Docker Compose for Machine Learning

This repository contains a Docker Compose configuration file (docker-compose-ml.yml) to set up a JupyterLab environment with Python 3.8 for machine learning tasks.

1. Prerequisites

Before you begin, ensure you have Docker and Docker Compose installed on your system.

Docker Compose Structure

Below is the Docker Compose configuration for setting up a JupyterLab environment with Python 3.8.

version: '3'
services:
  jupyterlab:
    image: jupyter/base-notebook:python-3.8
    ports:
      - "8888:8888"
    volumes:
      - ./notebooks:/home/jovyan/work
    environment:
      - JUPYTER_ENABLE_LAB=yes

JupyterLab Docker Setup

We have defined a service named jupyterlab with the following specifications:

• Docker image: jupyter/base-notebook:python-3.8, which includes Python 3.8 and JupyterLab pre-installed.
• Port Mapping: We map port 8888 from the container to port 8888 on your host machine for accessing JupyterLab in a web browser.
• Volume: We create a volume named ./notebooks and mount it to the /home/jovyan/work directory inside the container. This allows you to persist your JupyterLab notebooks and data outside the container.
• Environment Variable: We set the JUPYTER_ENABLE_LAB environment variable to "yes" to enable JupyterLab.

Running Docker Compose

To run the docker-compose-ml.yml file, use the following command:

docker-compose -f docker-compose-ml.yml up -d

2. Clone the Repository

Clone this repository to your local machine:

git clone https://github.com/your-username/docker-ml.git
cd docker-ml