title: ÐÐ»Ð°Ð½Ð¸ÑÐ¾Ð²ÑÐ¸Ðº ÑÐ°Ð·Ð´ÐµÐ»ÐµÐ½Ð¸Ñ Ð¿ÑÐ¾ÑÐµÑÑÐ° Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð½Ð° Ð¸ÑÐ¿Ð¾Ð»Ð½Ð¸ÑÐµÐ»ÐµÐ¹ ÑÐ°Ð·Ð»Ð¸ÑÐ½Ð¾Ð¹ Ð¼Ð¾ÑÐ½Ð¾ÑÑÐ¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Scheduler for distributing neural network training process among executors of different power
Creators: name: ÐÐ°Ð·Ð°ÑÐµÐ², ÐÐ°Ð²ÐµÐ»
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÑÑÑÐ¾Ð¹ÑÑÐ²Ð°-Ð²Ð¾Ð»Ð¾Ð½ÑÐµÑÑ
Ð¿Ð»Ð°Ð½Ð¸ÑÐ¾Ð²ÑÐ¸Ðº Ð·Ð°Ð´Ð°Ñ
Ð²ÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½ÑÐµ Ð¼Ð¾ÑÐ½Ð¾ÑÑÐ¸
Ð±ÑÐ°ÑÐ·ÐµÑÐ½Ð¾Ðµ ÑÐ°ÑÑÐ¸ÑÐµÐ½Ð¸Ðµ
Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ ÑÐ²ÐµÑÑÐºÐ¸
devices volunteers
task scheduler
computing power
browser extension
convolution algorithm

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description: Objective: Development and implementation of a scheduler for effective distribution of the neural network training process among executors with different levels of computing power. Methods: The work includes an analysis of existing distributed learning approaches, development of algorithms for assessing device performance, dynamic task distribution considering current loads and resource capabilities, and the development and implementation of a browser extension for background task execution. Main results: The study analyzed existing methods of distributed learning, identified their advantages and disadvantages. A task scheduler was developed that considers the computing power of different volunteers and optimizes task distribution among them. Additionally, a browser extension was developed and implemented to allow background task execution. Tests showed the effectiveness of the developed solution in accelerating the training process and improving the overall system performance. Application area: The developed scheduler can be used for distributed neural network training, which is especially relevant given the rapidly growing data volumes and increasing model complexity. This solution is suitable for use in scientific research, large corporations, and other organizations involved in machine learning and artificial intelligence. The developed task scheduler for distributed neural network training, including the use of a browser extension for background task execution, has demonstrated its effectiveness in improving system performance and accelerating the training process. Further research may focus on optimizing task distribution algorithms and expanding the functionality of the browser extension.