Develop Efficient AI Solutions with Accelerated Machine Learning
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Overview
Tailor your approach to developing efficient AI solutions with accelerated machine learning.
This expert-level workshop focuses on techniques for maximizing AI solution acceleration using components of the Intel® AI Analytics Toolkit. Workloads performed by CPUs and GPUs from Intel are surveyed, and implementations using key components, such as Intel® Extension for Scikit-learn* and Data Parallel Essentials for Python* language, are presented.
Topics covered include:
- Enabling patching and unpatching of scikit-learn—from fine-grained to global—for optimizing functions.
- Applying "compute follows data" principles via several algorithms including K-means, pairwise distance, and principal component analysis (PCA).
- A demo of data parallel Python with high-performing code targeting Intel CPUs and GPUs.
- Numba-dpex (a Numba* data-parallel extension), including examples of data-parallel code inside @numba.jit decorator and @kernel decorator functions readied to offload to a SYCL* device.
- How to write Python native extensions more easily using data parallel control (dpctl), a companion library based on SYCL.
Accelerate data science and AI pipelines-from preprocessing through machine learning-and provide interoperability for efficient model development.
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