qnnpack: Quantized Neural Networks PACKage

Contents

  1. Overview of package
  2. Overview of package
    1. General usage
  3. Availability of package by cluster

Overview of package

General information about package
Package: qnnpack
Description: Quantized Neural Networks PACKage
For more information: https://github.com/facebookincubator/gloo
Categories:
License: OpenSource (BSD)

General usage information

The Quantized Neural Networks PACKage (QNNPack) is a mobile-optimized library for low-precision high-performance neural network inference. QNNPACK provides implementation of common neural network operators on quantized 8-bit tensors.

QNNPACK is not intended to be directly used by machine learning researchers; instead it provides low-level performance primitives for high-level deep learning frameworks.

The following environmental variables have been defined:

  • \$QNNPACK_ROOT has been set to the root of the qnnpack installation
  • \$QNNPACK_LIBDIR points to the directory containing the libraries
  • \$QNNPACK_INCDIR points to the directory containing the header files

You will probably wish to use these by adding the following flags to your compilation command (e.g. to CFLAGS in your Makefile):

  • -I\$QNNPACK_INCDIR
and the following flags to your link command (e.g. LDFLAGS in your Makefile):
  • -L\$QNNPACK_LIBDIR -Wl,-rpath,\$QNNPACK_LIBDIR

Available versions of the package qnnpack, by cluster

This section lists the available versions of the package qnnpackon the different clusters.

Available versions of qnnpack on the Deepthought2 cluster (RHEL8)

Available versions of qnnpack on the Deepthought2 cluster (RHEL8)
Version Module tags CPU(s) optimized for GPU ready?
master qnnpack/master ivybridge Y
2019-08-28 qnnpack/2019-08-28 ivybridge, x86_64, zen Y

Available versions of qnnpack on the Juggernaut cluster

Available versions of qnnpack on the Juggernaut cluster
Version Module tags CPU(s) optimized for GPU ready?
2019-08-28 qnnpack/2019-08-28 skylake_avx512, x86_64 Y
2019-08-28 qnnpack/2019-08-28 x86_64 Y