# Technical Documentation: ufoym/deepo
> ℹ️ **Provenance:** Hybrid Fusion: `ufoym/deepo` (README + 2 In-Tree Chapters) · [CodeWiki Reference](https://codewiki.google/github.com/ufoym/deepo) · Recency: Active (< 180 days)
## 1. Project Overview & Quickstart (ufoym/deepo)
[](https://hub.docker.com/r/ufoym/deepo)
---
***Deepo*** is an open framework for painlessly assembling specialized [*Docker*](http://www.docker.com/) 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](#Build) 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](https://en.wikipedia.org/wiki/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](#Available-tags)
- support [GPU acceleration](#GPU) (CUDA and cuDNN included) and also work in [CPU-only mode](#CPU)
- run on Linux ([CPU](#CPU)/[GPU](#GPU)), Windows ([CPU](#CPU)), and macOS ([CPU](#CPU))
---
# Table of contents
- [Quick Start](#Quick-Start)
- [GPU Version](#GPU)
- [Installation](#Installation)
- [Usage](#Usage)
- [CPU Version](#CPU)
- [Installation](#Installation-cpu)
- [Usage](#Usage-cpu)
- [Customization](#Customization)
- [Unhappy with all-in-one solution?](#One)
- [Jupyter support](#Jupyter)
- [Build your own customized image](#Build)
- [Comparison to Alternatives](#Comparison)
- [Tags](#Tags)
- [Available Tags](#Available-tags)
- [Deprecated Tags](#Deprecated-tags)
- [Citation](#Citation)
- [Contributing](#Contributing)
- [Licensing](#Licensing)
---
# Quick Start
## GPU Version
### Installation
#### Step 1. Install [Docker](https://docs.docker.com/engine/installation/) and [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html).
#### Step 2. Pull the all-in-one image from [Docker Hub](https://hub.docker.com/r/ufoym/deepo)
```bash
docker pull ufoym/deepo
```
### Usage
Verify that GPU access works inside a container:
```bash
docker run --gpus all --rm ufoym/deepo nvidia-smi
```
If this does not work, check [the issues section of the NVIDIA Container Toolkit GitHub](https://github.com/NVIDIA/nvidia-container-toolkit/issues) — many solutions are already documented. To launch an interactive shell in a persistent container:
```bash
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:
```bash
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`:
```bash
docker run --gpus all -it --ipc=host ufoym/deepo bash
```
## CPU Version
### Installation
#### Step 1. Install [Docker](https://docs.docker.com/engine/installation/).
#### Step 2. Pull the all-in-one image from [Docker Hub](https://hub.docker.com/r/ufoym/deepo)
```bash
docker pull ufoym/deepo:cpu
```
### Usage
Launch an interactive shell:
```bash
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:
```bash
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`:
```bash
docker run -it --ipc=host ufoym/deepo:cpu bash
```
_You are now ready to begin your journey._
```
$ python
```
```python
>>> import tensorflow
>>> import torch
>>> import keras
>>> import mxnet
>>> import chainer
>>> import paddle
```
```
$ darknet
```
```
usage: darknet
```
# Customization
The `docker pull ufoym/deepo` command from [Quick Start](#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:
```bash
docker pull ufoym/deepo:tensorflow
```
## Jupyter Support
#### Step 1. Pull the all-in-one image
```bash
docker pull ufoym/deepo
```
#### Step 2. Run the image
```bash
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
```bash
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`:
```bash
python generate.py Dockerfile pytorch keras
```
Or with CUDA 11.3 and cuDNN 8:
```bash
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:
```bash
python generate.py Dockerfile pytorch keras python==3.8
```
#### Step 3. Build the Dockerfile
```bash
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](https://www.ubuntu.com) | 16.04 | 14.04 | 14.04 | 20.04
[cuda](https://developer.nvidia.com/cuda-zone) | X | 8.0 | 6.5-8.0 | 11.3/None
[cudnn](https://developer.nvidia.com/cudnn) | X | v5 | v2-5 | v8
[onnx](https://onnx.ai) | X | X | X | O
[tensorflow](http://www.tensorflow.org) | O | O | O | O
[pytorch](http://pytorch.org) | X | X | X | O
[keras](https://keras.io) | O | O | O | O
[mxnet](http://mxnet.incubator.apache.org) | X | X | X | O
[chainer](https://chainer.org) | X | X | X | O
[darknet](https://pjreddie.com/darknet/) | X | X | X | O
[paddlepaddle](https://www.paddlepaddle.org.cn/) | 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](http://www.tensorflow.org) | `tensorflow-py38-cu113` `tensorflow-py38` `tensorflow` | `tensorflow-py38-cpu` `tensorflow-cpu`
[PyTorch](http://pytorch.org) | `pytorch-py38-cu113` `pytorch-py38` `pytorch` | `pytorch-py38-cpu` `pytorch-cpu`
[Keras](https://keras.io) | `keras-py38-cu113` `keras-py38` `keras` | `keras-py38-cpu` `keras-cpu`
[MXNet](http://mxnet.incubator.apache.org) | `mxnet-py38-cu113` `mxnet-py38` `mxnet` | `mxnet-py38-cpu` `mxnet-cpu`
[Chainer](https://chainer.org) | `chainer-py38-cu113` `chainer-py38` `chainer` | `chainer-py38-cpu` `chainer-cpu`
[Darknet](https://pjreddie.com/darknet/) | `darknet-cu113` `darknet` | `darknet-cpu`
[PaddlePaddle](https://www.paddlepaddle.org.cn/) | `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](http://deeplearning.net/software/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](http://www.tensorflow.org) | `tensorflow-py36-cu113` | `tensorflow-py36-cu111` | `tensorflow-py36-cu101` | `tensorflow-py36-cu100` | `tensorflow-py36-cu90` | `tensorflow-py27-cu90` `tensorflow-py27` | | `tensorflow-py27-cpu`
[Sonnet](https://github.com/deepmind/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](http://pytorch.org) | `pytorch-py36-cu113` | `pytorch-py36-cu111` | `pytorch-py36-cu101` | `pytorch-py36-cu100` | `pytorch-py36-cu90` | `pytorch-py27-cu90` `pytorch-py27` | | `pytorch-py27-cpu`
[Keras](https://keras.io) | `keras-py36-cu113` | `keras-py36-cu111` | `keras-py36-cu101` | `keras-py36-cu100` | `keras-py36-cu90` | `keras-py27-cu90` `keras-py27` | | `keras-py27-cpu`
[Lasagne](http://lasagne.readthedocs.io) | `lasagne-py36-cu113` | `lasagne-py36-cu111` | `lasagne-py36-cu101` | `lasagne-py36-cu100` | `lasagne-py36-cu90` | `lasagne-py27-cu90` `lasagne-py27` | | `lasagne-py27-cpu`
[MXNet](http://mxnet.incubator.apache.org) | `mxnet-py36-cu113` | `mxnet-py36-cu111` | `mxnet-py36-cu101` | `mxnet-py36-cu100` | `mxnet-py36-cu90` | `mxnet-py27-cu90` `mxnet-py27` | | `mxnet-py27-cpu`
[CNTK](http://cntk.ai) | `cntk-py36-cu113` | `cntk-py36-cu111` | `cntk-py36-cu101` | `cntk-py36-cu100` | `cntk-py36-cu90` | `cntk-py27-cu90` `cntk-py27` | | `cntk-py27-cpu`
[Chainer](https://chainer.org) | `chainer-py36-cu113` | `chainer-py36-cu111` | `chainer-py36-cu101` | `chainer-py36-cu100` | `chainer-py36-cu90` | `chainer-py27-cu90` `chainer-py27` | | `chainer-py27-cpu`
[Caffe](http://caffe.berkeleyvision.org) | `caffe-py36-cu113` | `caffe-py36-cu111` | `caffe-py36-cu101` | `caffe-py36-cu100` | `caffe-py36-cu90` | `caffe-py27-cu90` `caffe-py27` | | `caffe-py27-cpu`
[Caffe2](https://caffe2.ai) | | | | | `caffe2-py36-cu90` `caffe2-py36` `caffe2` | `caffe2-py27-cu90` `caffe2-py27` | `caffe2-py36-cpu` `caffe2-cpu` | `caffe2-py27-cpu`
[Torch](http://torch.ch/) | `torch-cu113` | `torch-cu111` | `torch-cu101` | `torch-cu100` | `torch-cu90` | `torch-cu90` `torch` | | `torch-cpu`
[Darknet](https://pjreddie.com/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](https://github.com/ufoym/deepo/blob/master/LICENSE).
## 2. In-Tree Documentation Chapters (ufoym/deepo)
## File: README.md
[](https://hub.docker.com/r/ufoym/deepo)
---
***Deepo*** is an open framework for painlessly assembling specialized [*Docker*](http://www.docker.com/) 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](#Build) 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](https://en.wikipedia.org/wiki/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](#Available-tags)
- support [GPU acceleration](#GPU) (CUDA and cuDNN included) and also work in [CPU-only mode](#CPU)
- run on Linux ([CPU](#CPU)/[GPU](#GPU)), Windows ([CPU](#CPU)), and macOS ([CPU](#CPU))
---
# Table of contents
- [Quick Start](#Quick-Start)
- [GPU Version](#GPU)
- [Installation](#Installation)
- [Usage](#Usage)
- [CPU Version](#CPU)
- [Installation](#Installation-cpu)
- [Usage](#Usage-cpu)
- [Customization](#Customization)
- [Unhappy with all-in-one solution?](#One)
- [Jupyter support](#Jupyter)
- [Build your own customized image](#Build)
- [Comparison to Alternatives](#Comparison)
- [Tags](#Tags)
- [Available Tags](#Available-tags)
- [Deprecated Tags](#Deprecated-tags)
- [Citation](#Citation)
- [Contributing](#Contributing)
- [Licensing](#Licensing)
---
# Quick Start
## GPU Version
### Installation
#### Step 1. Install [Docker](https://docs.docker.com/engine/installation/) and [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html).
#### Step 2. Pull the all-in-one image from [Docker Hub](https://hub.docker.com/r/ufoym/deepo)
```bash
docker pull ufoym/deepo
```
### Usage
Verify that GPU access works inside a container:
```bash
docker run --gpus all --rm ufoym/deepo nvidia-smi
```
If this does not work, check [the issues section of the NVIDIA Container Toolkit GitHub](https://github.com/NVIDIA/nvidia-container-toolkit/issues) — many solutions are already documented. To launch an interactive shell in a persistent container:
```bash
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:
```bash
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`:
```bash
docker run --gpus all -it --ipc=host ufoym/deepo bash
```
## CPU Version
### Installation
#### Step 1. Install [Docker](https://docs.docker.com/engine/installation/).
#### Step 2. Pull the all-in-one image from [Docker Hub](https://hub.docker.com/r/ufoym/deepo)
```bash
docker pull ufoym/deepo:cpu
```
### Usage
Launch an interactive shell:
```bash
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:
```bash
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`:
```bash
docker run -it --ipc=host ufoym/deepo:cpu bash
```
_You are now ready to begin your journey._
```
$ python
```
```python
>>> import tensorflow
>>> import torch
>>> import keras
>>> import mxnet
>>> import chainer
>>> import paddle
```
```
$ darknet
```
```
usage: darknet
```
# Customization
The `docker pull ufoym/deepo` command from [Quick Start](#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:
```bash
docker pull ufoym/deepo:tensorflow
```
## Jupyter Support
#### Step 1. Pull the all-in-one image
```bash
docker pull ufoym/deepo
```
#### Step 2. Run the image
```bash
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
```bash
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`:
```bash
python generate.py Dockerfile pytorch keras
```
Or with CUDA 11.3 and cuDNN 8:
```bash
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:
```bash
python generate.py Dockerfile pytorch keras python==3.8
```
#### Step 3. Build the Dockerfile
```bash
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](https://www.ubuntu.com) | 16.04 | 14.04 | 14.04 | 20.04
[cuda](https://developer.nvidia.com/cuda-zone) | X | 8.0 | 6.5-8.0 | 11.3/None
[cudnn](https://developer.nvidia.com/cudnn) | X | v5 | v2-5 | v8
[onnx](https://onnx.ai) | X | X | X | O
[tensorflow](http://www.tensorflow.org) | O | O | O | O
[pytorch](http://pytorch.org) | X | X | X | O
[keras](https://keras.io) | O | O | O | O
[mxnet](http://mxnet.incubator.apache.org) | X | X | X | O
[chainer](https://chainer.org) | X | X | X | O
[darknet](https://pjreddie.com/darknet/) | X | X | X | O
[paddlepaddle](https://www.paddlepaddle.org.cn/) | 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](http://www.tensorflow.org) | `tensorflow-py38-cu113` `tensorflow-py38` `tensorflow` | `tensorflow-py38-cpu` `tensorflow-cpu`
[PyTorch](http://pytorch.org) | `pytorch-py38-cu113` `pytorch-py38` `pytorch` | `pytorch-py38-cpu` `pytorch-cpu`
[Keras](https://keras.io) | `keras-py38-cu113` `keras-py38` `keras` | `keras-py38-cpu` `keras-cpu`
[MXNet](http://mxnet.incubator.apache.org) | `mxnet-py38-cu113` `mxnet-py38` `mxnet` | `mxnet-py38-cpu` `mxnet-cpu`
[Chainer](https://chainer.org) | `chainer-py38-cu113` `chainer-py38` `chainer` | `chainer-py38-cpu` `chainer-cpu`
[Darknet](https://pjreddie.com/darknet/) | `darknet-cu113` `darknet` | `darknet-cpu`
[PaddlePaddle](https://www.paddlepaddle.org.cn/) | `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](http://deeplearning.net/software/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](http://www.tensorflow.org) | `tensorflow-py36-cu113` | `tensorflow-py36-cu111` | `tensorflow-py36-cu101` | `tensorflow-py36-cu100` | `tensorflow-py36-cu90` | `tensorflow-py27-cu90` `tensorflow-py27` | | `tensorflow-py27-cpu`
[Sonnet](https://github.com/deepmind/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](http://pytorch.org) | `pytorch-py36-cu113` | `pytorch-py36-cu111` | `pytorch-py36-cu101` | `pytorch-py36-cu100` | `pytorch-py36-cu90` | `pytorch-py27-cu90` `pytorch-py27` | | `pytorch-py27-cpu`
[Keras](https://keras.io) | `keras-py36-cu113` | `keras-py36-cu111` | `keras-py36-cu101` | `keras-py36-cu100` | `keras-py36-cu90` | `keras-py27-cu90` `keras-py27` | | `keras-py27-cpu`
[Lasagne](http://lasagne.readthedocs.io) | `lasagne-py36-cu113` | `lasagne-py36-cu111` | `lasagne-py36-cu101` | `lasagne-py36-cu100` | `lasagne-py36-cu90` | `lasagne-py27-cu90` `lasagne-py27` | | `lasagne-py27-cpu`
[MXNet](http://mxnet.incubator.apache.org) | `mxnet-py36-cu113` | `mxnet-py36-cu111` | `mxnet-py36-cu101` | `mxnet-py36-cu100` | `mxnet-py36-cu90` | `mxnet-py27-cu90` `mxnet-py27` | | `mxnet-py27-cpu`
[CNTK](http://cntk.ai) | `cntk-py36-cu113` | `cntk-py36-cu111` | `cntk-py36-cu101` | `cntk-py36-cu100` | `cntk-py36-cu90` | `cntk-py27-cu90` `cntk-py27` | | `cntk-py27-cpu`
[Chainer](https://chainer.org) | `chainer-py36-cu113` | `chainer-py36-cu111` | `chainer-py36-cu101` | `chainer-py36-cu100` | `chainer-py36-cu90` | `chainer-py27-cu90` `chainer-py27` | | `chainer-py27-cpu`
[Caffe](http://caffe.berkeleyvision.org) | `caffe-py36-cu113` | `caffe-py36-cu111` | `caffe-py36-cu101` | `caffe-py36-cu100` | `caffe-py36-cu90` | `caffe-py27-cu90` `caffe-py27` | | `caffe-py27-cpu`
[Caffe2](https://caffe2.ai) | | | | | `caffe2-py36-cu90` `caffe2-py36` `caffe2` | `caffe2-py27-cu90` `caffe2-py27` | `caffe2-py36-cpu` `caffe2-cpu` | `caffe2-py27-cpu`
[Torch](http://torch.ch/) | `torch-cu113` | `torch-cu111` | `torch-cu101` | `torch-cu100` | `torch-cu90` | `torch-cu90` `torch` | | `torch-cpu`
[Darknet](https://pjreddie.com/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](https://github.com/ufoym/deepo/blob/master/LICENSE).
---
## File: scripts/README.md
# How to generate `docker/Dockerfile.*` and CI configuration
#### Step 1. Generate scripts for generating dockerfiles
```bash
python make-gen-docker.py
```
This should generate `gen-docker.sh`.
#### Step 2. Generate dockerfiles
```bash
bash gen-docker.sh
```
This should generate `docker/Dockerfile.*`.
#### Step 3. Generate the CI workflow for GitHub Actions
```bash
python make-ci.py
```
This should generate `.github/workflows/dockerimage.yml`.
#### All-in-one
```bash
bash build.sh
```
This runs `clean.sh` → `make-gen-docker.py` → `gen-docker.sh` → `make-ci.py` in sequence.
--- METRICS ---
- Files Extracted: 3
- Estimated Token Budget: ~8449 tokens
- Recency Window: Active (< 180 days)
- Canonical Reference: https://codewiki.google/github.com/ufoym/deepo