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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Ray
生态及其他
|
233
比较了
Ray
、
Dask
和
Spark
支持的最常见任务类型。
表 11.5：Ray、Dask 和 Spark 的任务类型比较
任务类型 Dask Spark Ray
结构化数据处理 一级支持 一级支持 通过 Ray Dataset 和集成进行支持
，
但不是一级
底层并行 通过任务进行
一级支持
无 通过任务和执行器进行一级支持
深度学习任务 支持
，
但不是
一级
支持
，
但不是
一级
通过若干 ML 库进行一级支持
11.2.2 Ray AIR
和更广泛的
ML
生态
Ray AIR
主要关注
AI
计算，例如通过
Ray Train
提供各种类型的分布式训练，
但它的目标不是涵盖
AI
任务的每个方面。例如，
AIR
选择与
ML
实验的跟踪
工具和监控工具以及数据存储解决方案进行集成，而不是提供原生解决方案。
表
11.6
列出了该生态的补充组件。
表 11.6：生态的补充组件
类别 示例
ML 跟踪和可观测性 MLflow
、
Weights & Biases
、
Arize 等
训练框架 PyTorch
、
TensorFlow
、
Lightning
、
JAX 等
ML 特征存储 Feast
、
Tecton 等
Ray AIR
可以替代某些工具。例如，有许多特定于框架的工具包，如
TorchX
或
TFX
，与各自的框架紧密集成。相比之下，
AIR
不局限于框架，没有绑定厂商，
并提供类似的工具
注
10
。
12
另外，再简要讨论一下
Ray AIR
与云服务。一些主要的云服务提供了全面的工
具包来处理
Python
中的
ML
任务。比如，
AWS SageMaker
是一个出色的一体化 ...
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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.