Pod-first development - Runpod Documentation

Developing machine learning applications often requires powerful GPUs, making local development challenging. Instead of repeatedly deploying your worker to Serverless for testing, you can develop on a Pod first and then deploy the same Docker image to Serverless when ready. This “Pod-first” workflow lets you develop and test interactively in a GPU environment, then seamlessly transition to Serverless for production. You’ll use a Pod as your cloud-based development machine with tools like Jupyter Notebooks and SSH, catching issues early before deploying your worker to Serverless.

To get started quickly, you can clone this repository for a pre-configured template for a dual-mode worker.

Requirements

You’ve created a Runpod account.
You’ve installed Python 3.x and Docker and configured them for your command line.
Basic understanding of Docker concepts and shell scripting.

Step 1: Set up your project structure

Create a directory for your project and the necessary files:

mkdir dual-mode-worker
cd dual-mode-worker
touch handler.py start.sh Dockerfile requirements.txt

This creates the following project structure:

dual-mode-worker

handler.py

start.sh

Dockerfile

requirements.txt

Step 2: Create the handler

This Python script will check for a MODE_TO_RUN environment variable to determine whether to run in Pod or Serverless mode. Add the following code to handler.py:

handler.py

import os
import asyncio
import runpod

# Use the MODEL environment variable; fallback to a default if not set
mode_to_run = os.getenv("MODE_TO_RUN", "pod")
model_length_default = 25000

print("------- ENVIRONMENT VARIABLES -------")
print("Mode running: ", mode_to_run)
print("------- -------------------- -------")

async def handler(event):
    inputReq = event.get("input", {})
    return inputReq

if mode_to_run == "pod":
    async def main():
        prompt = "Hello World"
        requestObject = {"input": {"prompt": prompt}}
        response = await handler(requestObject)
        print(response)

    asyncio.run(main())
else: 
    runpod.serverless.start({
        "handler": handler,
        "concurrency_modifier": lambda current: 1,
    })

Key features:

MODE_TO_RUN = os.getenv("MODE_TO_RUN", "pod"): Reads the mode from an environment variable, defaulting to pod.
async def handler(event): Your core logic.
if mode_to_run == "pod" ... else: This conditional controls what happens when the script is executed directly.
- In pod mode, it runs a sample test call to your handler function, allowing for quick iteration.
- In serverless” mode, it starts the Runpod Serverless worker.

Step 3: Create the `start.sh` script

The start.sh script serves as the entrypoint for your Docker container and manages different operational modes. It reads the MODE_TO_RUN environment variable and configures the container accordingly. Add the following code to start.sh:

#!/bin/bash
set -e  # Exit the script if any statement returns a non-true return value

# Set workspace directory from env or default
WORKSPACE_DIR="${WORKSPACE_DIR:-/workspace}"

# Start nginx service
start_nginx() {
    echo "Starting Nginx service..."
    service nginx start
}

# Execute script if exists
execute_script() {
    local script_path=$1
    local script_msg=$2
    if [[ -f ${script_path} ]]; then
        echo "${script_msg}"
        bash ${script_path}
    fi
}

# Setup ssh
setup_ssh() {
    if [[ $PUBLIC_KEY ]]; then
        echo "Setting up SSH..."
        mkdir -p ~/.ssh
        echo "$PUBLIC_KEY" >> ~/.ssh/authorized_keys
        chmod 700 -R ~/.ssh
        # Generate SSH host keys if not present
        generate_ssh_keys
        service ssh start
        echo "SSH host keys:"
        cat /etc/ssh/*.pub
    fi
}

# Generate SSH host keys
generate_ssh_keys() {
    ssh-keygen -A
}

# Export env vars
export_env_vars() {
    echo "Exporting environment variables..."
    printenv | grep -E '^RUNPOD_|^PATH=|^_=' | awk -F = '{ print "export " $1 "=\"" $2 "\"" }' >> /etc/rp_environment
    echo 'source /etc/rp_environment' >> ~/.bashrc
}

# Start jupyter lab
start_jupyter() {
    echo "Starting Jupyter Lab..."
    mkdir -p "$WORKSPACE_DIR" && \
    cd / && \
    nohup jupyter lab --allow-root --no-browser --port=8888 --ip=* --NotebookApp.token='' --NotebookApp.password='' --FileContentsManager.delete_to_trash=False --ServerApp.terminado_settings='{"shell_command":["/bin/bash"]}' --ServerApp.allow_origin=* --ServerApp.preferred_dir="$WORKSPACE_DIR" &> /jupyter.log &
    echo "Jupyter Lab started without a password"
}

# Call Python handler if mode is serverless or both
call_python_handler() {
    echo "Calling Python handler.py..."
    python $WORKSPACE_DIR/handler.py
}

# ---------------------------------------------------------------------------- #
#                               Main Program                                   #
# ---------------------------------------------------------------------------- #

start_nginx

echo "Pod Started"

setup_ssh

case $MODE_TO_RUN in
    serverless)
        echo "Running in serverless mode"
        call_python_handler
        ;;
    pod)
        echo "Running in pod mode"
        start_jupyter
        ;;
    *)
        echo "Invalid MODE_TO_RUN value: $MODE_TO_RUN. Expected 'serverless', 'pod', or 'both'."
        exit 1
        ;;
esac

export_env_vars

echo "Start script(s) finished"

sleep infinity

Here are some key features of this script:

case $MODE_TO_RUN in ... esac: This structure directs the startup based on the mode.
serverless mode: Executes handler.py, which then starts the Runpod Serverless worker. exec replaces the shell process with the Python process.
pod mode: Starts up the JupyterLab server for Pod development, then runs sleep infinity to keep the container alive so you can connect to it (e.g., via SSH or docker exec). You would then manually run python /app/handler.py inside the Pod to test your handler logic.

Step 4: Create the `Dockerfile`

Create a Dockerfile that includes your handler and startup script:

# Use an official Runpod base image
FROM runpod/pytorch:2.0.1-py3.10-cuda11.8.0-devel-ubuntu22.04

# Environment variables
ENV PYTHONUNBUFFERED=1 

# Supported modes: pod, serverless
ARG MODE_TO_RUN=pod
ENV MODE_TO_RUN=$MODE_TO_RUN

# Set up the working directory
ARG WORKSPACE_DIR=/app
ENV WORKSPACE_DIR=${WORKSPACE_DIR}
WORKDIR $WORKSPACE_DIR

# Install dependencies in a single RUN command to reduce layers and clean up in the same layer to reduce image size
RUN apt-get update --yes --quiet && \
    DEBIAN_FRONTEND=noninteractive apt-get install --yes --quiet --no-install-recommends \
    software-properties-common \
    gpg-agent \
    build-essential \
    apt-utils \
    ca-certificates \
    curl && \
    add-apt-repository --yes ppa:deadsnakes/ppa && \
    apt-get update --yes --quiet && \
    DEBIAN_FRONTEND=noninteractive apt-get install --yes --quiet --no-install-recommends

# Create and activate a Python virtual environment
RUN python3 -m venv /app/venv
ENV PATH="/app/venv/bin:$PATH"

# Install Python packages
RUN pip install --no-cache-dir \
    asyncio \
    requests \
    runpod

# Install requirements.txt
COPY requirements.txt ./requirements.txt
RUN pip install --no-cache-dir --upgrade pip && \
    pip install --no-cache-dir -r requirements.txt
    
# Delete's the default start.sh file from Runpod (so we can replace it with our own below)
RUN rm ../start.sh

# Copy all of our files into the container
COPY handler.py $WORKSPACE_DIR/handler.py
COPY start.sh $WORKSPACE_DIR/start.sh

# Make sure start.sh is executable
RUN chmod +x start.sh

# Make sure that the start.sh is in the path
RUN ls -la $WORKSPACE_DIR/start.sh

# depot build -t justinrunpod/pod-server-base:1.0 . --push --platform linux/amd64
CMD $WORKSPACE_DIR/start.sh

Key features of this Dockerfile:

FROM runpod/pytorch:2.0.1-py3.10-cuda11.8.0-devel-ubuntu22.04: Starts with a Runpod base image that comes with nginx, runpodctl, and other helpful base packages.
ARG WORKSPACE_DIR=/workspace and ENV WORKSPACE_DIR=${WORKSPACE_DIR}: Allows the workspace directory to be set at build time.
WORKDIR $WORKSPACE_DIR: Sets the working directory to the value of WORKSPACE_DIR.
COPY requirements.txt ./requirements.txt and RUN pip install ...: Installs Python dependencies.
COPY . .: Copies all application files into the workspace directory.
ENV MODE_TO_RUN="pod": Sets the default operational mode to “pod”. This can be overridden at runtime.
CMD ["$WORKSPACE_DIR/start.sh"]: Specifies start.sh as the command to run when the container starts.

Step 5: 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.

Now you’re ready to build your Docker image and push it to Docker Hub:

Build your Docker image

Build your Docker image, replacing YOUR_USERNAME with your Docker Hub username and choosing a suitable image name:

docker build --platform linux/amd64 --tag YOUR_USERNAME/dual-mode-worker .

The --platform linux/amd64 flag is important for compatibility with Runpod’s infrastructure.

Push the image to your container registry

docker push YOUR_USERNAME/dual-mode-worker:latest

You might need to run docker login first.

Step 6: Testing in Pod mode

Now that you’ve finished building our Docker image, let’s explore how you would use the Pod-first development workflow in practice. Deploy the image to a Pod by following these steps:

Navigate to the Pods page in the Runpod console.
Click Deploy.
Select your preferred GPU.
Under Container Image, enter YOUR_USERNAME/dual-mode-worker:latest.
Under Public Environment Variables, select Add environment variable and add:
- Key: MODE_TO_RUN
- Value: pod
Click Deploy.

Once your Pod is running, you can:

Connect via the web terminal, JupyterLab, or SSH to test your handler interactively.
Debug and iterate on your code.
Test GPU-specific operations.
Edit handler.py within the Pod and re-run it for rapid iteration.

Step 7: Deploy to a Serverless endpoint

Once you’re confident with your handler.py logic tested in Pod mode, you’re ready to deploy your dual-mode worker to a Serverless endpoint.

Navigate to the Serverless page in the Runpod console.
Click New Endpoint.
Click Import from Docker Registry.
In the Container Image field, enter your Docker image URL: docker.io/YOUR_USERNAME/dual-mode-worker:latest, then click Next***.
Under Environment Variables, add:
- Key: MODE_TO_RUN
- Value: serverless
Configure your endpoint settings (GPU type, workers, etc.).
Click Deploy Endpoint.

The same image will be used for your workers, but start.sh will now direct them to run in Serverless mode, using the runpod.serverless.start() function to process requests.

Step 8: Test your endpoint

After deploying your endpoint in to Serverless mode, you can test it by sending API requests to your endpoint.

Navigate to your endpoint’s detail page in the Runpod console.
Click the Requests tab.
Use the following JSON as test input:

{
    "input": {
        "prompt": "Hello World!",
    }
}

Click Run.

After a few moments for initialization and processing, you should see output similar to this:

{
    "delayTime": 12345, // This will vary
    "executionTime": 3050, // This will be around 3000ms + overhead
    "id": "some-unique-id",
    "output": {
        "output": "Processed prompt: 'Hello Serverless World!' after 3s in Serverless mode."
    },
    "status": "COMPLETED"
}

Explore the Pod-first development workflow

Congratulations! You’ve successfully built, deployed, and tested a dual-mode Serverless worker.

Now, let’s explore the recommended iteration process for a Pod-first development workflow:

Develop using Pod mode

Deploy your initial Docker image to a Runpod Pod, ensuring MODE_TO_RUN is set to pod (or rely on the Dockerfile default).
Connect to your Pod (via SSH or web terminal).
Navigate to the /app directory.
As you develop, install any necessary Python packages (pip install PACKAGE_NAME) or system dependencies (apt-get install PACKAGE_NAME).
Iterate on your handler.py script. Test your changes frequently by running python handler.py directly in the Pod’s terminal. This will execute the test harness you defined in the elif MODE_TO_RUN == "pod": block, giving you immediate feedback.

Update your Docker image

Once you’re satisfied with a set of changes and have new dependencies:

Add new Python packages to your requirements.txt file.
Add system installation commands (e.g., RUN apt-get update && apt-get install -y PACKAGE_NAME) to your Dockerfile.
Ensure your updated handler.py is saved.

Re-deploy and test in Serverless mode

Re-deploy your worker image to a Serverless endpoint using Docker Hub or GitHub.
During deployment, ensure that the MODE_TO_RUN environment variable for the endpoint is set to serverless.

For instructions on how to set environment variables during deployment, see Manage endpoints.

After your endpoint is deployed, you can test it by sending API requests.

This iterative loop (write your handler, update the Docker image, test in Pod mode, then deploy to Serverless) enables you to rapidly develop and debug your Serverless workers.

​Requirements

​Step 1: Set up your project structure

​Step 2: Create the handler

​Step 3: Create the start.sh script

​Step 4: Create the Dockerfile

​Step 5: Build and push your Docker image

​Step 6: Testing in Pod mode

​Step 7: Deploy to a Serverless endpoint

​Step 8: Test your endpoint

​Explore the Pod-first development workflow

Requirements

Step 1: Set up your project structure

Step 2: Create the handler

Step 3: Create the `start.sh` script

Step 4: Create the `Dockerfile`

Step 5: Build and push your Docker image

Step 6: Testing in Pod mode

Step 7: Deploy to a Serverless endpoint

Step 8: Test your endpoint

Explore the Pod-first development workflow