# Unteranwendungen – Mounts { #sub-applications-mounts }

Wenn Sie zwei unabhängige FastAPI-Anwendungen mit deren eigenen unabhängigen OpenAPI und deren eigenen Dokumentationsoberflächen benötigen, können Sie eine Hauptanwendung haben und dann eine (oder mehrere) Unteranwendung(en) „mounten“.

## Eine **FastAPI**-Anwendung mounten { #mounting-a-fastapi-application }

   *       then sequentially accessing it could result in other processes dying. This is solvable
   *       via madvise(2), but that obviously doesn't exist in java.
   *   <li>Ordinary copy. Kernel copies bytes into a kernel buffer, from a kernel buffer into a
   *       userspace buffer (byte[] or ByteBuffer), then copies them from that buffer into the
   *       destination channel.
   * </ol>
   *

  TF_DeleteStatus(s);
  return NULL;
}

// A compute function. This will never actually get called in this test, it's
// just nice to know that it compiles.
void compute(void* kernel, TF_OpKernelContext* ctx) {
  TF_Tensor* input;
  TF_Status* s = TF_NewStatus();
  TF_GetInput(ctx, 0, &input, s);
  TF_DeleteTensor(input);
  TF_DeleteStatus(s);
}

// Exercises tensorflow's C API.

	stats, err := readStat(statsFile)
	if err != nil {
		return IOStats{}, err
	}
	if len(stats) < 11 {
		return IOStats{}, fmt.Errorf("found invalid format while reading %v", statsFile)
	}
	// refer https://www.kernel.org/doc/Documentation/block/stat.txt
	iostats = IOStats{
		ReadIOs:      stats[0],
		ReadMerges:   stats[1],
		ReadSectors:  stats[2],
		ReadTicks:    stats[3],
		WriteIOs:     stats[4],

      - name: Enable KVM group perms
        # https://github.blog/changelog/2023-02-23-hardware-accelerated-android-virtualization-on-actions-windows-and-linux-larger-hosted-runners/
        run: |
          echo 'KERNEL=="kvm", GROUP="kvm", MODE="0666", OPTIONS+="static_node=kvm"' | sudo tee /etc/udev/rules.d/99-kvm4all.rules
          sudo udevadm control --reload-rules
          sudo udevadm trigger --name-match=kvm

issues with Docker 1.9.1 can be found below.
  * CPU hardcapping will be enabled by default for containers with CPU limit set,
if supported by the kernel. You should either adjust your CPU limit, or set CPU
request only, if you want to avoid hardcapping. If the kernel does not support
CPU Quota, NodeStatus will contain a warning indicating that CPU Limits cannot
be enforced.

		_ = setMaxResources(globalServerCtxt)
	})

	// Verify kernel release and version.
	if oldLinux() {
		warnings = append(warnings, color.YellowBold("Detected Linux kernel version older than 4.0 release, there are some known potential performance problems with this kernel version. MinIO recommends a minimum of 4.x linux kernel version for best performance"))
	}

	maxProcs := runtime.GOMAXPROCS(0)

	if err != nil {
		buf, err = os.ReadFile(cgroupV1MemLimitFile)
	}
	if err != nil {
		return 0
	}
	limit, err = strconv.ParseUint(strings.TrimSpace(string(buf)), 10, 64)
	if err != nil {
		// The kernel can return valid but non integer values
		// but still, no need to interpret more
		return 0
	}
	if limit >= 100*humanize.TiByte {
		// No limit set, or unreasonably high. Ignore
		return 0
	}
	return limit
}

| `minio_node_scanner_versions_scanned`      | Total number of object versions scanned since server start. |
| `minio_node_syscall_read_total`            | Total read SysCalls to the kernel. /proc/[pid]/io syscr.    |
| `minio_node_syscall_write_total`           | Total write SysCalls to the kernel. /proc/[pid]/io syscw.   |
| `minio_usage_last_activity_nano_seconds`   | Time elapsed (in nano seconds) since last scan activity.    |

# Bucket Metrics

        mean, var = tf.nn.moments(self.kernel, axes=[0, 1, 2], keepdims=True)
        return self.convolution_op(inputs, (self.kernel - mean) / tf.sqrt(var +
        1e-10))` Alternatively, you can override `convolution_op`: `python class
        StandardizedConv2D(tf.keras.Layer): def convolution_op(self, inputs,
        kernel): mean, var = tf.nn.moments(kernel, axes=[0, 1, 2],