菜单
Databricks AI Runtime (`air`) CLI — the command-line tool for submitting and managing GPU training workloads on Databricks serverless compute. Use for: running `air` workloads, custom Docker image setup, environment configuration, and troubleshooting `air` jobs.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/databricks/databricks-agent-skills --skill databricks-ai-runtime命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
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正在显示 SKILL.md
| name | databricks-ai-runtime |
| description | Databricks AI Runtime (`air`) CLI — the command-line tool for submitting and managing GPU training workloads on Databricks serverless compute. Use for: running `air` workloads, custom Docker image setup, environment configuration, and troubleshooting `air` jobs. |
| compatibility | Requires databricks-air CLI. See the [installation guide](https://docs.databricks.com/aws/en/machine-learning/ai-runtime/cli/installation) to get started. |
| metadata | {"version":"0.1.0"} |
air) CLIDatabricks 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>.
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:
air register image