Building a Python Development Podman Container for AI and Data Science

Creating a robust development environment is crucial for AI and data science projects. Here’s how you can build a Podman container with Python 3.12, essential libraries, Jupyter Lab, and Visual Studio Code. Containers - DALL-E 3.0

Dockerfile

Here’s the Dockerfile to set up the container: Dockerfile

# Use an Ubuntu base image
FROM ubuntu:20.04

# Update packages and install dependencies
RUN apt-get update && apt-get install -y \
    software-properties-common \
    wget \
    curl \
    git \
    build-essential

# Add the Python 3.12 PPA and install Python 3.12
RUN add-apt-repository ppa:deadsnakes/ppa && \
    apt-get update && \
    apt-get install -y python3.12 python3.12-venv python3.12-dev python3-pip

# Create a symbolic link so python points to python3.12
RUN ln -s /usr/bin/python3.12 /usr/bin/python && \
    ln -s /usr/bin/pip3 /usr/bin/pip

# Install required Python libraries
RUN pip install --upgrade pip && \
    pip install openai streamlit pandas numpy openpyxl jupyterlab

# Install Visual Studio Code Server
RUN wget -qO- https://aka.ms/install-vscode-server/setup.sh | bash

# Expose ports
EXPOSE 8888
EXPOSE 8080

# Create a non-root user to work with
RUN useradd -ms /bin/bash devuser

# Switch to the created user
USER devuser
WORKDIR /home/devuser

# Command to start Jupyter Lab
CMD ["jupyter", "lab", "--ip=0.0.0.0", "--port=8888", "--no-browser", "--allow-root"]

Step-by-Step Guide to Building the Container

1. Create the Dockerfile:

Open a terminal and create a new directory for your project:
```
mkdir python_dev_container
cd python_dev_container
```
Create a file named Dockerfile:
```
nano Dockerfile
```
Copy and paste the Dockerfile content provided above into the Dockerfile and save the changes.

2. Build the Podman Image:

Ensure you have Podman installed on your system. If not, you can install it following the instructions here.
In the directory where you created the Dockerfile, run the following command to build the image:
```
podman build -t python_dev:latest .
```

3. Run the Container:

Once the image is built, run the container using:

podman run -it -p 8888:8888 -p 8080:8080 python_dev:latest

This will start the container and expose ports 8888 for Jupyter Lab and 8080 for Visual Studio Code.

4. Access Jupyter Lab and Visual Studio Code:

Open your browser and go to http://localhost:8888 to access Jupyter Lab. For Visual Studio Code, open your browser and go to http://localhost:8080. This container provides a complete development environment with Python 3.12, necessary libraries, Jupyter Lab, and Visual Studio Code, all ready to use.