Speed Machine Learning with the Model Zoo for Intel Architecture

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Intel has launched a Model Zoo for Intel Architecture, an open-sourced collection of optimized machine learning inference applications that demonstrates how to get the best performance on Intel platforms. The project contains more than 20 pre-trained models, benchmarking scripts, best practice documents, and step-by-step tutorials for running deep learning (DL) models optimized for Intel Xeon Scalable processors.

Are you a data scientist who wants to optimize the performance of your machine learning (ML) inference workloads? Perhaps you’ve heard of the Intel Optimization for TensorFlow and the Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN), but have not yet seen a working application in your domain that takes full advantage of Intel’s optimizations.

With the Model Zoo, you can easily:

  • Learn which AI topologies and applications Intel has optimized to run on its hardware
  • Benchmark the performance of optimized models on Intel hardware
  • Get started efficiently running optimized models in the cloud or on bare metal

The latest release of the Model Zoo features optimized models for the TensorFlow framework and benchmarking scripts for both 32-bit floating point (FP32) and 8-bit integer (Int8) precision. Most commercial DL applications today use FP32 precision for training and inference, though 8-bit multipliers can be used for inference with minimal to no loss in accuracy. The Int8 models were created using post-training quantization techniques for reduced model size and lower latency.

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