serverless_gpu.launcher

Main launcher module for distributed serverless GPU compute.

This module provides the core functionality for launching and managing distributed functions on serverless GPU infrastructure. It includes:

  • The distributed decorator for executing functions on remote GPU resources

  • Job submission and monitoring capabilities

  • Integration with Databricks jobs API and MLflow for tracking

  • Environment synchronization and dependency management

  • Support for multi-GPU and multi-node distributed workloads

The main entry point is the distributed function which can be used as a decorator or called directly to execute functions on serverless GPU compute resources.

Functions

distributed(gpus[, gpu_type, remote, run_async])

Decorator to launch a function on remote GPUs or local GPUs.

Classes

DistributedFunction(gpus, gpu_type, remote, ...)

serverless_gpu.launcher.distributed(gpus, gpu_type=None, remote=False, run_async=False)[source]

Decorator to launch a function on remote GPUs or local GPUs.

remote GPUs: gpus that are not attached to your notebook but you have access to local GPUs: the gpus that are attached to the notebook

Parameters

  • gpus (int, optional) – Number of GPUs to use. Must be 1, 2, 4, 8 or a multiple of 8 for remote GPUs.

  • gpu_type (Optional[Union[GPUType, str]], optional) – The GPU type to use. Defaults to None. Required if remote is True.

  • remote (bool) – Whether to run the function on remote GPUs. Defaults to False.

  • run_async (bool) – Whether to run the function asynchronously. Defaults to False.