May 2024
Intermediate
252 pages
5h 31m
Chinese
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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14
|
第
1
章
Python
工具。因此，
Ray
在设计上注重高性能和异构计算任务
注
4
。为了更好地
理解这些要点，我们接下来仔细研究
Ray
的设计原则。
1
1.1.2 Ray
的设计原则
Ray
构建于若干设计原则之上。
Ray
的
API
简单且通用、计算模型能提供充分
的灵活性、系统架构兼具高性能和可扩展性。下面进行详细介绍。
简洁性和抽象性
Ray
的
API
不仅高度简洁，而且易于上手（详见第
2
章）。无论是利用笔记本计
算机上的所有
CPU
内核，还是充分利用集群中的所有机器，都能轻松实现。开
发者只需修改一两行代码，
Ray
代码基本保持不变。与任何优秀的分布式系统
一样，
Ray
在后台管理任务分发和协调。这样用户就不必考虑分布式计算的机
制。
Ray
提供了良好的抽象层，能让用户专注于自身工作。
由于
Ray
的
API
具有广泛的适用性并符合
Python
编码风格，因此很容易将
Ray
与其他工具集成。例如，
Ray
的执行器（
actor
）可以调用现有的分布式
Python
计算任务，计算任务也可以调用执行器。在这个意义上，因为
Ray
兼具高性能
和强大的灵活性，支持在不同的系统和框架之间进行通信，所以非常适合作为
分布式计算任务中的“胶水代码”。
灵活性和异构性
对于
AI
计算任务，特别是在处理强化学习等任务时，用户需要灵活的编程模
型。
Ray
的
API
支持编写灵活和可组合的代码。简单来说，如果你能用
Python
表明计算任务，就可以通过
Ray
进行分布式处理。当然，用户需要确保有足够
的资源，并且要谨慎考虑分发的任务。但是，
Ray
不会限制开发者的操作。
Ray ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.