This repository contains applications and tools to help understand and develop Deep Learning Applications for NVIDIA AGX Platforms (DRIVE, Jetson and CLARA). The AGX Family is based around the Xavier SoC, a high performance Aarch64 based processor that is automotive safety grade. On board are a number of accelerators to help accelerate Deep Learning workloads. These include a Volta Based Integrated GPU, multiple Deep Learning Accelerators (DLA), multiple Programmable Vision Accelerators (PVA) as well as other ISPs and Video processors. For more information on Xavier check https://developer.nvidia.com/drive/drive-agx.

This repo uses bazel via a tool called dazel (https://github.com/nadirizr/dazel) to manage builds and cross-compilation inside a docker container.

1. Install Docker

   • https://docs.docker.com/install/

2. Install NVIDIA-Docker

   • https://github.com/NVIDIA/nvidia-docker

3. Install Dazel

   • pip3 install dazel

4. Build the relevant docker container using one of the Dockerfiles provided in //docker

   • More precise instructions can be found in that directory's (README.md)

5. Modify Dockerfile.dazel to be based on the image you just built

   • e.g. FROM nvidia/drive_pdk:5.1.3.0

Dazel behaves like bazel but runs the compilation in a specified docker container. Therefore traditional bazel commands work like:

dazel build //plugins/dali/TensorRTInferOp:libtensorrtinferop.so

You will find the associated binaries in //bazel-out/k8-fastbuild/plugins/dali/TensorRTInferOp/libtensorrtinferop.so

The AGX platforms are aarch64 based, so we need to cross compile the applications:

There are two supported toolchains:

In order to use this toolchain you must and have built a container that supports aarch64-linux (Dockerfiles will have names that contain aarch64-linux or both)

To cross-compile targets for aarch64-linux append the following flag to your build command: --config=D5L-toolchain

• e.g. dazel build //plugins/dali/TensorRTInferOp:libtensorrtinferop.so --config=D5L-toolchain

You will find the associated binaries in //bazel-out/aarch64-fastbuild/plugins/dali/TensorRTInferOp/libtensorrtinferop.so

Note: D5L-toolchain is aliased to L4T-toolchain for Jetson users' convenience

In order to use this toolchain you must obtain the QNX Toolchain and have built a container that supports QNX (Dockerfiles will have names that contain aarch64-qnx or both)

To cross-compile targets for aarch64-qnx append the following flag to your build command: --config=D5Q-toolchain

• e.g. dazel build //plugins/dali/TensorRTInferOp:libtensorrtinferop.so --config=D5Q-toolchain

You will find the associated binaries in //bazel-out/aarch64-fastbuild/plugins/dali/TensorRTInferOp/libtensorrtinferop.so

If you want to run a target in a container, use a command similar to the following:

docker run --runtime=nvidia -v $(realpath bazel-bin):/DL4AGX -it <NAME OF ENV DOCKER IMAGE> /DL4AGX/<PATH TO YOUR SAMPLE IN bazel-bin>

This application demonstrates how to use DALI (https://github.com/NVIDIA/DALI) and TensorRT (https://developer.nvidia.com/tensorrt) in order to create accelerated inference pipelines that leverage more than one accelerator on the Xavier SoC.

If you rebuild a container but have not changed the name of it, dazel may not pick up that the environment has changed. To trigger a manual rebuild of the environment do:

touch Dockerfile.dazel