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Networks (NN) and Deep Neural Networks (DNN)","Angular","Frameworks"],"sub_categories":["NN/DNN Software Frameworks"],"readme":"![deepo](https://user-images.githubusercontent.com/2270240/32102393-aecf573c-bb4e-11e7-811c-dc673cae7b9c.png)\n\n![workflows](https://github.com/ufoym/deepo/workflows/deepo%20CI/badge.svg)\n[![docker](https://img.shields.io/docker/pulls/ufoym/deepo.svg)](https://hub.docker.com/r/ufoym/deepo)\n![license](https://img.shields.io/github/license/ufoym/deepo.svg)\n\n\n---\n\n***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.\n\nAt the core of Deepo is a Dockerfile generator that\n- lets you [customize your deep learning environment](#Build) with Lego-like modules\n  - describe your environment in a single command line\n  - Deepo generates Dockerfiles following best practices\n  - and handles all the configuration for you\n- automatically resolves dependencies\n  - Deepo knows which combinations of CUDA, cuDNN, Python, PyTorch, TensorFlow, etc. are compatible\n  - picks the right versions on your behalf\n  - and determines the correct installation order via [topological sorting](https://en.wikipedia.org/wiki/Topological_sorting)\n\nWe also provide a series of pre-built Docker images that\n- let you instantly set up common deep learning research environments\n- support widely used [deep learning frameworks](#Available-tags)\n- support [GPU acceleration](#GPU) (CUDA and cuDNN included) and also work in [CPU-only mode](#CPU)\n- run on Linux ([CPU](#CPU)/[GPU](#GPU)), Windows ([CPU](#CPU)), and macOS ([CPU](#CPU))\n\n---\n\n# Table of contents\n- [Quick Start](#Quick-Start)\n  - [GPU Version](#GPU)\n    - [Installation](#Installation)\n    - [Usage](#Usage)\n  - [CPU Version](#CPU)\n    - [Installation](#Installation-cpu)\n    - [Usage](#Usage-cpu)\n- [Customization](#Customization)\n  - [Unhappy with all-in-one solution?](#One)\n  - [Jupyter support](#Jupyter)\n  - [Build your own customized image](#Build)\n- [Comparison to Alternatives](#Comparison)\n- [Tags](#Tags)\n  - [Available Tags](#Available-tags)\n  - [Deprecated Tags](#Deprecated-tags)\n- [Citation](#Citation)\n- [Contributing](#Contributing)\n- [Licensing](#Licensing)\n\n---\n\n\u003ca name=\"Quick-Start\"/\u003e\n\n# Quick Start\n\n\n\u003ca name=\"GPU\"/\u003e\n\n## GPU Version\n\n\u003ca name=\"Installation\"/\u003e\n\n### Installation\n\n#### 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).\n\n#### Step 2. Pull the all-in-one image from [Docker Hub](https://hub.docker.com/r/ufoym/deepo)\n\n```bash\ndocker pull ufoym/deepo\n```\n\n\u003ca name=\"Usage\"/\u003e\n\n### Usage\n\nVerify that GPU access works inside a container:\n```bash\ndocker run --gpus all --rm ufoym/deepo nvidia-smi\n```\nIf 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:\n\n```bash\ndocker run --gpus all -it ufoym/deepo bash\n```\n\nTo share data and configuration between the host (your machine or VM) and the container, use the `-v` option:\n```bash\ndocker run --gpus all -it -v /host/data:/data -v /host/config:/config ufoym/deepo bash\n```\nThis 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.\n\nNote 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`:\n```bash\ndocker run --gpus all -it --ipc=host ufoym/deepo bash\n```\n\n\n\u003ca name=\"CPU\"/\u003e\n\n## CPU Version\n\n\u003ca name=\"Installation-cpu\"/\u003e\n\n### Installation\n\n#### Step 1. Install [Docker](https://docs.docker.com/engine/installation/).\n\n#### Step 2. Pull the all-in-one image from [Docker Hub](https://hub.docker.com/r/ufoym/deepo)\n\n```bash\ndocker pull ufoym/deepo:cpu\n```\n\n\u003ca name=\"Usage-cpu\"/\u003e\n\n### Usage\n\nLaunch an interactive shell:\n```bash\ndocker run -it ufoym/deepo:cpu bash\n```\n\nTo share data and configuration between the host (your machine or VM) and the container, use the `-v` option:\n```bash\ndocker run -it -v /host/data:/data -v /host/config:/config ufoym/deepo:cpu bash\n```\nThis 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.\n\nNote 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`:\n```bash\ndocker run -it --ipc=host ufoym/deepo:cpu bash\n```\n\n\n_You are now ready to begin your journey._\n\n\n```\n$ python\n```\n```python\n\u003e\u003e\u003e import tensorflow\n\u003e\u003e\u003e import torch\n\u003e\u003e\u003e import keras\n\u003e\u003e\u003e import mxnet\n\u003e\u003e\u003e import chainer\n\u003e\u003e\u003e import paddle\n```\n\n```\n$ darknet\n```\n```\nusage: darknet \u003cfunction\u003e\n```\n\n\u003ca name=\"Customization\"/\u003e\n\n# Customization\n\nThe `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.\n\n\u003ca name=\"One\"/\u003e\n\n## Unhappy with all-in-one solution?\n\nIf you prefer a single framework instead of the all-in-one image, simply append a tag with the framework name.\nFor example, to pull TensorFlow only:\n```bash\ndocker pull ufoym/deepo:tensorflow\n```\n\n\u003ca name=\"Jupyter\"/\u003e\n\n## Jupyter Support\n\n#### Step 1. Pull the all-in-one image\n\n```bash\ndocker pull ufoym/deepo\n```\n\n#### Step 2. Run the image\n```bash\ndocker 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'\n```\n\n\n\u003ca name=\"Build\"/\u003e\n\n## Build Your Own Customized Image with Lego-like Modules\n\n#### Step 1. Set up the generator\n\n```bash\ngit clone https://github.com/ufoym/deepo.git\ncd deepo/generator\n```\n\n#### Step 2. Generate a customized Dockerfile\n\nFor example, to create an image with `pytorch` and `keras`:\n```bash\npython generate.py Dockerfile pytorch keras\n```\nOr with CUDA 11.3 and cuDNN 8:\n```bash\npython generate.py Dockerfile pytorch keras --cuda-ver 11.3.1 --cudnn-ver 8\n```\n\nThis 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.\n\nYou can also specify the Python version:\n```bash\npython generate.py Dockerfile pytorch keras python==3.8\n```\n\n#### Step 3. Build the Dockerfile\n\n```bash\ndocker build -t my/deepo .\n```\n\nThis may take several minutes, as some libraries are compiled from source.\n\n\n\u003ca name=\"Comparison\"/\u003e\n\n# Comparison to Alternatives\n\n\n.                                                  | modern-deep-learning | dl-docker          | jupyter-deeplearning | Deepo\n:------------------------------------------------: | :------------------: | :----------------: | :------------------: | :----------------:\n [ubuntu](https://www.ubuntu.com)                  | 16.04                | 14.04              | 14.04                | 20.04\n [cuda](https://developer.nvidia.com/cuda-zone)    | X                    | 8.0                | 6.5-8.0              | 11.3/None\n [cudnn](https://developer.nvidia.com/cudnn)       | X                    | v5                 | v2-5                 | v8\n [onnx](https://onnx.ai)                           | X                    | X                  | X                    | O\n [tensorflow](http://www.tensorflow.org)            | O                    | O                  | O                    | O\n [pytorch](http://pytorch.org)                      | X                    | X                  | X                    | O\n [keras](https://keras.io)                          | O                    | O                  | O                    | O\n [mxnet](http://mxnet.incubator.apache.org)         | X                    | X                  | X                    | O\n [chainer](https://chainer.org)                     | X                    | X                  | X                    | O\n [darknet](https://pjreddie.com/darknet/)           | X                    | X                  | X                    | O\n [paddlepaddle](https://www.paddlepaddle.org.cn/)   | X                    | X                  | X                    | O\n\n\n\n\u003ca name=\"Tags\"/\u003e\n\n# Tags\n\n\n\u003ca name=\"Available-tags\"/\u003e\n\n## Available Tags\n\n\n.                                                  | CUDA 11.3 / Python 3.8                                    | CPU-only / Python 3.8\n:------------------------------------------------: | :-------------------------------------------------------: | :-----------------------------------------:\n all-in-one                                        | `latest` `all` `all-py38` `py38-cu113` `all-py38-cu113`   | `all-py38-cpu` `all-cpu` `py38-cpu` `cpu`\n [TensorFlow](http://www.tensorflow.org)           | `tensorflow-py38-cu113` `tensorflow-py38` `tensorflow`    | `tensorflow-py38-cpu` `tensorflow-cpu`\n [PyTorch](http://pytorch.org)                     | `pytorch-py38-cu113` `pytorch-py38` `pytorch`             | `pytorch-py38-cpu` `pytorch-cpu`\n [Keras](https://keras.io)                         | `keras-py38-cu113` `keras-py38` `keras`                   | `keras-py38-cpu` `keras-cpu`\n [MXNet](http://mxnet.incubator.apache.org)        | `mxnet-py38-cu113` `mxnet-py38` `mxnet`                   | `mxnet-py38-cpu` `mxnet-cpu`\n [Chainer](https://chainer.org)                    | `chainer-py38-cu113` `chainer-py38` `chainer`             | `chainer-py38-cpu` `chainer-cpu`\n [Darknet](https://pjreddie.com/darknet/)          | `darknet-cu113` `darknet`                                 | `darknet-cpu`\n [PaddlePaddle](https://www.paddlepaddle.org.cn/)  | `paddle-cu113` `paddle`                                   | `paddle-cpu`\n\n\n\u003ca name=\"Deprecated-tags\"/\u003e\n\n## Deprecated Tags\n\n.                                                  | 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\n:------------------------------------------------: | :----------------------------: | :----------------------------: | :----------------------------: | :----------------------------: | :------------------------------------------: | :--------------------------------:       | :-----------------------------------------: | :----------------------------------------:\n 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`\n all-in-one with jupyter                           |                                |                                |                                |                                | `all-jupyter-py36-cu90`                      | `all-py27-jupyter` `py27-jupyter`        |                                             | `all-py27-jupyter-cpu` `py27-jupyter-cpu`\n [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`\n [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`\n [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`\n [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`\n [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`\n [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`\n [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`\n [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`\n [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`\n [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`\n [Caffe2](https://caffe2.ai)                       |                                |                                |                                |                                | `caffe2-py36-cu90` `caffe2-py36` `caffe2`    | `caffe2-py27-cu90` `caffe2-py27`         | `caffe2-py36-cpu` `caffe2-cpu`              | `caffe2-py27-cpu`\n [Torch](http://torch.ch/)                         | `torch-cu113`                  | `torch-cu111`                  | `torch-cu101`                  | `torch-cu100`                  | `torch-cu90`                                 | `torch-cu90` `torch`                     |                                             | `torch-cpu`\n [Darknet](https://pjreddie.com/darknet/)          | `darknet-cu113`                | `darknet-cu111`                | `darknet-cu101`                | `darknet-cu100`                | `darknet-cu90`                               | `darknet-cu90` `darknet`                 |                                             | `darknet-cpu`\n\n\n\u003ca name=\"Citation\"/\u003e\n\n# Citation\n```\n@misc{ming2017deepo,\n    author = {Ming Yang},\n    title = {Deepo: Set up a deep learning environment with a single command line.},\n    year = {2017},\n    publisher = {GitHub},\n    journal = {GitHub repository},\n    howpublished = {\\url{https://github.com/ufoym/deepo}}\n}\n```\n\u003ca name=\"Contributing\"/\u003e\n\n# Contributing\n\nWe 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.\n\n\u003ca name=\"Licensing\"/\u003e\n\n# Licensing\n\nDeepo is [MIT licensed](https://github.com/ufoym/deepo/blob/master/LICENSE).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fufoym%2Fdeepo","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fufoym%2Fdeepo","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fufoym%2Fdeepo/lists"}