Python CLI for AI Runtime (Deprecated)
This documentation has been retired and might not be updated.
The Python-based air CLI, installed with the databricks-air package, is now deprecated and is no longer actively maintained.
Use the Databricks CLI for new workloads. See Use the Databricks CLI with AI Runtime.
The air command-line interface submits and manages distributed training workloads on AI Runtime, the on-demand serverless GPU compute platform. The CLI uses YAML-based job configuration, integrates with MLflow, and supports workspace-based and git-based code workflows.
air CLI is compliant with the same compliance standards as AI Runtime. For the supported standards and exceptions, see compliance security profile.
When to use the CLI
Use the AI Runtime CLI when you want to:
- Submit GPU training workloads from your laptop and code editor without opening a notebook.
- Define training jobs declaratively in YAML so they can be checked into source control.
- Run long-running training jobs or multi-node distributed training — workloads that need to outlive an interactive session or span more than one node.
For the in-notebook Python API (@distributed and @ray_launch), see Distributed training in notebooks instead.
To work interactively on a single GPU node over SSH — from your terminal or IDE, rather than submitting a workload — use databricks ssh connect. See Connect to Databricks using an SSH tunnel.