Intro to Docker

This tutorial introduces containers via teaching how to use docker. This part is not designed for HPC but understanding the idea of a container is important.

If you understand docker then skip to here.

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Docker is a tool that packages applications and their dependencies into containers, ensuring they run the same way on any system. It's useful because it:

1. Ensures Consistency across different environments (e.g., development, testing, production).

2. Isolates Applications, preventing conflicts and improving security.

3. Makes Deployment Easy by packaging everything needed to run an app in one container.

4. Increases Efficiency since containers are lightweight and use less resources compared to traditional virtual machines.

In short, Docker simplifies running and deploying applications by making them portable and consistent.

Download sample code

git clone https://github.com/sanjeev-one/Intro-to-Supercomputing-24---Duke-IEEE.git

The dockerfile tells docker how to setup the containner:

```dockerfile
# Use the official Python image with version 3.8
FROM python:3.8-slim

# Set the working directory in the container
WORKDIR /app

# Copy the current directory contents into the container at /app
COPY . .

# Install any dependencies specified in requirements.txt
RUN pip install --no-cache-dir -r requirements.txt

# Run ml_app.py when the container launches
CMD ["python", "ml_app.py"]

```

Build and Run the Docker Container

Build the Docker Image:

In a local terminal that has the directory with ml_app.py open: (This may need logging into docker with docker login and using a dockerhub account)

docker build -t ml-app .

Run the Container Locally:

docker run ml-app

This will print the model's accuracy and the prediction for the sample flower measurements in your terminal.

Push to Docker Hub and Deploy on a VM - skip if doing workshop

Log In to Docker Hub: needs https://hub.docker.com/account

docker login

Tag and Push the Image: (change username to your docker hub account username)

docker tag ml-app username/ml-app
docker push username/ml-app

On the VM, Pull and Run the Image:

You can follow here to connect to a jetstream 2 vm

1. Pull the Docker Image:

   docker pull dukeieee/ml-app

   This command downloads the Docker image dukeieee/ml-app from Docker Hub to your local system or VM. The image contains the Python ML app and its required dependencies.

2. Run the Docker Container:

   docker run dukeieee/ml-app

   This command starts a container using the downloaded image. The app will train a machine learning model on the iris dataset and print the model's accuracy and predictions directly to your terminal.

This approach ensures that the app runs with all necessary dependencies, regardless of the environment, providing a consistent and reproducible setup.