Build and Run Sample Applications for DRIVE OS 6.x Linux

Note: To access the GPU from a Docker Container, please ensure that you have NVIDIA Container Toolkit installed. Installation instructions for NVIDIA Container Toolkit can be found at https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html for your host development environment distribution. An NVIDIA GPU and appropriate CUDA drivers must also be available on the host to run GPU-accelerated applications, including samples.

Note: If you are compiling and running samples in Docker and you want to preserve the compiled samples, please keep in mind that Docker containers are a temporary environment that will essentially be deleted when the container is stopped. To preserve the changes made in a running container, please refer to the Docker official documentation on committing Docker images: https://docs.docker.com/engine/reference/commandline/commit.

Alternatively, if the container was started with a mounted ${WORKSPACE}, you may copy or move the compiled binaries to the mounted ${WORKSPACE} before exiting the container so that the compiled binaries will be available on the host.

Graphics Applications

For boards that are pre-flashed or flashed with the custom GDM desktop file system (default for SDK Manager and Docker installations), the Bubble sample application is available in the /opt/nvidia/drive-linux/samples/ folder.

To run a basic X11 sample, use the following commands:

$ cd /opt/nvidia/drive-linux/samples/opengles2/bubble/x11 
$ ./bubble -fps

To run other graphics samples for X11 and the supported window systems, see Building and Running Samples and Window Systems.

CUDA

Documentation is available online: CUDA Toolkit Documentation v11.4.4.

The installations of CUDA Host x86 and Linux AArch64 are in the /usr/local/cuda-11.4/ folder.
The source code files of all CUDA samples are in the /usr/local/cuda-11.4/samples folder.

How to Build the CUDA Samples for the Linux Target

Host cross-compile is supported for DRIVE OS releases only. After you finish installing CUDA x86 and cross-compile packages, perform the following steps:

Install the CUDA sample sources to a directory on your x86 development host where you do not need root privileges to write, such as the $HOME directory as shown in the following example:
```
$ cd ~/
```
- Use the following command to run the cuda-install-samples-11.4.sh script from the CUDA installation in the x86 host file system:
```
$ <$NV_WORKSPACE>/drive-linux/filesystem/targetfs/usr/local/cuda-11.4/bin/cuda-install-samples-11.4.sh .
```
  Where $NV_WORKSPACE is:
  - Docker: /drive
- A successful run will show the following:
```
Copying samples to ./NVIDIA_CUDA-11.4_Samples now...
Finished copying samples.
```
  Samples will be installed to the directory ~/NVIDIA_CUDA-11.4_Samples.

$ cd ~/NVIDIA_CUDA-11.4_Samples
$ sudo make SMS=87 TARGET_ARCH=aarch64 TARGET_OS=linux TARGET_FS=$NV_WORKSPACE/drive-linux_src/filesystem/targetfs

Note: This will build the samples for the Orin GPU (SMS=87). To build for other GPUs, replace the SMS value with the target compute version.????

How to Run the CUDA Samples

To run a CUDA sample application,

Copy the sample files of your choice to the target.
From the target, run the sample application.

For example:

$ cd ~/
$ rcp -r NVIDIA_CUDA-11.4_Samples <username>@<host>:/home/<username>

From the target:

$ cd ~/NVIDIA_CUDA-11.4_Samples/bin/aarch64/linux/release/
$ ./deviceQueryDrv

TensorRT

After you finish installing the TensorRT packages, the /usr/src/tensorrt folder is created on the development host.

For more information about:

TensorRT Developer Guide and API Reference, see NVIDIA TensorRT Documentation.

How to cross-compile TensorRT samples, see Sample Support Guide in NVIDIA TensorRT Documentation.

How to Build the TensorRT Samples

On the development host:

$ cd /usr/src/tensorrt/samples
$ sudo make TARGET=aarch64

How to Run the TensorRT Samples on the Target

To run a TensorRT sample application,

Copy the sample files of your choice to the target.
From the target, run the sample application.

For example, from the host:

$ scp -r /usr/src/tensorrt <username>@<target ip address>:/home/nvidia/tensorrt

From the target:

$ cd /home/nvidia/tensorrt
$ ./bin/sample_algorithm_selector

For further information, the TensorRT Developer guide and API reference documents are available at https://docs.nvidia.com/deeplearning/tensorrt/index.html, including sample cross-compile information at https://docs.nvidia.com/deeplearning/tensorrt/sample-support-guide/index.html#cross-compiling-linux.