Quanto dos recursos do sistema você quer consumir/utilizar? Pode ser fácil pensar "não muito", mas, na realidade, você provavelmente vai querer consumir **o máximo possível sem travar**.

On the other hand, if you have 2 servers and you are using **100% of their CPU and RAM**, at some point one process will ask for more memory, and the server will have to use the disk as "memory" (which can be thousands of times slower), or even **crash**. Or one process might need to do some computation and would have to wait until the CPU is free again.

## Zusammenfassung

Sie können **Gunicorn** (oder auch Uvicorn) als Prozessmanager mit Uvicorn-Workern verwenden, um **Multikern-CPUs** zu nutzen und **mehrere Prozesse parallel** auszuführen.

Sie können diese Tools und Ideen nutzen, wenn Sie **Ihr eigenes Deployment-System** einrichten und sich dabei selbst um die anderen Deployment-Konzepte kümmern.

## Recap

You can use multiple worker processes with the `--workers` CLI option with the `fastapi` or `uvicorn` commands to take advantage of **multi-core CPUs**, to run **multiple processes in parallel**.

You could use these tools and ideas if you are setting up **your own deployment system** while taking care of the other deployment concepts yourself.

| `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` |

| `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.                             |

## Recapitular

Você pode usar vários processos de trabalho com a opção CLI `--workers` com os comandos `fastapi` ou `uvicorn` para aproveitar as vantagens de **CPUs multi-core** e executar **vários processos em paralelo**.

Você pode usar essas ferramentas e ideias se estiver configurando **seu próprio sistema de implantação** enquanto cuida dos outros conceitos de implantação.

        *   `tf.raw_ops.Bucketize` op on CPU.
        *   `tf.where` op for data types
            `tf.int32`/`tf.uint32`/`tf.int8`/`tf.uint8`/`tf.int64`.
        *   `tf.random.normal` op for output data type `tf.float32` on CPU.
        *   `tf.random.uniform` op for output data type `tf.float32` on CPU.
        *   `tf.random.categorical` op for output data type `tf.int64` on CPU.

*   `tensorflow.experimental.tensorrt`:

   
   
   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.

		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",