Python-dldocker集所有深度学习于一身的Docker镜像

[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'll have to build the GPU version locally using Option 2 below. #### Option 2: Build the Docker image locally Alternatively, you can build the images locally. Also, since the GPU version is not available in Docker Hub at the moment, you'll have to follow this if you want to GPU version. Note that this will take an hour or two depending on your machine since it compiles a few libraries from scratch. ```bash git clone https://github.com/saiprashanths/dl-docker.git cd dl-docker ``` **CPU Version** ```bash docker build -t floydhub/dl-docker:cpu -f Dockerfile.cpu . ``` **GPU Version** ```bash docker build -t floydhub/dl-docker:gpu -f Dockerfile.gpu . ``` This will build a Docker image named `dl-docker` and tagged either `cpu` or `gpu` depending on the tag your specify. Also note that the appropriate `Dockerfile.<architecture>` has to be used. ## Running the Docker image as a Container Once we've built the image, we have all the frameworks we need installed in it. We can now spin up one or more containers using this image, and you should be ready to [go deeper](http://imgur.com/gallery/BvuWRxq) **CPU Version** ```bash docker run -it -p 8888:8888 -p 6006:6006 -v /sharedfolder:/root/sharedfolder floydhub/dl-docker:cpu bash ``` **GPU Version** ```bash nvidia-docker run -it -p 8888:8888 -p 6006:6006 -v /sharedfolder:/root/sharedfolder floydhub/dl-docker:gpu bash ``` Note the use of `nvidia-docker` rather than just `docker` | Parameter | Explanation | |----------------|-------------| |`-it` | This creates an interactive terminal you can use to iteract with your container | |`-p 8888:8888 -p 6006:6006` | This exposes the ports inside the container so they can be accessed from the host. The format is `-p <host-port>:<container-port>`. The default iPython Notebook runs on port 8888 and Tensorboard on 6006 | |`-v /sharedfolder:/root/sharedfolder/` | This shares the folder `/sharedfolder` on your host machine to `/root/sharedfolder/` inside your container. Any data written to this folder by the container will be persistent. You can modify this to anything of the format `-v /local/shared/folder:/shared/folder/in/container/`. See [Docker container persistence](#docker-container-persistence) |`floydhub/dl-docker:cpu` | This the image that you want to run. The format is `image:tag`. In our case, we use the image `dl-docker` and tag `gpu` or `cpu` to spin up the appropriate image | |`bash` | This provides the default command when the container is started. Even if this was not provided, bash is the default command and just starts a Bash session. You can modify this to be whatever you'd like to be executed when your container starts. For example, you can execute `docker run -it -p 8888:8888 -p 6006:6006 floydhub/dl-docker:cpu jupyter notebook`. This will execute the command `jupyter notebook` and starts your Jupyter Notebook for you when the container starts ## Some common scenarios ### Jupyter Notebooks The container comes pre-installed with iPython and iTorch Notebooks, and you can use these to work with the deep learning frameworks. If you spin up the docker container with `docker-run -p <host-port>:<container-port>` (as shown above in the [instructions](#running-the-docker-image-as-a-container)), you will have access to these ports on your host and can access them at `http://127.0.0.1:<host-port>`. The default iPython notebook uses port 8888 and Tensorboard uses port 6006. Since we expose both these ports when we run the container, we can access them both from the localhost. However, you still need to start the Notebook inside the container to be able to access it from the host. You can either do this from the container terminal by executing `jupyter notebook` or you can pass this command in directly while spinning up your container using the `docker run -it -p 8888:8888 -p 6006:6006 floydhub/dl-docker:cpu jupyter notebook` CLI. The Jupyter Notebook has both Python (for TensorFlow, Caffe, Theano, Ker