runpod-worker-helloworld

"Hello World" on RunPod serverless

---

• Setup
• Local testing
• Deploy to Dockerhub
  • Automatically deploy with Github Actions
• Use your Docker image on RunPod serverless
• Interact with your RunPod API
  • Health status
  • Access the API: async
  • Access the API: synchronous
• Acknowledgements

---

Setup

• Clone the repo to your computer
• Create an env with conda: conda create --name runpod-helloworld python=3.10
• Activate the env: conda activate runpod-helloworld
• Install the runpod lib: pip install runpod
• Make sure to have Docker installed on your computer

Local testing

Execute python rp_handler.py, which will then output something like this:

--- Starting Serverless Worker |  Version 1.2.6 ---
INFO   | Using test_input.json as job input.
DEBUG  | Retrieved local job: {'input': {'greeting': 'world'}, 'id': 'local_test'}
INFO   | local_test | Started
DEBUG  | local_test | Handler output: Hello world
DEBUG  | local_test | run_job return: {'output': 'Hello world'}
INFO   | Job local_test completed successfully.
INFO   | Job result: {'output': 'Hello world'}
INFO   | Local testing complete, exiting.

Deploy to Dockerhub

• Create an account on Dockerhub if you don't have one already
• Login to your account: docker login
• Build your Docker image like this docker build -t <dockerhub_username>/<repository_name>:<tag> --platform linux/amd64 ., in this case: docker build -t timpietruskyblibla/runpod-worker-helloworld:latest --platform linux/amd64.
  • Note: We need to specify the platform here, as this is what RunPod requires. If you don't do this, you might see an error like exec python failed: Exec format error when you run your worker on RunPod
• After the image was created, you can see it when you run docker images, which provides a list of all images that exist on your computer
• Push your Docker image to Dockerhub like this docker push <dockerhub_username>/<repository_name>:<tag>, in this case: docker push timpietruskyblibla/runpod-worker-helloworld:latest
• Once this is done, you can check your Dockerhub account to find the image

Automatically deploy with Github Actions

The repo contains two workflows that publishes the image to Docker hub using Github Actions:

• docker-dev.yml: Creates the image and pushes it to Docker hub with the dev tag on every push to the main branch
• docker-release.yml: Creates the image and pushes it to Docker hub with the latest and the release tag. It will only be triggered when you create a release on GitHub

If you want to use this, you should add these secrets to your repository:

Configuration Variable	Description	Example Value
DOCKERHUB_USERNAME	Your Docker Hub username.	your-username
DOCKERHUB_TOKEN	Your Docker Hub token for authentication.	your-token
DOCKERHUB_REPO	The repository on Docker Hub where the image will be pushed.	timpietruskyblibla
DOCKERHUB_IMG	The name of the image to be pushed to Docker Hub.	runpod-worker-helloworld

Use your Docker image on RunPod serverless

• Create a new template by clicking on New Template
• In the dialog, configure:
  • Template Name: runpod-worker-helloworld (it can be anything you want)
  • Container Image: <dockerhub_username>/<repository_name>:tag, in this case: timpietruskyblibla/runpod-worker-helloworld:latest
• You can leave everything as it is, as this repo is public
• Click on Save Template
• Navigate to Serverless > Endpoints and click on New Endpoint
• In the dialog, configure:
  • Endpoint Name: hellworld
  • Select Template: runpow-worker-helloworld (or what ever name you gave your template)
  • Active Workers: 0 (keep this low, as we just want to test the hello world)
  • Max Workers: 1 (also keep this low, we just want to test the bare minimum)
  • Idle Timeout: 5 (leave the default)
  • Flash Boot: enabled (doesn't cost more, but provides faster boot of our worker, which is good)
  • Advanced: Leave the defaults
  • Select a GPU that has some availability
  • GPUs/Worker: 1 (keep this low as we are just testing, we don't need multiple GPUs for a hello world)
• Click deploy
• Your endpoint will be created, you can click on it to see the dashboard and also the different API methods that are available:
  • runsync
  • run
  • status
  • cancel
  • health

Interact with your RunPod API

• In the User Settings click on API Keys and then on the API Key button
• Save the generated key somewhere, as you will not be able to see it again when you navigate away from the page
• Use cURL or any other tool to access the API using the API key and your Endpoint-ID:
  • Replace <api_key> with your key
  • Replace <endpoint_id> with the ID of the endpoint, you find that when you click on your endpoint, it's part of the URLs shown at the bottom of the first box

Health status

curl -H "Authorization: Bearer <api_key>" https://api.runpod.ai/v2/<endpoint_id>/health

Access the API: async

This will return an id that you can then use in the status endpoint to find out if your job was processed.

# Returns a JSON with the id of the job (<job_id>), use that in the status endpoint
curl -X POST -H "Authorization: Bearer <api_key>" -H "Content-Type: application/json" -d '{"input": {"greeting": "world"}}' https://api.runpod.ai/v2/<endpoint_id>/run

# Returns the output of our script, in this case {"delayTime":17956,"executionTime":137,"id":"<job_id>","output":"Hello world","status":"COMPLETED"}
curl -H "Authorization: Bearer <api_key>" https://api.runpod.ai/v2/<endpoint_id>/status/<job_id>

Access the API: synchronous

This endpoint will wait until the job is done and provides the output of our API as the response:

curl -X POST -H "Authorization: Bearer <api_key>" -H "Content-Type: application/json" -d '{"input": {"greeting": "world"}}' https://api.runpod.ai/v2/<endpoint_id>/runsync

# {"delayTime":2218,"executionTime":138,"id":"<job_id>","output":"Hello world","status":"COMPLETED"}

Acknowledgements

• Thanks to Justin Merrell from RunPod for this nice getting started guide that was used to create this hello-world guide