Overview
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What is Serverless GPU API?
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Serverless GPU API is a light-weight, intuitive library for launching multi-GPU workloads from Databricks notebooks. 
It's designed to make distributed computing on Databricks simple and accessible.

Key Features
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* **Easy 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

Architecture
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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

Use Cases
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Serverless GPU API is ideal for:

* Machine learning model training at scale
* Distributed data processing
* GPU-accelerated computations
* Research and experimentation workflows

Distributed Execution Details
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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

Best Practices
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* Always specify gpu_type when using remote=True
* Use async execution for non-blocking workloads like sweeps

Limitations
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* Pip environment size is limited to 10GB.