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80 lines
2.8 KiB
Plaintext
80 lines
2.8 KiB
Plaintext
OpenEmbedded/Yocto BSP layer for NVIDIA Tegra X1/X2/AGX/K1
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==========================================================
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Boards supported:
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* Jetson-TK1 development kit (Linux4Tegra R21.7)
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* Jetson-TX1 development kit (Linux4Tegra R32.2.1, JetPack 4.2.2
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* Jetson-TX2 development kit (Linux4Tegra R32.2.1, JetPack 4.2.2)
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* Jetson AGX Xavier development kit (Linux4Tegra R32.2, JetPack 4.2.2)
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* Jetson Nano development kit (Linux4Tegra R32.2.1, JetPack 4.2.2)
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Also supported:
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* Jetson-TX2i module (Linux4Tegra R32.2.1, JetPack 4.2.2)
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This layer depends on:
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URI: git://git.openembedded.org/openembedded-core
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branch: master
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LAYERSERIES_COMPAT: warrior
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PLEASE NOTE
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-----------
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* Starting with JetPack 4.2, packages outside the L4T BSP can
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only be downloaded with an NVIDIA Developer Network login.
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So to use CUDA 10, cuDNN, and any other packages that require
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a Devnet login, you **must** create a Devnet account and
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download the JetPack packages you need for your builds using
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NVIDIA SDK Manager.
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You must then set the variable NVIDIA_DEVNET_MIRROR to
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"file://path/to/the/downloads" in your build configuration
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(e.g., local.conf) to make them available to your bitbake
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builds.
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* The SDK Manager downloads a different package of CUDA host-side
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tools depending on whether you are running Ubuntu 16.04
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or 18.04. If you downloaded the Ubuntu 16.04 package, you
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should add
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CUDA_BINARIES_NATIVE = "cuda-binaries-ubuntu1604-native"
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to your build configuration so the CUDA recipes can find
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them. Otherwise, the recipes will default to looking for
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the Ubuntu 18.04 package.
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* The tensorrt 5.1.6 packages for Xavier are different from
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those for TX1/TX2, even though the deb files have the same
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name. If you need to build for Xavier and another platform
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and include tensorrt 5.1.6, create a subdirectory called
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"P2888" under your NVIDIA_DEVNET_MIRROR directory, and copy
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the Xavier tensorrt packages there. The non-Xavier copies
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should go in the NVIDIA_DEVNET_MIRROR top level.
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* CUDA 10 supports up through gcc 7 only, and some NVIDIA-provided
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binary libraries appear to be compiled with g++ 7 and cause linker
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failures when building applications with g++ 6, so **only** gcc 7
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should be used if you intend to use CUDA. (For Jetson-TK1, CUDA 6.5
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supports up through gcc 5.x only.)
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Selecting the toolchain version
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-------------------------------
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Toolchain version selection is usually a distro configuration setting,
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but you can also set this in your build/conf/local.conf file. To use
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gcc 7 instead of gcc 8, set:
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GCCVERSION = "7.%"
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but you will also need the gcc 7 toolchain recipes in one of your layers,
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since it was retired from OE-Core in favor of gcc 8.
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Contributing
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------------
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Please use GitHub (https://github.com/madisongh/meta-tegra) to submit
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issues or pull requests, or add to the documentation on the wiki.
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Contributions are welcome!
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