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Machine learning platform compatible with multiple algorithm frameworks

A machine learning and framework technology, applied in instruments, computing, computing models, etc., can solve problems such as high development and operation and maintenance costs, waste of hardware resources, and unfavorable data sharing.

Inactive Publication Date: 2018-12-21
WUXI XUELANG DIGITAL TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

If a separate cluster is built for each computing framework, there will be a large waste of hardware resources, high development and operation and maintenance costs, and it is not conducive to data sharing

Method used

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  • Machine learning platform compatible with multiple algorithm frameworks
  • Machine learning platform compatible with multiple algorithm frameworks

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Embodiment Construction

[0013] The present invention will be further described below in conjunction with drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, but not to limit the present invention. In addition, it should be noted that, for the convenience of description, only parts related to the present invention are shown in the drawings but not all content. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field of the invention. The terms used herein in the description of the present invention are for the purpose of describing specific embodiments only, and are not intended to limit the present invention. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0014] In this embodiment, the machine learning platform compatible with multip...

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Abstract

The invention discloses a machine learning platform compatible with a plurality of algorithm frameworks, which uniformly performs resource scheduling and user isolation by building a cluster compatible machine learning framework of all mainstream. The mainstream machine learning framework is built on top of the Hadoop + Spark cluster and uses RDD as the data store. The invention has the followingadvantages: first, unified resource management is carried out by using yarn, thus inheriting the advantages of all yarns; 2, use Spark as that unified bottom compute framework and Spark RDD as the unified data storage, thus inheriting all the advantages of Spark; Third, scheduling multiple computing frameworks to support the native advantages of all frameworks, including synchronous / asynchronous training, model / data parallel computing, on-line prediction, etc. Fourth, the integration of heterogeneous computing framework is completed; Fifthly, the bottom layer supports CPU and GPU.

Description

technical field [0001] The invention relates to a machine learning platform, in particular to a machine learning platform compatible with multiple algorithm frameworks. Background technique [0002] Machine learning has been developed for decades. In different periods, in order to solve problems in different scenarios, a variety of machine learning frameworks have emerged, such as the traditional machine learning framework ScikitLearn, the distributed parallel computing framework Spark ML, and the deep learning framework TensorFlow , Caffe, Intel BigDL, etc. In actual scenarios, it is often necessary to use these heterogeneous computing frameworks at the same time to solve specific problems to achieve optimal results. If a separate cluster is built for each computing framework, there will be a large waste of hardware resources, high development and operation and maintenance costs, and it is not conducive to data sharing. Contents of the invention [0003] The purpose of ...

Claims

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Application Information

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IPC IPC(8): G06N99/00G06F9/50G06F17/30
CPCG06F9/5027G06F9/5066
Inventor 王峰
Owner WUXI XUELANG DIGITAL TECH CO LTD
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