---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: f6081aa31b3038b84a8ced06ca1b9d7bea84d531b75240f8dd9bf9c872008e9f
  pageDirectory: concepts
  sources:
    - ai-runtime-cli-command-reference-databricks-on-aws.md
  confidence: 1
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - air-logs-command
    - ALC
    - air logs
  citations:
    - file: ai-runtime-cli-command-reference-databricks-on-aws.md
    - file: ai-runtime-cli-quickstart-databricks-on-aws.md
    - file: track-runs-with-mlflow-and-the-jobs-run-page-databricks-on-aws.md
title: air logs command
description: Streams or downloads logs for a run, with targeting of specific nodes via --node and directory download via --download-to
tags:
  - cli
  - logging
  - debugging
timestamp: "2026-06-19T17:29:44.456Z"
---

# air logs command

The `air logs` command streams or downloads logs for a run submitted to [AI Runtime](/concepts/ai-runtime.md). It is part of the [AI Runtime CLI](/concepts/ai-runtime-cli.md) (Beta), which provides a command-line interface for managing AI workloads on Databricks. ^[ai-runtime-cli-command-reference-databricks-on-aws.md]

## Syntax

```bash
air logs [flags]
```

## Flags

| Flag | Purpose |
| --- | --- |
| `--node` | Target logs for a specific compute node in a distributed workload. |
| `--download-to` | Write logs to a local directory instead of streaming them to the terminal. |

^[ai-runtime-cli-command-reference-databricks-on-aws.md]

## Description

The `air logs` command retrieves the output generated by a workload run. By default, logs are streamed from node 0 (the first compute node). For distributed workloads that run across multiple nodes, use the `--node` flag to view logs from a particular node. Use `--download-to` to save logs to a local directory for offline inspection instead of streaming them to the terminal. ^[ai-runtime-cli-quickstart-databricks-on-aws.md]

The run ID required as an argument can be obtained from `air run` (which prints the ID) or from `air list runs`. You can also obtain it from the Databricks Jobs page. ^[ai-runtime-cli-quickstart-databricks-on-aws.md, track-runs-with-mlflow-and-the-jobs-run-page-databricks-on-aws.md]

Logs retrieved via `air logs` are also available as artifacts on the corresponding [MLflow Run](/concepts/mlflow-run.md). The workload submission creates both a Databricks job run and an [MLflow Run](/concepts/mlflow-run.md); the [MLflow Run](/concepts/mlflow-run.md) stores logs, system metrics, and custom metrics. ^[track-runs-with-mlflow-and-the-jobs-run-page-databricks-on-aws.md]

## Related Commands

| Command | Purpose |
| --- | --- |
| `air run` | Submit a workload defined by a YAML file; use `--watch` to stream logs until completion. |
| `air get run` | Show metadata, status, and configuration for a specific run. |
| `air list runs` | List recent runs, using `--limit` and `--active` filters. |
| `air cancel` | Cancel a running workload. |

^[ai-runtime-cli-command-reference-databricks-on-aws.md]

## See Also

- AI Runtime CLI quickstart
- Workload YAML reference
- Track runs with MLflow and the Jobs run page

^[ai-runtime-cli-command-reference-databricks-on-aws.md]

## Sources

- ai-runtime-cli-command-reference-databricks-on-aws.md
- ai-runtime-cli-quickstart-databricks-on-aws.md
- track-runs-with-mlflow-and-the-jobs-run-page-databricks-on-aws.md

# Citations

1. [ai-runtime-cli-command-reference-databricks-on-aws.md](/references/ai-runtime-cli-command-reference-databricks-on-aws-a236d04f.md)
2. [ai-runtime-cli-quickstart-databricks-on-aws.md](/references/ai-runtime-cli-quickstart-databricks-on-aws-87563c23.md)
3. [track-runs-with-mlflow-and-the-jobs-run-page-databricks-on-aws.md](/references/track-runs-with-mlflow-and-the-jobs-run-page-databricks-on-aws-f3444863.md)
