TFCI_BAZEL_COMMON_ARGS="--repo_env=HERMETIC_PYTHON_VERSION=$TFCI_PYTHON_VERSION --repo_env=USE_PYWRAP_RULES=True --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_WHEEL_NAME_ARG="--repo_env=WHEEL_NAME=tensorflow"

En ese caso, podría ser mejor tener solo 2 servidores y usar un mayor porcentaje de sus recursos (CPU, memoria, disco, ancho de banda de red, etc.).

Nesse caso, seria melhor ter apenas 2 servidores e usar uma porcentagem maior de seus recursos (CPU, memória, disco, largura de banda de rede, etc).

[main]
summary=Maximum server performance for MinIO

[vm]
transparent_hugepage=madvise

[sysfs]
/sys/kernel/mm/transparent_hugepage/defrag=defer+madvise
/sys/kernel/mm/transparent_hugepage/khugepaged/max_ptes_none=0

[cpu]
force_latency=1
governor=performance
energy_perf_bias=performance
min_perf_pct=100

[sysctl]
fs.xfs.xfssyncd_centisecs=72000
net.core.busy_read=50
net.core.busy_poll=50
kernel.numa_balancing=1

| `minio_system_cpu_load_perc`  | CPU load average 1min (percentage). <br><br>Type: gauge | `server` |
| `minio_system_cpu_nice`       | CPU nice time. <br><br>Type: gauge                      | `server` |
| `minio_system_cpu_steal`      | CPU steal time. <br><br>Type: gauge                     | `server` |

        }
    }

    /**
     * Calibrates the CPU load.
     *
     * @return true if the CPU load is within the acceptable range, false otherwise.
     */
    public boolean calibrateCpuLoad() {
        return calibrateCpuLoad(0L);
    }

    /**
     * Calibrates the CPU load with a timeout.
     *
     * @param timeoutInMillis The timeout in milliseconds.

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.).

In that case, it could be better to have only 2 servers and use a higher percentage of their resources (CPU, memory, disk, network bandwidth, etc).

```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

if [[ "$TFCI_WHL_NUMPY_VERSION" == 1 ]]; then
  # Uninstall tf nightly wheel built with numpy1.
  "$python" -m pip uninstall -y tf_nightly_numpy1
  # Install tf nightly cpu wheel built with numpy2.x from PyPI in numpy1.x env.
  "$python" -m pip install tf-nightly-cpu
  if [[ "$TFCI_WHL_IMPORT_TEST_ENABLE" == "1" ]]; then
    "$python" -c 'import tensorflow as tf; t1=tf.constant([1,2,3,4]); t2=tf.constant([5,6,7,8]); print(tf.add(t1,t2).shape)'