Dl-Docker

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dl-docker is an all-in-one Docker image for deep learning, providing popular frameworks like TensorFlow, Theano, and Torch. It supports both CPU and GPU configurations, including CUDA and cuDNN, for easy setup.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is dl-docker?

dl-docker offers an all-in-one Docker image designed for deep learning, simplifying the setup process by pre-packaging popular frameworks such as TensorFlow, Caffe, Theano, Keras, and Torch. It supports both CPU and GPU configurations, with the GPU version including CUDA 8.0 and cuDNN v5. The image also comes with essential libraries like iPython/Jupyter Notebook, Numpy, SciPy, Pandas, Scikit Learn, Matplotlib, and OpenCV. Users can either pull pre-built CPU images from Docker Hub or build both CPU and GPU versions locally. This solution addresses the 'dependency hell' often encountered when installing multiple deep learning frameworks, providing an isolated and fully functional OS environment for development.

Best used for

Ideal for developers who need to quickly set up a comprehensive deep learning environment, manage complex framework dependencies, and ensure consistent development across projects. Especially valuable for those working with multiple deep learning frameworks simultaneously or needing a portable, isolated environment.

Common actions

set up deep learning environment

manage deep learning dependencies

run deep learning frameworks

create consistent dev environments

automated workflowworkflowsdeepfakeopen-sourcegithub copilotcollaboration"AI Agents"low-code/no-codeface swapping

Capabilities

Key features

Pre-installed deep learning frameworks
CPU and GPU support
CUDA and cuDNN included
Jupyter Notebook integration
Shared volume persistence
Automated Docker builds

Target Audience

developer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What deep learning frameworks are included in dl-docker?

dl-docker includes popular deep learning frameworks such as TensorFlow, Caffe, Theano, Keras, Lasagne, and Torch. It also comes with essential libraries like iPython/Jupyter Notebook, Numpy, SciPy, Pandas, Scikit Learn, Matplotlib, and OpenCV for a complete development environment.

Does dl-docker support both CPU and GPU?

Yes, dl-docker provides separate Dockerfiles and images for both CPU and GPU configurations. The GPU version includes CUDA 8.0 and cuDNN v5, but requires Nvidia drivers and nvidia-docker to be installed on the host machine for full functionality.

How can I ensure my data persists when using dl-docker containers?

To ensure data persistence, you can use Docker's shared volume feature. By mapping a folder from your host machine into the container using the `-v` flag, any data written to that shared folder from within the container will remain accessible even after the container is stopped or removed.

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