---

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

• lets you customize your deep learning environment with Lego-like modules
  • describe your environment in a single command line
  • Deepo generates Dockerfiles following best practices
  • and handles all the configuration for you
• automatically resolves dependencies
  • Deepo knows which combinations of CUDA, cuDNN, Python, PyTorch, TensorFlow, etc. are compatible
  • picks the right versions on your behalf
  • and determines the correct installation order via topological sorting

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

• let you instantly set up common deep learning research environments
• support widely used deep learning frameworks
• support GPU acceleration (CUDA and cuDNN included) and also work in CPU-only mode
• run on Linux (CPU/GPU), Windows (CPU), and macOS (CPU)

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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 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.