Databricks AI Runtime (air) CLI

Databricks AI Runtime (air) is a CLI tool for submitting GPU training workloads to Databricks serverless compute. It manages environment setup, distributed training configuration, and workload lifecycle — without requiring you to manage clusters or infrastructure.

A typical workload YAML looks like:

experiment_name: my-training-job
compute:
  num_accelerators: 1
  accelerator_type: GPU_1xA10
environment:
  dependencies:
    - mlflow
  version: "AI5"
command: echo "Hello World"

Submit with air run --file workload.yaml -p <databricks_config_profile>.

Bring your own custom Docker images

Use a custom Docker image instead of environment.version when your workload needs specific system libraries, CUDA extensions (flash-attn, apex, custom kernels), or dependencies that don't fit environment.dependencies.

Read docker-images.md for step-by-step guidance on:

• Using Databricks-provided base images
• Dockerfile patterns
• Pre-build compatibility checklist (CUDA/driver, PyTorch, NCCL, EFA/RDMA)
• Registering images with air register image