perfectly balanced among workers • No routing table required • Key semantics are not preserved: values of the same key might be routed to different workers • Workers are responsible for roughly • Consider the problem of throwing n balls to n bins sequentially (balls -> records, bins -> workers) • Bins are selected uniformly at random • At the end of the process, the maximum load is Θ(ln • Choose one among n workers • check the load of each worker and send the item to the least loaded one • load checking for every item can be expensive • Choose two workers at random and send the

Consumer Pattern • Front end (UI) or registry generates replication jobs (producer) • Backend workers handle replication (consumer) • Potential issues • Producers need to sleep or wait when buffer is blocking for producers • Dispatcher queues jobs • Dispatcher distributes jobs to available workers • Workers added back to available worker queue after jobs are completed Front End Registry Worker for stopping a job Worker Pool • Predefine a pool of available workers (default:3, not to overwhelm frontend tasks) • A list of workers and a channel for dispatching job harbor/src/jobservice/job/workerpool

callbacks=None, validation_data=None, validation_steps=None, class_weight=None, max_queue_size=10, workers=1, use_multiprocessing=False, shuffle=True, initial_epoch=0) 使用 Python 生成器逐批生成的数据，按批次训练模型。 生成 Sequence：如果未指定，将使用 len(generator) 作为步数。 • class_weight: 将类别映射为权重的字典。 • max_queue_size: 生成器队列的最大尺寸。 • workers: 使用的最大进程数量。 • use_multiprocessing: 如果 True，则使用基于进程的多线程。请注意，因为此实现依赖于多 进程，所以不应将不可传递的参数传递给生成器，因为它们不能被轻易地传递给子进程。 4.2.3.9 evaluate_generator evaluate_generator(self, generator, steps=None, max_queue_size=10, workers=1, use_multiprocessing=False) 在数据生成器上评估模型。 这个生成器应该返回与 test_on_batch 所接收的同样的数据。 参数 • generator:

is bound and listen for incoming logging event. Default is 4560. • the workers properties defines the number of threads (workers) which can deal with multiple clients in the same time. 1.2.4. File The decanter-collector-log-socket # # Decanter Log/Log4j Socket collector configuration # #port=4560 #workers=10 The decanter-collector-file feature installs the file collector: Now, you have to create a configuration the port property contains the port number where the network socket collector is listening • the workers property contains the number of worker thread the socket collector is using for connection command

configuration # #port=4560 #workers=10 • the port property defines the port number where the collector is bound and listen for incoming logging event. Default is 4560. • the workers properties defines the the number of threads (workers) which can deal with multiple clients in the same time. 1.2.4. FILE The Decanter File Collector is an event driven collector. It automatically reacts when new lines are Collector # Port number on which to listen #port=34343 # Number of worker threads to deal with #workers=10 # Protocol tcp(default) or udp #protocol=tcp # Unmarshaller to use # Unmarshaller is identified

256 def get_dataloader_workers(): #@save """使用4个进程来读取数据""" return 4 train_iter = data.DataLoader(mnist_train, batch_size, shuffle=True, num_workers=get_dataloader_workers()) 我们看一下读取训练数据所需的时间。 timer DataLoader(mnist_train, batch_size, shuffle=True, num_workers=get_dataloader_workers()), data.DataLoader(mnist_test, batch_size, shuffle=False, num_workers=get_dataloader_workers())) 下面，我们通过指定resize参数来测试load_data_f 公共抽象值得使用的原因，公共抽象即重 新定义具有更新语义的键－值存储（key‐value store）的抽象。 在许多工作节点和许多GPU中，梯度i的计算可以定义为 gi = � k∈workers � j∈GPUs gijk, (12.7.1) 其中gijk是在工作节点k的GPUj上拆分的梯度i的一部分。这个运算的关键在于它是一个交换归约（commu‐ tative reduct

Queue, Processor Workers）- 分布式追踪平台代理是一个网络 守护进程，侦听通过用户数据报协议(UDP)发送并发送到 Collector。这个代理应被放置在要 管理的应用程序的同一主机上。这通常是通过容器环境（如 Kubernetes）中的 sidecar 来实 现。 Jaeger Collector (Collector, Queue, Workers)- 与 Jaeger 代理类似，Jaeger spec: collector: options: {} 定义 Jaeger Collector 的配置选 项。 options: collector: num-workers: 从队列中拉取的 worker 数量。 整数，如 50。 options: collector: queue-size: Collector 队列的大小。 整数，如 2000。 隔，请将其设置为零。 200ms es: bulk: size: 在批量处理器决定提交更 新之前，批量请求可以处 理的字节数。 5000000 es: bulk: workers: 可以接收并将批量请求提 交 Elasticsearch 的 worker 数量。 1 表 表 3.12. ES TLS 配置参数 配置参数 参数 参数 描述 描述 值 值 默 默认值

changes: external workload and system performance • Identify bottleneck operators, straggler workers, skew • Enumerate scaling actions, predict their effects, and decide which and when to apply changes: external workload and system performance • Identify bottleneck operators, straggler workers, skew • Enumerate scaling actions, predict their effects, and decide which and when to apply changes: external workload and system performance • Identify bottleneck operators, straggler workers, skew • Enumerate scaling actions, predict their effects, and decide which and when to apply

Processor Workers）- 分布式追踪平台 (Jaeger) 代理是 一个网络守护进程，侦听通过用户数据报协议(UDP)发送并发送到 Collector。这个代理应被 放置在要管理的应用程序的同一主机上。这通常是通过容器环境（如 Kubernetes）中的 sidecar 来实现。 Jaeger Collector (Collector, Queue, Workers)- 与 Jaeger spec: collector: options: {} 定义 Jaeger Collector 的配置选 项。 options: collector: num-workers: 从队列中拉取的 worker 数量。 整数，如 50。 options: collector: queue-size: Collector 队列的大小。 整数，如 2000。 隔，请将其设置为零。 200ms es: bulk: size: 在批量处理器决定提交更 新之前，批量请求可以处 理的字节数。 5000000 es: bulk: workers: 可以接收并将批量请求提 交 Elasticsearch 的 worker 数量。 1 表 表 3.12. ES TLS 配置参数 配置参数 参数 参数 描述 描述 值 值 默 默认值

OperatorHub, you can find the Windows Machine Config Operator (WMCO) that allows you to add Windows workers to any OpenShift cluster running on AWS, Azure or vSphere. Windows is also supported via a BYOH (TKG) does not support Windows workers or workloads. However, Tanzu Kubernetes Grid Integrated Edition (TKGI) offers the possibility to be deployed in Windows workers. The documentation specifies that