Skip to main content
Last updated on

AI Runtime

AI Runtime is a fully managed, serverless GPU compute offering at Databricks for your training, fine-tuning, and broader deep learning workloads.

  • Serverless GPUs: On-demand A10s and H100s, accessible through the Databricks CLI, notebooks, jobs, and Ray.
  • Managed environments: pre-built environments and dataloaders ready for training and fine-tuning, or bring your own Docker container.
  • Built-in observability: experiment tracking via MLflow, logging, and debugging agents.
  • Orchestration: schedule your GPU workloads and use them in CI/CD pipelines.

Get started​

Learn about AI Runtime and run your first workload.

    • 🚀 Quickstart
    • Run your first workload on AI Runtime from your terminal.
    • 🧭 Overview
    • Learn the key things about AI Runtime: what GPUs are available, how it works, and limitations.
    • 💡 Examples
    • Browse notebook tutorials and runnable Databricks CLI recipes organized by task.

Connect to GPUs​

Access GPUs through the Databricks CLI, notebooks, Jobs, or via Ray.

    • Databricks CLI
    • Submit and manage distributed GPU training jobs from your laptop using YAML job configs.
    • Notebooks
    • Attach a notebook to AI Runtime and develop interactively, with no cluster setup.
    • Jobs
    • Schedule a GPU workload on AI Runtime as a recurring job and orchestrate it with upstream and downstream tasks.
    • Ray
    • Run Ray Core, Ray Data, Ray Train, and Ray Tune on AI Runtime.

Develop workloads​

Load data efficiently, configure your environment, and develop workloads with coding agents.

    • Load data into GPUs
    • Stream data from Unity Catalog volumes with fault-tolerant loaders built for high GPU utilization.
    • Pre-built environments
    • Start from a minimal Standard environment or a Databricks AI environment pre-loaded with ML frameworks.
    • Custom Docker images
    • Bring your own Docker image when your workload requires custom system libraries or dependencies.
    • Coding agents
    • Use Genie Code and the databricks-ai-runtime agent skill to create, submit, and manage GPU workloads.

Monitor, debug, and deploy​

Track experiments, inspect logs, diagnose failures, and schedule production workloads.

    • View logs
    • View training output, monitor GPU resource usage while your code runs, and debug with Genie Code.
    • Schedule workloads
    • Deploy training code with Declarative Automation Bundles, schedule runs, and build multi-task GPU and CPU workflows.

Guides​

Explore migration guides, tutorials and technical resources for AI Runtime.

On this page