Docker script

Generate a Docker script to create a simple Linux machine that has Python 3.10 (prebuilt) installed with following libraries: pandas, tensorflow, numpy

Of course! Here is a complete solution using a Dockerfile. This approach is considered best practice as it is clean, reproducible, and easy to maintain.

We will create two files:

1. Dockerfile: The script that tells Docker how to build the image.
2. requirements.txt: A file listing the Python libraries to install.

This separation makes it easy to update your Python libraries without changing the core Dockerfile logic.

Step 1: Create a requirements.txt file

Create a file named requirements.txt and add the names of the libraries you want to install.

requirements.txt

pandas
tensorflow
numpy

Note: While pandas and tensorflow both depend on numpy, listing it explicitly is fine and ensures it's included.

Step 2: Create the Dockerfile

Create a file named Dockerfile (with no extension) in the same directory. This script defines your Linux environment.

Dockerfile

# Step 1: Use an official Python 3.10 image as the base.
# 'slim-bullseye' is a good choice as it's smaller than the default
# but still has the necessary tools.
FROM python:3.10-slim-bullseye

# Step 2: Set the working directory inside the container.
# This is where your commands will run and files will be placed.
WORKDIR /app

# Step 3: Copy the requirements file into the container.
# This is done before installing to leverage Docker's layer caching.
# If requirements.txt doesn't change, Docker won't re-run the installation.
COPY requirements.txt .

# Step 4: Install the Python libraries.
# --no-cache-dir: Reduces the final image size by not storing the pip cache.
# --upgrade pip: Ensures we have the latest version of pip.
RUN pip install --no-cache-dir --upgrade pip && \
    pip install --no-cache-dir -r requirements.txt

# Step 5: Define the default command to run when the container starts.
# 'bash' will drop you into an interactive shell inside the Linux machine,
# allowing you to run python, check files, etc.
CMD ["bash"]

How to Use It

1. Save the files: Make sure both Dockerfile and requirements.txt are in the same empty directory.

2. Build the Docker image: Open your terminal, navigate to that directory, and run the following command. We'll name (-t) our image python-ml-env.

   docker build -t python-ml-env .
   

   (The . at the end is important; it tells Docker to look for the Dockerfile in the current directory.)

3. Run the container: Once the build is complete, you can start a container from the image. The -it flags make the session interactive.

   docker run -it --rm python-ml-env
   

   • -it gives you an interactive terminal.
   • --rm automatically removes the container when you exit, keeping your system clean.

You will be dropped into a bash prompt inside your new Linux environment, like this: root@<container_id>:/app#

1. Verify the installation: Inside the container's shell, you can start Python and import the libraries to confirm they are installed correctly.

   # Start the Python interpreter
   python3
   
   # Inside the Python interpreter, type:
   >>> import pandas as pd
   >>> import tensorflow as tf
   >>> import numpy as np
   >>> print(f"TensorFlow version: {tf.__version__}")
   >>> print(f"Pandas version: {pd.__version__}")
   >>> print(f"NumPy version: {np.__version__}")
   >>> exit()
   

To exit the container's bash shell, simply type exit.

Alternative (Simpler, Less Maintainable) Dockerfile

If you prefer to have everything in a single file for a very simple, one-off case, you can write the pip install command directly in the Dockerfile without a requirements.txt file.

Dockerfile (single-file version)

# Use an official Python 3.10 image
FROM python:3.10-slim-bullseye

# Install the specified libraries directly
RUN pip install pandas tensorflow numpy

# Drop into a bash shell on start
CMD ["bash"]

This version is less optimal because any change to the list of libraries will cause Docker to re-run the entire installation from scratch, ignoring any cache. The first method using requirements.txt is highly recommended for any serious project.