feat(accelerate): port multi gpu training

Co-authored-by: mshukor <mustafa.shukor97@gmail.com>
chore(processor): Add default names for preprocessor and postprocessor in constants
2026-07-29 12:39:41 +00:00 · 2025-08-12 16:10:20 +02:00 · 2025-08-11 18:00:25 +02:00 · 2025-08-11 13:56:03 +02:00 · 2025-08-09 00:21:42 +02:00 · 2025-08-08 10:46:14 +02:00
48 changed files with 489 additions and 140 deletions
@@ -30,7 +30,7 @@ pytest -sx tests/test_stuff.py::test_something
 ```

 ```bash
-python -m lerobot.scripts.train --some.option=true
+lerobot-train --some.option=true
 ```

 ## SECTION TO REMOVE BEFORE SUBMITTING YOUR PR
@@ -29,8 +29,8 @@ on:
 env:
  UV_VERSION: "0.8.0"
  PYTHON_VERSION: "3.10"
-  DOCKER_IMAGE_NAME_CPU: huggingface/lerobot-gpu:latest
-  DOCKER_IMAGE_NAME_GPU: huggingface/lerobot-cpu:latest
+  DOCKER_IMAGE_NAME_CPU: huggingface/lerobot-cpu:latest
+  DOCKER_IMAGE_NAME_GPU: huggingface/lerobot-gpu:latest

 # Ensures that only the latest commit is built, canceling older runs.
 concurrency:
@@ -44,7 +44,7 @@ test-end-to-end:
 	${MAKE} DEVICE=$(DEVICE) test-smolvla-ete-eval

 test-act-ete-train:
-	python -m lerobot.scripts.train \
+	lerobot-train \
 		--policy.type=act \
 		--policy.dim_model=64 \
 		--policy.n_action_steps=20 \
@@ -68,12 +68,12 @@ test-act-ete-train:
 		--output_dir=tests/outputs/act/

 test-act-ete-train-resume:
-	python -m lerobot.scripts.train \
+	lerobot-train \
 		--config_path=tests/outputs/act/checkpoints/000002/pretrained_model/train_config.json \
 		--resume=true

 test-act-ete-eval:
-	python -m lerobot.scripts.eval \
+	lerobot-eval \
 		--policy.path=tests/outputs/act/checkpoints/000004/pretrained_model \
 		--policy.device=$(DEVICE) \
 		--env.type=aloha \
@@ -82,7 +82,7 @@ test-act-ete-eval:
 		--eval.batch_size=1

 test-diffusion-ete-train:
-	python -m lerobot.scripts.train \
+	lerobot-train \
 		--policy.type=diffusion \
 		--policy.down_dims='[64,128,256]' \
 		--policy.diffusion_step_embed_dim=32 \
@@ -106,7 +106,7 @@ test-diffusion-ete-train:
 		--output_dir=tests/outputs/diffusion/

 test-diffusion-ete-eval:
-	python -m lerobot.scripts.eval \
+	lerobot-eval \
 		--policy.path=tests/outputs/diffusion/checkpoints/000002/pretrained_model \
 		--policy.device=$(DEVICE) \
 		--env.type=pusht \
@@ -115,7 +115,7 @@ test-diffusion-ete-eval:
 		--eval.batch_size=1

 test-tdmpc-ete-train:
-	python -m lerobot.scripts.train \
+	lerobot-train \
 		--policy.type=tdmpc \
 		--policy.device=$(DEVICE) \
 		--policy.push_to_hub=false \
@@ -137,7 +137,7 @@ test-tdmpc-ete-train:
 		--output_dir=tests/outputs/tdmpc/

 test-tdmpc-ete-eval:
-	python -m lerobot.scripts.eval \
+	lerobot-eval \
 		--policy.path=tests/outputs/tdmpc/checkpoints/000002/pretrained_model \
 		--policy.device=$(DEVICE) \
 		--env.type=xarm \
@@ -148,7 +148,7 @@ test-tdmpc-ete-eval:


 test-smolvla-ete-train:
-	python -m lerobot.scripts.train \
+	lerobot-train \
 		--policy.type=smolvla \
 		--policy.n_action_steps=20 \
 		--policy.chunk_size=20 \
@@ -171,7 +171,7 @@ test-smolvla-ete-train:
 		--output_dir=tests/outputs/smolvla/

 test-smolvla-ete-eval:
-	python -m lerobot.scripts.eval \
+	lerobot-eval \
 		--policy.path=tests/outputs/smolvla/checkpoints/000004/pretrained_model \
 		--policy.device=$(DEVICE) \
 		--env.type=aloha \
@@ -6,7 +6,7 @@

 <div align="center">

-[![Tests](https://github.com/huggingface/lerobot/actions/workflows/nightly.yml/badge.svg?branch=main)](https://github.com/huggingface/lerobot/actions/workflows/nighty.yml?query=branch%3Amain)
+[![Tests](https://github.com/huggingface/lerobot/actions/workflows/nightly.yml/badge.svg?branch=main)](https://github.com/huggingface/lerobot/actions/workflows/nightly.yml?query=branch%3Amain)
 [![Python versions](https://img.shields.io/pypi/pyversions/lerobot)](https://www.python.org/downloads/)
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/huggingface/lerobot/blob/main/LICENSE)
 [![Status](https://img.shields.io/pypi/status/lerobot)](https://pypi.org/project/lerobot/)
@@ -276,7 +276,7 @@ Check out [example 2](https://github.com/huggingface/lerobot/blob/main/examples/
 We also provide a more capable script to parallelize the evaluation over multiple environments during the same rollout. Here is an example with a pretrained model hosted on [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht):

 ```bash
-python -m lerobot.scripts.eval \
+lerobot-eval \
    --policy.path=lerobot/diffusion_pusht \
    --env.type=pusht \
    --eval.batch_size=10 \
@@ -288,10 +288,10 @@ python -m lerobot.scripts.eval \
 Note: After training your own policy, you can re-evaluate the checkpoints with:

 ```bash
-python -m lerobot.scripts.eval --policy.path={OUTPUT_DIR}/checkpoints/last/pretrained_model
+lerobot-eval --policy.path={OUTPUT_DIR}/checkpoints/last/pretrained_model
 ```

-See `python -m lerobot.scripts.eval --help` for more instructions.
+See `lerobot-eval --help` for more instructions.

 ### Train your own policy

@@ -303,7 +303,7 @@ A link to the wandb logs for the run will also show up in yellow in your termina

 \<img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/wandb.png" alt="WandB logs example"\>

-Note: For efficiency, during training every checkpoint is evaluated on a low number of episodes. You may use `--eval.n_episodes=500` to evaluate on more episodes than the default. Or, after training, you may want to re-evaluate your best checkpoints on more episodes or change the evaluation settings. See `python -m lerobot.scripts.eval --help` for more instructions.
+Note: For efficiency, during training every checkpoint is evaluated on a low number of episodes. You may use `--eval.n_episodes=500` to evaluate on more episodes than the default. Or, after training, you may want to re-evaluate your best checkpoints on more episodes or change the evaluation settings. See `lerobot-eval --help` for more instructions.

 #### Reproduce state-of-the-art (SOTA)

@@ -311,7 +311,7 @@ We provide some pretrained policies on our [hub page](https://huggingface.co/ler
 You can reproduce their training by loading the config from their run. Simply running:

 ```bash
-python -m lerobot.scripts.train --config_path=lerobot/diffusion_pusht
+lerobot-train --config_path=lerobot/diffusion_pusht
 ```

 reproduces SOTA results for Diffusion Policy on the PushT task.
@@ -9,7 +9,7 @@ To instantiate a camera, you need a camera identifier. This identifier might cha
 To find the camera indices of the cameras plugged into your system, run the following script:

 ```bash
-python -m lerobot.find_cameras opencv # or realsense for Intel Realsense cameras
+lerobot-find-cameras opencv # or realsense for Intel Realsense cameras
 ```

 The output will look something like this if you have two cameras connected:
@@ -719,7 +719,7 @@ Example configuration for training the [reward classifier](https://huggingface.c
 To train the classifier, use the `train.py` script with your configuration:

 ```bash
-python -m lerobot.scripts.train --config_path path/to/reward_classifier_train_config.json
+lerobot-train --config_path path/to/reward_classifier_train_config.json
 ```

 **Deploying and Testing the Model**
@@ -784,7 +784,7 @@ The reward classifier will automatically provide rewards based on the visual inp
 3. **Train the classifier**:

   ```bash
-   python -m lerobot.scripts.train --config_path src/lerobot/configs/reward_classifier_train_config.json
+   lerobot-train --config_path src/lerobot/configs/reward_classifier_train_config.json
   ```

 4. **Test the classifier**:
@@ -19,7 +19,7 @@ pip install -e ".[hopejr]"
 Before starting calibration and operation, you need to identify the USB ports for each HopeJR component. Run this script to find the USB ports for the arm, hand, glove, and exoskeleton:

 ```bash
-python -m lerobot.find_port
+lerobot-find-port
 ```

 This will display the available USB ports and their associated devices. Make note of the port paths (e.g., `/dev/tty.usbmodem58760433331`, `/dev/tty.usbmodem11301`) as you'll need to specify them in the `--robot.port` and `--teleop.port` parameters when recording data, replaying episodes, or running teleoperation scripts.
@@ -31,7 +31,7 @@ Before performing teleoperation, HopeJR's limbs need to be calibrated. Calibrati
 ### 1.1 Calibrate Robot Hand

 ```bash
-python -m lerobot.calibrate \
+lerobot-calibrate \
    --robot.type=hope_jr_hand \
    --robot.port=/dev/tty.usbmodem58760432281 \
    --robot.id=blue \
@@ -81,7 +81,7 @@ Once you have set the appropriate boundaries for all joints, click "Save" to sav
 ### 1.2 Calibrate Teleoperator Glove

 ```bash
-python -m lerobot.calibrate \
+lerobot-calibrate \
    --teleop.type=homunculus_glove \
    --teleop.port=/dev/tty.usbmodem11201 \
    --teleop.id=red \
@@ -120,7 +120,7 @@ Once calibration is complete, the system will save the calibration to `/Users/yo
 ### 1.3 Calibrate Robot Arm

 ```bash
-python -m lerobot.calibrate \
+lerobot-calibrate \
    --robot.type=hope_jr_arm \
    --robot.port=/dev/tty.usbserial-1110 \
    --robot.id=white
@@ -146,7 +146,7 @@ Use the calibration interface to set the range boundaries for each joint. Move e
 ### 1.4 Calibrate Teleoperator Exoskeleton

 ```bash
-python -m lerobot.calibrate \
+lerobot-calibrate \
    --teleop.type=homunculus_arm \
    --teleop.port=/dev/tty.usbmodem11201 \
    --teleop.id=black
@@ -178,7 +178,7 @@ Due to global variable conflicts in the Feetech middleware, teleoperation for ar
 ### Hand

 ```bash
-python -m lerobot.teleoperate \
+lerobot-teleoperate \
    --robot.type=hope_jr_hand \
    --robot.port=/dev/tty.usbmodem58760432281 \
    --robot.id=blue \
@@ -194,7 +194,7 @@ python -m lerobot.teleoperate \
 ### Arm

 ```bash
-python -m lerobot.teleoperate \
+lerobot-teleoperate \
    --robot.type=hope_jr_arm \
    --robot.port=/dev/tty.usbserial-1110 \
    --robot.id=white \
@@ -214,7 +214,7 @@ Record, Replay and Train with Hope-JR is still experimental.
 This step records the dataset, which can be seen as an example [here](https://huggingface.co/datasets/nepyope/hand_record_test_with_video_data/settings).

 ```bash
-python -m lerobot.record \
+lerobot-record \
    --robot.type=hope_jr_hand \
    --robot.port=/dev/tty.usbmodem58760432281 \
    --robot.id=right \
@@ -236,7 +236,7 @@ python -m lerobot.record \
 ### Replay

 ```bash
-python -m lerobot.replay \
+lerobot-replay \
    --robot.type=hope_jr_hand \
    --robot.port=/dev/tty.usbmodem58760432281 \
    --robot.id=right \
@@ -248,7 +248,7 @@ python -m lerobot.replay \
 ### Train

 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
  --dataset.repo_id=nepyope/hand_record_test_with_video_data \
  --policy.type=act \
  --output_dir=outputs/train/hopejr_hand \
@@ -263,7 +263,7 @@ python -m lerobot.scripts.train \
 This training run can be viewed as an example [here](https://wandb.ai/tino/lerobot/runs/rp0k8zvw?nw=nwusertino).

 ```bash
-python -m lerobot.record \
+lerobot-record \
  --robot.type=hope_jr_hand \
  --robot.port=/dev/tty.usbmodem58760432281 \
  --robot.id=right \
@@ -45,7 +45,7 @@ Note that the `id` associated with a robot is used to store the calibration file
 <hfoptions id="teleoperate_so101">
 <hfoption id="Command">
 ```bash
-python -m lerobot.teleoperate \
+lerobot-teleoperate \
    --robot.type=so101_follower \
    --robot.port=/dev/tty.usbmodem58760431541 \
    --robot.id=my_awesome_follower_arm \
@@ -101,7 +101,7 @@ With `rerun`, you can teleoperate again while simultaneously visualizing the cam
 <hfoptions id="teleoperate_koch_camera">
 <hfoption id="Command">
 ```bash
-python -m lerobot.teleoperate \
+lerobot-teleoperate \
    --robot.type=koch_follower \
    --robot.port=/dev/tty.usbmodem58760431541 \
    --robot.id=my_awesome_follower_arm \
@@ -174,7 +174,7 @@ Now you can record a dataset. To record 5 episodes and upload your dataset to th
 <hfoptions id="record">
 <hfoption id="Command">
 ```bash
-python -m lerobot.record \
+lerobot-record \
    --robot.type=so101_follower \
    --robot.port=/dev/tty.usbmodem585A0076841 \
    --robot.id=my_awesome_follower_arm \
@@ -376,7 +376,7 @@ You can replay the first episode on your robot with either the command below or
 <hfoptions id="replay">
 <hfoption id="Command">
 ```bash
-python -m lerobot.replay \
+lerobot-replay \
    --robot.type=so101_follower \
    --robot.port=/dev/tty.usbmodem58760431541 \
    --robot.id=my_awesome_follower_arm \
@@ -428,10 +428,10 @@ Your robot should replicate movements similar to those you recorded. For example

 ## Train a policy

-To train a policy to control your robot, use the [`python -m lerobot.scripts.train`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
+To train a policy to control your robot, use the [`lerobot-train`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:

 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
  --dataset.repo_id=${HF_USER}/so101_test \
  --policy.type=act \
  --output_dir=outputs/train/act_so101_test \
@@ -453,7 +453,7 @@ Training should take several hours. You will find checkpoints in `outputs/train/
 To resume training from a checkpoint, below is an example command to resume from `last` checkpoint of the `act_so101_test` policy:

 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
  --config_path=outputs/train/act_so101_test/checkpoints/last/pretrained_model/train_config.json \
  --resume=true
 ```
@@ -490,7 +490,7 @@ You can use the `record` script from [`lerobot/record.py`](https://github.com/hu
 <hfoptions id="eval">
 <hfoption id="Command">
 ```bash
-python -m lerobot.record  \
+lerobot-record  \
  --robot.type=so100_follower \
  --robot.port=/dev/ttyACM1 \
  --robot.cameras="{ up: {type: opencv, index_or_path: /dev/video10, width: 640, height: 480, fps: 30}, side: {type: intelrealsense, serial_number_or_name: 233522074606, width: 640, height: 480, fps: 30}}" \
@@ -121,10 +121,10 @@ If you uploaded your dataset to the hub you can [visualize your dataset online](

 ## Train a policy

-To train a policy to control your robot, use the [`python -m lerobot.scripts.train`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
+To train a policy to control your robot, use the [`lerobot-train`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:

 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
  --dataset.repo_id=${HF_USER}/il_gym \
  --policy.type=act \
  --output_dir=outputs/train/il_sim_test \
@@ -31,7 +31,7 @@ pip install -e ".[dynamixel]"
 To find the port for each bus servo adapter, run this script:

 ```bash
-python -m lerobot.find_port
+lerobot-find-port
 ```

 <hfoptions id="example">
@@ -98,7 +98,7 @@ For a visual reference on how to set the motor ids please refer to [this video](
 <hfoption id="Command">

 ```bash
-python -m lerobot.setup_motors \
+lerobot-setup-motors \
    --robot.type=koch_follower \
    --robot.port=/dev/tty.usbmodem575E0031751 # <- paste here the port found at previous step
 ```
@@ -174,7 +174,7 @@ Do the same steps for the leader arm but modify the command or script accordingl
 <hfoption id="Command">

 ```bash
-python -m lerobot.setup_motors \
+lerobot-setup-motors \
    --teleop.type=koch_leader \
    --teleop.port=/dev/tty.usbmodem575E0031751 \  # <- paste here the port found at previous step
 ```
@@ -211,7 +211,7 @@ Run the following command or API example to calibrate the follower arm:
 <hfoption id="Command">

 ```bash
-python -m lerobot.calibrate \
+lerobot-calibrate \
    --robot.type=koch_follower \
    --robot.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
    --robot.id=my_awesome_follower_arm  # <- Give the robot a unique name
@@ -249,7 +249,7 @@ Do the same steps to calibrate the leader arm, run the following command or API
 <hfoption id="Command">

 ```bash
-python -m lerobot.calibrate \
+lerobot-calibrate \
    --teleop.type=koch_leader \
    --teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
    --teleop.id=my_awesome_leader_arm  # <- Give the robot a unique name
@@ -60,7 +60,7 @@ First, we will assemble the two SO100/SO101 arms. One to attach to the mobile ba
 To find the port for each bus servo adapter, run this script:

 ```bash
-python -m lerobot.find_port
+lerobot-find-port
 ```

 <hfoptions id="example">
@@ -116,7 +116,7 @@ The instructions for configuring the motors can be found in the SO101 [docs](./s
 You can run this command to setup motors for LeKiwi. It will first setup the motors for arm (id 6..1) and then setup motors for wheels (9,8,7)

 ```bash
-python -m lerobot.setup_motors \
+lerobot-setup-motors \
    --robot.type=lekiwi \
    --robot.port=/dev/tty.usbmodem58760431551 # <- paste here the port found at previous step
 ```
@@ -174,7 +174,7 @@ The calibration process is very important because it allows a neural network tra
 Make sure the arm is connected to the Raspberry Pi and run this script or API example (on the Raspberry Pi via SSH) to launch calibration of the follower arm:

 ```bash
-python -m lerobot.calibrate \
+lerobot-calibrate \
    --robot.type=lekiwi \
    --robot.id=my_awesome_kiwi # <- Give the robot a unique name
 ```
@@ -193,7 +193,7 @@ Then, to calibrate the leader arm (which is attached to the laptop/pc). Run the
 <hfoption id="Command">

 ```bash
-python -m lerobot.calibrate \
+lerobot-calibrate \
    --teleop.type=so100_leader \
    --teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
    --teleop.id=my_awesome_leader_arm # <- Give the robot a unique name
@@ -54,7 +54,7 @@ If you don't have a gpu device, you can train using our notebook on [![Google Co
 Pass your dataset to the training script using `--dataset.repo_id`. If you want to test your installation, run the following command where we use one of the datasets we collected for the [SmolVLA Paper](https://huggingface.co/papers/2506.01844).

 ```bash
-cd lerobot && python -m lerobot.scripts.train \
+cd lerobot && lerobot-train \
  --policy.path=lerobot/smolvla_base \
  --dataset.repo_id=${HF_USER}/mydataset \
  --batch_size=64 \
@@ -73,7 +73,7 @@ cd lerobot && python -m lerobot.scripts.train \
 Fine-tuning is an art. For a complete overview of the options for finetuning, run

 ```bash
-python -m lerobot.scripts.train --help
+lerobot-train --help
 ```

 <p align="center">
@@ -97,7 +97,7 @@ Similarly for when recording an episode, it is recommended that you are logged i
 Once you are logged in, you can run inference in your setup by doing:

 ```bash
-python -m lerobot.record \
+lerobot-record \
  --robot.type=so101_follower \
  --robot.port=/dev/ttyACM0 \ # <- Use your port
  --robot.id=my_blue_follower_arm \ # <- Use your robot id
@@ -26,7 +26,7 @@ Unlike the SO-101, the motor connectors are not easily accessible once the arm i
 To find the port for each bus servo adapter, run this script:

 ```bash
-python -m lerobot.find_port
+lerobot-find-port
 ```

 <hfoptions id="example">
@@ -93,7 +93,7 @@ For a visual reference on how to set the motor ids please refer to [this video](
 <hfoption id="Command">

 ```bash
-python -m lerobot.setup_motors \
+lerobot-setup-motors \
    --robot.type=so100_follower \
    --robot.port=/dev/tty.usbmodem585A0076841  # <- paste here the port found at previous step
 ```
@@ -168,7 +168,7 @@ Do the same steps for the leader arm.
 <hfoptions id="setup_motors">
 <hfoption id="Command">
 ```bash
-python -m lerobot.setup_motors \
+lerobot-setup-motors \
    --teleop.type=so100_leader \
    --teleop.port=/dev/tty.usbmodem575E0031751  # <- paste here the port found at previous step
 ```
@@ -568,7 +568,7 @@ Run the following command or API example to calibrate the follower arm:
 <hfoption id="Command">

 ```bash
-python -m lerobot.calibrate \
+lerobot-calibrate \
    --robot.type=so100_follower \
    --robot.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
    --robot.id=my_awesome_follower_arm # <- Give the robot a unique name
@@ -606,7 +606,7 @@ Do the same steps to calibrate the leader arm, run the following command or API
 <hfoption id="Command">

 ```bash
-python -m lerobot.calibrate \
+lerobot-calibrate \
    --teleop.type=so100_leader \
    --teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
    --teleop.id=my_awesome_leader_arm # <- Give the robot a unique name
@@ -162,7 +162,7 @@ It is advisable to install one 3-pin cable in the motor after placing them befor
 To find the port for each bus servo adapter, connect MotorBus to your computer via USB and power. Run the following script and disconnect the MotorBus when prompted:

 ```bash
-python -m lerobot.find_port
+lerobot-find-port
 ```

 <hfoptions id="example">
@@ -240,7 +240,7 @@ Connect the usb cable from your computer and the power supply to the follower ar
 <hfoption id="Command">

 ```bash
-python -m lerobot.setup_motors \
+lerobot-setup-motors \
    --robot.type=so101_follower \
    --robot.port=/dev/tty.usbmodem585A0076841  # <- paste here the port found at previous step
 ```
@@ -316,7 +316,7 @@ Do the same steps for the leader arm.
 <hfoption id="Command">

 ```bash
-python -m lerobot.setup_motors \
+lerobot-setup-motors \
    --teleop.type=so101_leader \
    --teleop.port=/dev/tty.usbmodem575E0031751  # <- paste here the port found at previous step
 ```
@@ -353,7 +353,7 @@ Run the following command or API example to calibrate the follower arm:
 <hfoption id="Command">

 ```bash
-python -m lerobot.calibrate \
+lerobot-calibrate \
    --robot.type=so101_follower \
    --robot.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
    --robot.id=my_awesome_follower_arm # <- Give the robot a unique name
@@ -402,7 +402,7 @@ Do the same steps to calibrate the leader arm, run the following command or API
 <hfoption id="Command">

 ```bash
-python -m lerobot.calibrate \
+lerobot-calibrate \
    --teleop.type=so101_leader \
    --teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
    --teleop.id=my_awesome_leader_arm # <- Give the robot a unique name
@@ -62,7 +62,7 @@ By default, every field takes its default value specified in the dataclass. If a
 Let's say that we want to train [Diffusion Policy](../src/lerobot/policies/diffusion) on the [pusht](https://huggingface.co/datasets/lerobot/pusht) dataset, using the [gym_pusht](https://github.com/huggingface/gym-pusht) environment for evaluation. The command to do so would look like this:

 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
    --dataset.repo_id=lerobot/pusht \
    --policy.type=diffusion \
    --env.type=pusht
@@ -77,7 +77,7 @@ Let's break this down:
 Let's see another example. Let's say you've been training [ACT](../src/lerobot/policies/act) on [lerobot/aloha_sim_insertion_human](https://huggingface.co/datasets/lerobot/aloha_sim_insertion_human) using the [gym-aloha](https://github.com/huggingface/gym-aloha) environment for evaluation with:

 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
    --policy.type=act \
    --dataset.repo_id=lerobot/aloha_sim_insertion_human \
    --env.type=aloha \
@@ -90,7 +90,7 @@ We now want to train a different policy for aloha on another task. We'll change
 Looking at the [`AlohaEnv`](../src/lerobot/envs/configs.py) config, the task is `"AlohaInsertion-v0"` by default, which corresponds to the task we trained on in the command above. The [gym-aloha](https://github.com/huggingface/gym-aloha?tab=readme-ov-file#description) environment also has the `AlohaTransferCube-v0` task which corresponds to this other task we want to train on. Putting this together, we can train this new policy on this different task using:

 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
    --policy.type=act \
    --dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \
    --env.type=aloha \
@@ -127,7 +127,7 @@ Now, let's assume that we want to reproduce the run just above. That run has pro
 We can then simply load the config values from this file using:

 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
    --config_path=outputs/train/act_aloha_transfer/checkpoints/last/pretrained_model/ \
    --output_dir=outputs/train/act_aloha_transfer_2
 ```
@@ -137,7 +137,7 @@ python -m lerobot.scripts.train \
 Similarly to Hydra, we can still override some parameters in the CLI if we want to, e.g.:

 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
    --config_path=outputs/train/act_aloha_transfer/checkpoints/last/pretrained_model/ \
    --output_dir=outputs/train/act_aloha_transfer_2
    --policy.n_action_steps=80
@@ -148,7 +148,7 @@ python -m lerobot.scripts.train \
 `--config_path` can also accept the repo_id of a repo on the hub that contains a `train_config.json` file, e.g. running:

 ```bash
-python -m lerobot.scripts.train --config_path=lerobot/diffusion_pusht
+lerobot-train --config_path=lerobot/diffusion_pusht
 ```

 will start a training run with the same configuration used for training [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht)
@@ -160,7 +160,7 @@ Being able to resume a training run is important in case it crashed or aborted f
 Let's reuse the command from the previous run and add a few more options:

 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
    --policy.type=act \
    --dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \
    --env.type=aloha \
@@ -179,7 +179,7 @@ INFO 2025-01-24 16:10:56 ts/train.py:263 Checkpoint policy after step 100
 Now let's simulate a crash by killing the process (hit `ctrl`+`c`). We can then simply resume this run from the last checkpoint available with:

 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
    --config_path=outputs/train/run_resumption/checkpoints/last/pretrained_model/ \
    --resume=true
 ```
@@ -190,7 +190,7 @@ Another reason for which you might want to resume a run is simply to extend trai
 You could double the number of steps of the previous run with:

 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
    --config_path=outputs/train/run_resumption/checkpoints/last/pretrained_model/ \
    --resume=true \
    --steps=200000
@@ -224,7 +224,7 @@ In addition to the features currently in Draccus, we've added a special `.path`
 For example, we could fine-tune a [policy pre-trained on the aloha transfer task](https://huggingface.co/lerobot/act_aloha_sim_transfer_cube_human) on the aloha insertion task. We can achieve this with:

 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
    --policy.path=lerobot/act_aloha_sim_transfer_cube_human \
    --dataset.repo_id=lerobot/aloha_sim_insertion_human \
    --env.type=aloha \
@@ -270,7 +270,7 @@ We'll summarize here the main use cases to remember from this tutorial.
 #### Train a policy from scratch – CLI

 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
    --policy.type=act \  # <- select 'act' policy
    --env.type=pusht \  # <- select 'pusht' environment
    --dataset.repo_id=lerobot/pusht  # <- train on this dataset
@@ -279,7 +279,7 @@ python -m lerobot.scripts.train \
 #### Train a policy from scratch - config file + CLI

 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
    --config_path=path/to/pretrained_model \  # <- can also be a repo_id
    --policy.n_action_steps=80  # <- you may still override values
 ```
@@ -287,7 +287,7 @@ python -m lerobot.scripts.train \
 #### Resume/continue a training run

 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
    --config_path=checkpoint/pretrained_model/ \
    --resume=true \
    --steps=200000  # <- you can change some training parameters
@@ -296,7 +296,7 @@ python -m lerobot.scripts.train \
 #### Fine-tuning

 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
    --policy.path=lerobot/act_aloha_sim_transfer_cube_human \  # <- can also be a local path to a checkpoint
    --dataset.repo_id=lerobot/aloha_sim_insertion_human \
    --env.type=aloha \
@@ -18,7 +18,7 @@ Replays the actions of an episode from a dataset on a robot.
 Example:

 ```shell
-python -m lerobot.replay \
+lerobot-replay \
    --robot.type=so100_follower \
    --robot.port=/dev/tty.usbmodem58760431541 \
    --robot.id=black \
@@ -18,7 +18,7 @@ Helper to recalibrate your device (robot or teleoperator).
 Example:

 ```shell
-python -m lerobot.calibrate \
+lerobot-calibrate \
    --teleop.type=so100_leader \
    --teleop.port=/dev/tty.usbmodem58760431551 \
    --teleop.id=blue
@@ -60,7 +60,7 @@ class OpenCVCamera(Camera):
    or port changes, especially on Linux. Use the provided utility script to find
    available camera indices or paths:
    ```bash
-    python -m lerobot.find_cameras opencv
+    lerobot-find-cameras opencv
    ```

    The camera's default settings (FPS, resolution, color mode) are used unless
@@ -165,8 +165,7 @@ class OpenCVCamera(Camera):
            self.videocapture.release()
            self.videocapture = None
            raise ConnectionError(
-                f"Failed to open {self}."
-                f"Run `python -m lerobot.find_cameras opencv` to find available cameras."
+                f"Failed to open {self}.Run `lerobot-find-cameras opencv` to find available cameras."
            )

        self._configure_capture_settings()
@@ -51,7 +51,7 @@ class RealSenseCamera(Camera):

    Use the provided utility script to find available camera indices and default profiles:
    ```bash
-    python -m lerobot.find_cameras realsense
+    lerobot-find-cameras realsense
    ```

    A `RealSenseCamera` instance requires a configuration object specifying the
@@ -176,8 +176,7 @@ class RealSenseCamera(Camera):
            self.rs_profile = None
            self.rs_pipeline = None
            raise ConnectionError(
-                f"Failed to open {self}."
-                "Run `python -m lerobot.find_cameras realsense` to find available cameras."
+                f"Failed to open {self}.Run `lerobot-find-cameras realsense` to find available cameras."
            ) from e

        self._configure_capture_settings()
@@ -40,6 +40,9 @@ OPTIMIZER_STATE = "optimizer_state.safetensors"
 OPTIMIZER_PARAM_GROUPS = "optimizer_param_groups.json"
 SCHEDULER_STATE = "scheduler_state.json"

+PREPROCESSOR_DEFAULT_NAME = "robot_preprocessor"
+POSTPROCESSOR_DEFAULT_NAME = "robot_postprocessor"
+
 if "LEROBOT_HOME" in os.environ:
    raise ValueError(
        f"You have a 'LEROBOT_HOME' environment variable set to '{os.getenv('LEROBOT_HOME')}'.\n"
@@ -20,7 +20,7 @@ Helper to find the camera devices available in your system.
 Example:

 ```shell
-python -m lerobot.find_cameras
+lerobot-find-cameras
 ```
 """

@@ -18,7 +18,7 @@ Helper to find the USB port associated with your MotorsBus.
 Example:

 ```shell
-python -m lerobot.find_port
+lerobot-find-port
 ```
 """

@@ -222,7 +222,7 @@ class MotorsBus(abc.ABC):
    A MotorsBus subclass instance requires a port (e.g. `FeetechMotorsBus(port="/dev/tty.usbmodem575E0031751"`)).
    To find the port, you can run our utility script:
    ```bash
-    python -m lerobot.find_port.py
+    lerobot-find-port.py
    >>> Finding all available ports for the MotorsBus.
    >>> ["/dev/tty.usbmodem575E0032081", "/dev/tty.usbmodem575E0031751"]
    >>> Remove the usb cable from your MotorsBus and press Enter when done.
@@ -446,7 +446,7 @@ class MotorsBus(abc.ABC):
        except (FileNotFoundError, OSError, serial.SerialException) as e:
            raise ConnectionError(
                f"\nCould not connect on port '{self.port}'. Make sure you are using the correct port."
-                "\nTry running `python -m lerobot.find_port`\n"
+                "\nTry running `lerobot-find-port`\n"
            ) from e

    @abc.abstractmethod
@@ -15,6 +15,7 @@
 # limitations under the License.
 import torch

+from lerobot.constants import POSTPROCESSOR_DEFAULT_NAME, PREPROCESSOR_DEFAULT_NAME
 from lerobot.policies.act.configuration_act import ACTConfig
 from lerobot.processor import (
    DeviceProcessor,
@@ -45,6 +46,6 @@ def make_act_processor(
            features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
        ),
    ]
-    return RobotProcessor(steps=input_steps, name="robot_preprocessor"), RobotProcessor(
-        steps=output_steps, name="robot_postprocessor"
+    return RobotProcessor(steps=input_steps, name=PREPROCESSOR_DEFAULT_NAME), RobotProcessor(
+        steps=output_steps, name=POSTPROCESSOR_DEFAULT_NAME
    )
@@ -16,6 +16,7 @@
 # limitations under the License.
 import torch

+from lerobot.constants import POSTPROCESSOR_DEFAULT_NAME, PREPROCESSOR_DEFAULT_NAME
 from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig
 from lerobot.processor import (
    DeviceProcessor,
@@ -46,6 +47,6 @@ def make_diffusion_processor(
            features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
        ),
    ]
-    return RobotProcessor(steps=input_steps, name="robot_preprocessor"), RobotProcessor(
-        steps=output_steps, name="robot_postprocessor"
+    return RobotProcessor(steps=input_steps, name=PREPROCESSOR_DEFAULT_NAME), RobotProcessor(
+        steps=output_steps, name=POSTPROCESSOR_DEFAULT_NAME
    )
@@ -30,7 +30,7 @@ pip install -e ".[pi0]"

 Example of finetuning the pi0 pretrained model (`pi0_base` in `openpi`):
 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
 --policy.path=lerobot/pi0 \
 --dataset.repo_id=danaaubakirova/koch_test
 ```
@@ -38,7 +38,7 @@ python -m lerobot.scripts.train \
 Example of finetuning the pi0 neural network with PaliGemma and expert Gemma
 pretrained with VLM default parameters before pi0 finetuning:
 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
 --policy.type=pi0 \
 --dataset.repo_id=danaaubakirova/koch_test
 ```
@@ -19,6 +19,7 @@ from typing import Any
 import torch

 from lerobot.configs.types import PolicyFeature
+from lerobot.constants import POSTPROCESSOR_DEFAULT_NAME, PREPROCESSOR_DEFAULT_NAME
 from lerobot.policies.pi0.configuration_pi0 import PI0Config
 from lerobot.processor import (
    DeviceProcessor,
@@ -115,6 +116,6 @@ def make_pi0_processor(
        ),
    ]

-    return RobotProcessor(steps=input_steps, name="robot_preprocessor"), RobotProcessor(
-        steps=output_steps, name="robot_postprocessor"
+    return RobotProcessor(steps=input_steps, name=PREPROCESSOR_DEFAULT_NAME), RobotProcessor(
+        steps=output_steps, name=POSTPROCESSOR_DEFAULT_NAME
    )
@@ -25,14 +25,14 @@ Disclaimer: It is not expected to perform as well as the original implementation

 Example of finetuning the pi0+FAST pretrained model (`pi0_fast_base` in `openpi`):
 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
 --policy.path=lerobot/pi0fast_base \
 --dataset.repo_id=danaaubakirova/koch_test
 ```

 Example of training the pi0+FAST neural network with from scratch:
 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
 --policy.type=pi0fast \
 --dataset.repo_id=danaaubakirova/koch_test
 ```
@@ -16,6 +16,7 @@

 import torch

+from lerobot.constants import POSTPROCESSOR_DEFAULT_NAME, PREPROCESSOR_DEFAULT_NAME
 from lerobot.policies.pi0.configuration_pi0 import PI0Config
 from lerobot.processor import (
    DeviceProcessor,
@@ -46,6 +47,6 @@ def make_pi0fast_processor(
            features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
        ),
    ]
-    return RobotProcessor(steps=input_steps, name="robot_preprocessor"), RobotProcessor(
-        steps=output_steps, name="robot_postprocessor"
+    return RobotProcessor(steps=input_steps, name=PREPROCESSOR_DEFAULT_NAME), RobotProcessor(
+        steps=output_steps, name=POSTPROCESSOR_DEFAULT_NAME
    )
@@ -17,6 +17,7 @@

 import torch

+from lerobot.constants import POSTPROCESSOR_DEFAULT_NAME, PREPROCESSOR_DEFAULT_NAME
 from lerobot.policies.sac.configuration_sac import SACConfig
 from lerobot.processor import (
    DeviceProcessor,
@@ -47,6 +48,6 @@ def make_sac_processor(
            features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
        ),
    ]
-    return RobotProcessor(steps=input_steps, name="robot_preprocessor"), RobotProcessor(
-        steps=output_steps, name="robot_postprocessor"
+    return RobotProcessor(steps=input_steps, name=PREPROCESSOR_DEFAULT_NAME), RobotProcessor(
+        steps=output_steps, name=POSTPROCESSOR_DEFAULT_NAME
    )
@@ -28,7 +28,7 @@ pip install -e ".[smolvla]"

 Example of finetuning the smolvla pretrained model (`smolvla_base`):
 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
 --policy.path=lerobot/smolvla_base \
 --dataset.repo_id=danaaubakirova/svla_so100_task1_v3 \
 --batch_size=64 \
@@ -38,7 +38,7 @@ python -m lerobot.scripts.train \
 Example of finetuning a smolVLA. SmolVLA is composed of a pretrained VLM,
 and an action expert.
 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
 --policy.type=smolvla \
 --dataset.repo_id=danaaubakirova/svla_so100_task1_v3 \
 --batch_size=64 \
@@ -18,6 +18,7 @@ from typing import Any
 import torch

 from lerobot.configs.types import PolicyFeature
+from lerobot.constants import POSTPROCESSOR_DEFAULT_NAME, PREPROCESSOR_DEFAULT_NAME
 from lerobot.policies.smolvla.configuration_smolvla import SmolVLAConfig
 from lerobot.processor import (
    DeviceProcessor,
@@ -57,8 +58,8 @@ def make_smolvla_processor(
            features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
        ),
    ]
-    return RobotProcessor(steps=input_steps, name="robot_preprocessor"), RobotProcessor(
-        steps=output_steps, name="robot_postprocessor"
+    return RobotProcessor(steps=input_steps, name=PREPROCESSOR_DEFAULT_NAME), RobotProcessor(
+        steps=output_steps, name=POSTPROCESSOR_DEFAULT_NAME
    )


@@ -16,6 +16,7 @@
 # limitations under the License.
 import torch

+from lerobot.constants import POSTPROCESSOR_DEFAULT_NAME, PREPROCESSOR_DEFAULT_NAME
 from lerobot.policies.tdmpc.configuration_tdmpc import TDMPCConfig
 from lerobot.processor import (
    DeviceProcessor,
@@ -46,6 +47,6 @@ def make_tdmpc_processor(
            features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
        ),
    ]
-    return RobotProcessor(steps=input_steps, name="robot_preprocessor"), RobotProcessor(
-        steps=output_steps, name="robot_postprocessor"
+    return RobotProcessor(steps=input_steps, name=PREPROCESSOR_DEFAULT_NAME), RobotProcessor(
+        steps=output_steps, name=POSTPROCESSOR_DEFAULT_NAME
    )
@@ -17,6 +17,7 @@
 # limitations under the License.
 import torch

+from lerobot.constants import POSTPROCESSOR_DEFAULT_NAME, PREPROCESSOR_DEFAULT_NAME
 from lerobot.policies.vqbet.configuration_vqbet import VQBeTConfig
 from lerobot.processor import (
    DeviceProcessor,
@@ -47,6 +48,6 @@ def make_vqbet_processor(
            features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
        ),
    ]
-    return RobotProcessor(steps=input_steps, name="robot_preprocessor"), RobotProcessor(
-        steps=output_steps, name="robot_postprocessor"
+    return RobotProcessor(steps=input_steps, name=PREPROCESSOR_DEFAULT_NAME), RobotProcessor(
+        steps=output_steps, name=POSTPROCESSOR_DEFAULT_NAME
    )
@@ -18,7 +18,7 @@ Records a dataset. Actions for the robot can be either generated by teleoperatio
 Example:

 ```shell
-python -m lerobot.record \
+lerobot-record \
    --robot.type=so100_follower \
    --robot.port=/dev/tty.usbmodem58760431541 \
    --robot.cameras="{laptop: {type: opencv, camera_index: 0, width: 640, height: 480}}" \
@@ -36,7 +36,7 @@ python -m lerobot.record \

 Example recording with bimanual so100:
 ```shell
-python -m lerobot.record \
+lerobot-record \
  --robot.type=bi_so100_follower \
  --robot.left_arm_port=/dev/tty.usbmodem5A460851411 \
  --robot.right_arm_port=/dev/tty.usbmodem5A460812391 \
@@ -18,7 +18,7 @@ Replays the actions of an episode from a dataset on a robot.
 Examples:

 ```shell
-python -m lerobot.replay \
+lerobot-replay \
    --robot.type=so100_follower \
    --robot.port=/dev/tty.usbmodem58760431541 \
    --robot.id=black \
@@ -28,7 +28,7 @@ python -m lerobot.replay \

 Example replay with bimanual so100:
 ```shell
-python -m lerobot.replay \
+lerobot-replay \
  --robot.type=bi_so100_follower \
  --robot.left_arm_port=/dev/tty.usbmodem5A460851411 \
  --robot.right_arm_port=/dev/tty.usbmodem5A460812391 \
@@ -141,10 +141,10 @@ python lerobot/scripts/control_robot.py \

 ## Train a policy

-To train a policy to control your robot, use the [`python -m lerobot.scripts.train`](../src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
+To train a policy to control your robot, use the [`lerobot-train`](../src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:

 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
  --dataset.repo_id=${HF_USER}/aloha_test \
  --policy.type=act \
  --output_dir=outputs/train/act_aloha_test \
@@ -21,7 +21,7 @@ You want to evaluate a model from the hub (eg: https://huggingface.co/lerobot/di
 for 10 episodes.

 ```
-python -m lerobot.scripts.eval \
+lerobot-eval \
    --policy.path=lerobot/diffusion_pusht \
    --env.type=pusht \
    --eval.batch_size=10 \
@@ -32,7 +32,7 @@ python -m lerobot.scripts.eval \

 OR, you want to evaluate a model checkpoint from the LeRobot training script for 10 episodes.
 ```
-python -m lerobot.scripts.eval \
+lerobot-eval \
    --policy.path=outputs/train/diffusion_pusht/checkpoints/005000/pretrained_model \
    --env.type=pusht \
    --eval.batch_size=10 \
@@ -26,6 +26,7 @@ from torch.optim import Optimizer

 from lerobot.configs import parser
 from lerobot.configs.train import TrainPipelineConfig
+from lerobot.constants import POSTPROCESSOR_DEFAULT_NAME, PREPROCESSOR_DEFAULT_NAME
 from lerobot.datasets.factory import make_dataset
 from lerobot.datasets.sampler import EpisodeAwareSampler
 from lerobot.datasets.utils import cycle
@@ -152,6 +153,10 @@ def train(cfg: TrainPipelineConfig):

    if cfg.resume:
        step, optimizer, lr_scheduler = load_training_state(cfg.checkpoint_path, optimizer, lr_scheduler)
+        preprocessor.from_pretrained(cfg.checkpoint_path, config_filename=f"{PREPROCESSOR_DEFAULT_NAME}.json")
+        postprocessor.from_pretrained(
+            cfg.checkpoint_path, config_filename=f"{POSTPROCESSOR_DEFAULT_NAME}.json"
+        )

    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())
@@ -240,7 +245,9 @@ def train(cfg: TrainPipelineConfig):
        if cfg.save_checkpoint and is_saving_step:
            logging.info(f"Checkpoint policy after step {step}")
            checkpoint_dir = get_step_checkpoint_dir(cfg.output_dir, cfg.steps, step)
-            save_checkpoint(checkpoint_dir, step, cfg, policy, optimizer, lr_scheduler, preprocessor)
+            save_checkpoint(
+                checkpoint_dir, step, cfg, policy, optimizer, lr_scheduler, preprocessor, postprocessor
+            )
            update_last_checkpoint(checkpoint_dir)
            if wandb_logger:
                wandb_logger.log_policy(checkpoint_dir)
@@ -284,10 +291,8 @@ def train(cfg: TrainPipelineConfig):

    if cfg.policy.push_to_hub:
        policy.push_model_to_hub(cfg)
-        if preprocessor:
-            preprocessor.push_to_hub(cfg.policy.repo_id)
-        if postprocessor:
-            postprocessor.push_to_hub(cfg.policy.repo_id)
+        preprocessor.push_to_hub(cfg.policy.repo_id)
+        postprocessor.push_to_hub(cfg.policy.repo_id)


 def main():
@@ -0,0 +1,311 @@
+#!/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.
+import logging
+import time
+from contextlib import nullcontext
+from pprint import pformat
+from typing import Any, Callable
+
+import accelerate
+import torch
+from termcolor import colored
+from torch.amp import GradScaler
+from torch.optim import Optimizer
+
+from lerobot.common.datasets.factory import make_dataset
+from lerobot.common.datasets.sampler import EpisodeAwareSampler
+from lerobot.common.datasets.utils import cycle
+from lerobot.common.envs.factory import make_env
+from lerobot.common.optim.factory import make_optimizer_and_scheduler
+from lerobot.common.policies.factory import make_policy
+from lerobot.common.policies.pretrained import PreTrainedPolicy
+from lerobot.common.policies.utils import get_device_from_parameters
+from lerobot.common.utils.logging_utils import AverageMeter, MetricsTracker
+from lerobot.common.utils.random_utils import set_seed
+from lerobot.common.utils.train_utils import (
+    get_step_checkpoint_dir,
+    get_step_identifier,
+    load_training_state,
+    save_checkpoint,
+    update_last_checkpoint,
+)
+from lerobot.common.utils.utils import (
+    format_big_number,
+    get_safe_torch_device,
+    has_method,
+    init_logging,
+    is_launched_with_accelerate,
+)
+from lerobot.common.utils.wandb_utils import WandBLogger
+from lerobot.configs import parser
+from lerobot.configs.train import TrainPipelineConfig
+from lerobot.scripts.eval import eval_policy
+
+
+def update_policy(
+    train_metrics: MetricsTracker,
+    policy: PreTrainedPolicy,
+    batch: Any,
+    optimizer: Optimizer,
+    grad_clip_norm: float,
+    grad_scaler: GradScaler,
+    lr_scheduler=None,
+    use_amp: bool = False,
+    lock=None,
+    accelerator: Callable = None,
+) -> tuple[MetricsTracker, dict]:
+    start_time = time.perf_counter()
+
+    policy.train()
+
+    loss, output_dict = policy.forward(batch)
+
+    accelerator.backward(loss)
+    accelerator.unscale_gradients(optimizer=optimizer)
+    grad_norm = torch.nn.utils.clip_grad_norm_(
+        policy.parameters(),
+        grad_clip_norm,
+        error_if_nonfinite=False,
+    )
+    optimizer.step()
+
+    optimizer.zero_grad()
+
+    # Step through pytorch scheduler at every batch instead of epoch
+    if lr_scheduler is not None:
+        lr_scheduler.step()
+
+    if has_method(accelerator.unwrap_model(policy, keep_fp32_wrapper=True), "update"):
+        accelerator.unwrap_model(policy, keep_fp32_wrapper=True).update()
+
+    train_metrics.loss = loss.item()
+    train_metrics.grad_norm = grad_norm.item()
+    train_metrics.lr = optimizer.param_groups[0]["lr"]
+    train_metrics.update_s = time.perf_counter() - start_time
+    return train_metrics, output_dict
+
+
+@parser.wrap()
+def train(cfg: TrainPipelineConfig, accelerator: Callable):
+    cfg.validate()
+    logging.info(pformat(cfg.to_dict()))
+
+    if accelerator.is_main_process:
+        # Disable logging on non-main processes.
+        cfg.wandb.enable = False
+
+    if cfg.wandb.enable and cfg.wandb.project:
+        wandb_logger = WandBLogger(cfg)
+    else:
+        wandb_logger = None
+        logging.info(colored("Logs will be saved locally.", "yellow", attrs=["bold"]))
+
+    if cfg.seed is not None:
+        set_seed(cfg.seed, accelerator=accelerator)
+
+    # Check device is available
+    device = get_safe_torch_device(cfg.device, log=True, accelerator=accelerator)
+    torch.backends.cudnn.benchmark = True
+    torch.backends.cuda.matmul.allow_tf32 = True
+
+    logging.info("Creating dataset")
+    dataset = make_dataset(cfg)
+
+    # Create environment used for evaluating checkpoints during training on simulation data.
+    # On real-world data, no need to create an environment as evaluations are done outside train.py,
+    # using the eval.py instead, with gym_dora environment and dora-rs.
+    eval_env = None
+    if cfg.eval_freq > 0 and cfg.env is not None:
+        logging.info("Creating env")
+        eval_env = make_env(cfg.env, n_envs=cfg.eval.batch_size)
+
+    logging.info("Creating policy")
+    policy = make_policy(
+        cfg=cfg.policy,
+        device=device,
+        ds_meta=dataset.meta,
+    )
+    policy.to(device)
+    logging.info("Creating optimizer and scheduler")
+    optimizer, lr_scheduler = make_optimizer_and_scheduler(cfg, policy)
+    grad_scaler = GradScaler(device, enabled=cfg.use_amp)
+
+    step = 0  # number of policy updates (forward + backward + optim)
+
+    if cfg.resume:
+        step, optimizer, lr_scheduler = load_training_state(cfg.checkpoint_path, optimizer, lr_scheduler)
+
+    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())
+    if accelerator.is_main_process:
+        logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {cfg.output_dir}")
+        if cfg.env is not None:
+            logging.info(f"{cfg.env.task=}")
+        logging.info(f"{cfg.steps=} ({format_big_number(cfg.steps)})")
+        logging.info(f"{dataset.num_frames=} ({format_big_number(dataset.num_frames)})")
+        logging.info(f"{dataset.num_episodes=}")
+        logging.info(f"{num_learnable_params=} ({format_big_number(num_learnable_params)})")
+        logging.info(f"{num_total_params=} ({format_big_number(num_total_params)})")
+
+    # create dataloader for offline training
+    if hasattr(cfg.policy, "drop_n_last_frames"):
+        shuffle = False
+        sampler = EpisodeAwareSampler(
+            dataset.episode_data_index,
+            drop_n_last_frames=cfg.policy.drop_n_last_frames,
+            shuffle=True,
+        )
+    else:
+        shuffle = True
+        sampler = None
+
+    dataloader = torch.utils.data.DataLoader(
+        dataset,
+        num_workers=cfg.num_workers,
+        batch_size=cfg.batch_size,
+        shuffle=shuffle,
+        sampler=sampler,
+        pin_memory=device.type != "cpu",
+        drop_last=False,
+    )
+
+    policy, optimizer, dataloader, lr_scheduler = accelerator.prepare(
+        policy, optimizer, dataloader, lr_scheduler
+    )
+
+    dl_iter = cycle(dataloader)
+
+    policy.train()
+
+    train_metrics = {
+        "loss": AverageMeter("loss", ":.3f"),
+        "grad_norm": AverageMeter("grdn", ":.3f"),
+        "lr": AverageMeter("lr", ":0.1e"),
+        "update_s": AverageMeter("updt_s", ":.3f"),
+        "dataloading_s": AverageMeter("data_s", ":.3f"),
+    }
+
+    train_tracker = MetricsTracker(
+        cfg.batch_size,
+        dataset.num_frames,
+        dataset.num_episodes,
+        train_metrics,
+        initial_step=step,
+        accelerator=accelerator,
+    )
+    if accelerator.is_main_process:
+        logging.info("Start offline training on a fixed dataset")
+
+    for _ in range(step, cfg.steps):
+        start_time = time.perf_counter()
+        batch = next(dl_iter)
+        train_tracker.dataloading_s = time.perf_counter() - start_time
+
+        train_tracker, output_dict = update_policy(
+            train_tracker,
+            policy,
+            batch,
+            optimizer,
+            cfg.optimizer.grad_clip_norm,
+            grad_scaler=grad_scaler,
+            lr_scheduler=lr_scheduler,
+            use_amp=cfg.use_amp,
+            accelerator=accelerator,
+        )
+
+        # Note: eval and checkpoint happens *after* the `step`th training update has completed, so we
+        # increment `step` here.
+        step += 1
+        train_tracker.step()
+        is_log_step = cfg.log_freq > 0 and step % cfg.log_freq == 0 and accelerator.is_main_process
+        is_saving_step = step % cfg.save_freq == 0 or step == cfg.steps and accelerator.is_main_process
+        is_eval_step = cfg.eval_freq > 0 and step % cfg.eval_freq == 0 and accelerator.is_main_process
+
+        if is_log_step:
+            logging.info(train_tracker)
+            if wandb_logger:
+                wandb_log_dict = train_tracker.to_dict()
+                if output_dict:
+                    wandb_log_dict.update(output_dict)
+                wandb_logger.log_dict(wandb_log_dict, step)
+            train_tracker.reset_averages()
+
+        if cfg.save_checkpoint and is_saving_step:
+            logging.info(f"Checkpoint policy after step {step}")
+            checkpoint_dir = get_step_checkpoint_dir(cfg.output_dir, cfg.steps, step)
+            save_checkpoint(
+                checkpoint_dir,
+                step,
+                cfg,
+                accelerator.unwrap_model(policy),
+                optimizer,
+                lr_scheduler,
+            )
+            update_last_checkpoint(checkpoint_dir)
+            if wandb_logger:
+                wandb_logger.log_policy(checkpoint_dir)
+
+        accelerator.wait_for_everyone()
+
+        if cfg.env and is_eval_step:
+            step_id = get_step_identifier(step, cfg.steps)
+            logging.info(f"Eval policy at step {step}")
+
+            with torch.no_grad():
+                eval_info = eval_policy(
+                    env=eval_env,
+                    policy=accelerator.unwrap_model(policy),
+                    n_episodes=cfg.eval.n_episodes,
+                    videos_dir=cfg.output_dir / "eval" / f"videos_step_{step_id}",
+                    max_episodes_rendered=4,
+                    start_seed=cfg.seed,
+                )
+
+            eval_metrics = {
+                "avg_sum_reward": AverageMeter("∑rwrd", ":.3f"),
+                "pc_success": AverageMeter("success", ":.1f"),
+                "eval_s": AverageMeter("eval_s", ":.3f"),
+            }
+            eval_tracker = MetricsTracker(
+                cfg.batch_size,
+                dataset.num_frames,
+                dataset.num_episodes,
+                eval_metrics,
+                initial_step=step,
+                accelerator=None,
+            )
+            eval_tracker.eval_s = eval_info["aggregated"].pop("eval_s")
+            eval_tracker.avg_sum_reward = eval_info["aggregated"].pop("avg_sum_reward")
+            eval_tracker.pc_success = eval_info["aggregated"].pop("pc_success")
+            logging.info(eval_tracker)
+            if wandb_logger:
+                wandb_log_dict = {**eval_tracker.to_dict(), **eval_info}
+                wandb_logger.log_dict(wandb_log_dict, step, mode="eval")
+                wandb_logger.log_video(eval_info["video_paths"][0], step, mode="eval")
+
+    if eval_env:
+        eval_env.close()
+    if not accelerator or accelerator.is_main_process:
+        logging.info("End of training")
+
+
+if __name__ == "__main__":
+    init_logging()
+
+    # We set step_scheduler_with_optimizer False to prevent accelerate from
+    # adjusting the lr_scheduler steps based on the num_processes
+    accelerator = accelerate.Accelerator(step_scheduler_with_optimizer=False)
+    train(accelerator=accelerator)
@@ -18,7 +18,7 @@ Helper to set motor ids and baudrate.
 Example:

 ```shell
-python -m lerobot.setup_motors \
+lerobot-setup-motors \
    --teleop.type=so100_leader \
    --teleop.port=/dev/tty.usbmodem575E0031751
 ```
@@ -18,7 +18,7 @@ Simple script to control a robot from teleoperation.
 Example:

 ```shell
-python -m lerobot.teleoperate \
+lerobot-teleoperate \
    --robot.type=so101_follower \
    --robot.port=/dev/tty.usbmodem58760431541 \
    --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \
@@ -32,7 +32,7 @@ python -m lerobot.teleoperate \
 Example teleoperation with bimanual so100:

 ```shell
-python -m lerobot.teleoperate \
+lerobot-teleoperate \
  --robot.type=bi_so100_follower \
  --robot.left_arm_port=/dev/tty.usbmodem5A460851411 \
  --robot.right_arm_port=/dev/tty.usbmodem5A460812391 \
@@ -44,7 +44,7 @@ Below is the short version on how to train and run inference/eval:
 ### Train from scratch

 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
  --dataset.repo_id=${HF_USER}/<dataset> \
  --policy.type=act \
  --output_dir=outputs/train/<desired_policy_repo_id> \
@@ -59,7 +59,7 @@ _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
 ### Evaluate the policy/run inference

 ```bash
-python -m lerobot.record \
+lerobot-record \
  --robot.type=so100_follower \
  --dataset.repo_id=<hf_user>/eval_<dataset> \
  --policy.path=<hf_user>/<desired_policy_repo_id> \
@@ -13,7 +13,7 @@
 # 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.
-from typing import Any
+from typing import Any, Callable

 from lerobot.utils.utils import format_big_number

@@ -84,6 +84,7 @@ class MetricsTracker:
        "samples",
        "episodes",
        "epochs",
+        "accelerator",
    ]

    def __init__(
@@ -93,12 +94,14 @@ class MetricsTracker:
        num_episodes: int,
        metrics: dict[str, AverageMeter],
        initial_step: int = 0,
+        accelerator: Callable | None = None,
    ):
        self.__dict__.update(dict.fromkeys(self.__keys__))
        self._batch_size = batch_size
        self._num_frames = num_frames
        self._avg_samples_per_ep = num_frames / num_episodes
        self.metrics = metrics
+        self.accelerator = accelerator

        self.steps = initial_step
        # A sample is an (observation,action) pair, where observation and action
@@ -128,7 +131,7 @@ class MetricsTracker:
        Updates metrics that depend on 'step' for one step.
        """
        self.steps += 1
-        self.samples += self._batch_size
+        self.samples += self._batch_size * (self.accelerator.num_processes if self.accelerator else 1)
        self.episodes = self.samples / self._avg_samples_per_ep
        self.epochs = self.samples / self._num_frames

@@ -17,7 +17,7 @@ import random
 from collections.abc import Generator
 from contextlib import contextmanager
 from pathlib import Path
-from typing import Any
+from typing import Any, Callable, Generator

 import numpy as np
 import torch
@@ -164,7 +164,7 @@ 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: int, accelerator: Callable | None = None) -> None:
    """Set seed for reproducibility."""
    random.seed(seed)
    np.random.seed(seed)
@@ -172,6 +172,11 @@ def set_seed(seed) -> None:
    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]:
@@ -17,10 +17,9 @@ import time


 def busy_wait(seconds):
-    if platform.system() == "Darwin":
-        # On Mac, `time.sleep` is not accurate and we need to use this while loop trick,
+    if platform.system() == "Darwin" or platform.system() == "Windows":
+        # On Mac and Windows, `time.sleep` is not accurate and we need to use this while loop trick,
        # but it consumes CPU cycles.
-        # TODO(rcadene): find an alternative: from python 11, time.sleep is precise
        end_time = time.perf_counter() + seconds
        while time.perf_counter() < end_time:
            pass
@@ -32,6 +32,7 @@ from lerobot.datasets.utils import load_json, write_json
 from lerobot.optim.optimizers import load_optimizer_state, save_optimizer_state
 from lerobot.optim.schedulers import load_scheduler_state, save_scheduler_state
 from lerobot.policies.pretrained import PreTrainedPolicy
+from lerobot.processor.pipeline import RobotProcessor
 from lerobot.utils.random_utils import load_rng_state, save_rng_state


@@ -74,7 +75,8 @@ def save_checkpoint(
    policy: PreTrainedPolicy,
    optimizer: Optimizer,
    scheduler: LRScheduler | None = None,
-    preprocessor=None,
+    preprocessor: RobotProcessor | None = None,
+    postprocessor: RobotProcessor | None = None,
 ) -> None:
    """This function creates the following directory structure:

@@ -105,6 +107,8 @@ def save_checkpoint(
    cfg.save_pretrained(pretrained_dir)
    if preprocessor is not None:
        preprocessor.save_pretrained(pretrained_dir)
+    if postprocessor is not None:
+        postprocessor.save_pretrained(pretrained_dir)
    save_training_state(checkpoint_dir, step, optimizer, scheduler)


@@ -24,6 +24,7 @@ import time
 from copy import copy, deepcopy
 from datetime import datetime, timezone
 from pathlib import Path
+from typing import Callable
 from statistics import mean

 import numpy as np
@@ -56,13 +57,15 @@ def auto_select_torch_device() -> torch.device:


 # TODO(Steven): Remove log. log shouldn't be an argument, this should be handled by the logger level
-def get_safe_torch_device(try_device: str, log: bool = False) -> torch.device:
+def get_safe_torch_device(
+    try_device: str, log: bool = False, accelerator: Callable | None = None
+) -> torch.device:
    """Given a string, return a torch.device with checks on whether the device is available."""
    try_device = str(try_device)
    match try_device:
        case "cuda":
            assert torch.cuda.is_available()
-            device = torch.device("cuda")
+            device = accelerator.device if accelerator else torch.device("cuda")
        case "mps":
            assert torch.backends.mps.is_available()
            device = torch.device("mps")
@@ -116,6 +119,7 @@ def init_logging(
    display_pid: bool = False,
    console_level: str = "INFO",
    file_level: str = "DEBUG",
+    accelerator: Callable | None = None,
 ):
    def custom_format(record: logging.LogRecord) -> str:
        dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
@@ -152,6 +156,11 @@ def init_logging(
        file_handler.setLevel(file_level.upper())
        logger.addHandler(file_handler)

+    if accelerator is not None and not accelerator.is_main_process:
+        # Disable duplicate logging on non-main processes
+        logging.info(f"Setting logging level on non-main process {accelerator.process_index} to WARNING.")
+        logging.getLogger().setLevel(logging.WARNING)
+

 def format_big_number(num, precision=0):
    suffixes = ["", "K", "M", "B", "T", "Q"]
@@ -165,6 +174,10 @@ def format_big_number(num, precision=0):
    return num


+def is_launched_with_accelerate() -> bool:
+    return "ACCELERATE_MIXED_PRECISION" in os.environ
+
+
 def _relative_path_between(path1: Path, path2: Path) -> Path:
    """Returns path1 relative to path2."""
    path1 = path1.absolute()
Author	SHA1	Message	Date
AdilZouitine	37103baa07	feat(accelerate): port multi gpu training Co-authored-by: mshukor <mustafa.shukor97@gmail.com>	2025-08-12 16:10:20 +02:00
AdilZouitine	35c5d43255	chore(processor): Add default names for preprocessor and postprocessor in constants - Introduced `PREPROCESSOR_DEFAULT_NAME` and `POSTPROCESSOR_DEFAULT_NAME` constants for consistent naming across various processor implementations. - Updated processor creation in multiple policy files to utilize these constants, enhancing code readability and maintainability. - Modified the training script to load and save the preprocessor and postprocessor using the new constants.	2025-08-11 18:00:25 +02:00
Steven Palma	95c1e32aa5	Merge branch 'main' into user/azouitine/2025-7-4-convert-codebase-with-pipeline	2025-08-11 13:56:03 +02:00
Steven Palma	0878c6880f	fix(ci): inverted names (#1705 )	2025-08-09 00:21:42 +02:00
Caroline Pascal	11e6bd762a	fix(busy_wait): fix busy_wait implementation for Windows platforms and removing erronous TODO (#1695 )	2025-08-08 10:46:14 +02:00
Steven Palma	ce3b9f627e	chore(docs): prioritize use of entry points in docs + fix nightly badge (#1692 ) * chore(docs): fix typo in nightly badge * chore(docs): prioritize the use of entrypoints for consistency	2025-08-07 14:25:44 +02:00