Package: runpod
RunPod | Python LibraryWelcome to the official Python library for RunPod API & SDK.Table of ContentsTable of Contents💻 | Installation⚡ | Serverless Worker (SDK)Quick StartLocal Test Worker📚 | API Language Library (GraphQL Wrapper)EndpointsGPU Cloud (Pods)📁 | Directory🤝 | Community and Contributing💻 | Installation# Install the latest release versionpipinstallrunpod# or# Install the latest development version (main branch)pipinstallgit+https://github.com/runpod/runpod-python.gitPython 3.8 or higher is required to use the latest version of this package.⚡ | Serverless Worker (SDK)This python package can also be used to create a serverless worker that can be deployed to RunPod as a custom endpoint API.Quick StartCreate a python script in your project that contains your model definition and the RunPod worker start code. Run this python code as your default container start command:# my_worker.pyimportrunpoddefis_even(job):job_input=job["input"]the_number=job_input["number"]ifnotisinstance(the_number,int):return{"error":"Silly human, you need to pass an integer."}ifthe_number%2==0:returnTruereturnFalserunpod.serverless.start({"handler":is_even})Make sure that this file is ran when your container starts. This can be accomplished by calling it in the docker command when you set up a template atrunpod.io/console/serverless/user/templatesor by setting it as the default command in your Dockerfile.See ourblog postfor creating a basic Serverless API, or view thedetails docsfor more information.Local Test WorkerYou can also test your worker locally before deploying it to RunPod. This is useful for debugging and testing.pythonmy_worker.py--rp_serve_api📚 | API Language Library (GraphQL Wrapper)When interacting with the RunPod API you can use this library to make requests to the API.importrunpodrunpod.api_key="your_runpod_api_key_found_under_settings"EndpointsYou can interact with RunPod endpoints via arunorrun_syncmethod.endpoint=runpod.Endpoint("ENDPOINT_ID")run_request=endpoint.run({"your_model_input_key":"your_model_input_value"})# Check the status of the endpoint run requestprint(run_request.status())# Get the output of the endpoint run request, blocking until the endpoint run is complete.print(run_request.output())endpoint=runpod.Endpoint("ENDPOINT_ID")run_request=endpoint.run_sync({"your_model_input_key":"your_model_input_value"})# Returns the job results if completed within 90 seconds, otherwise, returns the job status.print(run_request)GPU Cloud (Pods)importrunpodrunpod.api_key="your_runpod_api_key_found_under_settings"# Get all my podspods=runpod.get_pods()# Get a specific podpod=runpod.get_pod(pod.id)# Create a podpod=runpod.create_pod("test","runpod/stack","NVIDIA GeForce RTX 3070")# Stop the podrunpod.stop_pod(pod.id)# Resume the podrunpod.resume_pod(pod.id)# Terminate the podrunpod.terminate_pod(pod.id)📁 | Directory.
├──docs# Documentation├──examples# Examples├──runpod# Package source code│├──api_wrapper# Language library - API (GraphQL)│├──cli# Command Line Interface Functions│├──endpoint# Language library - Endpoints│└──serverless# SDK - Serverless Worker└──tests# Package tests🤝 | Community and ContributingWe welcome both pull requests and issues onGitHub. Bug fixes and new features are encouraged, but please read ourcontributing guidefirst.