Dockerfile.cudasamples

1) Install NVidia SDK & download all packages for the Jetson NANO. Do not flash the board from SDK manager.
2) run ./prepare.sh <path_to_sdk_downloads_dir>
3) Build Dockerfile.cudasamples
4) In the app container, start the X display server and run the prebuilt sample apps:
    $ X &
    $ ./clock
    $ ./deviceQuery
    $ ./postProcessGL
    $ ./simpleGL
    $ ./simpleTexture3D
    $ ./smokeParticles

NOTE: CUDA runtime libraries are very large, some even reaching 100-250MB. Therefore not all sample apps have been built in order to keep image size low.
To build more examples please add them to the samples section of the Dockerfile.

If headless GPU computing needs to be performed, without CUDA runtime and without using a display, comment out the path of runtime libs as mentioned
in the docker file and remove xorg from the final image.
A GPU API only image can be cut down to less than 400 MB. With CUDA libraries installed the image size will go less than 900 MB whereas an image
with video output support - X display server - may reach around 1.3 GB.

Dockerfile.opencv

This dockerfile presents how OpenCV can be built with CUDA libraries inside a container.
a) Perform steps 1) and 2) from above but remove libcudnn debs to improve upload time
b) Build Dockerfile.opencv
c) In the app container run
   $ export DISPLAY=:0
   $ ./example_ximgproc_fourier_descriptors_demo
   $ ./example_ximgproc_paillou_demo corridor.jpg

NOTE: This example builds only a small subset of OpenCV & CUDA modules to keep build time and image size at a low point.
Building specific OpenCV modules is done by selecting them in the build args: cmake -D BUILD_LIST=module1,module2.
Depending on specific usecase, select modules may need to be added as dependencies, otherwise OpenCV build may fail.

In this format the resulting build image may reach around 1.05 GB.

To decrease uploaded files size and build time, the large repository deb file may be unpacked and only necessary dependencies
can be installed to suit specific project needs, like for instance only the compiler and necessary libraries.
Dockerfile should be adapted based on specific project needs.


IMPORTANT: Please consult and accept license terms for all the packages you intend to install and use in your Jetson Nano project.