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Azure Databricks offers flexible compute solutions tailored to different machine learning needs, ranging from managed cluster runtimes to fully serverless GPU environments.
| Compute | Description |
|---|---|
| AI Runtime | Serverless GPU compute environment optimized for custom single-node and multi-node deep learning workloads. |
| Databricks Runtime for Machine Learning | Classic compute environment with pre-built libraries for classic machine learning and deep learning workloads. |
AI Runtime (Preview)
Important
This feature is in Public Preview.
AI Runtime is a specialized offering within the Databricks serverless ecosystem. It is optimized for custom single-node and multi-node deep learning workloads, such as fine-tuning LLMs or training computer vision models. For an overview of how serverless compute fits into the Databricks architecture, see Serverless workspace architecture.
Key features include:
- Instant availability: Removes the need to manage underlying cluster infrastructure, allowing you to connect a notebook directly to serverless GPU resources.
- High-performance hardware: Provides access to A10 GPUs for cost-effective tasks.
- Managed environments: Offers a default base environment for full customization or an AI environment pre-loaded with common ML packages like Transformers and Ray.
- Flexible scaling: Supports distributed training across multiple GPUs and nodes.
Databricks Runtime for Machine Learning
Databricks Runtime for Machine Learning is a specialized runtime that automates the creation of compute resources with pre-built infrastructure. It is designed for users who want a comprehensive, ready-to-use environment for both classic machine learning and deep learning.
Key features include:
- Pre-installed libraries: Includes popular libraries like PyTorch, TensorFlow, and XGBoost, which receive frequent updates and optimized support.
- Compute versatility: Supports both CPU and GPU-based instance types, including AWS Graviton for improved price-to-performance.
- Optimization: Offers integration with Photon to accelerate Spark SQL, DataFrames, and feature engineering tasks.
- Access control: Requires dedicated access mode for secure data access through Unity Catalog.