-
-import logging
-from pathlib import Path
-
-import pandas as pd
-import torch
-from datasets import Dataset, Features, Image, Sequence, Value
-
-from lerobot.common.datasets.utils import (
-    hf_transform_to_torch,
-)
-from lerobot.common.datasets.video_utils import VideoFrame
-from lerobot.common.utils.utils import init_logging
-
-
-def check_format(raw_dir) -> bool:
-    assert raw_dir.exists()
-
-    leader_file = list(raw_dir.glob("*.parquet"))
-    if len(leader_file) == 0:
-        raise ValueError(f"Missing parquet files in '{raw_dir}'")
-    return True
-
-
-def load_from_raw(raw_dir: Path, out_dir: Path):
-    # Load data stream that will be used as reference for the timestamps synchronization
-    reference_files = list(raw_dir.glob("observation.images.cam_*.parquet"))
-    if len(reference_files) == 0:
-        raise ValueError(f"Missing reference files for camera, starting with  in '{raw_dir}'")
-    # select first camera in alphanumeric order
-    reference_key = sorted(reference_files)[0].stem
-    reference_df = pd.read_parquet(raw_dir / f"{reference_key}.parquet")
-    reference_df = reference_df[["timestamp_utc", reference_key]]
-
-    # Merge all data stream using nearest backward strategy
-    df = reference_df
-    for path in raw_dir.glob("*.parquet"):
-        key = path.stem  # action or observation.state or ...
-        if key == reference_key:
-            continue
-        modality_df = pd.read_parquet(path)
-        modality_df = modality_df[["timestamp_utc", key]]
-        df = pd.merge_asof(
-            df,
-            modality_df,
-            on="timestamp_utc",
-            direction="backward",
-        )
-
-    # Remove rows with a NaN in any column. It can happened during the first frames of an episode,
-    # because some cameras didnt start recording yet.
-    df = df.dropna(axis=0)
-
-    # Remove rows with episode_index -1 which indicates a failed episode
-    df = df[df["episode_index"] != -1]
-
-    # dora only use arrays, so single values are encapsulated into a list
-    df["episode_index"] = df["episode_index"].map(lambda x: x[0])
-    df["frame_index"] = df.groupby("episode_index").cumcount()
-    df = df.reset_index()
-    df["index"] = df.index
-
-    # set 'next.done' to True for the last frame of each episode
-    df["next.done"] = False
-    df.loc[df.groupby("episode_index").tail(1).index, "next.done"] = True
-
-    df["timestamp"] = df["timestamp_utc"].map(lambda x: x.timestamp())
-    # each episode starts with timestamp 0 to match the ones from the video
-    df["timestamp"] = df.groupby("episode_index")["timestamp"].transform(lambda x: x - x.iloc[0])
-
-    del df["timestamp_utc"]
-
-    # sanity check episode indices go from 0 to n-1
-    ep_ids = [ep_idx for ep_idx, _ in df.groupby("episode_index")]
-    expected_ep_ids = list(range(df["episode_index"].max() + 1))
-    assert ep_ids == expected_ep_ids, f"Episodes indices go from {ep_ids} instead of {expected_ep_ids}"
-
-    # Create symlink to raw videos directory (that needs to be absolute not relative)
-    out_dir.mkdir(parents=True, exist_ok=True)
-    videos_dir = out_dir / "videos"
-    videos_dir.symlink_to((raw_dir / "videos").absolute())
-
-    # sanity check the video paths are well formated
-    for key in df:
-        if "observation.images." not in key:
-            continue
-        for ep_idx in ep_ids:
-            video_path = videos_dir / f"{key}_episode_{ep_idx:06d}.mp4"
-            assert video_path.exists(), f"Video file not found in {video_path}"
-
-    data_dict = {}
-    for key in df:
-        # is video frame
-        if "observation.images." in key:
-            # we need `[0] because dora only use arrays, so single values are encapsulated into a list.
-            # it is the case for video_frame dictionary = [{"path": ..., "timestamp": ...}]
-            data_dict[key] = [video_frame[0] for video_frame in df[key].values]
-
-            # sanity check the video path is well formated
-            video_path = videos_dir.parent / data_dict[key][0]["path"]
-            assert video_path.exists(), f"Video file not found in {video_path}"
-        # is number
-        elif df[key].iloc[0].ndim == 0 or df[key].iloc[0].shape[0] == 1:
-            data_dict[key] = torch.from_numpy(df[key].values)
-        # is vector
-        elif df[key].iloc[0].shape[0] > 1:
-            data_dict[key] = torch.stack([torch.from_numpy(x.copy()) for x in df[key].values])
-        else:
-            raise ValueError(key)
-
-    # Get the episode index containing for each unique episode index
-    first_ep_index_df = df.groupby("episode_index").agg(start_index=("index", "first")).reset_index()
-    from_ = first_ep_index_df["start_index"].tolist()
-    to_ = from_[1:] + [len(df)]
-    episode_data_index = {
-        "from": from_,
-        "to": to_,
-    }
-
-    return data_dict, episode_data_index
-
-
-def to_hf_dataset(data_dict, video) -> Dataset:
-    features = {}
-
-    keys = [key for key in data_dict if "observation.images." in key]
-    for key in keys:
-        if video:
-            features[key] = VideoFrame()
-        else:
-            features[key] = Image()
-
-    features["observation.state"] = Sequence(
-        length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None)
-    )
-    if "observation.velocity" in data_dict:
-        features["observation.velocity"] = Sequence(
-            length=data_dict["observation.velocity"].shape[1], feature=Value(dtype="float32", id=None)
-        )
-    if "observation.effort" in data_dict:
-        features["observation.effort"] = Sequence(
-            length=data_dict["observation.effort"].shape[1], feature=Value(dtype="float32", id=None)
-        )
-    features["action"] = Sequence(
-        length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)
-    )
-    features["episode_index"] = Value(dtype="int64", id=None)
-    features["frame_index"] = Value(dtype="int64", id=None)
-    features["timestamp"] = Value(dtype="float32", id=None)
-    features["next.done"] = Value(dtype="bool", id=None)
-    features["index"] = Value(dtype="int64", id=None)
-
-    hf_dataset = Dataset.from_dict(data_dict, features=Features(features))
-    hf_dataset.set_transform(hf_transform_to_torch)
-    return hf_dataset
-
-
-def from_raw_to_lerobot_format(raw_dir: Path, out_dir: Path, fps=None, video=True, debug=False):
-    init_logging()
-
-    if debug:
-        logging.warning("debug=True not implemented. Falling back to debug=False.")
-
-    # sanity check
-    check_format(raw_dir)
-
-    if fps is None:
-        fps = 30
-    else:
-        raise NotImplementedError()
-
-    if not video:
-        raise NotImplementedError()
-
-    data_df, episode_data_index = load_from_raw(raw_dir, out_dir)
-    hf_dataset = to_hf_dataset(data_df, video)
-
-    info = {
-        "fps": fps,
-        "video": video,
-    }
-    return hf_dataset, episode_data_index, info

lerobot/common/datasets/push_dataset_to_hub/aloha_hdf5_format.py

@@ -43,8 +43,7 @@ def get_cameras(hdf5_data):
 
 
 def check_format(raw_dir) -> bool:
-    # only frames from simulation are uncompressed
+    compressed_images = None
-    compressed_images = "sim" not in raw_dir.name
 
     hdf5_paths = list(raw_dir.glob("episode_*.hdf5"))
     assert len(hdf5_paths) != 0
@@ -62,18 +61,20 @@ def check_format(raw_dir) -> bool:
             for camera in get_cameras(data):
                 assert num_frames == data[f"/observations/images/{camera}"].shape[0]
 
-                if compressed_images:
+                assert data[f"/observations/images/{camera}"].ndim in [2, 4]
-                    assert data[f"/observations/images/{camera}"].ndim == 2
+                if data[f"/observations/images/{camera}"].ndim == 2:
+                    assert compressed_images is None or compressed_images
+                    compressed_images = True
                 else:
+                    assert compressed_images is None or not compressed_images
+                    compressed_images = False
                     assert data[f"/observations/images/{camera}"].ndim == 4
                     b, h, w, c = data[f"/observations/images/{camera}"].shape
                     assert c < h and c < w, f"Expect (h,w,c) image format but ({h=},{w=},{c=}) provided."
+    return compressed_images
 
 
-def load_from_raw(raw_dir, out_dir, fps, video, debug):
+def load_from_raw(raw_dir, out_dir, fps, video, debug, compressed_images):
-    # only frames from simulation are uncompressed
-    compressed_images = "sim" not in raw_dir.name
-
     hdf5_files = list(raw_dir.glob("*.hdf5"))
     ep_dicts = []
     episode_data_index = {"from": [], "to": []}
@@ -199,12 +200,12 @@ def to_hf_dataset(data_dict, video) -> Dataset:
 
 def from_raw_to_lerobot_format(raw_dir: Path, out_dir: Path, fps=None, video=True, debug=False):
     # sanity check
-    check_format(raw_dir)
+    compressed_images = check_format(raw_dir)
 
     if fps is None:
         fps = 50
 
-    data_dir, episode_data_index = load_from_raw(raw_dir, out_dir, fps, video, debug)
+    data_dir, episode_data_index = load_from_raw(raw_dir, out_dir, fps, video, debug, compressed_images)
     hf_dataset = to_hf_dataset(data_dir, video)
 
     info = {

lerobot/common/envs/factory.py

@@ -28,11 +28,11 @@ def make_env(cfg: DictConfig, n_envs: int | None = None) -> gym.vector.VectorEnv
         raise ValueError("`n_envs must be at least 1")
 
     kwargs = {
-        # "obs_type": "pixels_agent_pos",
+        "obs_type": "pixels_agent_pos",
-        # "render_mode": "rgb_array",
+        "render_mode": "rgb_array",
         "max_episode_steps": cfg.env.episode_length,
-        # "visualization_width": 384,
+        "visualization_width": 384,
-        # "visualization_height": 384,
+        "visualization_height": 384,
     }
 
     package_name = f"gym_{cfg.env.name}"

lerobot/configs/default.yaml

@@ -10,6 +10,9 @@ hydra:
     name: default
 
 device: cuda  # cpu
+# `use_amp` determines whether to use Automatic Mixed Precision (AMP) for training and evaluation. With AMP,
+# automatic gradient scaling is used.
+use_amp: false
 # `seed` is used for training (eg: model initialization, dataset shuffling)
 # AND for the evaluation environments.
 seed: ???
@@ -17,6 +20,7 @@ dataset_repo_id: lerobot/pusht
 
 training:
   offline_steps: ???
+  # NOTE: `online_steps` is not implemented yet. It's here as a placeholder.
   online_steps: ???
   online_steps_between_rollouts: ???
   online_sampling_ratio: 0.5

lerobot/configs/env/aloha_thom.yaml

@@ -0,0 +1,14 @@
+# @package _global_
+
+fps: 50
+
+env:
+  name: aloha
+  task: AlohaInsertion-v0
+  from_pixels: True
+  pixels_only: False
+  image_size: [3, 480, 640]
+  episode_length: 500
+  fps: ${fps}
+  state_dim: 6
+  action_dim: 6

lerobot/configs/env/dora.yaml

@@ -1,14 +0,0 @@
-# @package _global_
-
-fps: 30
-
-env:
-  name: dora
-  task: DoraAloha-v0
-  # from_pixels: True
-  # pixels_only: False
-  # image_size: [3, 480, 640]
-  episode_length: 400
-  # fps: ${fps}
-  # state_dim: 14
-  # action_dim: 14

lerobot/configs/policy/act_thom.yaml

@@ -1,32 +1,14 @@
 # @package _global_
 
 seed: 1000
-dataset_repo_id: cadene/aloha_v2_static_dora_test
+dataset_repo_id: lerobot/aloha_sim_insertion_human
-
-override_dataset_stats:
-  observation.images.cam_right_wrist:
-    # stats from imagenet, since we use a pretrained vision model
-    mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
-    std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
-  observation.images.cam_left_wrist:
-    # stats from imagenet, since we use a pretrained vision model
-    mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
-    std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
-  observation.images.cam_high:
-    # stats from imagenet, since we use a pretrained vision model
-    mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
-    std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
-  observation.images.cam_low:
-    # stats from imagenet, since we use a pretrained vision model
-    mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
-    std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
 
 training:
-  offline_steps: 80000
+  offline_steps: 20000
   online_steps: 0
-  eval_freq: 99999999999999
+  eval_freq: 100000
-  save_freq: 1000
+  save_freq: 200
-  log_freq: 100
+  log_freq: 200
   save_model: true
 
   batch_size: 8
@@ -54,20 +36,14 @@ policy:
 
   input_shapes:
     # TODO(rcadene, alexander-soare): add variables for height and width from the dataset/env?
-    observation.images.cam_right_wrist: [3, 480, 640]
+    observation.images: [3, 480, 640]
-    observation.images.cam_left_wrist: [3, 480, 640]
-    observation.images.cam_high: [3, 480, 640]
-    observation.images.cam_low: [3, 480, 640]
     observation.state: ["${env.state_dim}"]
   output_shapes:
     action: ["${env.action_dim}"]
 
   # Normalization / Unnormalization
   input_normalization_modes:
-    observation.images.cam_right_wrist: mean_std
+    observation.images.front: mean_std
-    observation.images.cam_left_wrist: mean_std
-    observation.images.cam_high: mean_std
-    observation.images.cam_low: mean_std
     observation.state: mean_std
   output_normalization_modes:
     action: mean_std

lerobot/configs/policy/tdmpc.yaml

@@ -5,7 +5,8 @@ dataset_repo_id: lerobot/xarm_lift_medium
 
 training:
   offline_steps: 25000
-  online_steps: 25000
+  # TODO(alexander-soare): uncomment when online training gets reinstated
+  online_steps: 0  # 25000 not implemented yet
   eval_freq: 5000
   online_steps_between_rollouts: 1
   online_sampling_ratio: 0.5

lerobot/scripts/eval.py

@@ -46,6 +46,7 @@ import json
 import logging
 import threading
 import time
+from contextlib import nullcontext
 from copy import deepcopy
 from datetime import datetime as dt
 from pathlib import Path
@@ -520,7 +521,7 @@ def eval(
         raise NotImplementedError()
 
     # Check device is available
-    get_safe_torch_device(hydra_cfg.device, log=True)
+    device = get_safe_torch_device(hydra_cfg.device, log=True)
 
     torch.backends.cudnn.benchmark = True
     torch.backends.cuda.matmul.allow_tf32 = True
@@ -539,16 +540,17 @@ def eval(
         policy = make_policy(hydra_cfg=hydra_cfg, dataset_stats=make_dataset(hydra_cfg).stats)
     policy.eval()
 
-    info = eval_policy(
+    with torch.no_grad(), torch.autocast(device_type=device.type) if hydra_cfg.use_amp else nullcontext():
-        env,
+        info = eval_policy(
-        policy,
+            env,
-        hydra_cfg.eval.n_episodes,
+            policy,
-        max_episodes_rendered=10,
+            hydra_cfg.eval.n_episodes,
-        video_dir=Path(out_dir) / "eval",
+            max_episodes_rendered=10,
-        start_seed=hydra_cfg.seed,
+            video_dir=Path(out_dir) / "eval",
-        enable_progbar=True,
+            start_seed=hydra_cfg.seed,
-        enable_inner_progbar=True,
+            enable_progbar=True,
-    )
+            enable_inner_progbar=True,
+        )
     print(info["aggregated"])
 
     # Save info

lerobot/scripts/push_dataset_to_hub.py

@@ -84,14 +84,10 @@ def get_from_raw_to_lerobot_format_fn(raw_format):
         from lerobot.common.datasets.push_dataset_to_hub.umi_zarr_format import from_raw_to_lerobot_format
     elif raw_format == "aloha_hdf5":
         from lerobot.common.datasets.push_dataset_to_hub.aloha_hdf5_format import from_raw_to_lerobot_format
-    elif raw_format == "aloha_dora":
-        from lerobot.common.datasets.push_dataset_to_hub.aloha_dora_format import from_raw_to_lerobot_format
     elif raw_format == "xarm_pkl":
         from lerobot.common.datasets.push_dataset_to_hub.xarm_pkl_format import from_raw_to_lerobot_format
     else:
-        raise ValueError(
+        raise ValueError(raw_format)
-            f"The selected {raw_format} can't be found. Did you add it to `lerobot/scripts/push_dataset_to_hub.py::get_from_raw_to_lerobot_format_fn`?"
-        )
 
     return from_raw_to_lerobot_format
 
@@ -144,8 +140,7 @@ def push_videos_to_hub(repo_id, videos_dir, revision):
 
 
 def push_dataset_to_hub(
-    input_data_dir: Path,
+    data_dir: Path,
-    output_data_dir: Path,
     dataset_id: str,
     raw_format: str | None,
     community_id: str,
@@ -162,33 +157,34 @@ def push_dataset_to_hub(
 ):
     repo_id = f"{community_id}/{dataset_id}"
 
-    meta_data_dir = output_data_dir / "meta_data"
+    raw_dir = data_dir / f"{dataset_id}_raw"
-    videos_dir = output_data_dir / "videos"
+
+    out_dir = data_dir / repo_id
+    meta_data_dir = out_dir / "meta_data"
+    videos_dir = out_dir / "videos"
 
     tests_out_dir = tests_data_dir / repo_id
     tests_meta_data_dir = tests_out_dir / "meta_data"
     tests_videos_dir = tests_out_dir / "videos"
 
-    if output_data_dir.exists():
+    if out_dir.exists():
-        shutil.rmtree(output_data_dir)
+        shutil.rmtree(out_dir)
 
     if tests_out_dir.exists() and save_tests_to_disk:
         shutil.rmtree(tests_out_dir)
 
-    if not input_data_dir.exists():
+    if not raw_dir.exists():
-        download_raw(input_data_dir, dataset_id)
+        download_raw(raw_dir, dataset_id)
 
     if raw_format is None:
         # TODO(rcadene, adilzouitine): implement auto_find_raw_format
         raise NotImplementedError()
-        # raw_format = auto_find_raw_format(input_data_dir)
+        # raw_format = auto_find_raw_format(raw_dir)
 
     from_raw_to_lerobot_format = get_from_raw_to_lerobot_format_fn(raw_format)
 
     # convert dataset from original raw format to LeRobot format
-    hf_dataset, episode_data_index, info = from_raw_to_lerobot_format(
+    hf_dataset, episode_data_index, info = from_raw_to_lerobot_format(raw_dir, out_dir, fps, video, debug)
-        input_data_dir, output_data_dir, fps, video, debug
-    )
 
     lerobot_dataset = LeRobotDataset.from_preloaded(
         repo_id=repo_id,
@@ -202,7 +198,7 @@ def push_dataset_to_hub(
 
     if save_to_disk:
         hf_dataset = hf_dataset.with_format(None)  # to remove transforms that cant be saved
-        hf_dataset.save_to_disk(str(output_data_dir / "train"))
+        hf_dataset.save_to_disk(str(out_dir / "train"))
 
     if not dry_run or save_to_disk:
         # mandatory for upload
@@ -236,25 +232,19 @@ def push_dataset_to_hub(
             fname = f"{key}_episode_{episode_index:06d}.mp4"
             shutil.copy(videos_dir / fname, tests_videos_dir / fname)
 
-    if not save_to_disk and output_data_dir.exists():
+    if not save_to_disk and out_dir.exists():
         # remove possible temporary files remaining in the output directory
-        shutil.rmtree(output_data_dir)
+        shutil.rmtree(out_dir)
 
 
 def main():
     parser = argparse.ArgumentParser()
 
     parser.add_argument(
-        "--input-data-dir",
+        "--data-dir",
         type=Path,
         required=True,
-        help="Directory containing input raw datasets (e.g. `data/aloha_mobile_chair_raw` or `data/pusht_raw`).",
+        help="Root directory containing datasets (e.g. `data` or `tmp/data` or `/tmp/lerobot/data`).",
-    )
-    parser.add_argument(
-        "--output-data-dir",
-        type=Path,
-        required=True,
-        help="Root directory containing output dataset (e.g. `data/lerobot/aloha_mobile_chair` or `data/lerobot/pusht`).",
     )
     parser.add_argument(
         "--dataset-id",

lerobot/scripts/train.py

@@ -15,15 +15,15 @@
 # limitations under the License.
 import logging
 import time
+from contextlib import nullcontext
 from copy import deepcopy
 from pathlib import Path
 
-import datasets
 import hydra
 import torch
-from datasets import concatenate_datasets
-from datasets.utils import disable_progress_bars, enable_progress_bars
 from omegaconf import DictConfig
+from torch.cuda.amp import GradScaler
+from tqdm import tqdm
 
 from lerobot.common.datasets.factory import make_dataset
 from lerobot.common.datasets.utils import cycle
@@ -31,6 +31,7 @@ from lerobot.common.envs.factory import make_env
 from lerobot.common.logger import Logger, log_output_dir
 from lerobot.common.policies.factory import make_policy
 from lerobot.common.policies.policy_protocol import PolicyWithUpdate
+from lerobot.common.policies.utils import get_device_from_parameters
 from lerobot.common.utils.utils import (
     format_big_number,
     get_safe_torch_device,
@@ -69,7 +70,6 @@ def make_optimizer_and_scheduler(cfg, policy):
             cfg.training.adam_eps,
             cfg.training.adam_weight_decay,
         )
-        assert cfg.training.online_steps == 0, "Diffusion Policy does not handle online training."
         from diffusers.optimization import get_scheduler
 
         lr_scheduler = get_scheduler(
@@ -87,21 +87,40 @@ def make_optimizer_and_scheduler(cfg, policy):
     return optimizer, lr_scheduler
 
 
-def update_policy(policy, batch, optimizer, grad_clip_norm, lr_scheduler=None):
+def update_policy(
+    policy,
+    batch,
+    optimizer,
+    grad_clip_norm,
+    grad_scaler: GradScaler,
+    lr_scheduler=None,
+    use_amp: bool = False,
+):
     """Returns a dictionary of items for logging."""
-    start_time = time.time()
+    start_time = time.perf_counter()
+    device = get_device_from_parameters(policy)
     policy.train()
-    output_dict = policy.forward(batch)
+    with torch.autocast(device_type=device.type) if use_amp else nullcontext():
-    # TODO(rcadene): policy.unnormalize_outputs(out_dict)
+        output_dict = policy.forward(batch)
-    loss = output_dict["loss"]
+        # TODO(rcadene): policy.unnormalize_outputs(out_dict)
-    loss.backward()
+        loss = output_dict["loss"]
+    grad_scaler.scale(loss).backward()
+
+    # Unscale the graident of the optimzer's assigned params in-place **prior to gradient clipping**.
+    grad_scaler.unscale_(optimizer)
+
     grad_norm = torch.nn.utils.clip_grad_norm_(
         policy.parameters(),
         grad_clip_norm,
         error_if_nonfinite=False,
     )
 
-    optimizer.step()
+    # Optimizer's gradients are already unscaled, so scaler.step does not unscale them,
+    # although it still skips optimizer.step() if the gradients contain infs or NaNs.
+    grad_scaler.step(optimizer)
+    # Updates the scale for next iteration.
+    grad_scaler.update()
+
     optimizer.zero_grad()
 
     if lr_scheduler is not None:
@@ -115,7 +134,7 @@ def update_policy(policy, batch, optimizer, grad_clip_norm, lr_scheduler=None):
         "loss": loss.item(),
         "grad_norm": float(grad_norm),
         "lr": optimizer.param_groups[0]["lr"],
-        "update_s": time.time() - start_time,
+        "update_s": time.perf_counter() - start_time,
         **{k: v for k, v in output_dict.items() if k != "loss"},
     }
 
@@ -211,103 +230,6 @@ def log_eval_info(logger, info, step, cfg, dataset, is_offline):
     logger.log_dict(info, step, mode="eval")
 
 
-def calculate_online_sample_weight(n_off: int, n_on: int, pc_on: float):
-    """
-    Calculate the sampling weight to be assigned to samples so that a specified percentage of the batch comes from online dataset (on average).
-
-    Parameters:
-    - n_off (int): Number of offline samples, each with a sampling weight of 1.
-    - n_on (int): Number of online samples.
-    - pc_on (float): Desired percentage of online samples in decimal form (e.g., 50% as 0.5).
-
-    The total weight of offline samples is n_off * 1.0.
-    The total weight of offline samples is n_on * w.
-    The total combined weight of all samples is n_off + n_on * w.
-    The fraction of the weight that is online is n_on * w / (n_off + n_on * w).
-    We want this fraction to equal pc_on, so we set up the equation n_on * w / (n_off + n_on * w) = pc_on.
-    The solution is w = - (n_off * pc_on) / (n_on * (pc_on - 1))
-    """
-    assert 0.0 <= pc_on <= 1.0
-    return -(n_off * pc_on) / (n_on * (pc_on - 1))
-
-
-def add_episodes_inplace(
-    online_dataset: torch.utils.data.Dataset,
-    concat_dataset: torch.utils.data.ConcatDataset,
-    sampler: torch.utils.data.WeightedRandomSampler,
-    hf_dataset: datasets.Dataset,
-    episode_data_index: dict[str, torch.Tensor],
-    pc_online_samples: float,
-):
-    """
-    Modifies the online_dataset, concat_dataset, and sampler in place by integrating
-    new episodes from hf_dataset into the online_dataset, updating the concatenated
-    dataset's structure and adjusting the sampling strategy based on the specified
-    percentage of online samples.
-
-    Parameters:
-    - online_dataset (torch.utils.data.Dataset): The existing online dataset to be updated.
-    - concat_dataset (torch.utils.data.ConcatDataset): The concatenated dataset that combines
-      offline and online datasets, used for sampling purposes.
-    - sampler (torch.utils.data.WeightedRandomSampler): A sampler that will be updated to
-      reflect changes in the dataset sizes and specified sampling weights.
-    - hf_dataset (datasets.Dataset): A Hugging Face dataset containing the new episodes to be added.
-    - episode_data_index (dict): A dictionary containing two keys ("from" and "to") associated to dataset indices.
-      They indicate the start index and end index of each episode in the dataset.
-    - pc_online_samples (float): The target percentage of samples that should come from
-      the online dataset during sampling operations.
-
-    Raises:
-    - AssertionError: If the first episode_id or index in hf_dataset is not 0
-    """
-    first_episode_idx = hf_dataset.select_columns("episode_index")[0]["episode_index"].item()
-    last_episode_idx = hf_dataset.select_columns("episode_index")[-1]["episode_index"].item()
-    first_index = hf_dataset.select_columns("index")[0]["index"].item()
-    last_index = hf_dataset.select_columns("index")[-1]["index"].item()
-    # sanity check
-    assert first_episode_idx == 0, f"{first_episode_idx=} is not 0"
-    assert first_index == 0, f"{first_index=} is not 0"
-    assert first_index == episode_data_index["from"][first_episode_idx].item()
-    assert last_index == episode_data_index["to"][last_episode_idx].item() - 1
-
-    if len(online_dataset) == 0:
-        # initialize online dataset
-        online_dataset.hf_dataset = hf_dataset
-        online_dataset.episode_data_index = episode_data_index
-    else:
-        # get the starting indices of the new episodes and frames to be added
-        start_episode_idx = last_episode_idx + 1
-        start_index = last_index + 1
-
-        def shift_indices(episode_index, index):
-            # note: we dont shift "frame_index" since it represents the index of the frame in the episode it belongs to
-            example = {"episode_index": episode_index + start_episode_idx, "index": index + start_index}
-            return example
-
-        disable_progress_bars()  # map has a tqdm progress bar
-        hf_dataset = hf_dataset.map(shift_indices, input_columns=["episode_index", "index"])
-        enable_progress_bars()
-
-        episode_data_index["from"] += start_index
-        episode_data_index["to"] += start_index
-
-        # extend online dataset
-        online_dataset.hf_dataset = concatenate_datasets([online_dataset.hf_dataset, hf_dataset])
-
-    # update the concatenated dataset length used during sampling
-    concat_dataset.cumulative_sizes = concat_dataset.cumsum(concat_dataset.datasets)
-
-    # update the sampling weights for each frame so that online frames get sampled a certain percentage of times
-    len_online = len(online_dataset)
-    len_offline = len(concat_dataset) - len_online
-    weight_offline = 1.0
-    weight_online = calculate_online_sample_weight(len_offline, len_online, pc_online_samples)
-    sampler.weights = torch.tensor([weight_offline] * len_offline + [weight_online] * len(online_dataset))
-
-    # update the total number of samples used during sampling
-    sampler.num_samples = len(concat_dataset)
-
-
 def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = None):
     if out_dir is None:
         raise NotImplementedError()
@@ -316,11 +238,11 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
 
     init_logging()
 
-    if cfg.training.online_steps > 0 and cfg.eval.batch_size > 1:
+    if cfg.training.online_steps > 0:
-        logging.warning("eval.batch_size > 1 not supported for online training steps")
+        raise NotImplementedError("Online training is not implemented yet.")
 
     # Check device is available
-    get_safe_torch_device(cfg.device, log=True)
+    device = get_safe_torch_device(cfg.device, log=True)
 
     torch.backends.cudnn.benchmark = True
     torch.backends.cuda.matmul.allow_tf32 = True
@@ -338,6 +260,7 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
     # Create optimizer and scheduler
     # Temporary hack to move optimizer out of policy
     optimizer, lr_scheduler = make_optimizer_and_scheduler(cfg, policy)
+    grad_scaler = GradScaler(enabled=cfg.use_amp)
 
     num_learnable_params = sum(p.numel() for p in policy.parameters() if p.requires_grad)
     num_total_params = sum(p.numel() for p in policy.parameters())
@@ -358,14 +281,15 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
     def evaluate_and_checkpoint_if_needed(step):
         if step % cfg.training.eval_freq == 0:
             logging.info(f"Eval policy at step {step}")
-            eval_info = eval_policy(
+            with torch.no_grad(), torch.autocast(device_type=device.type) if cfg.use_amp else nullcontext():
-                eval_env,
+                eval_info = eval_policy(
-                policy,
+                    eval_env,
-                cfg.eval.n_episodes,
+                    policy,
-                video_dir=Path(out_dir) / "eval",
+                    cfg.eval.n_episodes,
-                max_episodes_rendered=4,
+                    video_dir=Path(out_dir) / "eval",
-                start_seed=cfg.seed,
+                    max_episodes_rendered=4,
-            )
+                    start_seed=cfg.seed,
+                )
             log_eval_info(logger, eval_info["aggregated"], step, cfg, offline_dataset, is_offline)
             if cfg.wandb.enable:
                 logger.log_video(eval_info["video_paths"][0], step, mode="eval")
@@ -389,36 +313,38 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
         num_workers=4,
         batch_size=cfg.training.batch_size,
         shuffle=True,
-        pin_memory=cfg.device != "cpu",
+        pin_memory=device.type != "cpu",
         drop_last=False,
     )
     dl_iter = cycle(dataloader)
 
     policy.train()
-    step = 0  # number of policy update (forward + backward + optim)
     is_offline = True
-    for offline_step in range(cfg.training.offline_steps):
+    for offline_step in tqdm(range(cfg.training.offline_steps)):
         if offline_step == 0:
             logging.info("Start offline training on a fixed dataset")
         batch = next(dl_iter)
 
         for key in batch:
-            batch[key] = batch[key].to(cfg.device, non_blocking=True)
+            batch[key] = batch[key].to(device, non_blocking=True)
 
-        train_info = update_policy(policy, batch, optimizer, cfg.training.grad_clip_norm, lr_scheduler)
+        train_info = update_policy(
+            policy,
+            batch,
+            optimizer,
+            cfg.training.grad_clip_norm,
+            grad_scaler=grad_scaler,
+            lr_scheduler=lr_scheduler,
+            use_amp=cfg.use_amp,
+        )
 
         # TODO(rcadene): is it ok if step_t=0 = 0 and not 1 as previously done?
-        if step % cfg.training.log_freq == 0:
+        if offline_step % cfg.training.log_freq == 0:
-            log_train_info(logger, train_info, step, cfg, offline_dataset, is_offline)
+            log_train_info(logger, train_info, offline_step, cfg, offline_dataset, is_offline)
 
         # Note: evaluate_and_checkpoint_if_needed happens **after** the `step`th training update has completed,
         # so we pass in step + 1.
-        evaluate_and_checkpoint_if_needed(step + 1)
+        evaluate_and_checkpoint_if_needed(offline_step + 1)
-
-        step += 1
-
-    # create an env dedicated to online episodes collection from policy rollout
-    online_training_env = make_env(cfg, n_envs=1)
 
     # create an empty online dataset similar to offline dataset
     online_dataset = deepcopy(offline_dataset)
@@ -436,58 +362,11 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
         num_workers=4,
         batch_size=cfg.training.batch_size,
         sampler=sampler,
-        pin_memory=cfg.device != "cpu",
+        pin_memory=device.type != "cpu",
         drop_last=False,
     )
-    dl_iter = cycle(dataloader)
-
-    online_step = 0
-    is_offline = False
-    for env_step in range(cfg.training.online_steps):
-        if env_step == 0:
-            logging.info("Start online training by interacting with environment")
-
-        policy.eval()
-        with torch.no_grad():
-            eval_info = eval_policy(
-                online_training_env,
-                policy,
-                n_episodes=1,
-                return_episode_data=True,
-                start_seed=cfg.training.online_env_seed,
-                enable_progbar=True,
-            )
-
-        add_episodes_inplace(
-            online_dataset,
-            concat_dataset,
-            sampler,
-            hf_dataset=eval_info["episodes"]["hf_dataset"],
-            episode_data_index=eval_info["episodes"]["episode_data_index"],
-            pc_online_samples=cfg.training.online_sampling_ratio,
-        )
-
-        policy.train()
-        for _ in range(cfg.training.online_steps_between_rollouts):
-            batch = next(dl_iter)
-
-            for key in batch:
-                batch[key] = batch[key].to(cfg.device, non_blocking=True)
-
-            train_info = update_policy(policy, batch, optimizer, cfg.training.grad_clip_norm, lr_scheduler)
-
-            if step % cfg.training.log_freq == 0:
-                log_train_info(logger, train_info, step, cfg, online_dataset, is_offline)
-
-            # Note: evaluate_and_checkpoint_if_needed happens **after** the `step`th training update has completed,
-            # so we pass in step + 1.
-            evaluate_and_checkpoint_if_needed(step + 1)
-
-            step += 1
-            online_step += 1
 
     eval_env.close()
-    online_training_env.close()
     logging.info("End of training")
 
 

poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.1 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
@@ -404,28 +404,28 @@ files = [
 
 [[package]]
 name = "cmake"
-version = "3.29.3"
+version = "3.29.2"
 description = "CMake is an open-source, cross-platform family of tools designed to build, test and package software"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "cmake-3.29.2-py3-none-macosx_10_10_universal2.macosx_10_10_x86_64.macosx_11_0_arm64.macosx_11_0_universal2.whl", hash = "sha256:1d40c5451d6467b20a0a6015a5a6b6dc86f61b83f71f935740485b259100a34e"},
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+    {file = "cmake-3.29.2-py3-none-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:ed3108e646cd65a4e23fa1cbe8123569a29334a3f2a8ce214d871406b161bedb"},
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+    {file = "cmake-3.29.2-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:40aafe612b03a9fa140cca4024ba60b74cd92372f3f349d8062cba1f021e5001"},
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+    {file = "cmake-3.29.2-py3-none-win_amd64.whl", hash = "sha256:23336c8ca01205d18d92ed8de6c54e570c352a58e378b7f9adc02ef00f433960"},
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+    {file = "cmake-3.29.2-py3-none-win_arm64.whl", hash = "sha256:e722a949f7c91084dba61f8f17a9854787182ab711ed0b84b1507b24a8e12e25"},
-    {file = "cmake-3.29.3.tar.gz", hash = "sha256:d04adb1a8b878e92a734742cb0db9c59e3828abcf8ec9c930eb8a01faa00c9df"},
+    {file = "cmake-3.29.2.tar.gz", hash = "sha256:6a4c1185cb2eca7263190a5754d0c9edf738d9e50bff464f78f48d0c05318e7c"},
 ]
 
 [package.extras]
@@ -767,26 +767,6 @@ files = [
 [package.dependencies]
 six = ">=1.4.0"
 
-[[package]]
-name = "dora-rs"
-version = "0.3.4"
-description = "`dora` goal is to be a low latency, composable, and distributed data flow."
-optional = true
-python-versions = "*"
-files = [
-    {file = "dora_rs-0.3.4-cp37-abi3-macosx_10_12_x86_64.whl", hash = "sha256:d1b738eea5a4966d731c26c6b6a0a50a491a24f7e9e335475f983cfc6f0da19e"},
-    {file = "dora_rs-0.3.4-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:80b724871618c78a4e5863938fa66724176cc40352771087aebe1e62a8141157"},
-    {file = "dora_rs-0.3.4-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3a3919e157b47dc1dbc74c040a73087a4485f0d1bee99b6adcdbc36559400fe2"},
-    {file = "dora_rs-0.3.4-cp37-abi3-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f7c95f6e5858fd651d6cd220e4f052e99db2944b9c37fb0b5402d60ac4b41a63"},
-    {file = "dora_rs-0.3.4-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:37d915fbbca282446235c98a9ca08389aa3ef3155d4e88c6c136326e9a830042"},
-    {file = "dora_rs-0.3.4-cp37-abi3-win32.whl", hash = "sha256:c9f7f22f65c884ec9bee0245ce98d0c7fad25dec0f982e566f844b5e8e58818f"},
-    {file = "dora_rs-0.3.4-cp37-abi3-win_amd64.whl", hash = "sha256:0a6a37f96a9f6e13b58b02a6ea75af192af5fbe4f456f6a67b1f239c3cee3276"},
-    {file = "dora_rs-0.3.4.tar.gz", hash = "sha256:05c5d0db0d23d7c4669995ae34db11cd636dbf91f5705d832669bd04e7452903"},
-]
-
-[package.dependencies]
-pyarrow = "*"
-
 [[package]]
 name = "einops"
 version = "0.8.0"
@@ -976,13 +956,13 @@ tqdm = ["tqdm"]
 
 [[package]]
 name = "gdown"
-version = "5.2.0"
+version = "5.1.0"
 description = "Google Drive Public File/Folder Downloader"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "gdown-5.2.0-py3-none-any.whl", hash = "sha256:33083832d82b1101bdd0e9df3edd0fbc0e1c5f14c9d8c38d2a35bf1683b526d6"},
+    {file = "gdown-5.1.0-py3-none-any.whl", hash = "sha256:421530fd238fa15d41ba43219a79fdc28efe8ac11022173abad333701b77de2c"},
-    {file = "gdown-5.2.0.tar.gz", hash = "sha256:2145165062d85520a3cd98b356c9ed522c5e7984d408535409fd46f94defc787"},
+    {file = "gdown-5.1.0.tar.gz", hash = "sha256:550a72dc5ca2819fe4bcc15d80d05d7c98c0b90e57256254b77d0256b9df4683"},
 ]
 
 [package.dependencies]
@@ -992,7 +972,7 @@ requests = {version = "*", extras = ["socks"]}
 tqdm = "*"
 
 [package.extras]
-test = ["build", "mypy", "pytest", "pytest-xdist", "ruff", "twine", "types-requests", "types-setuptools"]
+test = ["build", "mypy", "pytest", "pytest-xdist", "ruff", "twine", "types-requests"]
 
 [[package]]
 name = "gitdb"
@@ -1068,33 +1048,15 @@ mujoco = ">=2.3.7,<3.0.0"
 dev = ["debugpy (>=1.8.1)", "pre-commit (>=3.7.0)"]
 test = ["pytest (>=8.1.0)", "pytest-cov (>=5.0.0)"]
 
-[[package]]
-name = "gym-dora"
-version = "0.1.0"
-description = ""
-optional = true
-python-versions = "^3.10"
-files = []
-develop = true
-
-[package.dependencies]
-dora-rs = ">=0.3.4"
-gymnasium = ">=0.29.1"
-pyarrow = ">=12.0.0"
-
-[package.source]
-type = "directory"
-url = "gym_dora"
-
 [[package]]
 name = "gym-pusht"
-version = "0.1.4"
+version = "0.1.3"
 description = "A gymnasium environment for PushT."
 optional = true
 python-versions = "<4.0,>=3.10"
 files = [
-    {file = "gym_pusht-0.1.4-py3-none-any.whl", hash = "sha256:04ec628fac80d77cf4edf280ed528ab06f7b8997e4452c78999b4730c2d269ab"},
+    {file = "gym_pusht-0.1.3-py3-none-any.whl", hash = "sha256:feeb02493a03d1aacc45d43d6397962c50ed779ab7e4019d73af11d2f0b3831b"},
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+    {file = "gym_pusht-0.1.3.tar.gz", hash = "sha256:c8e9a5256035ba49841ebbc7c32a06c4fa2daa52f5fad80da941b607c4553e28"},
 ]
 
 [package.dependencies]
@@ -1614,153 +1576,165 @@ files = [
 
 [[package]]
 name = "lxml"
-version = "5.2.2"
+version = "5.2.1"
 description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
 optional = true
 python-versions = ">=3.6"
 files = [
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 [package.extras]
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 [package.dependencies]
@@ -2983,13 +2957,13 @@ files = [
 
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 description = "pytest: simple powerful testing with Python"
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 python-versions = ">=3.8"
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 [package.dependencies]
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 [[package]]
 name = "requests"
-version = "2.32.1"
+version = "2.31.0"
 description = "Python HTTP for Humans."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -3218,22 +3192,22 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
 name = "rerun-sdk"
-version = "0.16.0"
+version = "0.15.1"
 description = "The Rerun Logging SDK"
 optional = false
 python-versions = "<3.13,>=3.8"
 files = [
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 ]
 
 [package.dependencies]
 attrs = ">=23.1.0"
 numpy = ">=1.23,<2"
-pillow = ">=8.0.0"
+pillow = "*"
 pyarrow = ">=14.0.2"
 typing-extensions = ">=4.5"
 
@@ -3449,13 +3423,13 @@ test = ["array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "mpmath", "po
 
 [[package]]
 name = "sentry-sdk"
-version = "2.2.1"
+version = "2.1.1"
 description = "Python client for Sentry (https://sentry.io)"
 optional = false
 python-versions = ">=3.6"
 files = [
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 ]
 
 [package.dependencies]
@@ -3477,7 +3451,7 @@ django = ["django (>=1.8)"]
 falcon = ["falcon (>=1.4)"]
 fastapi = ["fastapi (>=0.79.0)"]
 flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
-grpcio = ["grpcio (>=1.21.1)", "protobuf (>=3.8.0)"]
+grpcio = ["grpcio (>=1.21.1)"]
 httpx = ["httpx (>=0.16.0)"]
 huey = ["huey (>=2)"]
 huggingface-hub = ["huggingface-hub (>=0.22)"]
@@ -3599,18 +3573,19 @@ test = ["pytest"]
 
 [[package]]
 name = "setuptools"
-version = "70.0.0"
+version = "69.5.1"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
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+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
-testing = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.1)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
+testing = ["build[virtualenv]", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6,!=8.1.1)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
+testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.2)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
 
 [[package]]
 name = "shapely"
@@ -3946,13 +3921,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "virtualenv"
-version = "20.26.2"
+version = "20.26.1"
 description = "Virtual Python Environment builder"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "virtualenv-20.26.2-py3-none-any.whl", hash = "sha256:a624db5e94f01ad993d476b9ee5346fdf7b9de43ccaee0e0197012dc838a0e9b"},
+    {file = "virtualenv-20.26.1-py3-none-any.whl", hash = "sha256:7aa9982a728ae5892558bff6a2839c00b9ed145523ece2274fad6f414690ae75"},
-    {file = "virtualenv-20.26.2.tar.gz", hash = "sha256:82bf0f4eebbb78d36ddaee0283d43fe5736b53880b8a8cdcd37390a07ac3741c"},
+    {file = "virtualenv-20.26.1.tar.gz", hash = "sha256:604bfdceaeece392802e6ae48e69cec49168b9c5f4a44e483963f9242eb0e78b"},
 ]
 
 [package.dependencies]
@@ -4228,13 +4203,13 @@ multidict = ">=4.0"
 
 [[package]]
 name = "zarr"
-version = "2.18.1"
+version = "2.18.0"
 description = "An implementation of chunked, compressed, N-dimensional arrays for Python"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "zarr-2.18.1-py3-none-any.whl", hash = "sha256:a1770d194eec4ec0a41a01295a6f724e1c3471d704d3aca906d3b3a7f8830245"},
+    {file = "zarr-2.18.0-py3-none-any.whl", hash = "sha256:7f8532b6a3f50f22e809e130e09353637ec8b5bb5e95a5a0bfaae91f63978b5d"},
-    {file = "zarr-2.18.1.tar.gz", hash = "sha256:28c360ed123e606c425a694a83300227a907cb86a995fc9eef620ecafbe5f92d"},
+    {file = "zarr-2.18.0.tar.gz", hash = "sha256:c3b7d2c85b8a42b0ad0ad268a36fb6886ca852098358c125c6b126a417e0a598"},
 ]
 
 [package.dependencies]
@@ -4249,23 +4224,22 @@ jupyter = ["ipytree (>=0.2.2)", "ipywidgets (>=8.0.0)", "notebook"]
 
 [[package]]
 name = "zipp"
-version = "3.18.2"
+version = "3.18.1"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.18.2-py3-none-any.whl", hash = "sha256:dce197b859eb796242b0622af1b8beb0a722d52aa2f57133ead08edd5bf5374e"},
+    {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
-    {file = "zipp-3.18.2.tar.gz", hash = "sha256:6278d9ddbcfb1f1089a88fde84481528b07b0e10474e09dcfe53dad4069fa059"},
+    {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
 ]
 
 [package.extras]
 docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
 
 [extras]
 aloha = ["gym-aloha"]
 dev = ["debugpy", "pre-commit"]
-dora = ["gym-dora"]
 pusht = ["gym-pusht"]
 test = ["pytest", "pytest-cov"]
 umi = ["imagecodecs"]
@@ -4274,4 +4248,4 @@ xarm = ["gym-xarm"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "ea4e8207316a8ec8a4b95d6a89cf488c8733a8e7ab43e5f669c889ee87f3bef3"
+content-hash = "e4834d67df32c8c617c259b0e59bb33ddaccde08fe940d771e74046cbffe3399"

pyproject.toml

@@ -46,7 +46,6 @@ h5py = ">=3.10.0"
 huggingface-hub = {extras = ["hf-transfer"], version = "^0.23.0"}
 gymnasium = ">=0.29.1"
 cmake = ">=3.29.0.1"
-gym-dora = { path = "gym_dora", optional = true, develop = true}
 gym-pusht = { version = ">=0.1.3", optional = true}
 gym-xarm = { version = ">=0.1.1", optional = true}
 gym-aloha = { version = ">=0.1.1", optional = true}
@@ -62,7 +61,6 @@ rerun-sdk = ">=0.15.1"
 
 
 [tool.poetry.extras]
-dora = ["gym-dora"]
 pusht = ["gym-pusht"]
 xarm = ["gym-xarm"]
 aloha = ["gym-aloha"]