Practical overview
When it fits
Use it when your workload is written in Python and you want to define, develop or submit GPU work on Runpod, including queued functions, one-off tasks or custom serverless endpoint workers.
Who it is for
- Python developers building GPU-backed inference or fine-tuning workloads.
- Teams creating custom serverless worker handlers and endpoint APIs.
- Developers who want to test worker code locally before deploying it to Runpod.
Documented uses
- Define a model handler and expose it through a custom endpoint API.
- Create an autoscaling GPU job queue for inference work.
- Run a GPU task remotely and retrieve its result.
- Configure models, network volumes, dependencies and environment secrets for workloads.
- Develop and locally test a serverless worker before cloud deployment.
Requirements & limits
The repository documents installation through pip or uv and requires Python 3.10 or later. Its setup instructions use rp login before CLI development and deployment.
- The package includes both an SDK and an rp command-line interface.
- Queue jobs and ephemeral tasks are separate workload patterns in the examples.
- Custom endpoint workers use a handler started with runpod.serverless.start.
- The README shows H100 configuration, model and volume mounts, dependency setup, and secrets as workload options.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Test a serverless worker locally
Run the documented local worker command before deploying a custom endpoint worker.
Before you start
- Python 3.10 or later
- A Python worker script such as my_worker.py
- A handler started with runpod.serverless.start
Steps
Create a Python script containing the model or handler logic.
Start the worker with runpod.serverless.start and pass the handler.
Run python my_worker.py --rp_serve_api to test the worker locally.
Expected outcome
The worker can be exercised locally as a development step before deployment to Runpod.
Guide limitations
- This documents local worker testing, not cloud deployment or endpoint invocation.
- The repository does not specify the result of running a particular user-written handler.
Set up and deploy with the Runpod CLI
Install the SDK, authenticate, open a live development session and deploy an endpoint.
Before you start
- Python 3.10 or later
- A Runpod account or credentials accepted by rp login
- A project file such as main.py
Steps
Install the package with pip install runpod or uv add runpod.
Authenticate with rp login.
Start a live development session with rp flash dev main.py.
Deploy production endpoints with rp flash deploy.
Expected outcome
The documented CLI workflow proceeds from package installation and authentication to development and endpoint deployment.
Guide limitations
- The README does not detail endpoint configuration or deployment results for a specific project.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 8 October 2026
- Original website title
- runpod/runpod-python · Official homepage title · retained in its source language