Nvidia Jetson Nano 的 Yocto Bitbake 食谱 Python whl 文件不在 PyPi 上

Yocto Bitbake Recipes for Nvidia Jetson Nano for Python whl files not on PyPi

我正在尝试为 NVIDIA 特定的 PyTorch 和 Tensorflow Python whl 包创建 2 个简单的 Yocto Python 食谱。目标是 Yocto 从 meta-tegra 层生成的 NVIDIA Jetson Nano 的 SD 卡映像。没有这些方法,我可以从 meta-tegra 成功编译和引导图像。

NVIDIA 自己编译并发布了“.whl”Python 包,它们可以在这里找到: https://devtalk.nvidia.com/default/topic/1048776/official-tensorflow-for-jetson-nano-/ https://devtalk.nvidia.com/default/topic/1049071/jetson-nano/pytorch-for-jetson-nano/

我已经尝试了以下方法,但是两个方法都失败了,出现了各种错误(未找到许可证、缺少 setup.py 等等)

SUMMARY = "NVIDIA's version of Python Torch"
DESCRIPTION = "NVIDIA's version of Python Torch"
HOMEPAGE = "https://nvidia.com"

SECTION = "devel/python"
LICENSE = "BSD-3-Clause"
LIC_FILES_CHKSUM = "file://LICENSE;md5=79aa8b7bc4f781210d6b5c06d6424cb0"

PR = "r0"
SRCNAME = "Pytorch"

SRC_URI = "https://nvidia.box.com/shared/static/j2dn48btaxosqp0zremqqm8pjelriyvs.whl"

SRC_URI[md5sum] = "9ec85425a64ca266abbfdeddbe92fb18"
SRC_URI[sha256sum] = "3b9b8f944962aaf550460409e9455d6d6b86083510b985306a8012d01d730b8b"

S = "${WORKDIR}/${SRCNAME}-${PV}"

inherit setuptools

CLEANBROKEN = "1"

SUMMARY = "NVIDIA's version of Python Tensorflow"
DESCRIPTION = "NVIDIA's version of Python Tensorflow"
HOMEPAGE = "https://nvidia.com"

SECTION = "devel/python"
LICENSE = "BSD-3-Clause"
LIC_FILES_CHKSUM = "file://generic_BSD-3-Clause;md5=79aa8b7bc4f781210d6b5c06d6424cb0"

PR = "r0"
SRCNAME = "Tensorflow-gpu"

SRC_URI = "https://developer.download.nvidia.com/compute/redist/jp/v42/tensorflow-gpu/tensorflow_gpu-1.13.1+nv19.5-cp36-cp36m-linux_aarch64.whl"

SRC_URI[md5sum] = "ae649a62c274d19d1d096d97284ec2ee"
SRC_URI[sha256sum] = "6639761eccf53cab550d4afb4c8a13dbfe1b1d8051c62e14f83199667ae42d1a"

S = "${WORKDIR}/${SRCNAME}-${PV}"

inherit setuptools

CLEANBROKEN = "1"

我相信我已经在 Yocto 中安装了依赖项。我如何从这些现有的 whl 文件创建 Yocto 配方?谢谢。

可能(未经测试)类似的东西需要添加到您的食谱中:

DEPENDS += 'pip-native'

do_install() {
    pip install ${S}/tensorflow_gpu-1.13.1+nv19.5-cp36-cp36m-linux_aarch64.whl
}

但可能需要进行更多调整。