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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
book

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
Ray
集群
|
191
Client
连接:
import ray
ray.init(address="ray://localhost:10001")
print(ray.cluster_resources())
@ray.remote
def test():
return 12
ray.get([test.remote() for i in range(12)])
使用这种方法,就可以直接在笔记本计算机上运行
Ray
程序(不需要通过
kubectl
或作业提交来传输代码)
9.2.3
公开
KubeRay
在先前的示例中,我们使用端口转发作为访问
Ray
主节点服务的方法。对于生
产环境,你可能想使用其他方法公开服务。以下介绍适用于在
Kubernetes
上运
行的服务。
默认情况下,
Ray
服务可以从运行
Ray
管理器的
Kubernetes
集群中的任何位置
进行访问。例如,要从与
Ray
集群相同的
Kubernetes
命名空间的
Pod
中使用
Ray
Client
,可使用
ray.init("ray://raycluster-complete-head-svc:10001")
要从另一个
Kubernetes
命名空间连接,可使用
ray.init("ray://raycluster-
complete-head-svc.default.svc.cluster.local:10001")
。如果
Ray
集群是
非默认命名空间,请使用该命名空间替换
default
如果从集群外部访问服务,可使用入口控制器。任何标准的入口控制器都应与
Ray Client ...
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Publisher Resources

ISBN: 9787111753384