derekmerck/keras-tf
derekmerck
Multi-arch Conda and Keras-TF Python Docker images for embedded systems.
下载次数: 0状态：社区镜像维护者：derekmerck仓库类型：镜像最近更新：7 年前
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Conda xArch Docker Image
https://travis-ci.org/derekmerck/docker-conda-xarch.svg?branch=master](https://travis-ci.org/derekmerck/docker-conda-xarch)
Derek Merck
<***>
Rhode Island Hospital and Brown University
Providence, RI
Multi-arch Conda and Keras-TF Python Docker images for embedded systems.
Conda on Arm
The official arm32 MiniConda is Python 2 from 2015. These images use https://github.com/jjhelmus/berryconda compiled by jjhelmus. He also explains how to build a JetsonConda in the https://github.com/conda/constructor repo.*
*Need tdd
libcondato the package manifest.
The official arm32 tensorflow wheels are available as http://ci.tensorflow.org/view/Nightly/. The wheel name for the python3 build has to be manipuated to remove the platform restriction tags. NVIDIA provides a recent https://devtalk.nvidia.com/default/topic/***/tensorflow-1-8-wheel-with-jetpack-3-2-/.
Use It
bash$ docker run derekmerck/conda:py2 $ docker run derekmerck/keras-tf:py2 $ docker run derekmerck/conda:latest $ docker run derekmerck/keras-tf:latest
Build It
This image is based on the resin/$ARCH-debian:stretch image. http://resin.io base images include a https://www.qemu.org cross-compiler to facilitate building images for low-power single-board computers on more powerful Intel-architecture desktops and servers.
docker-compose.yml contains build descriptions for all relevant architectures.
amd64
bash$ docker-compose build conda-py2-amd64 keras-tf-py2-amd64 $ docker-compose build conda-py3-amd64 keras-tf-py3-amd64
Desktop computers/vms, UP boards, and the https://www.intel.com/content/www/us/en/products/boards-kits/nuc.html are amd64 devices. The appropriate image can be built and pushed from https://travis-ci.org.
arm32v7
Most low-power single board computers such as the Raspberry Pi and Beagleboard are arm32v7 devices. Appropriate images can be cross-compiled and pushed from Travis CI.
bash$ docker-compose build conda-py2-arm32v7 keras-tf-py2-arm32v7 $ docker-compose build conda-py3-arm32v7 keras-tf-py3-arm32v7
arm64v8
The https://developer.nvidia.com/embedded/buy/jetson-tx2 uses a Tegra arm64v8 cpu. The appropriate image can be built natively and pushed from Packet.io, using a brief tenancy on a bare-metal Cavium ThunderX ARMv8 server.
bash$ apt update && apt upgrade $ curl -fsSL get.docker.com -o get-docker.sh $ sh get-docker.sh $ docker run hello-world $ apt install git python-pip $ pip install docker-compose $ git clone http://github.com/derekmerck/conda-xarch $ cd conda-xarch $ docker-compose build conda3-arm64v8
Although https://resin.io/blog/docker-builds-on-arm-servers-youre-not-crazy-your-builds-really-are-5x-faster/, the available ThunderX does not implement the arm32 instruction set, so it https://gitlab.com/gitlab-org/omnibus-gitlab/issues/2544.
Manifest It
After building new images, call manifest-it.py to push updated images and build the Docker
multi-architecture service mappings.
bash$ python3 manifest-it conda-xarch.manifest.yml
License
MIT
镜像拉取方式
您可以使用以下命令拉取该镜像。请将 <标签> 替换为具体的标签版本。如需查看所有可用标签版本，请访问 标签列表页面。
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