Deepo

Deepo is a series of Docker images that

• allows you to quickly set up your deep learning research environment
• supports almost all commonly used deep learning frameworks
• supports GPU acceleration (CUDA and cuDNN included), also works in CPU-only mode
• works on Linux (CPU version/GPU version), Windows (CPU version) and OS X (CPU version)

and their Dockerfile generator that

• allows you to customize your own environment with Lego-like modules
• automatically resolves the dependencies for you

---

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

For users in China who may suffer from slow speeds when pulling the image from the public Docker registry, you can pull deepo images from the China registry mirror by specifying the full path, including the registry, in your docker pull command, for example:

docker pull registry.docker-cn.com/ufoym/deepo

or

docker pull hub-mirror.c.163.com/ufoym/deepo

or

docker pull docker.mirrors.ustc.edu.cn/ufoym/deepo

Usage

Now you can try this command:

docker run --runtime=nvidia --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

docker run --runtime=nvidia -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.

docker run --runtime=nvidia -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 docker run.

docker run --runtime=nvidia -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

$ darknet

usage: darknet <function>

$ 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

Jupyter support

Step 1. pull the image with jupyter support

docker pull ufoym/deepo:all-jupyter

Step 2. run the image

docker run --runtime=nvidia -it -p 8888:8888 --ipc=host ufoym/deepo:all-jupyter 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	18.04
cuda	X	8.0	6.5-8.0	8.0-10.0/None
cudnn	X	v5	v2-5	v7
onnx	X	X	X	O
theano	X	O	O	O
tensorflow	O	O	O	O
sonnet	X	X	X	O
pytorch	X	X	X	O
keras	O	O	O	O
lasagne	X	O	O	O
mxnet	X	X	X	O
cntk	X	X	X	O
chainer	X	X	X	O
caffe	O	O	O	O
caffe2	X	X	X	O
torch	X	O	O	O
darknet	X	X	X	O

Tags

Available Tags

.	CUDA 10.0 / Python 3.6	CPU-only / Python 3.6
all-in-one	latest all all-py36 py36-cu100 all-py36-cu100	all-py36-cpu all-cpu py36-cpu cpu
all-in-one with jupyter	all-jupyter-py36-cu100 all-jupyter-py36 all-jupyter	all-py36-jupyter-cpu py36-jupyter-cpu
Theano	theano-py36-cu100 theano-py36 theano	theano-py36-cpu theano-cpu
TensorFlow	tensorflow-py36-cu100 tensorflow-py36 tensorflow	tensorflow-py36-cputensorflow-cpu
Sonnet	sonnet-py36-cu100 sonnet-py36 sonnet	sonnet-py36-cpu sonnet-cpu
PyTorch / Caffe2	pytorch-py36-cu100 pytorch-py36pytorch	pytorch-py36-cpu pytorch-cpu
Keras	keras-py36-cu100 keras-py36 keras	keras-py36-cpu keras-cpu
Lasagne	lasagne-py36-cu100 lasagne-py36lasagne	lasagne-py36-cpu lasagne-cpu
MXNet	mxnet-py36-cu100 mxnet-py36 mxnet	mxnet-py36-cpu mxnet-cpu
CNTK	cntk-py36-cu100 cntk-py36 cntk	cntk-py36-cpu cntk-cpu
Chainer	chainer-py36-cu100 chainer-py36chainer	chainer-py36-cpu chainer-cpu
Caffe	caffe-py36-cu100 caffe-py36 caffe	caffe-py36-cpu caffe-cpu
Torch	torch-cu100 torch	torch-cpu
Darknet	darknet-cu100 darknet	darknet-cpu

Deprecated 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	py36-cu90 all-py36-cu90	all-py27-cu90all-py27 py27-cu90		all-py27-cpupy27-cpu
all-in-one with jupyter	all-jupyter-py36-cu90	all-py27-jupyter py27-jupyter		all-py27-jupyter-cpu py27-jupyter-cpu
Theano	theano-py36-cu90	theano-py27-cu90 theano-py27		theano-py27-cpu
TensorFlow	tensorflow-py36-cu90	tensorflow-py27-cu90tensorflow-py27		tensorflow-py27-cpu
Sonnet	sonnet-py36-cu90	sonnet-py27-cu90 sonnet-py27		sonnet-py27-cpu
PyTorch	pytorch-py36-cu90	pytorch-py27-cu90 pytorch-py27		pytorch-py27-cpu
Keras	keras-py36-cu90	keras-py27-cu90keras-py27		keras-py27-cpu
Lasagne	lasagne-py36-cu90	lasagne-py27-cu90 lasagne-py27		lasagne-py27-cpu
MXNet	mxnet-py36-cu90	mxnet-py27-cu90mxnet-py27		mxnet-py27-cpu
CNTK	cntk-py36-cu90	cntk-py27-cu90cntk-py27		cntk-py27-cpu
Chainer	chainer-py36-cu90	chainer-py27-cu90 chainer-py27		chainer-py27-cpu
Caffe	caffe-py36-cu90	caffe-py27-cu90caffe-py27		caffe-py27-cpu
Caffe2	caffe2-py36-cu90 caffe2-py36 caffe2	caffe2-py27-cu90 caffe2-py27	caffe2-py36-cpucaffe2-cpu	caffe2-py27-cpu
Torch	torch-cu90	torch-cu90 torch		torch-cpu
Darknet	darknet-cu90	darknet-cu90darknet		darknet-cpu

Citation

@misc{ming2017deepo,
    author = {Ming Yang},
    title = {Deepo: set up deep learning environment in a single command line.},
    year = {2017},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {url{https://github.com/ufoym/deepo}}
}

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.