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Releases: ufoym/deepo

Deepo v2.0.0

27 Nov 15:04
@ufoym ufoym
v2.0.0
e96f620
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Deepo2 is now a series of Docker images that

and their Dockerfile generator that

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

Table of contents

Quick Start

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

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

$ 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.

I hate 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 https://hub.docker.com/r/ufoym/deepo/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.

Build your own customized image with Lego-like modules

Step 1. prepare generator

git clone https://github.com/ufoym/deepo.git
cd deepo/generator
pip install -r requirements.txt

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
cudnn ❌ v5 v2-5 v6
theano ❌ ✔️ ✔️ ✔️
tensorflow ✔️ ✔️ ✔️ ✔️
sonnet ❌ ❌ ❌ ✔️
pytorch ❌ ❌ ❌ ✔️
keras ✔️ ✔️ ✔️ ✔️
lasagne ❌ ✔️ ✔️ ✔️
mxnet ❌ ❌ ❌ ✔️
cntk ❌ ❌ ❌ ✔️
chainer ❌ ❌ ❌ ✔️
caffe ✔️ ✔️ ✔️ ✔️
torch ❌ ✔️ ✔️ ✔️
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Deepo v1.0.0

27 Nov 14:53
@ufoym ufoym
v1.0.0
e132dd2
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Deepo v1.0.0

Deepo is a Docker image with a full reproducible deep learning research environment. It contains most popular deep learning frameworks: theano, tensorflow, sonnet, pytorch, keras,
lasagne, mxnet, cntk, chainer, caffe, torch.

Quick Start

Installation

Step 1. Install Docker and nvidia-docker.

Step 2. Obtain the Deepo image

You can either directly download the image from Docker Hub, or build the image yourself.

Option 1: Get the image from Docker Hub (recommended)
docker pull ufoym/deepo
Option 2: Build the Docker image locally
git clone https://github.com/ufoym/deepo.git
cd deepo && docker build -t ufoym/deepo .

Note that this may take several hours as it compiles a few libraries from scratch.

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.

You are now ready to begin your journey.

tensorflow

$ python

>>> import tensorflow
>>> print(tensorflow.__name__, tensorflow.__version__)
tensorflow 1.3.0

sonnet

$ python

>>> import sonnet
>>> print(sonnet.__name__, sonnet.__path__)
sonnet ['/usr/local/lib/python3.5/dist-packages/sonnet']

pytorch

$ python

>>> import torch
>>> print(torch.__name__, torch.__version__)
torch 0.2.0_3

keras

$ python

>>> import keras
>>> print(keras.__name__, keras.__version__)
keras 2.0.8

mxnet

$ python

>>> import mxnet
>>> print(mxnet.__name__, mxnet.__version__)
mxnet 0.11.0

cntk

$ python

>>> import cntk
>>> print(cntk.__name__, cntk.__version__)
cntk 2.2

chainer

$ python

>>> import chainer
>>> print(chainer.__name__, chainer.__version__)
chainer 3.0.0

theano

$ python

>>> import theano
>>> print(theano.__name__, theano.__version__)
theano 0.10.0beta4+14.gb6e3768

lasagne

$ python

>>> import lasagne
>>> print(lasagne.__name__, lasagne.__version__)
lasagne 0.2.dev1

caffe

$ python

>>> import caffe
>>> print(caffe.__name__, caffe.__version__)
caffe 1.0.0

$ caffe --version

caffe version 1.0.0

torch

$ th

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

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
cudnn ❌ v5 v2-5 v6
theano ❌ ✔️ ✔️ ✔️
tensorflow ✔️ ✔️ ✔️ ✔️
sonnet ❌ ❌ ❌ ✔️
pytorch ❌ ❌ ❌ ✔️
keras ✔️ ✔️ ✔️ ✔️
lasagne ❌ ✔️ ✔️ ✔️
mxnet ❌ ❌ ❌ ✔️
cntk ❌ ❌ ❌ ✔️
chainer ❌ ❌ ❌ ✔️
caffe ✔️ ✔️ ✔️ ✔️
torch ❌ ✔️ ✔️ ✔️
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