This is how to create the Docker container for this chapter:
- Create a file under the docker folder called Dockerfile and place the following text into the file:
FROM base_image
This simply inherits from our base image that we built all the way back in Chapter 2, Data First, Easy Environment, and Data Prep.
- Install the graphing utilities and the Kaggle API among other needed libraries:
# Installations for graphing and analysis
RUN apt update && apt install -y python3-pydot python-pydot-ng graphviz
RUN pip3 install kaggle ipython pillow
- In Chapter 7, Using Simulated Images To Create Photo-Realistic Eyeballs with SimGAN, we learned how to get kaggle.json—use the same file here (placed in the docker folder) and copy it into the container:
# Copy Kaggle.json
COPY kaggle.json /root/.kaggle/kaggle.json
- We're going to download a dataset called the 3D MNIST dataset—this is simply a dataset that contains 3D models of MNIST in voxelized or point cloud format:
# Download the Data
RUN kaggle datasets download -d daavoo/3d-mnist
- Unzip the dataset into a folder called 3d-mnist inside the container and remove the ZIP file to save space:
RUN unzip 3d-mnist.zip -d 3d-mnist
RUN rm 3d-mnist.zip
- Set the working directory that we'll start the container in at the src folder level:
WORKDIR /src
After finishing this file, we'll create a few shell scripts to allow us to build, clean, and work with this new environment.