Intel® Optimization for TensorFlow*: Tips & Tricks for an AI & High-Performance Computing (HPC) Convergence
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This guided tutorial introduces a key machine-learning framework for optimizing AI inference workloads: the Intel® Optimization for TensorFlow*.
Preethi Venkatesh, an AI technical consulting engineer, discusses how developers can achieve different levels of optimizations and see performance benefits using this Intel-optimized tool. The session includes use-case demonstrations, case studies, and benchmarks. She also answers questions from the audience.
Get the Software
- Download the Intel Optimization for TensorFlow as part of the Intel® oneAPI AI Analytics Toolkit (which also includes PyTorch* and Intel® Distribution for Python*)
- Download the stand-alone tool
Preethi Venkatesh
Technical consulting engineer, Intel Corporation
Preethi helps customers use and adopt the Intel Distribution for Python and Intel® Data Analytics Acceleration Library through training, article publication, and open-source contributions. She joined Intel in 2017, coming from a four-year tour at Infosys Limited* where she was a business data analyst.
Preethi has a BA degree in instrumentation technology from Visvesvaraya Technological University in Belgaum, India, and an MA degree in information systems on data science from the University of Texas at Arlington.
Intel® oneAPI AI Analytics Toolkit
Accelerate end-to-end machine learning and data science pipelines with optimized deep learning frameworks and high-performing Python libraries.