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Build & Install from source

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

Build vAccel

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 vaccel:

  • build-essential
  • ninja-build
  • pkg-config
  • meson

You can install them using the following command:

apt-get install build-essential ninja-build pkg-config python3-pip 
pip install meson

Get the source code for vaccel:

git clone https://github.com/nubificus/vaccel --recursive

2. Building and installing the core runtime library

cd vaccel

# Configure the build directory with the default options and set build
# type to 'release'.
meson setup --buildtype=release build

# Compile the project
meson compile -C build

# Install the project to default directory (/usr/local)
meson install -C build

3. Building the plugins

Building the plugins is disabled, by default. You can enable building one or more plugins at configuration time by setting the corresponding options.

For example, replacing:

meson setup --buildtype=release build

with:

meson setup --buildtype=release -Dplugin-noop=enabled build

in the previous code snippet, will build and install both the core library and the noop backend plugin.

You can also configure a plugin after the initial configuration of your build directory by using:

meson setup --reconfigure -Dplugin-noop=enabled build

To view all available plugins and options/values you an run:

meson setup --buildtype=release build
meson configure build

vAccel specific options can be found in the Project Options section.

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:

meson setup --reconfigure -Dexamples=enabled build
meson compile -C build

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

Alternatively, to build the example manually you can use the provided pkg-config specification - make sure vAccel is installed globally or set the PKG_CONFIG_PATHenvironment variable.

$ # install vaccel to build/install
$ meson setup --reconfigure --prefix=<absolute/path/to/>build/install build
$ meson install -C build
$ # set path to the pkgconfig dir (so pkg-config will find vaccel.pc)
$ export PKG_CONFIG_PATH=<absolute/path/to/>build/install/lib/<multirarch-triplet>/pkgconfig
$
$ cd examples
$ gcc classify.c -o classify -Wall -Wextra $(pkg-config --cflags --libs vaccel)
$ 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!

For debug level output:

export VACCEL_DEBUG_LEVEL=4