Section 2 Dockerfile for Data Science Environment with Jupyter Lab

Dockerfile

# Use Python 3.12 as the base image
FROM python:3.12-slim

# Set the working directory in the container
WORKDIR /app

# Install system dependencies for data science packages
RUN apt-get update && apt-get install -y \
    build-essential \
    libcurl4-openssl-dev \
    libssl-dev \
    libffi-dev \
    && apt-get clean

# Install Jupyter Lab and common data science packages
RUN pip install --no-cache-dir \
    jupyterlab \
    numpy \
    pandas \
    matplotlib \
    scikit-learn \
    seaborn \
    scipy \
    plotly \
    statsmodels \
    && pip install --upgrade pip

# Expose Jupyter Lab's default port
EXPOSE 8888

# Set the default command to launch Jupyter Lab
CMD ["jupyter", "lab", "--ip='*'", "--port=8888", "--no-browser", "--allow-root"]

This Dockerfile creates a containerized environment for data science workflows, including the installation of Jupyter Lab and common data science packages.

1. Use Python 3.12 as the base image

The base image is set to Python 3.12 with a slim variant to reduce image size.

FROM python:3.12-slim

Click link to see specification of python3.12-slim page

2. Set the working directory in the container

This instruction sets the working directory to /app inside the container. All subsequent commands will be run from this directory.

WORKDIR /app

3. Install system dependencies for data science packages

This step installs system dependencies required for compiling and installing various Python packages used in data science.

RUN apt-get update && apt-get install -y \
    build-essential \
    libcurl4-openssl-dev \
    libssl-dev \
    libffi-dev \
    && apt-get clean

4. Install Jupyter Lab and common data science packages

The following Python packages are installed:

1. Jupyter Lab: The web-based interactive development environment for notebooks.
2. Data Science Libraries: Including numpy, pandas, matplotlib, scikit-learn, seaborn, scipy, and plotly.
   This installation is done using pip to ensure that packages are installed without caching.
   

   RUN pip install --no-cache-dir \
       jupyterlab \
       numpy \
       pandas \
       matplotlib \
       scikit-learn \
       seaborn \
       scipy \
       plotly \
       && pip install --upgrade pip
   

5. Expose Jupyter Lab's default port

Expose port 8888, the default port that Jupyter Lab listens on, making it accessible from outside the container.

EXPOSE 8888

6. Set the default command to launch Jupyter Lab

The default command launches Jupyter Lab with the following params options:

• --ip='*': Allows connections from any IP address.
• --port=8888: Specifies the port to run Jupyter Lab on.
• --no-browser: Prevents the automatic opening of a browser.
• --allow-root: Allows running Jupyter Lab as the root user inside the container.

7. Complete CMD command

CMD ["jupyter", "lab", "--ip='*'", "--port=8888", "--no-browser", "--allow-root"]

Format of CMD

Format of CMD:

CMD command param1 param2

This is executed in the shell (/bin/sh -c).

• Exec form (recommended):

CMD ["executable", "param1", "param2"]

This does not invoke a shell, which prevents issues like signal handling and allows the executable to run directly.

Summary of Workflow:

1. Base Image: Python 3.12-slim for a lightweight container.
2. System Dependencies: Essential packages for compiling and installing Python packages.
3. Python Packages: Installs Jupyter Lab and essential libraries for data science.
4. Expose Port: Makes Jupyter Lab accessible at port 8888.
5. Start Jupyter Lab: The container automatically starts Jupyter Lab with the appropriate configurations when run.