Welcome to the official Python library for RunPod API & SDK.
- Table of Contents
- π» | Installation
- β‘ | Serverless Worker (SDK)
- π | API Language Library (GraphQL Wrapper)
- π | Directory
- π€ | Community and Contributing
# Install the latest release version pip install runpod # or # Install the latest development version (main branch) pip install git+https://github.com/runpod/runpod-python.git
Python 3.8 or higher is required to use the latest version of this package.
This python package can also be used to create a serverless worker that can be deployed to RunPod as a custom endpoint API.
Create 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.py import runpod def is_even(job): job_input = job["input"] the_number = job_input["number"] if not isinstance(the_number, int): return {"error": "Silly human, you need to pass an integer."} if the_number % 2 == 0: return True return False runpod.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 at runpod.io/console/serverless/user/templates or by setting it as the default command in your Dockerfile.
See our blog post for creating a basic Serverless API, or view the details docs for more information.
You can also test your worker locally before deploying it to RunPod. This is useful for debugging and testing.
python my_worker.py --rp_serve_api
When interacting with the RunPod API you can use this library to make requests to the API.
import runpod runpod.api_key = "your_runpod_api_key_found_under_settings"
You can interact with RunPod endpoints via a run or run_sync method.
endpoint = runpod.Endpoint("ENDPOINT_ID") run_request = endpoint.run( {"your_model_input_key": "your_model_input_value"} ) # Check the status of the endpoint run request print(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 )
import runpod runpod.api_key = "your_runpod_api_key_found_under_settings" # Get all my pods pods = runpod.get_pods() # Get a specific pod pod = runpod.get_pod(pod.id) # Create a pod with GPU pod = runpod.create_pod("test", "runpod/stack", "NVIDIA GeForce RTX 3070") # Create a pod with CPU pod = runpod.create_pod("test", "runpod/stack", instance_id="cpu3c-2-4") # Stop the pod runpod.stop_pod(pod.id) # Resume the pod runpod.resume_pod(pod.id) # Terminate the pod runpod.terminate_pod(pod.id)
. βββ docs # Documentation βββ examples # Examples βββ runpod # Package source code β βββ api_wrapper # Language library - API (GraphQL) β βββ cli # Command Line Interface Functions β βββ endpoint # Language library - Endpoints β βββ serverless # SDK - Serverless Worker βββ tests # Package tests
We welcome both pull requests and issues on GitHub. Bug fixes and new features are encouraged, but please read our contributing guide first.