Isaac Lab Training

Isaac Lab reinforcement learning training with SKRL and RSL-RL backends. Both Azure ML and OSMO platforms support distributed GPU training with automatic checkpointing and MLflow experiment tracking.

📋 Prerequisites

Component	Requirement
Infrastructure	AKS cluster deployed via Infrastructure Guide
Azure ML	Extension installed via 02-deploy-azureml-extension.sh
OSMO	Control plane and backend via 03-deploy-osmo.sh
Terraform outputs	Available in infrastructure/terraform/ (or provide values via CLI / environment vars)
Azure CLI	az with ml extension for Azure ML submissions
OSMO CLI	osmo CLI installed and authenticated for OSMO submissions

🚀 Quick Start

Azure ML

./scripts/submit-azureml-training.sh \
  --task Isaac-Velocity-Rough-Anymal-C-v0 \
  --num-envs 2048 \
  --stream

OSMO (Object Storage)

./scripts/submit-osmo-training.sh \
  --task Isaac-Velocity-Rough-Anymal-C-v0 \
  --num-envs 2048

OSMO (Dataset Injection)

./scripts/submit-osmo-dataset-training.sh \
  --task Isaac-Velocity-Rough-Anymal-C-v0 \
  --dataset-name my-training-v1

Dataset injection provides named, reusable dataset versions across runs.

⚖️ Platform Selection

Aspect	Azure ML	OSMO
Submission	az ml job create via YAML templates	osmo workflow submit
Orchestration	AKS compute targets	KAI Scheduler / Volcano integration
Experiment tracking	MLflow (managed)	MLflow (Azure ML backend)
Dataset delivery	Azure ML datastores	Object storage (url:) or OSMO bucket upload
Monitoring	Azure ML Studio	OSMO UI Dashboard
Payload modes	Single (YAML template)	Object storage (url:) or dataset injection

Azure ML provides managed compute and experiment tracking through Azure ML Studio. OSMO adds distributed training coordination, KAI Scheduler integration, and a dataset versioning system.

⚙️ Training Configuration

Core parameters shared across platforms:

Parameter	Default	Description
--task	Isaac-Velocity-Rough-Anymal-C-v0	Isaac Lab task identifier
--num-envs	2048	Parallel simulation environments
--max-iterations	(unset)	Training iteration limit
--image	nvcr.io/nvidia/isaac-lab:2.3.2	Container image
--backend	skrl	Training backend: skrl or rsl_rl
--headless	true	Disable rendering

Values resolve in order: CLI arguments → environment variables → Terraform outputs.

Training Backends

Backend	Algorithms	Use Case
SKRL	PPO, IPPO, MAPPO, AMP, SAC	General-purpose RL with MLflow
RSL-RL	PPO, Distillation	Locomotion-focused, teacher-student

SKRL is the default backend and supports automatic MLflow metric logging via monkey-patching. See MLflow Integration for metric details.

🔄 Checkpoint Workflows

Four checkpoint modes control how training initializes:

Mode	Behavior
from-scratch	Default. No checkpoint loaded, training starts fresh.
warm-start	Load weights only. Resets optimizer and iteration counters.
resume	Load full state. Continues from exact training position.
fresh	Load model architecture only. Reinitializes all parameters.

Checkpoint Examples

# Resume interrupted training (Azure ML)
./scripts/submit-azureml-training.sh \
  --checkpoint-uri "runs:/abc123/checkpoint" \
  --checkpoint-mode resume

# Warm-start from a registered model (OSMO)
./scripts/submit-osmo-training.sh \
  --checkpoint-uri "models:/anymal-c-velocity/1" \
  --checkpoint-mode warm-start

Model Registration

Training scripts register checkpoints to Azure ML automatically. Override the model name or skip registration:

# Custom model name
./scripts/submit-azureml-training.sh \
  --register-checkpoint my-custom-model

# Skip registration
./scripts/submit-osmo-training.sh \
  --skip-register-checkpoint

💾 Dataset Injection (OSMO)

OSMO supports two payload delivery modes for training code:

Mode	Script	Size Limit	Versioning
Object storage (url:)	submit-osmo-training.sh	Unlimited	Per submit
Dataset injection	submit-osmo-dataset-training.sh	Unlimited	Automatic

Dataset injection uploads training/rl/ as a versioned OSMO dataset, mounted at /data/<dataset_name>/training in the container:

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

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

🔗 Related Documentation

• Experiment Tracking for MLflow setup
• MLflow Integration for SKRL metric logging internals
• AzureML Workflows for job template reference
• OSMO Workflows for workflow template reference
• Scripts Reference for full CLI parameter tables

🤖 Crafted with precision by ✨Copilot following brilliant human instruction, then carefully refined by our team of discerning human reviewers.