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Running a simple example

Building a vaccel application

We will use an example of image classification which can be found under the examples folder of the vAccel runtime repo.

You can build the example using the CMake of the repo:

mkdir build
cd build
ls examples
classify  CMakeFiles  cmake_install.cmake  Makefile

If, instead, you want to build by hand you need to define the include and library paths (if they are not in your respective default search paths) and also link with dl:

cd ../examples
gcc -Wall -Wextra -I${HOME}/.local/include -L${HOME}/.local/lib classification.c -o classify -lvaccel -ldl
classification.c  classify  CMakeLists.txt  images

Running the example

Having built our classify example, we need to prepare the vaccel environment for it to run:

  1. Define the path to (if not in the default search path):
export LD_LIBRARY_PATH=${HOME}/.local/lib
  1. Define the backend plugin to use for our application.

In this example, we will use the jetson plugin which implements the image classification operation using the Jetson Inference framework which uses TensorRT.

export VACCEL_BACKENDS=${HOME}/.local/lib/

Finally, the classification application needs the imagent models in the current working path. (TODO: Find link to download those). Once you have these, you can do:

classify  images  networks

VACCEL_IMAGENET_NETWORKS=$(pwd) ./classify images/banana_0.jpg 1
Initialized session with id: 1
Image size: 79281B
classification tags: 99.902% banana