Quick Start

GPU Version

Installation

Usage

Now you can try this command:

docker run --gpus all --rm ufoym/deepo nvidia-smi

CPU Version

Installation

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>

Customization

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 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