deepo

Setup and customize deep learning environment in seconds.

View the Project on GitHub

deepo

workflows docker license

Deepo is an open framework for painlessly assembling specialized Docker images for deep learning research. It provides a “Lego set” of dozens of standard components for preparing deep learning tools, along with a framework for composing them into custom Docker images.

At the core of Deepo is a Dockerfile generator that

We also provide a series of pre-built Docker images that

Table of contents

Quick Start

GPU Version

Installation

Step 1. Install Docker and NVIDIA Container Toolkit.

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

docker pull ufoym/deepo

Usage

Verify that GPU access works inside a container:

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

If this does not work, check the issues section of the NVIDIA Container Toolkit GitHub — many solutions are already documented. To launch an interactive shell in a persistent container:

docker run --gpus all -it ufoym/deepo bash

To share data and configuration between the host (your machine or VM) and the container, use the -v option:

docker run --gpus all -it -v /host/data:/data -v /host/config:/config ufoym/deepo bash

This makes /host/data on the host visible as /data inside the container, and /host/config as /config. This isolation helps prevent containerized experiments from accidentally overwriting or reading the wrong data.

Note that some frameworks (e.g., PyTorch) use shared memory for inter-process communication. If you use multiprocessing, the container’s default shared memory size may be insufficient. Increase it with --ipc=host or --shm-size:

docker run --gpus all -it --ipc=host ufoym/deepo bash

CPU Version

Installation

Step 1. Install Docker.

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

docker pull ufoym/deepo:cpu

Usage

Launch an interactive shell:

docker run -it ufoym/deepo:cpu bash

To share data and configuration between the host (your machine or VM) and the container, use the -v option:

docker run -it -v /host/data:/data -v /host/config:/config ufoym/deepo:cpu bash

This makes /host/data on the host visible as /data inside the container, and /host/config as /config. This isolation helps prevent containerized experiments from accidentally overwriting or reading the wrong data.

Note that some frameworks (e.g., PyTorch) use shared memory for inter-process communication. If you use multiprocessing, the container’s default shared memory size may be insufficient. Increase it with --ipc=host or --shm-size:

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

You are now ready to begin your journey.

$ python

>>> import tensorflow
>>> import torch
>>> import keras
>>> import mxnet
>>> import chainer
>>> import paddle

$ darknet

usage: darknet <function>

Customization

The docker pull ufoym/deepo command from Quick Start gives you a standard image containing every available deep learning framework. You can also customize your own environment.

Unhappy with all-in-one solution?

If you prefer a single framework instead of the all-in-one image, simply append a tag with the framework name. For example, to pull TensorFlow only:

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. Set up the generator

git clone https://github.com/ufoym/deepo.git
cd deepo/generator

Step 2. Generate a customized Dockerfile

For example, to create an image with pytorch and keras:

python generate.py Dockerfile pytorch keras

Or with CUDA 11.3 and cuDNN 8:

python generate.py Dockerfile pytorch keras --cuda-ver 11.3.1 --cudnn-ver 8

This generates a Dockerfile with everything needed to build pytorch and keras. The generator automatically resolves dependencies and topologically sorts them, so you don’t need to worry about missing packages or ordering.

You can also specify the Python version:

python generate.py Dockerfile pytorch keras python==3.8

Step 3. Build the Dockerfile

docker build -t my/deepo .

This may take several minutes, as some libraries are compiled from source.

Comparison to Alternatives

. modern-deep-learning dl-docker jupyter-deeplearning Deepo
ubuntu 16.04 14.04 14.04 20.04
cuda X 8.0 6.5-8.0 11.3/None
cudnn X v5 v2-5 v8
onnx X X X O
tensorflow O O O O
pytorch X X X O
keras O O O O
mxnet X X X O
chainer X X X O
darknet X X X O
paddlepaddle X X X O

Tags

Available Tags

. CUDA 11.3 / Python 3.8 CPU-only / Python 3.8
all-in-one latest all all-py38 py38-cu113 all-py38-cu113 all-py38-cpu all-cpu py38-cpu cpu
TensorFlow tensorflow-py38-cu113 tensorflow-py38 tensorflow tensorflow-py38-cpu tensorflow-cpu
PyTorch pytorch-py38-cu113 pytorch-py38 pytorch pytorch-py38-cpu pytorch-cpu
Keras keras-py38-cu113 keras-py38 keras keras-py38-cpu keras-cpu
MXNet mxnet-py38-cu113 mxnet-py38 mxnet mxnet-py38-cpu mxnet-cpu
Chainer chainer-py38-cu113 chainer-py38 chainer chainer-py38-cpu chainer-cpu
Darknet darknet-cu113 darknet darknet-cpu
PaddlePaddle paddle-cu113 paddle paddle-cpu

Deprecated Tags

. CUDA 11.3 / Python 3.6 CUDA 11.1 / Python 3.6 CUDA 10.1 / Python 3.6 CUDA 10.0 / Python 3.6 CUDA 9.0 / Python 3.6 CUDA 9.0 / Python 2.7 CPU-only / Python 3.6 CPU-only / Python 2.7
all-in-one py36-cu113 all-py36-cu113 py36-cu111 all-py36-cu111 py36-cu101 all-py36-cu101 py36-cu100 all-py36-cu100 py36-cu90 all-py36-cu90 all-py27-cu90 all-py27 py27-cu90   all-py27-cpu py27-cpu
all-in-one with jupyter         all-jupyter-py36-cu90 all-py27-jupyter py27-jupyter   all-py27-jupyter-cpu py27-jupyter-cpu
Theano theano-py36-cu113 theano-py36-cu111 theano-py36-cu101 theano-py36-cu100 theano-py36-cu90 theano-py27-cu90 theano-py27   theano-py27-cpu
TensorFlow tensorflow-py36-cu113 tensorflow-py36-cu111 tensorflow-py36-cu101 tensorflow-py36-cu100 tensorflow-py36-cu90 tensorflow-py27-cu90 tensorflow-py27   tensorflow-py27-cpu
Sonnet sonnet-py36-cu113 sonnet-py36-cu111 sonnet-py36-cu101 sonnet-py36-cu100 sonnet-py36-cu90 sonnet-py27-cu90 sonnet-py27   sonnet-py27-cpu
PyTorch pytorch-py36-cu113 pytorch-py36-cu111 pytorch-py36-cu101 pytorch-py36-cu100 pytorch-py36-cu90 pytorch-py27-cu90 pytorch-py27   pytorch-py27-cpu
Keras keras-py36-cu113 keras-py36-cu111 keras-py36-cu101 keras-py36-cu100 keras-py36-cu90 keras-py27-cu90 keras-py27   keras-py27-cpu
Lasagne lasagne-py36-cu113 lasagne-py36-cu111 lasagne-py36-cu101 lasagne-py36-cu100 lasagne-py36-cu90 lasagne-py27-cu90 lasagne-py27   lasagne-py27-cpu
MXNet mxnet-py36-cu113 mxnet-py36-cu111 mxnet-py36-cu101 mxnet-py36-cu100 mxnet-py36-cu90 mxnet-py27-cu90 mxnet-py27   mxnet-py27-cpu
CNTK cntk-py36-cu113 cntk-py36-cu111 cntk-py36-cu101 cntk-py36-cu100 cntk-py36-cu90 cntk-py27-cu90 cntk-py27   cntk-py27-cpu
Chainer chainer-py36-cu113 chainer-py36-cu111 chainer-py36-cu101 chainer-py36-cu100 chainer-py36-cu90 chainer-py27-cu90 chainer-py27   chainer-py27-cpu
Caffe caffe-py36-cu113 caffe-py36-cu111 caffe-py36-cu101 caffe-py36-cu100 caffe-py36-cu90 caffe-py27-cu90 caffe-py27   caffe-py27-cpu
Caffe2         caffe2-py36-cu90 caffe2-py36 caffe2 caffe2-py27-cu90 caffe2-py27 caffe2-py36-cpu caffe2-cpu caffe2-py27-cpu
Torch torch-cu113 torch-cu111 torch-cu101 torch-cu100 torch-cu90 torch-cu90 torch   torch-cpu
Darknet darknet-cu113 darknet-cu111 darknet-cu101 darknet-cu100 darknet-cu90 darknet-cu90 darknet   darknet-cpu

Citation

@misc{ming2017deepo,
    author = {Ming Yang},
    title = {Deepo: Set up a deep learning environment with 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 bug fixes, please go ahead and open a pull request directly. If you plan to contribute new features, utility functions, or extensions, please open an issue first to discuss your idea with us.

Licensing

Deepo is MIT licensed.