Machine learning calculation optimization method and platform

Abstract

The invention discloses a machine learning calculation optimization method and platform. The method comprises the following steps: segmenting a machine learning calculation graph into a data working sub-graph consisting of upstream nodes of stateful nodes and a training working sub-graph consisting of stateful nodes and downstream nodes thereof, and adding data sending nodes to the data working sub-graph at two sides of a segmented edge, and adding a data receiving node to the training work sub-graph. According to the method, data and training work in the same task can be decoupled, so that general computing resources participating in the data work during operation can be dynamically allocated; the problem that the running efficiency of a deep learning task is reduced due to the fact that enough preprocessing data cannot be provided for a special computing unit such as a GPU is solved. Furthermore, through combination with a scheduler, general computing resource scheduling can be carried out in a cluster range, a single-machine boundary is broken, and the overall hardware utilization efficiency of the platform is improved.

Description

technical field [0001] The present disclosure relates to the field of machine learning, and in particular to a machine learning calculation optimization method and platform. Background technique [0002] Currently, data processing and training for deep learning tasks reside in the same piece of code, compiled together and run on the same machine. However, the ratio of general-purpose computing resources (for example, CPU) and special-purpose computing resources (for example, GPU, ASIC) required by different deep learning tasks is quite different. meet mission requirements. And with the improvement of the computing power of a single dedicated computing resource, the general-purpose computing resources usually equipped in the prior art cannot provide enough data for the dedicated computing resources, which leads to the inefficiency of deep learning tasks caused by the mismatch of general-purpose and special-purpose computing capabilities. reduce. [0003] For this reason, t...

AI Technical Summary

Problems solved by technology

This scheme divides the computing graph based on the presence or absence of nodes and inserts communication nodes, so that the data work and training work in the same deep learning task are decoupled from each other, so that the general computing resources involved in data work can be dynamically allocated based on the efficiency of the runtime training work. Solve the problem of running deep learning tasks inefficiently due to the inability to provide enough pre-processed data to dedicated computing units such as GPUs

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