memorize something for the long term, you need to improve recall by repetition (i.e., increase the weight of that connection). Can we do the same with neural networks? Can we optimally prune the network latency gains with a minimal performance tradeoff. Next, the chapter goes over weight sharing using clustering. Weight sharing, and in particular clustering is a generalization of quantization. If you that lie within the same quantization bin, are mapped to the same quantized weight value. That is an implicit form for weight sharing. However, quantization falls behind in case the data that we are quantizing

architectures. A classical example is Quantization (see Figure 1-8), which tries to compress the weight matrix of a layer, by reducing its precision (eg., from 32-bit floating point values to 8-bit unsigned unsigned / signed integers). Quantization can generally be applied to any network which has a weight matrix. It can often help reduce the model size 2 - 8x, while also speeding up the inference latency. of continuous high-precision values to discrete fixed-point integer values. Another example is Pruning (see Figure 1-9), where weights that are not important for the network’s quality are removed / pruned

用户使用多个服务设置路由。每个服务负责应用程序的一个版本。 每个服务分配到一个 weight，进入每个服务的请求的比例等于 service_weight 除以 sum_of_weights。 每个服务的 weight 分布到该服务的端点，使得端点 weight 的总和等于服务 weight。 路由最多可有四个服务。服务的 weight 可以在 0 到 256 范围内。当 weight 等于 0 时，服务不参与负载 均衡，但继续为现有的持久连接服务。当服务 均衡，但继续为现有的持久连接服务。当服务 weight 不为 0 时，每个端点的最小 weight 为 1。因此， 具有大量端点的服务会得到高于预期值的 weight。在本例中，减少 pod 数量以获得预期的负载均衡 weight。 流程 流程 设置 A/B 环境： 1. 创建两个应用程序并使用不同的名称。它们各自创建一个 Deployment 对象。应用程序是同一程 序的不同版本；一个是当前生产版本，另一个是提议的新版本。 查看应用程序，以确保可以看到预期的版本。 3. 当您部署路由时，路由器会根据为服务指定的 weight 来均衡流量。此时，存在具有默认 weight=1 的单一服务，因此所有请求都会进入该服务。添加其他服务作为 alternateBackend 并 调整 weight，即可激活 A/B 设置。这可通过 oc set route-backends 命令或编辑路由来完成。 如果将

用户使用多个服务设置路由。每个服务负责应用程序的一个版本。 每个服务分配到一个 weight，进入每个服务的请求的比例等于 service_weight 除以 sum_of_weights。 每个服务的 weight 分布到该服务的端点，使得端点 weight 的总和等于服务 weight。 路由最多可有四个服务。服务的 weight 可以在 0 到 256 范围内。当 weight 等于 0 时，服务不参与负载 均衡，但继续为现有的持久连接服务。当服务 均衡，但继续为现有的持久连接服务。当服务 weight 不为 0 时，每个端点的最小 weight 为 1。因此， 具有大量端点的服务会得到高于预期值的 weight。在本例中，减少 pod 数量以获得预期的负载均衡 weight。 流程 流程 设置 A/B 环境： 1. 创建两个应用程序并使用不同的名称。它们各自创建一个 Deployment 对象。应用程序是同一程 序的不同版本；一个是当前生产版本，另一个是提议的新版本。 查看应用程序，以确保可以看到预期的版本。 3. 当您部署路由时，路由器会根据为服务指定的 weight 来均衡流量。此时，存在具有默认 weight=1 的单一服务，因此所有请求都会进入该服务。添加其他服务作为 alternateBackend 并 调整 weight，即可激活 A/B 设置。这可通过 oc set route-backends 命令或编辑路由来完成。 注意

attention domain. The attention (Luong) mechanism learns three weight matrices namely WQ (query weight), WK (key weight) and WV (value weight) which are used to compute the query, key and value matrices number of elements in each sequence and d represents the number of dimensions of each element. The weight matrices WQ, WK, and WV are identically shaped as (d, dk). The query, key and the value matrices learned. In the next chapter we will explore some more advanced model compression techniques like pruning.

the ability of resource isolation and sharing to a certain extent. It will send queries to a high-weight pool to get more executors for execution. In essence, resource isolation such as CPU/memory/IO should and sharing. No one would like to restart the server and stop it from serving to adjust some pool’s weight or increase the total computing resources. High Availability Limitations The community edition 4 # spark.yarn.am.memory 2g # spark.yarn.am.memoryOverhead 1024 Dynamic Partition Pruning # spark.sql.optimizer.dynamicPartitionPruning.enabled true # spark.sql.optimizer.dy

the ability of resource isolation and sharing to a certain extent. It will send queries to a high-weight pool to get more executors for execution. In essence, resource isolation such as CPU/memory/IO should and sharing. No one would like to restart the server and stop it from serving to adjust some pool’s weight or increase the total computing resources. High Availability Limitations The community edition 4 # spark.yarn.am.memory 2g # spark.yarn.am.memoryOverhead 1024 Dynamic Partition Pruning # spark.sql.optimizer.dynamicPartitionPruning.enabled true # spark.sql.optimizer.dy

deployments may not exist either because the deployment was successful # or due to deployment pruning or manual deletion of the deployment oc logs --version=1 dc/mysql # Return a snapshot of ruby-container going to b to 10%% of the traffic going to a oc set route-backends web --adjust b=10%% # Set weight of b to 10 oc set route-backends web --adjust b=10 第 第 2 章 章 OPENSHIFT CLI (OC) 65 2.5.1.131 更新角色绑定或集群角色绑定中的用户、组或服务帐户 用法示例 用法示例 2.5.1.134. oc set triggers 更新一个或多个对象上的触发器 用法示例 用法示例 # Set the weight to all backends to zero oc set route-backends web --zero # Set the labels and selector before

deployments may not exist either because the deployment was successful # or due to deployment pruning or manual deletion of the deployment oc logs --version=1 dc/mysql # Return a snapshot of ruby-container going to a oc set route-backends web --adjust b=10%% # Set weight of b to 10 oc set route-backends web --adjust b=10 # Set the weight to all backends to zero oc set route-backends web --zero OpenShift

the ability of resource isolation and sharing to a certain extent. It will send queries to a high-weight pool to get more executors for execution. In essence, resource isolation such as CPU/memory/IO should and sharing. No one would like to restart the server and stop it from serving to adjust some pool’s weight or increase the total computing resources. High Availability Limitations The community edition 4 # spark.yarn.am.memory 2g # spark.yarn.am.memoryOverhead 1024 Dynamic Partition Pruning # spark.sql.optimizer.dynamicPartitionPruning.enabled true # spark.sql.optimizer.dy