Learn how to create, deploy, and test a custom Serverless worker.
For an even faster start, clone the worker-basic repository for a pre-configured template for building and deploying Serverless workers. After cloning the repository, skip to step 6 of this tutorial to deploy and test the endpoint.

​
What you’ll learn

In this tutorial you’ll learn how to:
  • Set up your development environment.
  • Create a handler file.
  • Test your handler locally.
  • Build a Docker image for deployment.
  • Deploy and test your worker on the Runpod console.

​
Requirements

​
Step 1: Create a Python virtual environment

First, set up a virtual environment to manage your project dependencies.
1

Create a virtual environment

Run this command in your local terminal:
# Create a Python virtual environment
python3 -m venv venv
2

Activate the virtual environment

source venv/bin/activate
3

Install the Runpod SDK

pip install runpod

​
Step 2: Create a handler file

Create a file named rp_handler.py and add the following code: 
import runpod
import time  

def handler(event):
#   This function processes incoming requests to your Serverless endpoint.
#
#    Args:
#        event (dict): Contains the input data and request metadata
#       
#    Returns:
#       Any: The result to be returned to the client
    
    # Extract input data
    print(f"Worker Start")
    input = event['input']
    
    prompt = input.get('prompt')  
    seconds = input.get('seconds', 0)  

    print(f"Received prompt: {prompt}")
    print(f"Sleeping for {seconds} seconds...")
    
    # You can replace this sleep call with your own Python code
    time.sleep(seconds)  
    
    return prompt 

# Start the Serverless function when the script is run
if __name__ == '__main__':
    runpod.serverless.start({'handler': handler })
This is a bare-bones handler that processes a JSON object and outputs a prompt string contained in the input object. You can replace the time.sleep(seconds) call with your own Python code for generating images, text, or running any machine learning workload.

​
Step 3: Create a test input file

You’ll need to create an input file to properly test your handler locally. Create a file named test_input.json and add the following code: 
{
    "input": {
        "prompt": "Hey there!"
    }
}

​
Step 4: Test your handler locally

Run your handler to verify that it works correctly: 
python rp_handler.py
You should see output similar to this: 
--- Starting Serverless Worker |  Version 1.7.9 ---
INFO   | Using test_input.json as job input.
DEBUG  | Retrieved local job: {'input': {'prompt': 'Hey there!'}, 'id': 'local_test'}
INFO   | local_test | Started.
Worker Start
Received prompt: Hey there!
Sleeping for 0 seconds...
DEBUG  | local_test | Handler output: Hey there!
DEBUG  | local_test | run_job return: {'output': 'Hey there!'}
INFO   | Job local_test completed successfully.
INFO   | Job result: {'output': 'Hey there!'}
INFO   | Local testing complete, exiting.

​
Step 5: Create a Dockerfile

Create a file named Dockerfile with the following content: 
FROM python:3.10-slim

WORKDIR /

# Install dependencies
RUN pip install --no-cache-dir runpod

# Copy your handler file
COPY rp_handler.py /

# Start the container
CMD ["python3", "-u", "rp_handler.py"]

​
Step 6: Build and push your Docker image

Instead of building and pushing your image via Docker Hub, you can also deploy your worker from a GitHub repository.
Before you can deploy your worker on Runpod Serverless, you need to push it to Docker Hub:
1

Build your Docker image

Build your Docker image, specifying the platform for Runpod deployment, replacing [YOUR_USERNAME] with your Docker username:
docker build --platform linux/amd64 --tag [YOUR_USERNAME]/serverless-test .
2

Push the image to your container registry

docker push [YOUR_USERNAME]/serverless-test:latest

​
Step 7: Deploy your worker using the Runpod console

To deploy your worker to a Serverless endpoint:
  1. Go to the Serverless section of the Runpod console.
  2. Click New Endpoint.
  3. Under Custom Source, select Docker Image, then click Next.
  4. In the Container Image field, enter your Docker image URL: docker.io/yourusername/serverless-test:latest.
  5. (Optional) Enter a custom name for your endpoint, or use the randomly generated name.
  6. Under Worker Configuration, check the box for 16 GB GPUs.
  7. Leave the rest of the settings at their defaults.
  8. Click Create Endpoint.
The system will redirect you to a dedicated detail page for your new endpoint.

​
Step 8: Test your worker

To test your worker, click the Requests tab in the endpoint detail page:
On the left you should see the default test request: 
{
    "input": {
        "prompt": "Hello World"
    }
}
Leave the default input as is and click Run. The system will take a few minutes to initialize your workers. When the workers finish processing your request, you should see output on the right side of the page similar to this: 
{
    "delayTime": 15088,
    "executionTime": 60,
    "id": "04f01223-4aa2-40df-bdab-37e5caa43cbe-u1",
    "output": "Hello World",
    "status": "COMPLETED",
    "workerId": "uhbbfre73gqjwh"
}
Congratulations! You’ve successfully deployed and tested your first Serverless worker.

​
Next steps

Now that you’ve learned the basics, you’re ready to: