A Dockerfile declaratively packages the software needed to run distributed model training. The image can serve as the common runtime for worker Pods and later workflow steps. section 9.2.2
A practical containerization workflow uses the Dockerfile to:
- Start from the Python 3.9 base image. section 9.2.2
- Copy the distributed-training script, then rebuild and re-import the image when additional workflow scripts are added. section 9.4.1
Distributed-training image
FROM python:3.9 RUN pip install tensorflow==2.11.0 tensorflow_datasets==4.7.0 COPY multi-worker-distributed-training.py /
The Dockerfile supplies the executable environment, while Kubernetes volumes preserve trained models and checkpoints after worker Pods are deleted. Treat image contents and persistent storage as separate deployment concerns. section 9.2.2
