}
	}
	uaAppend("; ", "")

	if cpus, err := gopsutilcpu.Info(); err == nil && len(cpus) > 0 {
		cpuMap := make(map[string]struct{}, len(cpus))
		coreMap := make(map[string]struct{}, len(cpus))
		for i := range cpus {
			cpuMap[cpus[i].PhysicalID] = struct{}{}
			coreMap[cpus[i].CoreID] = struct{}{}
		}
		cpu := cpus[0]

## Recap { #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.

Wie viele Systemressourcen möchten Sie verbrauchen/nutzen? Sie mögen „nicht viel“ denken, aber in Wirklichkeit möchten Sie tatsächlich **so viel wie möglich ohne Absturz** verwenden.

Por otro lado, si tienes 2 servidores y estás usando **100% de su CPU y RAM**, en algún momento un proceso pedirá más memoria y el servidor tendrá que usar el disco como "memoria" (lo cual puede ser miles de veces más lento), o incluso **colapsar**. O un proceso podría necesitar hacer algún cálculo y tendría que esperar hasta que la CPU esté libre de nuevo.

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.

		if nErr.Err != nil {
			peersLogOnceIf(logger.SetReqInfo(ctx, reqInfo), nErr.Err, nErr.Host.String())
		}
	}
}

// GetCPUs - Get all CPU information.
func (sys *NotificationSys) GetCPUs(ctx context.Context) []madmin.CPUs {
	reply := make([]madmin.CPUs, len(sys.peerClients))

	g := errgroup.WithNErrs(len(sys.peerClients))
	for index, client := range sys.peerClients {
		if client == nil {
			continue
		}

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**.

| `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` |

# See https://developer.nvidia.com/cuda-gpus#compute
# `compute_XY` enables PTX embedding in addition to SASS. PTX
# is forward compatible beyond the current compute capability major
# release while SASS is only forward compatible inside the current
# major release. Example: sm_80 kernels can run on sm_89 GPUs but
# not on sm_90 GPUs. compute_80 kernels though can also run on sm_90 GPUs.

        `.predict` is available for Cloud TPUs, Cloud TPU, for all types of
        Keras models (sequential, functional and subclassing models).
    *   Automatic outside compilation is now enabled for Cloud TPUs. This allows
        `tf.summary` to be used more conveniently with Cloud TPUs.
    *   Dynamic batch sizes with DistributionStrategy and Keras are supported on
        Cloud TPUs.