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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
An all-in-one Docker image for deep learning. Contains all the popular DL frameworks (TensorFlow, Theano, Torch, Caffe, etc.)
| Date | Stars |
|---|---|
| 2026-07-31 | 3855 |
| 2026-08-03 | 3855 |
| 2026-08-04 | 3855 |
| 2026-08-06 | 3855 |
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[Website](https://www.floydhub.com) • [Docs](https://docs.floydhub.com) • [Forum](https://forum.floydhub.com) • [Twitter](https://twitter.com/floydhub_) • [We're Hiring](https://angel.co/floydhub) [](https://www.floydhub.com) ## All-in-one Docker image for Deep Learning Here are Dockerfiles to get you up and running with a fully functional deep learning machine. It contains all the popular deep learning frameworks with CPU and GPU support (CUDA and cuDNN included). The CPU version should work on Linux, Windows and OS X. The GPU version will, however, only work on Linux machines. See [OS support](#what-operating-systems-are-supported) for details If you are not familiar with Docker, but would still like an all-in-one solution, start here: [What is Docker?](#what-is-docker). If you know what Docker is, but are wondering why we need one for deep learning, [see this](#why-do-i-need-a-docker) ## Update: I've built a quick tool, based on dl-docker, to run your DL project on the cloud with zero setup. You can start running your Tensorflow project on AWS in <30seconds using Floyd. See [www.floydhub.com](https://www.floydhub.com). It's free to try out. ### Happy to take feature requests/feedback and answer questions - mail me [email protected]. ## Specs This is what you get out of the box when you create a container with the provided image/Dockerfile: * Ubuntu 14.04 * [CUDA 8.0](https://developer.nvidia.com/cuda-toolkit) (GPU version only) * [cuDNN v5](https://developer.nvidia.com/cudnn) (GPU version only) * [Tensorflow](https://www.tensorflow.org/) * [Caffe](http://caffe.berkeleyvision.org/) * [Theano](http://deeplearning.net/software/theano/) * [Keras](http://keras.io/) * [Lasagne](http://lasagne.readthedocs.io/en/latest/) * [Torch](http://torch.ch/) (includes nn, cutorch, cunn and cuDNN bindings) * [iPython/Jupyter Notebook](http://jupyter.org/) (including iTorch kernel) * [Numpy](http://www.numpy.org/), [SciPy](https://www.scipy.org/), [Pandas](http://pandas.pydata.org/), [Scikit Learn](http://scikit-learn.org/), [Matplotlib](http://matplotlib.org/) * [OpenCV](http://opencv.org/) * A few common libraries used for deep learning ## Setup ### Prerequisites 1. Install Docker following the installation guide for your platform: [https://docs.docker.com/engine/installation/](https://docs.docker.com/engine/installation/) 2. **GPU Version Only**: Install Nvidia drivers on your machine either from [Nvidia](http://www.nvidia.com/Download/index.aspx?lang=en-us) directly or follow the instructions [here](https://github.com/saiprashanths/dl-setup#nvidia-drivers). Note that you _don't_ have to install CUDA or cuDNN. These are included in the Docker container. 3. **GPU Version Only**: Install nvidia-docker: [https://github.com/NVIDIA/nvidia-docker](https://github.com/NVIDIA/nvidia-docker), following the instructions [here](https://github.com/NVIDIA/nvidia-docker/wiki/Installation). This will install a replacement for the docker CLI. It takes care of setting up the Nvidia host driver environment inside the Docker containers and a few other things. ### Obtaining the Docker image You have 2 options to obtain the Docker image #### Option 1: Download the Docker image from Docker Hub Docker Hub is a cloud based repository of pre-built images. You can download the image directly from here, which should be _much faster_ than building it locally (a few minutes, based on your internet speed). Here is the automated build page for `dl-docker`: [https://hub.docker.com/r/floydhub/dl-docker/](https://hub.docker.com/r/floydhub/dl-docker/). The image is automatically built based on the `Dockerfile` in the Github repo. **CPU Version** ```bash docker pull floydhub/dl-docker:cpu ``` **GPU Version** An automated build for the GPU image is not available currently due to timeout restrictions in Docker's automated build process. I'll look into solving this in the future, but for now you'
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Charlie Harrington · @folioco · United States
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Shashank Srikant · MIT
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e2eb61e4b21a2e85, llm:Description: 'An all-in-one Docker image for deep learning. Contains all the popular DL frameworks (TensorFlow, Theano, Torch, Caffe, etc.)'
matched fp:e2eb61e4b21a2e85, llm:Description: 'An all-in-one Docker image for deep learning. Contains all the popular DL frameworks (TensorFlow, Theano, Torch, Caffe, etc.)'
matched fp:e2eb61e4b21a2e85, llm:Description: 'An all-in-one Docker image for deep learning. Contains all the popular DL frameworks (TensorFlow, Theano, Torch, Caffe, etc.)'