info.Name = devName
				qst, err := bfs.SysBlockDeviceQueueStats(devName)
				if err != nil { // Mostly not found error
					// Check if there is a parent device:
					//   e.g. if the mount is based on /dev/nvme0n1p1, let's calculate the
					//        real device name (nvme0n1) to get its sysfs information
					parentDevPath, e := os.Readlink("/sys/class/block/" + devName)
					if e == nil {

[CUDA-enabled GPU cards](https://www.tensorflow.org/install/gpu) *(Ubuntu and
Windows)*:

```
$ pip install tensorflow
```

Other devices (DirectX and MacOS-metal) are supported using
[Device Plugins](https://www.tensorflow.org/install/gpu_plugins#available_devices).

A smaller CPU-only package is also available:

```
$ pip install tensorflow-cpu
```

## Why distributed MinIO?

MinIO in distributed mode can help you setup a highly-available storage system with a single object storage deployment. With distributed MinIO, you can optimally use storage devices, irrespective of their location in a network.

### Data protection

access policies for applications/users to interact with MinIO object storage.

Calling AssumeRoleWithWebIdentity does not require the use of MinIO root or IAM credentials. Therefore, you can distribute an application (for example, on mobile devices) that requests temporary security credentials without including MinIO long lasting credentials in the application. Instead, the identity of the caller is validated by using a JWT id_token from the web identity provider. The temporary security credentials...

It was created to generate the HTML in the backend, not to create APIs used by a modern frontend (like React, Vue.js and Angular) or by other systems (like <abbr title="Internet of Things">IoT</abbr> devices) communicating with it.

### <a href="https://www.django-rest-framework.org/" class="external-link" target="_blank">Django REST Framework</a> { #django-rest-framework }

        decisions.
*   PluggableDevice
    *   Third-party devices can now connect to TensorFlow as plug-ins through
        [StreamExecutor C API](https://github.com/tensorflow/community/blob/master/rfcs/20200612-stream-executor-c-api.md).
        and
        [PluggableDevice](https://github.com/tensorflow/community/blob/master/rfcs/20200624-pluggable-device-for-tensorflow.md)
        interface.

  csession.CloseAndDelete(s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  TF_DeleteGraph(graph);
  TF_DeleteStatus(s);
}

// If `device` is non-empty, run Min op on that device.
// Otherwise run it on the default device (CPU).
void RunMinTest(const string& device, bool use_XLA) {
  TF_Status* s = TF_NewStatus();
  TF_Graph* graph = TF_NewGraph();

  // Make a placeholder operation.

 * in the list of remote repositories. This is done either explicitly using the
 * {@link org.apache.maven.api.services.VersionResolver VersionResolver} service, or implicitly when resolving
 * an artifact.</p>
 *
 * <p><dfn>Artifact resolution</dfn> is the process of {@linkplain org.apache.maven.api.services.VersionResolver
 * resolving the version} and then downloading the file.</p>
 *

      entryPoints = ["http"]
      service = "app"
      rule = "PathPrefix(`/api/v1`)"
      middlewares = ["api-stripprefix"]

  [http.services]

    [http.services.app]
      [http.services.app.loadBalancer]
        [[http.services.app.loadBalancer.servers]]
          url = "http://127.0.0.1:8000"
```

This file configures Traefik to use the path prefix `/api/v1`.

import org.apache.commons.cli.Option;
import org.apache.commons.cli.ParseException;
import org.apache.maven.api.cli.Options;
import org.apache.maven.api.cli.ParserRequest;
import org.apache.maven.api.services.Interpolator;
import org.apache.maven.api.services.InterpolatorException;
import org.apache.maven.jline.MessageUtils;

import static java.util.Objects.requireNonNull;
import static org.apache.maven.cling.invoker.CliUtils.createInterpolator;