Quick Start
This should work and enables Deepo to use the GPU from inside a docker container. If this does not work, search the issues section on the nvidia-docker GitHub – many solutions are already documented. To get an interactive shell to a container that will not be automatically deleted after you exit do
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.
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 nvidia-docker run .
CPU Version
Installation
Step 1. Install Docker.
Step 2. Obtain the all-in-one image from Docker Hub
Usage
Now you can try this command:
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.
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 .
You are now ready to begin your journey.
Customization
Note that docker pull ufoym/deepo mentioned in Quick Start will give you a standard image containing all available deep learning frameworks. You can customize your own environment as well.
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:
Other python versions
Note that all python-related images use Python 3.6 by default. If you are unhappy with Python 3.6 , you can also specify other python versions:
Currently, we support Python 2.7 and Python 3.6 .
See Available Tags for a complete list of all available tags. These pre-built images are all built from docker/Dockerfile.* and circle.yml . See How to generate docker/Dockerfile.* and circle.yml if you are interested in how these files are generated.
Jupyter support
Step 1. pull the image with jupyter support
Note that the tag could be either of all-py36-jupyter , py36-jupyter , all-py27-jupyter , or py27-jupyter .
Step 2. run the image
Build your own customized image with Lego-like modules
Step 1. prepare generator
Step 2. generate your customized Dockerfile
For example, if you like pytorch and lasagne , then
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:
Step 3. build your Dockerfile
This may take several minutes as it compiles a few libraries from scratch.
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.
Quick start
This should work and allow Deepo to use the GPU from within the docker container.
If this doesn’t work, please searchNvidia-docker part of the problem on GitHub- A number of solutions have been documented. To send an interactive shell to a container that does not automatically delete when you exit do
If you want to share data and configuration between a host (computer or VM) and a container that uses Deepo, use the -v option, for example
This will make /host/data Host /data Visible in the container, and /host/config as /config . This isolation reduces the chances of containerized experiments covering or using erroneous data.
Note that some frameworks (such as PyTorch) use shared memory to share data between processes, so if you use multiprocessing, the default shared memory segment size that the container runs is not enough, you should use —ipc=host or —shm-size Command line options increase shared memory size to nvidia-docker run 。
CPU version
installation
Step 2. FromDocker HubAccess to integration Mirror
usage
Now you can try the following commands:
If you want to share data and configuration between a host (computer or VM) and a container that uses Deepo, use the -v option, for example
This will make /host/data Host /data Visible in the container, and /host/config as /config . This isolation reduces the chances of containerized experiments covering or using erroneous data.
Note that some frameworks (such as PyTorch) use shared memory to share data between processes, so if you use multiprocessing, the default shared memory segment size that the container runs is not enough, you should use —ipc=host or —shm-size Command line options increase shared memory size to docker run 。
You are now ready to start your journey.
custom made
Please note," Quick start"in docker pull ufoym/deepo Mentioned will provide you with a standard that includes all available deep learning frameworks Mirror . You can also customize your environment.
Not satisfied with the integrated solution?
If you prefer a particular framework rather than an integrated one Mirror Just attach a label with the name of the frame.
Take tensorflow as an example:
Other python versions
Please note, Python 3.6 By default all python related Mirror Both are used. If you are not satisfied Python 3.6 You can also specify other python versions:
Currently, we support Python 2.7 with Python 3.6 。
For a complete list of all available tags, seeAvailablemark. These pre-built Mirror All by docker/Dockerfile.* And constructed circle.yml . SeeHow to generate docker/Dockerfile.* with circle.yml If you are interested in how these files are generated.
Jupyter support
Step 1. Use jupyter support to pull the image
Please note that the label can be arbitrary all-py36-jupyter ， py36-jupyter ， all-py27-jupyter ,or py27-jupyter 。
Step 2. run Mirror
Build your own custom image with a module like Lego
Step 1. Prepare the generator
Step 2. Generate a custom Dockerfile
For example, if you like pytorch with lasagne ,then
This should generate a Dockerfile that contains the build pytorch And all content lasagne . Note that the generator can handle automatic dependency processing and topological sorting of lists. Therefore, you don’t have to worry about missing dependencies and list order.
You can also specify the version of Python:
Step 3. Build your Dockerfile
This may take a few minutes because itStart from scratchCompile some libraries.
Special
We thank all the contributions. If you plan to respond to a bug fix, please do not discuss it further. If you plan to offer new features, utility features or extensions, first open a question and discuss it with us.
deepo
Setup and customize deep learning environment in seconds.
PLEASE NOTE, THE DEEP LEARNING FRAMEWORK WAR IS OVER, THIS PROJECT IS NO LONGER BEING MAINTAINED.
Deepo is an open framework to assemble specialized docker images for deep learning research without pain. It provides a “lego set” of dozens of standard components for preparing deep learning tools and a framework for assembling them into custom docker images.
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ufoym / deepo Public archive
Setup and customize deep learning environment in seconds.
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ufoym/deepo
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README.md
PLEASE NOTE, THE DEEP LEARNING FRAMEWORK WAR IS OVER, THIS PROJECT IS NO LONGER BEING MAINTAINED.
Deepo is an open framework to assemble specialized docker images for deep learning research without pain. It provides a “lego set” of dozens of standard components for preparing deep learning tools and a framework for assembling them into custom docker images.
At the core of Deepo is a Dockerfile generator that
- allows you to customize your deep learning environment with Lego-like modules
- define your environment in a single command line,
- then deepo will generate Dockerfiles with best practices
- and do all the configuration for you
- deepo knows which combos (CUDA/cuDNN/Python/PyTorch/Tensorflow, . tons of dependancies) are compatible
- and will pick the right versions for you
- and arrange sequence of installation procedures using topological sorting
We also prepare a series of pre-built docker images that
- allows you to instantly set up common deep learning research environment
- supports almost all commonly used deep learning frameworks
- supports GPU acceleration (CUDA and cuDNN included), also works in CPU-only mode
- works on Linux (CPU version/GPU version), Windows (CPU version) and OS X (CPU version)
Table of contents
Quick Start
GPU Version
Installation
Step 1. Install Docker and nvidia-docker.
Step 2. Obtain the all-in-one image from Docker Hub
For users in China who may suffer from slow speeds when pulling the image from the public Docker registry, you can pull deepo images from the China registry mirror by specifying the full path, including the registry, in your docker pull command, for example:
Usage
Now you can try this command:
This should work and enables Deepo to use the GPU from inside a docker container. If this does not work, search the issues section on the nvidia-docker GitHub — many solutions are already documented. To get an interactive shell to a container that will not be automatically deleted after you exit do
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.
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 .
CPU Version
Installation
Step 1. Install Docker.
Step 2. Obtain the all-in-one image from Docker Hub
Usage
Now you can try this command:
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.
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 .
You are now ready to begin your journey.
Customization
Note that docker pull ufoym/deepo mentioned in Quick Start will give you a standard image containing all available deep learning frameworks. You can customize your own environment as well.
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:
Jupyter support
Step 1. pull the all-in-one image
Step 2. run the image
Build your own customized image with Lego-like modules
Step 1. prepare generator
Step 2. generate your customized Dockerfile
For example, if you like pytorch and lasagne , then
or with CUDA 11.1 and CUDNN 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:
Step 3. build your Dockerfile
This may take several minutes as it compiles a few libraries from scratch.
Comparison to alternatives
. modern-deep-learning dl-docker jupyter-deeplearning Deepo ubuntu 16.04 14.04 14.04 18.04 cuda X 8.0 6.5-8.0 8.0-10.2/None cudnn X v5 v2-5 v7 onnx X X X O theano X O O O tensorflow O O O O sonnet X X X O pytorch X X X O keras O O O O lasagne X O O O mxnet X X X O cntk X X X O chainer X X X O caffe O O O O caffe2 X X X O torch X O O O darknet X X X O paddlepaddle X X X O 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
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.