Current Jetson Nano image is based on Ubuntu distro, This project will try to deploy a opensuse version. Furthermore, I will take a closer look on deep learning framework, and learn how they use hardware accelerator.

First, boot up Jeston nano with Ubuntu, and deploy Tensorflow(Keras), Pytorch(Caffee2), MXNet, the most popular DL framework today, on it. Understand how those frameworks take advantage of hardware accelerator.

Second, build a new image with our kernel and rootfs.

Last, try to install DL frameworks from our ARM64 repo, checking current status.

Reference:

https://elinux.org/Jetson/Nano/Upstream


Comments

  • lyan
    4 months ago by lyan | Reply

    First part is almost done. Board is booted, and DL framworks are installed. Just could not find so much information about how its GPU work with arm cpu. There is a PCIe controller inside, maybe it is used pcie or just AXI bus, really wish its driver is open source, add-emoji

  • lyan
    4 months ago by lyan | Reply

    Had an upgrade issue with Ubuntu, "files list file for package 'libacl1:arm64' is missing final newline", the solution is removing all "libacl1:arm64*" from "/var/lib/dpkg/info", there will be more same errors, just repeat it.

  • lyan
    4 months ago by lyan | Reply

    Did a investigation on DL framworks, just some basic stuff, but it's good to me since I am more interested in how they work with hardware accelerator or how to improve. Most of them could work with Nvidia GPU by CUDA, and a few could work with AMD GPU and FPGA with OpenCL, and few of them could be supported directly by ASICs(TPU,NPU).

    =================== Tensorflow(google), most popular today, current version 2.0,

    Pytorch(FB), caffe2 is merged in pytorch now.

    Mxnet(Amazon, Nvidia)

    There are some others:

    Theano: stop develop since 2017

    Keras, user-friendly API for tensorflow and theano

    CNTK(MicroSoft), Cognitive Toolkit

    FastAI, A library based on Pytorch

    Reference: https://docs.nvidia.com/deeplearning/frameworks/install-tf-jetson-platform/index.html https://www.tensorflow.org/learn https://pytorch.org/get-started/locally/ https://mxnet.apache.org/versions/master/architecture/index.html

  • lyan
    4 months ago by lyan | Reply

    a pretty useful link:

    https://developer.nvidia.com/embedded/linux-tegra

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