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Tensorflow-based training model storage method, driver and calculation server

A computing server and training model technology, applied in computing, instrumentation, program control design, etc., can solve problems such as failure to save training models

Active Publication Date: 2018-08-24
HUAWEI TECH CO LTD
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  • Claims
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AI Technical Summary

Problems solved by technology

Therefore, this application provides a Tensorflow-based training model storage method, driver, and computing server, the purpose of which is to enable the computing server and parameter server that save the training model to run on the same computing device, so as to solve the problem of failure to save the training model

Method used

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  • Tensorflow-based training model storage method, driver and calculation server
  • Tensorflow-based training model storage method, driver and calculation server
  • Tensorflow-based training model storage method, driver and calculation server

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Embodiment Construction

[0019] The training model preservation method described in this application can be applied in figure 2 In the Tensorflow On Spark architecture shown. and figure 1 compared to, figure 2 The shown Tensorflow On Spark architecture adds a storage system, and improves SparkDriver, computing server, and parameter server, so that the computing server and parameter server that save the model run on the same computing device.

[0020] figure 2 The computing device shown is a device running an operating system, and may include a physical machine, a virtual machine, or a Docker container.

[0021] image 3 The Tensorflow-based training model preservation method disclosed in the embodiment of the present application includes the following steps:

[0022] S301: After the Spark Driver schedules the training tasks to multiple computing servers, it obtains an Internet Protocol (Internet Protocol, IP) address of the parameter server, and stores the IP address of the parameter server in...

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Abstract

The invention provides a Tensorflow-based training model storage method. The method comprises the following steps of: obtaining and storing a first identifier and a second identifier by a driver, wherein the first identifier is an identifier of calculation equipment, in which a parameter server is operated, and the second identifier is an identifier of a calculation server which is operated in thesame calculation equipment with the parameter server; and obtaining the first identifier and the second identifier from the driver by the calculation server, and when the identifier of the calculation equipment, in which the calculation server is operated, is same as the first identifier and the identifier of the calculation identifier is same as the second identifier, storing a training model soas to improve the training model storage success rate.

Description

technical field [0001] The present application relates to the field of electronic information, in particular to a Tensorflow-based training model storage method, driver, and computing server. Background technique [0002] Tensorflow Tensorflow is a machine learning model produced by Google, which provides distributed machine learning and deep learning capabilities. [0003] figure 1 It is a structural schematic diagram of Tensorflow On Spark, a common architecture of Tensorflow. Tensorflow On Spark includes the following logical units: Spark driver Driver, computing server and parameter server. Among them, Spark Driver dispatches training tasks to multiple computing servers and distributes training data to each computing server. The calculation server executes the training process according to the training tasks and training data, and obtains the feedback value of the model parameters. The parameter server corrects the model parameters (for example, the model parameters in...

Claims

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Application Information

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IPC IPC(8): G06F9/48G06F9/50
CPCG06F9/485G06F9/5027
Inventor 袁建勇余远铭王超
Owner HUAWEI TECH CO LTD
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