README, README.md: swap content and symlink

Signed-off-by: Matt Madison <matt@madison.systems>
This commit is contained in:
Matt Madison
2020-01-08 16:10:52 -08:00
parent 7baa5933a9
commit ea05215513
2 changed files with 84 additions and 84 deletions

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OpenEmbedded/Yocto BSP layer for NVIDIA Jetson TX1/TX2/AGX Xavier/Nano
======================================================================
Boards supported:
* Jetson-TX1 development kit (Linux4Tegra R32.3.1, JetPack 4.3)
* Jetson-TX2 development kit (Linux4Tegra R32.3.1, JetPack 4.3)
* Jetson AGX Xavier development kit (Linux4Tegra R32.3.1, JetPack 4.3)
* Jetson Nano development kit (Linux4Tegra R32.3.1, JetPack 4.3)
Also supported thanks to community support:
* Jetson-TX2i module (Linux4Tegra R32.3.1, JetPack 4.3)
* Jetson-TX2 4GB module (Linux4Tegra R32.3.1, JetPack 4.3)
* Jetson AGX Xavier 8GB module (Linux4Tegra R32.3.1, JetPack 4.3)
This layer depends on:
URI: git://git.openembedded.org/openembedded-core
branch: master
LAYERSERIES_COMPAT: zeus
PLEASE NOTE
-----------
* Starting with JetPack 4.2, packages outside the L4T BSP can
only be downloaded with an NVIDIA Developer Network login.
So to use CUDA 10, cuDNN, and any other 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/$USER/Downloads/nvidia/sdkm_downloads`
**Note** Starting with L4T R32.3.1 and JetPack 4.3, The Tegra
Multimedia API kit has moved to JetPack, so **all builds**
now require you to set up an SDK Manager downloads area.
* The SDK Manager downloads a different package of CUDA host-side
tools depending on whether you are running Ubuntu 16.04
or 18.04. If you downloaded the Ubuntu 16.04 package, you
should add
CUDA_BINARIES_NATIVE = "cuda-binaries-ubuntu1604-native"
to your build configuration so the CUDA recipes can find
them. Otherwise, the recipes will default to looking for
the Ubuntu 18.04 package.
* The TensorRT 6.0.1 packages for Xavier are different from
those for TX1/TX2, even though the deb files have the same
name. To prevent mixups during the build, the recipe here
expects to find the Xavier packages in a `DLA` subdirectory
under `${NVIDIA_DEVNET_MIRROR}`, and non-Xavier packages
in a `NoDLA` subdirectory.
If you need to include TensorRT in your builds, you **must**
create the subdirectory and move all of the TensorRT packages
downloaded by the SDK Manager there. Xavier example:
$ cd ~/Downloads/nvidia/sdkm_downloads
$ mkdir DLA
$ mv tensorrt*.deb libnvinfer*.deb DLA/
* CUDA 10 supports up through gcc 7 only, and some NVIDIA-provided
binary libraries appear to be compiled with g++ 7 and cause linker
failures when building applications with g++ 6, so **only** gcc 7
should be used if you intend to use CUDA. See the following wiki
pages for instructions on including gcc 7 in your builds:
[Using gcc7 from the contrib layer](https://github.com/madisongh/meta-tegra/wiki/Using-gcc7-from-the-contrib-layer)
[Using linaro gcc7 for CUDA support](https://github.com/madisongh/meta-tegra/wiki/Using-linaro-gcc7-for-CUDA-support)
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!

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OpenEmbedded/Yocto BSP layer for NVIDIA Jetson TX1/TX2/AGX Xavier/Nano
======================================================================
Boards supported:
* Jetson-TX1 development kit (Linux4Tegra R32.3.1, JetPack 4.3)
* Jetson-TX2 development kit (Linux4Tegra R32.3.1, JetPack 4.3)
* Jetson AGX Xavier development kit (Linux4Tegra R32.3.1, JetPack 4.3)
* Jetson Nano development kit (Linux4Tegra R32.3.1, JetPack 4.3)
Also supported thanks to community support:
* Jetson-TX2i module (Linux4Tegra R32.3.1, JetPack 4.3)
* Jetson-TX2 4GB module (Linux4Tegra R32.3.1, JetPack 4.3)
* Jetson AGX Xavier 8GB module (Linux4Tegra R32.3.1, JetPack 4.3)
This layer depends on:
URI: git://git.openembedded.org/openembedded-core
branch: master
LAYERSERIES_COMPAT: zeus
PLEASE NOTE
-----------
* Starting with JetPack 4.2, packages outside the L4T BSP can
only be downloaded with an NVIDIA Developer Network login.
So to use CUDA 10, cuDNN, and any other 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/$USER/Downloads/nvidia/sdkm_downloads`
**Note** Starting with L4T R32.3.1 and JetPack 4.3, The Tegra
Multimedia API kit has moved to JetPack, so **all builds**
now require you to set up an SDK Manager downloads area.
* The SDK Manager downloads a different package of CUDA host-side
tools depending on whether you are running Ubuntu 16.04
or 18.04. If you downloaded the Ubuntu 16.04 package, you
should add
CUDA_BINARIES_NATIVE = "cuda-binaries-ubuntu1604-native"
to your build configuration so the CUDA recipes can find
them. Otherwise, the recipes will default to looking for
the Ubuntu 18.04 package.
* The TensorRT 6.0.1 packages for Xavier are different from
those for TX1/TX2, even though the deb files have the same
name. To prevent mixups during the build, the recipe here
expects to find the Xavier packages in a `DLA` subdirectory
under `${NVIDIA_DEVNET_MIRROR}`, and non-Xavier packages
in a `NoDLA` subdirectory.
If you need to include TensorRT in your builds, you **must**
create the subdirectory and move all of the TensorRT packages
downloaded by the SDK Manager there. Xavier example:
$ cd ~/Downloads/nvidia/sdkm_downloads
$ mkdir DLA
$ mv tensorrt*.deb libnvinfer*.deb DLA/
* CUDA 10 supports up through gcc 7 only, and some NVIDIA-provided
binary libraries appear to be compiled with g++ 7 and cause linker
failures when building applications with g++ 6, so **only** gcc 7
should be used if you intend to use CUDA. See the following wiki
pages for instructions on including gcc 7 in your builds:
[Using gcc7 from the contrib layer](https://github.com/madisongh/meta-tegra/wiki/Using-gcc7-from-the-contrib-layer)
[Using linaro gcc7 for CUDA support](https://github.com/madisongh/meta-tegra/wiki/Using-linaro-gcc7-for-CUDA-support)
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!