Harshavardhana <******@****.***> 1711791711 -0700

)

config_setting(
    name = "linux_aarch64",
    values = {"cpu": "aarch64"},
    visibility = ["//visibility:public"],
)

config_setting(
    name = "linux_armhf",
    values = {"cpu": "armhf"},
    visibility = ["//visibility:public"],
)

config_setting(
    name = "linux_x86_64",
    values = {"cpu": "k8"},
    visibility = ["//visibility:public"],
)

config_setting(

## 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。

| `minio_node_cpu_avg_system`          | CPU system time.                           |
| `minio_node_cpu_avg_system_avg`      | CPU system time (avg).                     |
| `minio_node_cpu_avg_system_max`      | CPU system time (max).                     |
| `minio_node_cpu_avg_idle`            | CPU idle time.                             |

build:android_arm64 --config=android
build:android_arm64 --cpu=arm64-v8a
build:android_arm64 --fat_apk_cpu=arm64-v8a
build:android_x86 --config=android
build:android_x86 --cpu=x86
build:android_x86 --fat_apk_cpu=x86
build:android_x86_64 --config=android
build:android_x86_64 --cpu=x86_64
build:android_x86_64 --fat_apk_cpu=x86_64

# Build everything statically for Android since all static libs are later

```bash
tensorflow/tools/ci_build/ci_build.sh CPU tensorflow/tools/ci_build/ci_sanity.sh
```

This will catch most license, Python coding style and BUILD file issues that
may exist in your changes.

#### Running unit tests

There are two ways to run TensorFlow unit tests.

1.  Using tools and libraries installed directly on your system.

    Refer to the

| `minio_system_cpu_load_perc`  | `gauge` | CPU load average 1min (percentage) | `server` |
| `minio_system_cpu_nice`       | `gauge` | CPU nice time                      | `server` |
| `minio_system_cpu_steal`      | `gauge` | CPU steal time                     | `server` |
| `minio_system_cpu_system`     | `gauge` | CPU system time                    | `server` |

// 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

streaming compression due to its stability and performance.

This algorithm is specifically optimized for machine generated content.
Write throughput is typically at least 500MB/s per CPU core,
and scales with the number of available CPU cores.
Decompression speed is typically at least 1GB/s.

This means that in cases where raw IO is below these numbers
compression will not only reduce disk usage but also help increase system throughput.

// 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