Deepo2 is now 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: theano, tensorflow, sonnet, pytorch, keras, lasagne, mxnet, cntk, chainer, caffe, torch

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
• Usage
• Customization
• I hate all-in-one solution
• Other python versions
• Build your own customized image
• Comparison to Alternatives
• Contributing
• Licensing

Step 1. Install Docker and nvidia-docker.

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

:::bash
docker pull ufoym/deepo

:::bash
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

:::bash
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.

:::bash
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.

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

You are now ready to begin your journey.

:::$ python```
:::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>

<a name="Customization"/>

# Customization

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

<a name="One"/>

## 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:
:::bash

docker pull ufoym/deepo:tensorflow

<a name="Python"/>

## 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:
:::bash

docker pull ufoym/deepo:py27

:::bash

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/](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`](https://github.com/ufoym/deepo/tree/master/scripts) if you are interested in how these files are generated.

<a name="Build"/>

## Build your own customized image with Lego-like modules

#### Step 1. prepare generator

:::bash

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
:::bash

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:
:::bash

python generate.py Dockerfile pytorch lasagne python==3.6

#### Step 3. build your Dockerfile

:::bash

docker build -t my/deepo .

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


<a name="Comparison"/>

# Comparison to alternatives
.                                                  | modern-deep-learning | dl-docker          | jupyter-deeplearning | Deepo
:------------------------------------------------: | :------------------: | :----------------: | :------------------: | :----------------:
 [ubuntu](https://www.ubuntu.com)                  | 16.04                | 14.04              | 14.04                | 16.04
 [cuda](https://developer.nvidia.com/cuda-zone)    | :x:                  | 8.0                | 6.5-8.0              | 8.0
 [cudnn](https://developer.nvidia.com/cudnn)       | :x:                  | v5                 | v2-5                 | v6
 [theano](http://deeplearning.net/software/theano) | :x:                  | :heavy_check_mark: | :heavy_check_mark:   | :heavy_check_mark:
 [tensorflow](http://www.tensorflow.org)           | :heavy_check_mark:   | :heavy_check_mark: | :heavy_check_mark:   | :heavy_check_mark:
 [sonnet](https://github.com/deepmind/sonnet)      | :x:                  | :x:                | :x:                  | :heavy_check_mark:
 [pytorch](http://pytorch.org)                     | :x:                  | :x:                | :x:                  | :heavy_check_mark:
 [keras](https://keras.io)                         | :heavy_check_mark:   | :heavy_check_mark: | :heavy_check_mark:   | :heavy_check_mark:
 [lasagne](http://lasagne.readthedocs.io)          | :x:                  | :heavy_check_mark: | :heavy_check_mark:   | :heavy_check_mark:
 [mxnet](http://mxnet.incubator.apache.org)        | :x:                  | :x:                | :x:                  | :heavy_check_mark:
 [cntk](http://cntk.ai)                            | :x:                  | :x:                | :x:                  | :heavy_check_mark:
 [chainer](https://chainer.org)                    | :x:                  | :x:                | :x:                  | :heavy_check_mark:
 [caffe](http://caffe.berkeleyvision.org)          | :heavy_check_mark:   | :heavy_check_mark: | :heavy_check_mark:   | :heavy_check_mark:
 [torch](http://torch.ch/)                         | :x:                  | :heavy_check_mark: | :heavy_check_mark:   | :heavy_check_mark: