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(

A smaller CPU-only package is also available:

```
$ pip install tensorflow-cpu
```

To update TensorFlow to the latest version, add `--upgrade` flag to the above
commands.

*Nightly binaries are available for testing using the
[tf-nightly](https://pypi.python.org/pypi/tf-nightly) and
[tf-nightly-cpu](https://pypi.python.org/pypi/tf-nightly-cpu) packages on PyPi.*

## 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

# ==============================================================================
TFCI_BAZEL_COMMON_ARGS="--repo_env=TF_PYTHON_VERSION=$TFCI_PYTHON_VERSION --config release_cpu_linux"
TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_cpu
TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow_cpu"
TFCI_DOCKER_ENABLE=1
TFCI_DOCKER_IMAGE=tensorflow/build:2.16-python${TFCI_PYTHON_VERSION}
TFCI_DOCKER_PULL_ENABLE=1

```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` |

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.