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docs/zh/docs/deployment/concepts.md
## 资源利用率 您的服务器是一个**资源**,您可以通过您的程序消耗或**利用**CPU 上的计算时间以及可用的 RAM 内存。 您想要消耗/利用多少系统资源? 您可能很容易认为“不多”,但实际上,您可能希望在不崩溃的情况下**尽可能多地消耗**。 如果您支付了 3 台服务器的费用,但只使用了它们的一点点 RAM 和 CPU,那么您可能**浪费金钱** 💸,并且可能 **浪费服务器电力** 🌎,等等。 在这种情况下,最好只拥有 2 台服务器并使用更高比例的资源(CPU、内存、磁盘、网络带宽等)。 另一方面,如果您有 2 台服务器,并且正在使用 **100% 的 CPU 和 RAM**,则在某些时候,一个进程会要求更多内存,并且服务器将不得不使用磁盘作为“内存” (这可能会慢数千倍),甚至**崩溃**。 或者一个进程可能需要执行一些计算,并且必须等到 CPU 再次空闲。
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tensorflow/c/eager/c_api_distributed_test.cc
EXPECT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status); const char task0_name[] = "/job:localhost/replica:0/task:0/device:CPU:0"; const char task1_name[] = "/job:localhost/replica:0/task:1/device:CPU:0"; const char task2_name[] = "/job:localhost/replica:0/task:2/device:CPU:0"; // Create one variable per task. TFE_TensorHandle* h0 = TestVariable(ctx, 1.0, task1_name);
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.github/workflows/arm-cd.yml
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ci/official/envs/linux_arm64
TFCI_BAZEL_COMMON_ARGS="--repo_env=TF_PYTHON_VERSION=$TFCI_PYTHON_VERSION --config release_arm64_linux" TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_arm64 # Note: this is not set to "--cpu", because that changes the package name # to tensorflow_cpu. These ARM builds are supposed to have the name "tensorflow" # despite lacking Nvidia CUDA support. TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow" TFCI_DOCKER_ENABLE=1
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tensorflow/c/eager/c_api_cluster_test.cc
EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); const char remote_device_name[] = "/job:localhost/replica:0/task:1/device:CPU:0"; const char local_device_name[] = "/job:localhost/replica:0/task:0/device:CPU:0"; CheckRemoteMatMulExecutesOK(ctx, remote_device_name, local_device_name); TFE_Executor* executor = TFE_ContextGetExecutorForThread(ctx);
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docs/de/docs/deployment/concepts.md
In diesem Fall könnte es besser sein, nur zwei Server zu haben und einen höheren Prozentsatz von deren Ressourcen zu nutzen (CPU, Arbeitsspeicher, Festplatte, Netzwerkbandbreite, usw.).
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common-protos/k8s.io/api/autoscaling/v2beta2/generated.proto
// ContainerResourceMetricSource indicates how to scale on a resource metric known to // Kubernetes, as specified in requests and limits, describing each pod in the // current scale target (e.g. CPU or memory). The values will be averaged // together before being compared to the target. Such metrics are built in to // Kubernetes, and have special scaling options on top of those available to
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manifests/charts/gateways/istio-egress/templates/autoscale.yaml
scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: {{ $gateway.name }} metrics: - type: Resource resource: name: cpu target: type: Utilization averageUtilization: {{ $gateway.cpu.targetAverageUtilization }} ---
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src/main/java/org/codelibs/fess/app/web/api/admin/stats/ApiAdminStatsAction.java
processFileDescriptorObj.max = processProbe.getMaxFileDescriptorCount(); final ProcessCpuObj processCpuObj = new ProcessCpuObj(); processObj.cpu = processCpuObj; processCpuObj.percent = processProbe.getProcessCpuPercent(); processCpuObj.total = processProbe.getProcessCpuTotalTime();
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common-protos/k8s.io/api/autoscaling/v1/generated.proto
optional int32 desiredReplicas = 4; // currentCPUUtilizationPercentage is the current average CPU utilization over all pods, represented as a percentage of requested CPU, // e.g. 70 means that an average pod is using now 70% of its requested CPU. // +optional optional int32 currentCPUUtilizationPercentage = 5; } // MetricSpec specifies how to scale based on a single metric
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