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/deepoOption 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-smiThis 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 bashIf 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 bashThis 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 1.3.0sonnet
$ 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_3keras
$ python
>>> import keras
>>> print(keras.__name__, keras.__version__)
keras 2.0.8mxnet
$ python
>>> import mxnet
>>> print(mxnet.__name__, mxnet.__version__)
mxnet 0.11.0cntk
$ python
>>> import cntk
>>> print(cntk.__name__, cntk.__version__)
cntk 2.2chainer
$ python
>>> import chainer
>>> print(chainer.__name__, chainer.__version__)
chainer 3.0.0theano
$ python
>>> import theano
>>> print(theano.__name__, theano.__version__)
theano 0.10.0beta4+14.gb6e3768lasagne
$ python
>>> import lasagne
>>> print(lasagne.__name__, lasagne.__version__)
lasagne 0.2.dev1caffe
$ 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 |
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| tensorflow |
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| sonnet |
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| pytorch |
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| keras |
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| lasagne |
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| mxnet |
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| cntk |
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| chainer |
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| caffe |
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| torch |
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