admin/lerobot
1
0
Fork 0
mirror of https://github.com/huggingface/lerobot.git synced 2026-07-21 17:01:53 +00:00
Code Issues Packages Projects Releases Wiki Activity

docs(groot): update Training & hardware Evaluation commands

Browse Source 
Replace the multi-GPU accelerate-launch Training snippet with the current
single-command 'uv run lerobot-train' N1.7 recipe (relative actions excluding
gripper, bf16, flash attention, chunk/n_action_steps=16, bs64/20k steps).

Replace the bimanual 'Evaluate in your hardware setup' rollout example with the
SO-101 follower RTC 'uv run lerobot-rollout' command (strategy.type=base,
inference.type=rtc, wrist+front cameras, place-the-vial task).

Docs-only; no source/test changes.
This commit is contained in:
nv-sachdevkartik
2026-07-02 12:41:13 +00:00
parent 1ce1c01337
commit 9ec4530248
1 changed files with 40 additions and 36 deletions
Download Patch File Download Diff File Expand all files Collapse all files
docs/source/groot.mdx
+40 -36
View File
@@ -77,25 +77,31 @@ To use GR00T N1.7:
Here's a complete training command for finetuning the base GR00T model on your own dataset: Here's a complete training command for finetuning the base GR00T model on your own dataset:
```bash ```bash
# Using a multi-GPU setup uv run lerobot-train \
accelerate launch \ --dataset.repo_id=sreetz-nv/so101-clean-up-vials-into-rack-50_20260628_131121 \
--multi_gpu \ --dataset.image_transforms.enable=true \
--num_processes=$NUM_GPUS \
$(which lerobot-train) \
--output_dir=$OUTPUT_DIR \
--save_checkpoint=true \
--batch_size=$BATCH_SIZE \
--steps=$NUM_STEPS \
--save_freq=$SAVE_FREQ \
--log_freq=$LOG_FREQ \
--policy.push_to_hub=true \
--policy.type=groot \ --policy.type=groot \
--policy.repo_id=$REPO_ID \ --policy.device=cuda \
--policy.tune_diffusion_model=false \ --policy.base_model_path=nvidia/GR00T-N1.7-3B \
--dataset.repo_id=$DATASET_ID \ --policy.embodiment_tag=new_embodiment \
--wandb.enable=true \ --policy.chunk_size=16 \
--wandb.disable_artifact=true \ --policy.n_action_steps=16 \
--job_name=$JOB_NAME --policy.use_relative_actions=true \
--policy.relative_exclude_joints='["gripper"]' \
--policy.use_bf16=true \
--policy.use_flash_attention=true \
--policy.push_to_hub=true \
--policy.repo_id=sreetz-nv/so101-clean-up-vials-into-rack-50-groot-n17-relact-bs64-20k-20260628_johnny_42 \
--seed=42 \
--batch_size=64 \
--steps=20000 \
--save_checkpoint=true \
--use_policy_training_preset=true \
--env_eval_freq=0 \
--eval_steps=0 \
--log_freq=100 \
--output_dir=outputs/train/sreetz-nv/so101-clean-up-vials-into-rack-50-groot-n17-relact-bs64-20k-20260628_johnny_42 \
--job_name=so101-clean-up-vials-into-rack-50-groot-n17-relact-bs64-20k-20260628_johnny_42
``` ```
## Performance Results ## Performance Results
@@ -153,25 +159,23 @@ Use `eval.n_episodes >= 50` per suite when reporting success rates.
Once you have trained your model using your parameters you can run inference in your downstream task. Follow the instructions in [Policy Deployment (lerobot-rollout)](./inference). For example: Once you have trained your model using your parameters you can run inference in your downstream task. Follow the instructions in [Policy Deployment (lerobot-rollout)](./inference). For example:
```bash ```bash
lerobot-rollout\ uv run lerobot-rollout \
--strategy.type=sentry \ --strategy.type=base \
--strategy.upload_every_n_episodes=5 \ --inference.type=rtc \
--robot.type=bi_so_follower \ --policy.path=outputs/train/sreetz-nv/so101-clean-up-vials-into-rack-50-groot-n17-relact-bs64-20k-20260628_johnny_42/checkpoints/020000/pretrained_model/ \
--robot.left_arm_port=/dev/ttyACM1 \ --policy.base_model_path=nvidia/GR00T-N1.7-3B \
--robot.right_arm_port=/dev/ttyACM0 \ --robot.type=so101_follower \
--robot.id=bimanual_follower \ --robot.port=/dev/ttyACM0 \
--robot.cameras='{ right: {"type": "opencv", "index_or_path": 0, "width": 640, "height": 480, "fps": 30}, --robot.id=orange_andrew \
left: {"type": "opencv", "index_or_path": 2, "width": 640, "height": 480, "fps": 30}, --robot.cameras='{ wrist: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30, fourcc: "MJPG"}, front: {type: opencv, index_or_path: 2, width: 640, height: 480, fps: 30, fourcc: "MJPG"} }' \
top: {"type": "opencv", "index_or_path": 4, "width": 640, "height": 480, "fps": 30}, --task="place the vial in the rack" \
}' \ --duration=60 \
--device=cuda \
--display_data=true \ --display_data=true \
--dataset.repo_id=<user>/eval_groot-bimanual \ --inference.rtc.enabled=false \
--dataset.single_task="Grab and handover the red cube to the other arm" \ --inference.rtc.execution_horizon=8 \
--dataset.streaming_encoding=true \ --inference.queue_threshold=0 \
--dataset.encoder_threads=2 \ --policy.n_action_steps=8
# --dataset.rgb_encoder.vcodec=auto \
--policy.path=<user>/groot-bimanual \ # your trained model
--duration=600
``` ```
## License ## License