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Important
The AI Runtime CLI is in Beta.
This page walks through submitting your first training job with the AI Runtime CLI. Before starting, install the CLI and configure authentication.
Step 1: Write a YAML config
Create train.yaml describing the workload. The minimal config requires an experiment name, an environment, a compute spec, and a command:
experiment_name: my-first-air-run
compute:
num_accelerators: 1
accelerator_type: GPU_1xA10
command: echo "hello AIR!"
For the full field reference, see Workload YAML reference.
Step 2: Submit the run
Submit the workload:
air run --file train.yaml
The CLI uploads your local code, submits the job, and prints a run ID.
To watch logs until completion, add --watch:
air run --file train.yaml --watch
Step 3: Inspect the run
Check status:
air get run <run-id>
Stream or download logs:
air logs <run-id>
air logs <run-id> --node 2
air logs <run-id> --download-only ./logs/
List recent runs:
air list runs --limit 10
air list runs --active
Cancel a run:
air cancel <run-id>
Common patterns
Override YAML fields from the command line:
air run --file train.yaml --override compute.num_accelerators=32 timeout_minutes=120
Validate the config without submitting:
air run --file train.yaml --dry-run
Make a submission safely retryable:
air run --file train.yaml --idempotency-key my-unique-key
If the same key has been used before, the existing run is returned instead of creating a new one.