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Table of contents


Inference-Engine supports research in concurrent, large-batch inference and training of deep, feed-forward neural networks. Inference-Engine targets high-performance computing (HPC) applications with performance-critical inference and training needs. The initial target application is in situ training of a cloud microphysics model proxy for the Intermediate Complexity Atmospheric Research (ICAR) model. Such a proxy must support concurrent inference at every grid point at every time step of an ICAR run. For validation purposes, Inference-Engine also supports the export and import of neural networks to and from Python by the companion package nexport.

The features of Inference-Engine that make it suitable for use in HPC applications include

  1. Implementation in Fortran 2018.
  2. Exposing concurrency via
  • Elemental, implicitly pure inference procedures,
  • An elemental and implicitly pure activation strategy, and
  • A pure training subroutine,
  1. Gathering network weights and biases into contiguous arrays for efficient memory access patterns, and
  2. User-controlled mini-batch size facilitating in situ training at application runtime.

Making Inference-Engine's infer functions and train subroutines pure facilitates invoking those procedures inside Fortran do concurrent constructs, which some compilers can offload automatically to graphics processing units (GPUs). The use of contiguous arrays facilitates spatial locality in memory access patterns. User control of mini-batch size facilitates in-situ training at application runtime.

The available optimizers for training neural networks are

  1. Stochastic gradient descent
  2. Adam (recommended)


Building Inference-Engine requires a Fortran 2018 compiler. With gfortran, the required minimum compiler version is 13.

Building and Testing

GNU (gfortran)


To build, and test Inference-Engine with gfortran in your PATH and your present working directory set to your local copy of the inference-engine repository, enter the following commands in macOS Terminal window (using the default zsh shell or bash):


whereupon the trailing output will provide instructions for running the codes in the example subdirectory.

Linux (including the Windows Subsystem for Linux)

The above script assumes that you have either have fpm installed and or that the script can use Homebrew to install it. If neither is true, please install fpm and then build and test Inference-Engine with the following command:

fpm test

Intel (ifx) -- under development

As of this writing, ifx compiles all of Inference-Engine and all tests pass except tests involving training. We are working with Intel on supporting training with ifx. If you would like to build Inference-Engine and run the tests, please execute the following command

fpm test --compiler ifx --flag "-coarray -coarray-num-images=1"

NAG (nagfor) -- under development

As of this writing, nagfor compiles all of Inference-Engine and passes only tests that involve neither inference nor training. We are working with NAG on supporting inference and training with nagfor.

fpm test --compiler nagfor --flag "-fpp -f2018 -coarray=single"

HPE ( -- under development

As of this writing, the Cray Compiler Environment (CCE) Fortran compiler does not build Inference-Engine. Building with the CCE ftn compiler wrapper requires an additional trivial wrapper. With a shell script named of the following form in your PATH


ftn "$@"

execute the following command:

fpm test --compiler


The example subdirectory contains demonstrations of several intended use cases.

Configuring a Training Run

To see the format for a JSON configuration file that defines the hyperparameters and a new network configuration for a training run, execute the provided training-configuration output example program:

% ./build/ run --example print-training-configuration
Project is up to date
     "hyperparameters": {
         "mini-batches" : 10,
         "learning rate" : 1.50000000,
         "optimizer" : "adam"
     "network configuration": {
         "skip connections" : false,
         "nodes per layer" : [2,72,2],
         "activation function" : "sigmoid"

As of this writing, the JSON file format is fragile. Because an Intel ifx compiler bug prevents using our preferred JSON interface, rojff, Inference-Engine currently uses a very restricted JSON subset written and read by the sourcery utility's string_t type-bound procedures. For this to work, it is important to keep input files as close as possible to the exact form shown above. In particular, do not split, combine or reorder lines. Adding or removing whitespace should be ok.


Please see the Inference-Engine GitHub Pages site for HTML documentation generated by ford.