Overview
To run machine learning algorithms based on the big data algorithm library, you need to download the binary packages from Huawei support website and decompress the packages to obtain the machine learning analysis algorithm library packages boostkit-ml-kernel_2.11-1.3.0-spark2.3.2-aarch64.jar, boostkit-xgboost4j-kernel-2.11-1.3.0-spark2.3.2-aarch64.jar, and libboostkit_xgboost_kernel.so. In addition, boostkit-ml-core_2.11-1.3.0-spark2.3.2.jar, boostkit-ml-acc_2.11-1.3.0-spark2.3.2.jar, boostkit-xgboost4j_2.11-1.3.0.jar, and boostkit-xgboost4j-spark2.3.2_2.11-1.3.0.jar compiled from the open source adaptation code of the library are required. boostkit-ml-kernel-client_2.11-1.3.0-spark2.3.2.jar is the dependency library during application development and does not need to be deployed in the Spark cluster. It is used only during compilation in the development phase.
This document uses the BoostKit algorithm package based on Spark 2.3.2 as an example and also applies to the BoostKit algorithm package based on Spark 2.4.6.
Table 1 lists the compilation outputs.
Output |
Remarks |
How to Obtain |
|---|---|---|
boostkit-xgboost4j_2.11-1.3.0.jar |
Adaptation packages required by the XGBoost algorithm, which can be compiled from the open source adaptation code |
|
boostkit-xgboost4j-spark2.3.2_2.11-1.3.0.jar |
||
boostkit-ml-acc_2.11-1.3.0-spark2.3.2.jar |
Files required by machine learning algorithms except XGBoost. boostkit-ml-kernel-client_2.11-1.3.0-spark2.3.2.jar is the dependency library for application development and does not need to be deployed in the Spark cluster. It is used only during compilation in the development phase. |
|
boostkit-ml-core_2.11-1.3.0-spark2.3.2.jar |
||
boostkit-ml-kernel-client_2.11-1.3.0-spark2.3.2.jar |