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Ray 分布式机器学习:利用Ray 进行大模型的数据处理、训练、推理和部署
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Ray 分布式机器学习:利用Ray 进行大模型的数据处理、训练、推理和部署

by Max Pumperla, Edward Oakes, Richard Liaw
May 2024
Intermediate
252 pages
5h 31m
Chinese
China Machine Press
Content preview from Ray 分布式机器学习:利用Ray 进行大模型的数据处理、训练、推理和部署
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1
1.4 Ray
的生态
Ray
的高级库功能十分强大,本书将深入探讨这些高级库。虽然它们对于数据
科学很重要,但我们并不希望给人一种
Ray
是万金油解决方案的印象。通常,
最好和最成功的框架是能与现有解决方案和编程思想进行集成的框架。所以最
好专注于自己的核心优势,并利用其他工具填补不足,
Ray
在这方面做得非常
出色。
在本书中,特别是第
11
章,我们将讨论许多基于
Ray
的强大的第三方库。
Ray
生态还与许多现有工具进行了集成。例如,
Ray Dataset
Ray
的数据加载和计
算库。如果你已经使用了
Spark
18
Dask
等数据处理引擎,则可以将这些工
具与
Ray
一起使用。具体来说,你可以使用
Dask-on-Ray
调度器在
Ray
集群上
运行整个
Dask
生态,或者可以使用
Spark on Ray
项目(
https://oreil.ly/J1D5I
Spark
计算任务与
Ray
集成。同样,
Modin
项目(
https://oreil.ly/brGPJ
)是
Pandas DataFrames
的分布式即插即用替代方案,它使用
Ray
(或
Dask
)作为分
布式执行引擎(
Pandas on Ray
”)。
1
Ray
并不试图替代所有这些工具,而是在与它们集成的同时,仍然可以访问原生
Ray Dataset
库。在第
11
章中,我们将详细介绍
Ray
与其他生态工具的关系。
许多
Ray
库的重要特性之一是它们无缝集成了常用工具作为后端。
Ray
通常创
建常见的接口,而不是创建新标准
19
。这些接口支持以分布式方式运行任务,
这是大多数相应的后端所没有或功能欠缺的。例如 ...
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Publisher Resources

ISBN: 9787111753384