Serverless GPU API is a light-weight, intuitive library for launching multi-GPU workloads from Databricks notebooks. Itâs designed to make distributed computing on Databricks simple and accessible.
Key FeaturesïEasy Integration: Works seamlessly with Databricks notebooks
Multi-GPU Support: Efficiently utilize multiple GPUs for your workloads
Flexible Configuration: Customizable compute resources and runtime settings
Comprehensive Logging: Built-in logging and monitoring capabilities
Serverless GPU API consists of several key components:
Compute Manager: Handles resource allocation and management
Runtime Environment: Manages Python environments and dependencies
Launcher: Orchestrates job execution and monitoring
Serverless GPU API is ideal for:
Machine learning model training at scale
Distributed data processing
GPU-accelerated computations
Research and experimentation workflows
When running in distributed mode:
The function is serialized and distributed across the specified number of GPUs
Each GPU runs a copy of the function with the same parameters
The environment is synchronized across all nodes
Results are collected and returned from all GPUs
Always specify gpu_type when using remote=True
Use async execution for non-blocking workloads like sweeps
Pip environment size is limited to 10GB.
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