Why Dockers for ML?
Docker has become an indispensable tool for machine learning, offering a powerful solution to many of the challenges faced by data scientists and ML engineers. This containerisation technology provides a standardised, portable, reproducible environment for developing, testing, and deploying ML models.
By encapsulating all necessary dependencies, libraries, and configurations, Docker ensures consistency across different systems and eliminates the infamous "it works on my machine" problem 😉.
- Introduction to Docker - An introduction to Docker, its architecture, and why it is useful in software development and machine learning.
- docker_files - Contains examples of Dockerfiles for different use cases.
- assets - Contains images used in the markdown files.
- Docker for Data Science: An Introduction
- Containerization: Docker and Kubernetes for Machine Learning
- Docker Documentation
- NVIDIA Container toolkit
- Best Practices When Working With Docker for Machine Learning https://neptune.ai/blog/best-practices-docker-for-machine-learning
Feel free to add things that you found helpful :)