Integrating other frameworks


This section details some of the configuration options and some of the environment variables that can be used to tune for optimal performance when your system already has a version of nGraph installed with one or more of our supported Working with Backends.

Regardless of the framework, after the Build and Test step, a good place to start usually involves making the libraries available to the framework. On Linux* systems built on Intel® Architecture, that command tends to looks something like:

export NGRAPH_CPP_BUILD_PATH=path/to/ngraph_dist/
export LD_LIBRARY_PATH=path/to/ngraph_dist/lib/

Find or display version

If you’re working with the Python API, the following command may be useful:

python3 -c "import ngraph as ng; print('nGraph version: ',ng.__version__)";

To manually build a newer version than is available from the latest PyPI (Python Package Index), see our nGraph Python API documentation.


FMV stands for Function Multi-Versioning, and it can also provide a number of generic ways to patch or bring architecture-based optimizations to the Operating System that is handling your ML environment. See the GCC wiki for details.

If your nGraph build is a Neural Network configured on Clear Linux* OS for Intel® Architecture, and it includes at least one older CPU, the following article may be helpful.

Training Deep Neural Networks

Before tweaking various environment variables, be aware that how the computation gets executed depends on the data layout that the model is using. NHWC and NCHW are common layouts in Deep Learning models. Your ultimate runtime can vary greatly – even when all other factors are exactly the same – when this detail is overlooked.

For CPU (and most cuDNN) backends, the preferred layout is currently NCHW.

  • N – Number of images per batch

  • C – Channel of the image (expressed as a number like 3 for RGB and 1 for grayscale)

  • H – Height of the image

  • W – Width of the image

Intel® Math Kernel Library for Deep Neural Networks


Intel® MKL-DNN is automatically enabled as part of an nGraph default build; you do not need to add it separately or as an additional component to be able to use these configuration settings.

The following KMP options were originally optimized for models using the Intel® MKL-DNN to train models with the NCHW data layout; however, other configurations can be explored.

nGraph-enabled Intel® Xeon®

The list below includes recommendations on data layout, parameters, and application configuration to achieve best performance running DNN workloads on Intel® Xeon® (CPU processor) systems.


The number of threads set by OMP_NUM_THREADS ought not exceed the number of physical cores. The threads should be pinned to their respective physical cores and activated as follows:

  • When HT=off, KMP_AFFINITY=compact,granularity=fine

  • When HT=on, KMP_AFFINITY=compact,1,0,granularity=fine

Memory allocation

Buffer pointers should be aligned on 64-byte boundaries. NUMA policy should be configured for local memory allocation (numactl --localloc).

Convolution shapes

  • When running inference, or training for forward-propagation and weight updates, for best performance:

    • the number of input channels should be 1, 3, or a multiple of SIMD-width (8 for AVX2 systems, 16 for AVX512 systems).

    • the number of output channels should be a multiple of SIMD-width (8 for AVX2 systems, 16 for AVX512 systems).

  • When training backward propagation, the number of input and output channels should be a multiple of SIMD-width (8 for AVX2 systems, 16 for AVX512 systems),

    • padding should not exceed \(0.5x\) where \(x\) is the kernel size.

    • kernel width should be less than 14.


The best resource for this configuration option is the Intel® OpenMP* docs at the following link: Intel OpenMP documentation. OMP_NUM_THREADS defaults to the number of logical cores. To check the number of cores on your system, you can run the following on the command-line to see the details of your CPU:

$ lscpu

Intra-op and inter-op parallelism

  • intra_op_parallelism_threads

  • inter_op_parallelism_threads

Some frameworks, like TensorFlow*, use these settings to improve performance; however, they are often not sufficient for optimal performance. Framework-based adjustments cannot access the underlying NUMA configuration in multi-socket Intel® Xeon® processor-based platforms, which is a key requirement for many kinds of inference-engine computations. See the next section on NUMA performance to learn more about this performance feature available to systems utilizing nGraph.

NUMA performance

NUMA stands for Non-Uniform Memory Access. It indicates how each CPU can access memory attached to each socket.

Without the “knowledge” of CPU socket and NUMA configuration, a simple thread affinity (as in the case of thread pool) does not lead to optimal performance. In fact, it can sometimes prohibitively decrease throughput; a core from socket 0 might have to continually access cache lines from the memory bank of socket 1, increasing bandwidth demands on the Intel® Ultra-Path Interconnect (Intel® UPI). This situation is exacerbated with larger number of sockets found in 4, 8, and 16-socket systems. We believe that users need to be aware of system level optimizations in addition to framework specific configuration parameters to achieve the best performance for NN workloads on CPU platforms. The nGraph Compiler stack runs on transformers handled by Intel® Architecture (IA), and thus can make more efficient use of the underlying hardware.