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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.
| Get started | Description |
|---|---|
| 🚀 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.
| Feature | Description |
|---|---|
| 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.
| Feature | Description |
|---|---|
| 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.
| Feature | Description |
|---|---|
| MLflow for debugging | Track experiments, metrics, and runs with MLflow. |
| 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. |
| Serve trained models | Deploy models trained on AI Runtime to Model Serving. |
Guides
Explore migration guides, tutorials and technical resources for AI Runtime.
| Resource | Description |
|---|---|
| Why use AI Runtime | Compare AI Runtime with classic compute and Databricks Runtime ML for cost, developer experience, and scale. |
| Migrate classic GPU workloads | Move an existing deep learning workload from a classic Azure Databricks cluster to AI Runtime. |
| Optimize your workload performance | Improve training throughput and make long-running jobs more resilient to failures. |