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Build & install from source

To build the components of vAccel we need the vAccelRT core library and a backend plugin that implements the operation we want to execute.

Build vAccelRT

This repo includes the core runtime system, the exec backend plugin and a debug plugin for testing (noop).

1. Cloning and preparing the build directory

In Ubuntu-based systems, you need to have the following packages to build vaccelrt:

  • cmake
  • build-essential

You can install them using the following command:

sudo apt-get install -y cmake build-essential

Get the source code for vaccelrt:

git clone https://github.com/cloudkernels/vaccelrt --recursive

Prepare the build directory:

cd vaccelrt
mkdir build
cd build

2. Building the core runtime library

# This sets the installation path to /usr/local, and the current build
# type to 'Release'. The other option is the 'Debug' build
cmake ../ -DCMAKE_INSTALL_PREFIX=/usr/local -DCMAKE_BUILD_TYPE=Release
make
make install

3. Building the plugins

Building the plugins is disabled, by default. You can enable building one or more plugins at configuration time of CMake by setting the corresponding variable of the following table to ON

Backend Plugin Variable Default
noop BUILD_PLUGIN_NOOP OFF
exec BUILD_PLUGIN_EXEC OFF

For example:

cmake -DBUILD_PLUGIN_NOOP=ON ..

will enable building the noop backend plugin.

Building a vaccel application

We will use an example of image classification which can be found under the examples folder of this project.

You can build the example using the following directive in the build directory:

cmake -DBUILD_EXAMPLES=ON ..
make

A number of example binaries have been built:

# ls examples
classify          detect          exec_generic     minmax          pose             pynq_parallel    segment_generic  tf_inference
classify_generic  depth           detect_generic   minmax_generic  pose_generic     pynq_vector_add  sgemm            tf_model
depth_generic     exec            Makefile         noop            pynq_array_copy  segment          sgemm_generic    tf_saved_model

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/usr/local/include -L/usr/local/lib classify.c -o classify -lvaccel -ldl
$ ls classify.c classify
classify.c  classify

Running a vaccel application

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

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

In this example, we will use the noop plugin:

export VACCEL_BACKENDS=/usr/local/lib/libvaccel-noop.so
  1. Finally, you can do:
./classify images/example.jpg 1

which should dump the following output:

$ ./classify images/example.jpg 1
Initialized session with id: 1
Image size: 79281B
[noop] Calling Image classification for session 1
[noop] Dumping arguments for Image classification:
[noop] len_img: 79281
[noop] will return a dummy result
classification tags: This is a dummy classification tag!

Alternatively from the build directory:

$ cd ../build
$ ./examples/classify ../examples/images/example.jpg 1
Initialized session with id: 1
Image size: 79281B
[noop] Calling Image classification for session 1
[noop] Dumping arguments for Image classification:
[noop] len_img: 79281
[noop] will return a dummy result
classification tags: This is a dummy classification tag!