A series of Docker images (and their generator) that allows you to quickly set up your deep learning research environment.
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README.md

deepo

CircleCI docker license

Deepo is a series of Docker images that

and their Dockerfile generator that


Table of contents


Quick Start

GPU Version

Installation

Step 1. Install Docker and nvidia-docker.

Step 2. Obtain the all-in-one image from Docker Hub

docker pull ufoym/deepo

Usage

Now you can try this command:

nvidia-docker run --rm ufoym/deepo nvidia-smi

This should work and enables Deepo to use the GPU from inside a docker container. If this does not work, search the issues section on the nvidia-docker GitHub -- many solutions are already documented. To get an interactive shell to a container that will not be automatically deleted after you exit do

nvidia-docker run -it ufoym/deepo bash

If you want to share your data and configurations between the host (your machine or VM) and the container in which you are using Deepo, use the -v option, e.g.

nvidia-docker run -it -v /host/data:/data -v /host/config:/config ufoym/deepo bash

This will make /host/data from the host visible as /data in the container, and /host/config as /config. Such isolation reduces the chances of your containerized experiments overwriting or using wrong data.

Please note that some frameworks (e.g. PyTorch) use shared memory to share data between processes, so if multiprocessing is used the default shared memory segment size that container runs with is not enough, and you should increase shared memory size either with --ipc=host or --shm-size command line options to nvidia-docker run.

nvidia-docker run -it --ipc=host ufoym/deepo bash

CPU Version

Installation

Step 1. Install Docker.

Step 2. Obtain the all-in-one image from Docker Hub

docker pull ufoym/deepo:cpu

Usage

Now you can try this command:

docker run -it ufoym/deepo:cpu bash

If you want to share your data and configurations between the host (your machine or VM) and the container in which you are using Deepo, use the -v option, e.g.

docker run -it -v /host/data:/data -v /host/config:/config ufoym/deepo:cpu bash

This will make /host/data from the host visible as /data in the container, and /host/config as /config. Such isolation reduces the chances of your containerized experiments overwriting or using wrong data.

Please note that some frameworks (e.g. PyTorch) use shared memory to share data between processes, so if multiprocessing is used the default shared memory segment size that container runs with is not enough, and you should increase shared memory size either with --ipc=host or --shm-size command line options to docker run.

docker run -it --ipc=host ufoym/deepo:cpu bash

You are now ready to begin your journey.

$ python

>>> import tensorflow
>>> import sonnet
>>> import torch
>>> import keras
>>> import mxnet
>>> import cntk
>>> import chainer
>>> import theano
>>> import lasagne
>>> import caffe
>>> import caffe2

$ caffe --version

caffe version 1.0.0

$ th

 │  ______             __   |  Torch7
 │ /_  __/__  ________/ /   |  Scientific computing for Lua.
 │  / / / _ \/ __/ __/ _ \  |  Type ? for help
 │ /_/  \___/_/  \__/_//_/  |  https://github.com/torch
 │                          |  http://torch.ch
 │
 │th>

Customization

Note that docker pull ufoym/deepo mentioned in Quick Start will give you a standard image containing all available deep learning frameworks. You can customize your own environment as well.

Unhappy with all-in-one solution?

If you prefer a specific framework rather than an all-in-one image, just append a tag with the name of the framework. Take tensorflow for example:

docker pull ufoym/deepo:tensorflow

Other python versions

Note that all python-related images use Python 3.6 by default. If you are unhappy with Python 3.6, you can also specify other python versions:

docker pull ufoym/deepo:py27
docker pull ufoym/deepo:tensorflow-py27

Currently, we support Python 2.7 and Python 3.6.

See Available Tags for a complete list of all available tags. These pre-built images are all built from docker/Dockerfile.* and circle.yml. See How to generate docker/Dockerfile.* and circle.yml if you are interested in how these files are generated.

Jupyter support

Step 1. pull the image with jupyter support

docker pull ufoym/deepo:all-py36-jupyter

Note that the tag could be either of all-py36-jupyter, py36-jupyter, all-py27-jupyter, or py27-jupyter.

Step 2. run the image

nvidia-docker run -it -p 8888:8888 --ipc=host ufoym/deepo:all-jupyter-py36 jupyter notebook --no-browser --ip=0.0.0.0 --allow-root --NotebookApp.token= --notebook-dir='/root'

Build your own customized image with Lego-like modules

Step 1. prepare generator

git clone https://github.com/ufoym/deepo.git
cd deepo/generator

Step 2. generate your customized Dockerfile

For example, if you like pytorch and lasagne, then

python generate.py Dockerfile pytorch lasagne

This should generate a Dockerfile that contains everything for building pytorch and lasagne. Note that the generator can handle automatic dependency processing and topologically sort the lists. So you don't need to worry about missing dependencies and the list order.

You can also specify the version of Python:

python generate.py Dockerfile pytorch lasagne python==3.6

Step 3. build your Dockerfile

docker build -t my/deepo .

This may take several minutes as it compiles a few libraries from scratch.

Comparison to alternatives

. modern-deep-learning dl-docker jupyter-deeplearning Deepo
ubuntu 16.04 14.04 14.04 16.04
cuda 8.0 6.5-8.0 8.0/9.0/None
cudnn v5 v2-5 v7
onnx ✔️
theano ✔️ ✔️ ✔️
tensorflow ✔️ ✔️ ✔️ ✔️
sonnet ✔️
pytorch ✔️
keras ✔️ ✔️ ✔️ ✔️
lasagne ✔️ ✔️ ✔️
mxnet ✔️
cntk ✔️
chainer ✔️
caffe ✔️ ✔️ ✔️ ✔️
caffe2 ✔️
torch ✔️ ✔️ ✔️

Available Tags

. CUDA 9.0 / Python 3.6 CUDA 9.0 / Python 2.7 CPU-only / Python 3.6 CPU-only / Python 2.7
all-in-one latest all all-py36 py36-cu90 all-py36-cu90 all-py27-cu90 all-py27 py27-cu90 all-py36-cpu all-cpu py36-cpu cpu all-py27-cpu py27-cpu
all-in-one with jupyter all-jupyter-py36-cu90 all-jupyter-py36 all-jupyter all-py27-jupyter py27-jupyter all-py36-jupyter-cpu py36-jupyter-cpu all-py27-jupyter-cpu py27-jupyter-cpu
theano theano-py36-cu90 theano-py36 theano theano-py27-cu90 theano-py27 theano-py36-cpu theano-cpu theano-py27-cpu
tensorflow tensorflow-py36-cu90 tensorflow-py36 tensorflow tensorflow-py27-cu90 tensorflow-py27 tensorflow-py36-cpu tensorflow-cpu tensorflow-py27-cpu
sonnet sonnet-py36-cu90 sonnet-py36 sonnet sonnet-py27-cu90 sonnet-py27 sonnet-py36-cpu sonnet-cpu sonnet-py27-cpu
pytorch pytorch-py36-cu90 pytorch-py36 pytorch pytorch-py27-cu90 pytorch-py27 pytorch-py36 pytorch pytorch-py27
keras keras-py36-cu90 keras-py36 keras keras-py27-cu90 keras-py27 keras-py36-cpu keras-cpu keras-py27-cpu
lasagne lasagne-py36-cu90 lasagne-py36 lasagne lasagne-py27-cu90 lasagne-py27 lasagne-py36-cpu lasagne-cpu lasagne-py27-cpu
mxnet mxnet-py36-cu90 mxnet-py36 mxnet mxnet-py27-cu90 mxnet-py27 mxnet-py36-cpu mxnet-cpu mxnet-py27-cpu
cntk cntk-py36-cu90 cntk-py36 cntk cntk-py27-cu90 cntk-py27 cntk-py36-cpu cntk-cpu cntk-py27-cpu
chainer chainer-py36-cu90 chainer-py36 chainer chainer-py27-cu90 chainer-py27 chainer-py36-cpu chainer-cpu chainer-py27-cpu
caffe caffe-py36-cu90 caffe-py36 caffe caffe-py27-cu90 caffe-py27 caffe-py36-cpu caffe-cpu caffe-py27-cpu
caffe2 caffe2-py36-cu90 caffe2-py36 caffe2 caffe2-py27-cu90 caffe2-py27 caffe2-py36-cpu caffe2-cpu caffe2-py27-cpu
torch torch-cu90 torch torch-cu90 torch torch-cpu torch-cpu

Contributing

We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.

Licensing

Deepo is MIT licensed.