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Progressive Knowledge - HuaweiCloud

Kunpeng BoostKit for Big Data Machine Learning Algorithm Library

The algorithm library is compatible with native Spark APIs and does not require any modification to upper-layer applications. It optimizes the multi-core affinity and implementation principles of Spark algorithms to dramatically increase the computing speed without impairing the computing precision.

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Learning Path

Deepen your understanding of the Kunpeng BoostKit for Big Data Machine Learning Algorithm Library with beginner to expert level resources.

Overview

Kunpeng BoostKit for Big Data provides a machine learning algorithm library, which has deep optimization in algorithm principles and Kunpeng affinity based on the native Spark algorithms, bringing about higher algorithm execution efficiency.

Use

In projects where native Spark algorithms are used, you can directly invoke the Kunpeng BoostKit for Big Data Machine Learning Algorithm Library without modifying the code.

Acceptance

Learn how to generate test datasets and execute machine learning algorithm test cases.

Deployment

To execute machine learning algorithms, obtain the JAR package compiled by the algorithm library adaptation code and the JAR package of the core algorithm, and then deploy and configure the algorithm library.

Tuning

Learn how to tune the parameters of machine learning algorithm models to accelerate the model convergence, increase the model precision, and improve the algorithm performance.

Secondary Development

For projects that do not invoke native Spark algorithms, refer to the example projects and algorithm APIs to perform secondary development.