| `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.                             |

		cpuUser:           "CPU user time",
		cpuSystem:         "CPU system time",
		cpuIdle:           "CPU idle time",
		cpuIOWait:         "CPU ioWait time",
		cpuSteal:          "CPU steal time",
		cpuNice:           "CPU nice time",
		cpuLoad1:          "CPU load average 1min",
		cpuLoad5:          "CPU load average 5min",
		cpuLoad15:         "CPU load average 15min",

   
   
   Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load.
   
   Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
   
      * Try Google Colab to use TensorFlow.

#     elinux_aarch64:  Embedded Linux options for aarch64 (ARM64) CPU support.
#     elinux_armhf:    Embedded Linux options for armhf (ARMv7) CPU support.
#
# Release build options (for all operating systems)
#     release_base:                    Common options for all builds on all operating systems.
#     release_cpu_linux:               Toolchain and CUDA options for Linux CPU builds.

    /**
     * Data transfer object representing process CPU statistics.
     */
    public static class ProcessCpuObj {
        /**
         * Default constructor.
         */
        public ProcessCpuObj() {
            // Default constructor
        }

        /** CPU usage percentage for the process. */
        public short percent;
        /** Total CPU time used by the process in milliseconds. */

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.*

			cpuMap[cpus[i].PhysicalID] = struct{}{}
			coreMap[cpus[i].CoreID] = struct{}{}
		}
		cpu := cpus[0]
		uaAppend(" CPU ", fmt.Sprintf("(total_cpus:%d, total_cores:%d; vendor:%s; family:%s; model:%s; stepping:%d; model_name:%s)",
			len(cpuMap), len(coreMap), cpu.VendorID, cpu.Family, cpu.Model, cpu.Stepping, cpu.ModelName))
	}
	uaAppend(")", "")

	return strings.Join(userAgentParts, "")
}

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).