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开源软件名称:Deepo开源软件地址:https://gitee.com/mirrors/Deepo开源软件介绍:Deepo is a series of Docker images that
and their Dockerfile generator that
Table of contentsQuick StartGPU VersionInstallationStep 1. Install Docker and nvidia-docker.Step 2. Obtain the all-in-one image from Docker Hubdocker pull ufoym/deepo For users in China who may suffer from slow speeds when pulling the image from the public Docker registry, you can pull docker pull registry.docker-cn.com/ufoym/deepo UsageNow you can try this command: docker run --gpus all --rm ufoym/deepo nvidia-smi 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 docker run --gpus all -it ufoym/deepo bash 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. docker run --gpus all -it -v /host/data:/data -v /host/config:/config ufoym/deepo bash This will make 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 docker run --gpus all -it --ipc=host ufoym/deepo bash CPU VersionInstallationStep 1. Install Docker.Step 2. Obtain the all-in-one image from Docker Hubdocker pull ufoym/deepo:cpu UsageNow you can try this command: docker run -it ufoym/deepo:cpu bash 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. docker run -it -v /host/data:/data -v /host/config:/config ufoym/deepo:cpu bash This will make 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 docker run -it --ipc=host ufoym/deepo:cpu bash You are now ready to begin your journey.
>>> import tensorflow>>> import sonnet>>> import torch>>> import keras>>> import mxnet>>> import cntk>>> import chainer>>> import theano>>> import lasagne>>> import caffe>>> import paddle
caffe version 1.0.0
usage: darknet <function> CustomizationNote that 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: docker pull ufoym/deepo:tensorflow Jupyter supportStep 1. pull the all-in-one imagedocker pull ufoym/deepo Step 2. run the imagedocker 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 modulesStep 1. prepare generatorgit clone https://github.com/ufoym/deepo.gitcd deepo/generator Step 2. generate your customized DockerfileFor example, if you like python generate.py Dockerfile pytorch lasagne or with CUDA 11.1 and CUDNN 8 python generate.py Dockerfile pytorch lasagne --cuda-ver 11.1 --cudnn-ver 8 This should generate a Dockerfile that contains everything for building You can also specify the version of Python: python generate.py Dockerfile pytorch lasagne python==3.6 Step 3. build your Dockerfiledocker build -t my/deepo . This may take several minutes as it compiles a few libraries from scratch. Comparison to alternatives
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Citation@misc{ming2017deepo, author = {Ming Yang}, title = {Deepo: set up deep learning environment in a single command line.}, year = {2017}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/ufoym/deepo}}} ContributingWe 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. LicensingDeepo is MIT licensed. |
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