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CatalogContainersIGX - Yocto Builder PB October 2023 (PB 23h2)

IGX - Yocto Builder PB October 2023 (PB 23h2)

Logo for IGX - Yocto Builder PB October 2023 (PB 23h2)
Associated Products
Features
Description
OpenEmbedded/Yocto Build Container for NVIDIA Holoscan PB October 2023 (PB 23h2)
Publisher
NVIDIA
Latest Tag
23.10.05
Modified
April 16, 2024
Compressed Size
1.58 GB
Multinode Support
No
Multi-Arch Support
No
23.10.05 (Latest) Security Scan Results

Linux / amd64

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What Is Holoscan OpenEmbedded Builder?

OpenEmbedded/Yocto Build Container for NVIDIA Holoscan

This container image contains runtime dependencies, scripts, and the

NVIDIA-proprietary binary packages that are required to build an OpenEmbedded

BSP image for NVIDIA Holoscan Developer Kits.

What Is Holoscan OpenEmbedded Builder Production Branch October 2023?

The Holoscan OpenEmbedded Builder Production Branch, exclusively available with NVIDIA AI Enterprise, is a 9-month supported, API-stable branch that includes monthly fixes for high and critical software vulnerabilities. This branch provides a stable and secure environment for building your mission-critical AI applications. The Holoscan production branch releases every six months with a three-month overlap in between two releases.

Getting started with Holoscan OpenEmbedded Builder Production Branch

Before you start, ensure that your environment is set up by following one of the deployment guides available in the NVIDIA AI Enterprise Documentation.

How to use the Holoscan Build Container

The following documentation provides information specific to the usage of the

Holoscan Build Container, and may be missing information from the main

documentation that may be useful to know when configuring or using the BSP.

Please see the main README file for additional documentation.

Note: the main README file can be found at

meta-tegra-holoscan/README.md after following the `1. Setting up

the Local Development Environment` section, below.

Also note that building a BSP for NVIDIA Holoscan requires a significant

amount of resources, and at least 300GB of free disk space is required to build. See the System Requirements section in the main README for more

details.

1. Setting up the Local Development Environment

While it would be possible to build an OE image directly from source that is

stored within a container, doing so would mean that any additions or

modifications to the build recipes would also only live inside the running

container and so would be lost whenever the container terminates. Instead, this

container operates by initially setting up a local host volume with all of the

recipes, dependencies, and initial configuration that is needed for the BSP

build such that all of the recipes, configuration, and build cache is stored in

persistent storage on the host and thus is not limited to the lifespan of a

single container runtime.

In order to perform this initial setup navigate to the directory into which you

would like to initialize the development environment and run the following

(making sure IMAGE matches the name and tag of this container image):

$ export IMAGE="nvcr.io/nvstaging/nvaie/yocto-builder-pb23h2-igx:23.10.05"
$ docker run -it --rm -v $(pwd):/workspace --network host ${IMAGE} setup.sh ${IMAGE} $(id -u) $(id -g)

This setup processes initializes the following:

  1. The OE recipes and dependencies in these folders:
poky
meta-openembedded
meta-virtualization
meta-tegra
meta-tegra-holoscan
  1. A sample build configuration in the build folder.

  2. Wrapper script bitbake.sh, which runs a build container and passes the

arguments to the script to the container's bitbake command.

  1. A flash.sh script to flash the device with the built image.

  2. A .buildimage file which contains the name of the container image.

This is used by the bitbake.sh script and prevents the need to export

an IMAGE environment variable anytime a build is performed.

2. Configure the Image

The OE image configuration file is created by the previous step and is written

to build/conf/local.conf. This file is based on the default local.conf that

is created by the Poky environment setup script (oe-init-build-env)

and has various NVIDIA configuration defaults and samples added to it.

For example, the MACHINE configuration in this template file is set to

igx-orin-devkit; the GPU configration is set to use the dGPU; and CUDA,

TensorRT, Holoscan SDK, and the HoloHub sample applications are installed by

default. This configuration can be used as-is to build a BSP for the IGX Orin

Developer Kit using the A6000 dGPU, but it may be neccessary to change this

configuration to use the iGPU or to add additional components like Rivermax or

support for third-party hardware such as AJA video capture cards or Emergent

high-speed cameras. See the Build Configuration section in the main README

for more details.

To see the additional configuration that is added to this file relative to the

standard OpenEmbedded local.conf, as well as some documentation as to what

additional components offered by this meta-tegra-holoscan layer may be enabled,

scroll down to the "BEGIN NVIDIA CONFIGURATION" section in this file.

3. Build the Image

Once the image has been configured in the local host development tree, the

container image is used again for the actual bitbake build process. This

can be done using the bitbake.sh build wrapper that is written to the

root of the development directory. This script simply runs the bitbake

process in the container and passes the arguments to the script to this

process. For example, to build a Holoscan reference image, use the following:

$ ./bitbake.sh core-image-holoscan

This build is expected to take at least an hour with build times of 3 or 4

hours being expected on machines with slower hardware or internet connections.

Note: For a list of different image targets that are available to build,

see the Yocto Project Images List.

Note: If the build fails due to unavailable resource errors, try the

build again. Builds are extremely resource-intensive, and having a number of

particularly large tasks running in parallel can exceed even 32GB of system

memory usage. Repeating the build can often reschedule the tasks so that

they can succeed. If errors are still encountered, try lowering the value

of BB_NUMBER_THREADS

in build/conf/local.conf to reduce the maximum number of tasks that BitBake

should run in parallel at any one time.

Using the default configuration, the above script will build the BSP image and

write the final output to:

build/tmp/deploy/images/igx-orin-devkit/core-image-holoscan-igx-orin-devkit.tegraflash.tar.gz

4. Flash the Image

The flash.sh script can be used to flash the BSP image that is output by the

previous step onto the Holoscan Developer Kit hardware. For example, to flash the

core-image-holoscan image that was produced by the previous step, connect the

developer kit to the host via the USB-C debug port, put it into recovery

mode, ensure the developer kit is visible to the host using lsusb, then run:

$ ./flash.sh core-image-holoscan

Note: If the doflash.sh command fails due to a No such file: 'dtc' error, install the device tree compiler (dtc) using the following:

$ sudo apt-get install device-tree-compiler

For instructions on how to put the developer kit into recovery mode and how to

check that it is visible using lsusb, see the developer kit user guide:

Note that flashing the device will require root privileges and so you may be

asked for a sudo password by this script.

Once flashed, the Holoscan Developer Kit can then be disconnected from the host

system and booted. A display, keyboard, and mouse should be attached to the

developer kit before it is booted. The display connection depends on the GPU

configuration that was used for the build: the iGPU configuration uses the

onboard Tegra display connection while the dGPU configuration uses one of the

connections on the discrete GPU. Please refer to the developer kit user guide

for diagrams showing the locations of these display connections. During boot

you will see a black screen with only a cursor for a few moments before an X11

terminal or GUI appears (depending on your image type).

Running the Holoscan SDK and HoloHub Applications

When the core-image-holoscan reference image is used, the Holoscan SDK and

Holohub apps are built into the image, including some tweaks to make running the

samples even easier. Upon boot, the core-image-holoscan image presents a

Matchbox UI with icons for a variety of Holoscan SDK and Holohob sample

applications, all of which can be run with just a single click.

Note that the first execution of these samples will rebuild the model engine

files and it will take a few minutes before the application fully loads. These

engine files are then cached and will significantly reduce launch times for

successive executions. Check the console windows with the application logs for

additional information.

While a handful of graphical Holoscan applications have icons installed on the

desktop, many more are console-only and must be launched from a console.

When the holoscan-sdk component is installed, the Holoscan SDK is installed

into the image in the /opt/nvidia/holoscan directory, with examples present in

the examples subdirectory. Due to relative data paths being used by the apps,

these examples should be run from the /opt/nvidia/holoscan directory. To run

the C++ version of an example, simply run the executable in the example's cpp

subdirectory:

$ cd /opt/nvidia/holoscan
$ ./examples/hello_world/cpp/hello_world

To run the Python version of an example, run the application in the example's

python subdirectory using python3:

$ cd /opt/nvidia/holoscan
$ python3 ./examples/hello_world/python/hello_world.py

When the holohub-apps component is installed, the HoloHub sample applications

are installed into the image in the /opt/nvidia/holohub directory, with the

applications present in the applications subdirectory. Due to relative data

paths being used by the apps, these applications should be run from the

/opt/nvidia/holohub directory. To run the C++ version of an application,

simply run the executable in the applications's cpp subdirectory:

$ cd /opt/nvidia/holohub
$ ./applications/endoscopy_tool_tracking/cpp/endoscopy_tool_tracking

To run the Python version of an application, run the application in the

python subdirectory using python3:

$ cd /opt/nvidia/holohub
$ python3 ./applications/endoscopy_tool_tracking/python/endoscopy_tool_tracking.py

Note that the first execution of the samples will rebuild the model engine files

and it will take a few minutes before the application fully loads. These engine

files are then cached and will significantly reduce launch times for successive

executions.

Security Vulnerabilities in Open Source Packages

Please review the Security Scanning tab to view the latest security scan results.

For certain open-source vulnerabilities listed in the scan results, NVIDIA provides a response in the form of a Vulnerability Exploitability eXchange (VEX) document. The VEX information can be reviewed and downloaded from the Security Scanning tab.

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