tingatepai/deepo repository overview

• automatically resolves the dependencies for you
  • deepo knows which combos (CUDA/cuDNN/Python/PyTorch/Tensorflow, ..., tons of dependancies) are compatible
  • and will pick the right versions for you
  • and arrange sequence of installation procedures using topological sorting⁠

We also prepare a series of pre-built docker images that

• allows you to instantly set up common 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)

⁠Table of contents

• Quick Start⁠
  • GPU Version⁠
    • Installation⁠
    • Usage⁠
  • CPU Version⁠
    • Installation⁠
    • Usage⁠
• Customization⁠
  • Unhappy with all-in-one solution?⁠
  • Jupyter support⁠
  • Build your own customized image⁠
• Comparison to Alternatives⁠
• Tags⁠
  • Available Tags⁠
  • Deprecated Tags⁠
• Citation⁠
• Contributing⁠
• Licensing⁠

⁠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

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

⁠Usage

Now you can try this command:

docker run --gpus all --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 --gpus all -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 --gpus all -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 --gpus all -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 paddle

$ caffe --version

caffe version 1.0.0

$ darknet

usage: darknet <function>

⁠Jupyter support

⁠Step 1. pull the all-in-one image

docker pull ufoym/deepo

⁠Step 2. run the image

docker run --gpus all -it -p 8888:8888 -v /home/u:/root --ipc=host ufoym/deepo jupyter lab --no-browser --ip=0.0.0.0 --allow-root --LabApp.allow_origin='*' --LabApp.root_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

or with CUDA 11.1 and CUDNN 8

python generate.py Dockerfile pytorch lasagne --cuda-ver 11.1 --cudnn-ver 8

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

⁠Tags

⁠Available Tags

⁠Deprecated Tags

⁠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