curioustauseef / meta-tegra

BSP layer for NVIDIA Jetson platforms, based on L4T

OpenEmbedded/Yocto BSP layer for NVIDIA Jetson TX1/TX2/AGX Xavier/Nano

Linux4Tegra release: R32.4.2 JetPack release: 4.4 Developer Preview

Boards supported:

• Jetson-TX1 development kit
• Jetson-TX2 development kit
• Jetson AGX Xavier development kit
• Jetson Nano development kit
• Jetson Nano eMMC module with rev B01 carrier board

Experimental support:

• Jetson Xavier NX Development Kit
• Jetson Xavier NX eMMC module in dev kit or Nano carrier board

Also supported thanks to community support:

• Jetson-TX2i module
• Jetson-TX2 4GB module
• Jetson AGX Xavier 8GB module

This layer depends on: URI: git://git.openembedded.org/openembedded-core branch: master LAYERSERIES_COMPAT: dunfell

PLEASE NOTE

• NVIDIA recommends using L4T R32.3.1/JetPack 4.3 for production use. The JetPack release supported here is labeled a "developer preview".

• Some packages outside the L4T BSP can only be downloaded with an NVIDIA Developer Network login - in particular, the CUDA host-side tools.

  To use any packages that require a Devnet login, you must create a Devnet account and download the JetPack packages you need for your builds using NVIDIA SDK Manager.

  You must then set the variable NVIDIA_DEVNET_MIRROR to "file://path/to/the/downloads" in your build configuration (e.g., local.conf) to make them available to your bitbake builds. This can be the NVIDIA SDK Manager downloads directory, /home/$USER/Downloads/nvidia/sdkm_downloads

• The SDK Manager downloads a different package of CUDA host-side tools depending on whether you are running Ubuntu 16.04 or 18.04. If you downloaded the Ubuntu 16.04 package, you should add

  CUDA_BINARIES_NATIVE = "cuda-binaries-ubuntu1604-native"
  

  to your build configuration so the CUDA recipes can find them. Otherwise, the recipes will default to looking for the Ubuntu 18.04 package.

• CUDA 10.2 supports up through gcc 8 only. Pre-built binaries in the BSP appear to be compatible with gcc 7 and 8 only. So use only gcc 7 or gcc 8 if you intend to use CUDA. Recipes for gcc 8 have been imported from the OE-Core warrior branch (the last version of OE-Core to supply gcc 8) to make it easier to use this older toolchain.

  See this wiki page for information on adding the meta-tegra/contrib layer to your builds and configuring them for GCC 8.

Contributing

Please use GitHub (https://github.com/madisongh/meta-tegra) to submit issues or pull requests, or add to the documentation on the wiki. Contributions are welcome!