Script Examples

Detailed submission examples for training, inference, and pipeline workflows on OSMO and Azure ML platforms.

[!NOTE] For CLI argument reference and script inventory, see Script Reference.

OSMO Dataset Training

The submit-osmo-dataset-training.sh script uploads training/rl/ as a versioned OSMO dataset and enables dataset reuse across runs.

Dataset Submission Example

# Default dataset configuration
./submit-osmo-dataset-training.sh --task Isaac-Velocity-Rough-Anymal-C-v0

# Custom dataset bucket and name
./submit-osmo-dataset-training.sh \
  --dataset-bucket custom-bucket \
  --dataset-name my-training-v1 \
  --task Isaac-Velocity-Rough-Anymal-C-v0

# With checkpoint resume
./submit-osmo-dataset-training.sh \
  --task Isaac-Velocity-Rough-Anymal-C-v0 \
  --checkpoint-uri "runs:/abc123/checkpoint" \
  --checkpoint-mode resume

Dataset Parameters

Parameter	Default	Description
--dataset-bucket	training	OSMO bucket for training code
--dataset-name	training-code	Dataset name (auto-versioned)
--training-path	training/rl	Local folder to upload

The script stages files to exclude __pycache__ and build artifacts via .amlignore patterns before upload.

LeRobot Behavioral Cloning

The submit-osmo-lerobot-training.sh script submits LeRobot training workflows supporting ACT and Diffusion policy architectures. It trains from HuggingFace Hub datasets or Azure Blob datasets and installs runtime dependencies exported at build time from training/il/lerobot/uv.lock.

LeRobot Submission Examples

# ACT policy with default MLflow tracking
./submit-osmo-lerobot-training.sh -d user/my-dataset

# Diffusion policy with Azure MLflow
./submit-osmo-lerobot-training.sh \
  -d user/my-dataset \
  -p diffusion \
  -r my-model-name

# Train from Azure Blob Storage
./submit-osmo-lerobot-training.sh \
  --blob-url https://account.blob.core.windows.net/datasets/pusht \
  -r pusht-model

# Fine-tune from pre-trained policy
./submit-osmo-lerobot-training.sh \
  -d user/my-dataset \
  --policy-repo-id user/pretrained-act \
  --training-steps 50000 \
  --batch-size 16

LeRobot Parameters

Parameter	Default	Description
--dataset-repo-id	Required for HuggingFace; dataset for Blob sources	HuggingFace dataset repository ID or logical local dataset name
--blob-url	(none)	Direct Azure Blob dataset URL; repeatable
--policy-type	act	Policy: act, diffusion
--job-name	lerobot-act-training	Job identifier
--policy-repo-id	(none)	Pre-trained policy for fine-tuning
--training-steps	100000	Total training iterations
--batch-size	32	Training batch size
--learning-rate	1e-4	Optimizer learning rate
--save-freq	5000	Checkpoint save frequency

LeRobot Inference

The submit-osmo-lerobot-inference.sh script evaluates trained LeRobot policies from HuggingFace Hub. Downloads the policy, runs evaluation, and optionally registers the model to Azure ML.

LeRobot Inference Examples

# Evaluate a trained policy
./submit-osmo-lerobot-inference.sh --policy-repo-id user/trained-act-policy

# Evaluate with model registration
./submit-osmo-lerobot-inference.sh \
  --policy-repo-id user/trained-act-policy \
  -r my-evaluated-model

# Diffusion policy evaluation
./submit-osmo-lerobot-inference.sh \
  --policy-repo-id user/trained-diffusion \
  -p diffusion \
  --eval-episodes 50

Inference Parameters

Parameter	Default	Description
--policy-repo-id	(required)	HuggingFace policy repository
--policy-type	act	Policy: act, diffusion
--eval-episodes	10	Number of evaluation episodes
--register-model	(none)	Model name for Azure ML registration
--dataset-repo-id	(none)	Dataset for environment replay

AzureML LeRobot Training

The submit-azureml-lerobot-training.sh script submits LeRobot training directly to Azure ML instead of OSMO. It registers an environment, compiles runtime dependencies from training/il/lerobot/pyproject.toml, and submits via az ml job create.

AzureML LeRobot Examples

# ACT policy training
./submit-azureml-lerobot-training.sh -d user/my-dataset

# With model registration and log streaming
./submit-azureml-lerobot-training.sh \
  -d user/my-dataset \
  -r my-act-model \
  --stream

# Custom environment and compute
./submit-azureml-lerobot-training.sh \
  -d user/my-dataset \
  --image custom-registry.io/lerobot:latest \
  --compute my-gpu-cluster

End-to-End Pipeline

The run-lerobot-pipeline.sh script orchestrates the full LeRobot lifecycle: training → polling → inference → model registration. It delegates to the individual submission scripts and polls OSMO workflow status between stages.

Pipeline Stages

Stage	Action	Script Used
1	Submit training workflow	submit-osmo-lerobot-training.sh
2	Poll workflow status until completion	osmo workflow query
3	Submit inference/evaluation workflow	submit-osmo-lerobot-inference.sh

Pipeline Examples

# Full pipeline: train → evaluate → register
./run-lerobot-pipeline.sh \
  -d lerobot/aloha_sim_insertion_human \
  --policy-repo-id user/my-act-policy \
  -r my-act-model

# Async mode (submit training and exit)
./run-lerobot-pipeline.sh \
  -d user/my-dataset \
  --skip-wait

# Diffusion pipeline
./run-lerobot-pipeline.sh \
  -d user/my-dataset \
  --policy-repo-id user/my-diffusion \
  -p diffusion \
  --training-steps 100000 \
  -r my-diffusion-model

# Skip inference (training only with polling)
./run-lerobot-pipeline.sh \
  -d user/my-dataset \
  --skip-inference

Pipeline Parameters

Parameter	Default	Description
--dataset-repo-id	(required)	HuggingFace dataset repository
--policy-repo-id	(required*)	HuggingFace policy target repo
--policy-type	act	Policy: act, diffusion
--register-model	(none)	Azure ML model registration name
--poll-interval	60	Status check interval (seconds)
--timeout	720	Training timeout (minutes)
--skip-wait	disabled	Async mode: submit and exit
--skip-inference	disabled	Skip inference stage

Related Documentation

• Script Reference for CLI arguments and script inventory
• Reference Hub for all reference documentation

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