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Two problems continuously faced by machine learning engineers and data scientists are (1) efficiently managing AI pipelines from development to deployment and (2) running those pipelines in such a way as to reduce costs and resource use.

Microsoft and Intel have collaborated to create a solution that addresses both by incorporating Intel AI optimizations into the Azure Machine Learning platform. Sign up for this session to learn what it is and how to take advantage of it.

Key takeaways:

• An overview of the solution, which integrates Intel optimizations for Python frameworks such as PyTorch, TensorFlow, and Scikit-learn on the Azure ML platform
• How to enable in your Azure workloads using the above-mentioned framework optimizations to reduce cloud costs and development time, increase resource utilization, and improve ML pipeline speed

Includes a demo.

Sign up today.

Skill level: Intermediate

Featured software

Get the following Intel extensions standalone from GitHub or as part of the Intel® oneAPI AI Analytics Toolkit

• TensorFlow
• PyTorch
• Scikit-learn

Download code samples

• Intel TensorFlow – Getting Started
• Intel PyTorch – Optimizing Workloads
• Intel Scikit-learn – Getting Started
• See all code samples

Rachel Oberman
Intel AI Technical Consulting Engineer

Savita Mittal
Principal Software Engineer, Microsoft

This session addresses the challenge of speeding up the linear algebra oneMKL kernel on GPUs through OpenMP. (This is important because it allows you to maintain the same code base for CPU and GPU OpenMP acceleration.)

You’ll learn the technique via a demonstration of how to solve a 1000x1000 complex number matrix by calling the GPU version of the oneMKL subroutine on Intel® Data Center GPU Max Series processors.

Key takeaways:

• An overview of OpenMP, including how it’s a native solution for Fortran users to take advantage of Intel GPU hardware and its offload capabilities
• How to dispatch a oneMKL math kernel, such as solving a complex number matrix, to the latest Intel GPU
• Compiling the code example using the latest Intel® Fortran Compiler
• Using OpenMP data construct to manage data movement between the host and target devices
• How to monitor and optimize host-target data movement by the OpenMP runtime library

Sign up today.

Skill level: Intermediate

Featured software

Download the following standalone or as part of the Intel® oneAPI Base Toolkit

• Intel oneAPI Math Kernel Library
• Intel Fortran Compiler – Windows | Linux | macOS

Download code samples

• oneMKL – Solving Linear Systems
• Fortran-OpenMP Offload to GPU
• Intel Fortran Compiler Co-array Tutorial
• See all code samples

Shiquan Su
Software Technical Consulting Engineer

Barbara Perz
Technical Consulting Engineer

Operational AI describes using models and algorithms to integrate AI into day-to-day customer experiences and business processes.

This session explores the concept generally, plus dives into a methodology for achieving it—combining MLOps† components and AI optimizations to deploy performant and scalable AI solutions in production while ensuring that individual components of an AI system are optimized across the stack.

Key takeaways:

• Practical examples of implementing MLOps components—such as model registries, data versioning, and monitoring—with AI tools such as model compression and Intel-optimized AI frameworks
• How implementation of software and hardware optimizations can help optimize critical parts of the ML lifecycle
• How to use the combo of MLOps/AI optimization to maximize ROI and AI system quality

This session will take advantage of the latest Intel hardware and software available in the Intel® Developer Cloud.

Sign up today.

Skill level: Intermediate

Featured software

Get the following tools standalone or as part of the Intel® AI Analytics Toolkit

• Intel® Neural Compressor
• Intel® Extension for PyTorch
• Intel® Extension for Scikit-learn

Download code samples

• Fine-Tuning Text Classification Model with Intel Neural Compressor
• Get Started with Intel Extension for PyTorch
• Intel Fortran Compiler Co-array Tutorial
• See all code samples

†MLOps (machine learning operations) productionizes the operational rigor of the ML lifecycle.

Eduardo Alvarez
Software Technical Consulting Engineer

Even though GPUs are the dominant paradigm for data-parallel computations, software outlives hardware. Thus, standard approaches are needed to increase the longevity of heterogeneous software, regardless of hardware target.

OpenMP is such an approach.

This session focuses on how industry-standard OpenMP provides a rich set of directives to address the limitations of Fortran, a mature and modern programming language that, nonetheless, evolves slowly.

Topics covered:

• Differences between Fortran and OpenMP in regard to supporting heterogeneous parallelism
• Using detailed code examples to illustrate when ISO Fortran is sufficient and when Fortran+OpenMP is a better alternative to achieve heterogeneous acceleration
• Using OpenMP to explicitly control data transfer between disjoint memories to overcome the control issues of host-device data transfer using Fortran DO CONCURRENT loops to offload computations to an accelerator

Sign up today.

Skill level: Intermediate

Featured software

Get the Intel Fortran Compiler standalone or as part of the Intel® oneAPI Base Toolkit

Download code samples

• Fortran-OpenMP Matrix Multiply Sample
• ISO3DfD OpenMP Offload
• See all code samples

Henry Gabb
Senior Principal Engineer, Intel

Shiquan Su
Software Technical Consulting Engineer, Intel

Regardless of whether medical science is your thing, this session is a good one for anyone interested in materially improving performance on Intel CPUs of computationally challenging, algorithm-heavy applications.

Sign up to hear how two researchers accomplished it for a critical epistasis disease-detection application that uses datasets spanning millions of genetic markers in multiple high-order combinations; this effectively expands datasets into the hundreds of trillions of samples that must be quickly and accurately evaluated.

Find out how they leveraged oneAPI tools to transform their legacy code into multi-architecture state-of-the-art code, plus the results they achieved on Intel CPUs, including Intel® Xeon® CPU Max Series with HBM.

This session focuses on Intel’s results from its 2023 MLPerf v.3.0 submissions specific to thedata center category. The workload tasks were run on 4th Gen Intel® Xeon® Scalable processors. (Fun fact: Intel remains the only data center CPU vendor to have MLPerf Inference results on a broad set of models.)

Sign up today.

Skill level: Intermediate

Featured software

Get the following tools standalone or as part of the Intel® oneAPI Base Toolkit

• Intel® oneAPI DPC++/C++ Compiler
• Intel® Advisor
• Intel® DPC++ Compatibility Tool

Download code samples

• Matrix Multiply with Intel Advisor
• Compatibility Tool : Migrate CUDA to DPC++/SYCL with Vector-Add
• See all code samples

Aleksandar Ilic
Assistant Professor, Universidade de Lisboa and Senior Researcher SIPS GROUP, INESC-ID

Ricardo Nobre
Researcher at INESC-ID | High-Performance Computing Architectures and Systems (HPCAS)

Join parallel programming expert James Reinders for an inside look at the 2024.0 release. Plus get a tour of the new Intel® Developer Cloud, where you can try out the tools on the latest Intel® CPUs, GPUs, and FPGAs.

They will share:

• A brief overview of the oneAPI programming model and standard
• A summary of new developments, features, and improvements across a select group of optimized tools such as oneMKL, oneDNN, oneTBB, Intel® IPP, Intel® Distribution for Python, and Intel® VTune™ Profiler.
• A live Q&A session that gives participants an open forum for oneAPI-related inquiries and discussions

Sign up today.

Skill level: All

Featured software

• Get started with oneAPI by downloading the Intel® oneAPI Base Toolkit—the foundational set of tools and libraries for developing high-performance, data-centric applications across Intel® CPUs, GPUs, and FPGAs.
• Bookmark the full complement of code samples

James Reinders
Engineer & Author, Intel

Kent Moffat
Product Marketing Manager, oneAPI Tools, Intel