11 Docker Container Images for Generative AI & ML Projects
Author(s): Youssef Hosni
Originally published on Towards AI.
Docker containers offer significant advantages for machine learning by ensuring consistent, portable, and reproducible environments across different systems.
By encapsulating all dependencies, libraries, and configurations in a container, Docker eliminates compatibility issues and the “it works on my machine” problem.
This makes it easier to move ML projects between development, cloud, or production environments without worrying about differences in setup. Additionally, Docker enables scalability and isolation, allowing machine learning workflows to be easily scaled using tools like Kubernetes, and ensuring that dependencies do not conflict between different projects.
In this article, we will explore 11 Docker container images for Generative AI and machine learning projects. These include tools for development environments, deep learning frameworks, machine learning lifecycle management, workflow orchestration, and large language models.
I. Machine Learning & Data Science
PythonJupyter Notebook data science stack
II. Generative AI & Deep Learning
3. Hugging Face Transformers
4. NVIDIA CUDA deep learning runtime
5. TensorFlow
6. PyTorch
7. Ollama
8. Qdrant
III. Workflow Orchestration & ML Lifecycle Management
9. Airflow
10. MLflow
11. Kubeflow Notebooks
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