👀 Views: 0 💬 Answers: 1 📅 Created: 2025-09-21

docker linux machine-learning Python

While refactoring a machine learning pipeline built in Python, I've moved to a Docker-based setup to ensure consistency across different environments. The pipeline uses libraries like TensorFlow 2.11 and scikit-learn 1.0.2, which are crucial for model training and evaluation. Running the Docker container on Linux (Ubuntu 20.04), I noticed that the model training phase fails due to file permission issues, particularly when trying to access training data stored in a mounted volume. My `docker-compose.yml` looks something like this: ```yaml version: '3.8' services: ml_pipeline: image: python:3.9 volumes: - ./data:/app/data command: python train_model.py ``` The error logs indicate `Permission denied` when accessing files inside `/app/data`. I've tried changing the ownership of the local `data` directory with: ```bash sudo chown -R $(whoami):$(whoami) ./data ``` However, this doesn't seem to resolve the issue when the container runs. I also attempted to switch the user in the Dockerfile like so: ```Dockerfile FROM python:3.9 WORKDIR /app COPY . . RUN pip install tensorflow==2.11 scikit-learn==1.0.2 USER root ``` This approach didn’t help either, as the permission errors persist. Another thing I noticed is that some of the libraries might have specific dependencies that are missing; during the build process, I received warnings about certain optional libraries not being installed, although they shouldn’t be essential for basic training tasks. Is there a best practice for handling file permissions with Docker on Linux, especially for machine learning projects? Also, how can I ensure that all required packages are included without cluttering the container image? Would switching to a different base image help in this scenario? Any insights on how to effectively manage dependencies in this context would be greatly appreciated. This issue appeared after updating to Python latest. Any help would be greatly appreciated!