Merk
Tilgang til denne siden krever autorisasjon. Du kan prøve å logge på eller endre kataloger.
Tilgang til denne siden krever autorisasjon. Du kan prøve å endre kataloger.
Important
This feature is in Public Preview.
AI Runtime is a serverless GPU compute offering at Databricks intended for deep learning workloads. You can use AI Runtime to train and fine-tune custom models using your favorite frameworks and get state-of-the-art efficiency, performance, and quality.
Get started
Run your first workload in minutes using our quickstart guides.
| Get started | Description |
|---|---|
| 🚀 Databricks CLI quickstart | Coming from a Slurm or Kubernetes cluster? Train on AI Runtime from your terminal in minutes. |
| 📓 Notebook quickstart | Attach your notebook to a GPU in seconds and develop interactively. |
| ⚡ Ray | Coming from a Ray cluster? Run Ray on AI Runtime with dashboard support. |
| 🧭 Overview | Learn the key things about AI Runtime in two minutes: available GPUs, how it works, and limitations. |
Features
AI Runtime is a full-stack GPU platform, with many ways to connect to GPUs, built-in observability and debugging tools, and pre-built environments.
Connect to Serverless GPUs: Reach serverless GPUs from wherever you work.
| Feature | Description |
|---|---|
| Notebooks | Attach a notebook to serverless GPU compute and develop interactively, with no cluster setup. |
| IDE over SSH | Connect from your IDE or terminal over an SSH tunnel to work on a GPU node directly. |
| Databricks CLI | Submit and manage distributed GPU training jobs from your laptop using YAML job configs. |
| AI agents | Use the databricks-ai-runtime agent skill to submit, monitor, and manage GPU training jobs from your AI agent. |
| Ray | Run Ray Core, Ray Data, Ray Train, and Ray Tune on serverless GPU compute. |
Managing Dependencies: Control the Python and system libraries your workload runs with.
| Feature | Description |
|---|---|
| Pre-built environments | Start from a minimal Standard environment or a Databricks AI environment pre-loaded with ML frameworks. |
Loading data into GPUs: Feed training data to your GPUs efficiently.
| Feature | Description |
|---|---|
| Efficient data loaders | Stream data from Unity Catalog volumes with fault-tolerant loaders built for high GPU utilization. |
Debugging and Observability: Track experiments, inspect output, and diagnose failures.
| Feature | Description |
|---|---|
| MLflow for deep learning | Track experiments, metrics, and runs with MLflow. |
| Logs viewer | View training output and monitor GPU resource usage while your code runs. |
| Debugging with agents | Use Genie Code to generate training code, resolve environment issues, and debug GPU failures. |
Scheduling and real-time serving: Move from interactive development to scheduled jobs and endpoints.
| Feature | Description |
|---|---|
| Schedule GPU workloads | Deploy training code with Declarative Automation Bundles, schedule runs, and build multi-task GPU and CPU workflows. |
| Serve your models | Deploy your trained models behind a scalable endpoint with Model Serving. |
Examples
Clone end-to-end examples and run them on AI Runtime in minutes, from the CLI or in notebooks.
| Example | Description |
|---|---|
| Classic machine learning | GPU-accelerated XGBoost, zero-shot tabular prediction, and time series forecasting. |
| Recommendation systems | Train deep-learning recommender models, such as two-tower architectures. |
| Computer vision | Object detection and image classification workloads on GPUs. |
| Post-training OSS models (LLMs) | Fine-tune and post-train open-source LLMs with LoRA, full fine-tuning, and libraries like TRL, Unsloth, and Axolotl. |
| Batch inference | Run large-scale batch inference with Ray Data and vLLM across multiple GPUs. |
| Multi-GPU distributed training | Scale training across multiple GPUs and nodes with DDP, FSDP, and DeepSpeed. |
Guides
Task-focused guides for migrating workloads to AI Runtime and getting the most out of your GPUs.
| Guide | Description |
|---|---|
| Migrate from Slurm | Map your Slurm batch scripts, distributed launchers, and shared storage onto the Databricks CLI. |
| Migrate classic GPU workloads | Move an existing deep learning workload from a classic Databricks cluster to AI Runtime. |
| Performance and resiliency | Improve training throughput and make long-running jobs more resilient to failures. |
Additional resources
Find helpful content: the latest product updates and how AI Runtime works under the hood.
| Resource | Description |
|---|---|
| Product updates | See the latest AI Runtime product updates and announcements. |
| How AI Runtime works under the hood | Read how Databricks keeps GPUs reliable across AI Runtime. |