.desc("Launch the Maven Encryption tool (script option).")
                .build());
        options.addOption(Option.builder()
                .longOpt(YJP)
                .desc("Launch the JVM with Yourkit profiler (script option).")
                .build());

        // Adding this back to make Maven fail if used
        options.addOption(Option.builder("llr")
                .longOpt("legacy-local-repository")

                    .get());
            options.addOption(Option.builder()
                    .longOpt(YJP)
                    .desc("Launch the JVM with Yourkit profiler (script option).")
                    .get());

            // Deprecated
            options.addOption(Option.builder(ALTERNATE_GLOBAL_SETTINGS)
                    .longOpt("global-settings")

            "//tensorflow/core/common_runtime/eager:execute",
            "//tensorflow/core/common_runtime/eager:placement_utils",
            "//tensorflow/core/common_runtime/eager:tensor_handle",
            "//tensorflow/core/profiler/lib:traceme",
            "@com_google_absl//absl/algorithm:container",
            "@com_google_absl//absl/types:span",
            "@com_google_absl//absl/types:variant",
        ],
    }) + [

- Fixed a 1.26 regression scheduling bug by ensuring that preemption is skipped when a PreFilter plugin returns `UnschedulableAndUnresolvable` ([#119953](https://github.com/kubernetes/kubernetes/pull/119953), [@sanposhiho](https://github.com/sanposhiho)) [SIG Scheduling]

		References: []Reference{
			{"Refer", "User", "UserRefer", "user_profiles", "", true},
			{"Kind", "User", "Kind", "user_profiles", "", true},
			{"ProfileRefer", "Profile", "ProfileR", "user_profiles", "", false},
			{"Kind", "Profile", "Kind", "user_profiles", "", false},
		},
	})
}

func TestMany2ManyOverrideJoinForeignKey(t *testing.T) {
	type Profile struct {
		gorm.Model

func (sys *NotificationSys) StartProfiling(ctx context.Context, profiler string) []NotificationPeerErr {
	ng := WithNPeers(len(sys.peerClients))
	for idx, client := range sys.peerClients {
		if client == nil {
			continue
		}
		client := client
		ng.Go(ctx, func() error {
			return client.StartProfiling(ctx, profiler)
		}, idx, *client.host)
	}
	return ng.Wait()
}

     */
    private void deleteBySessionId(final String sessionId) {
        try {
            urlFilterService.delete(sessionId);
        } catch (final Exception e) {
            logger.warn("Failed to delete UrlFilter: sessionId={}", sessionId, e);
        }
        try {
            urlQueueService.delete(sessionId);
        } catch (final Exception e) {

    analyzing input pipeline performance with TF Profiler.

*   [`tf.distribute.TPUStrategy`](https://www.tensorflow.org/api_docs/python/tf/distribute/TPUStrategy)
    is now a stable API and no longer considered experimental for TensorFlow.
    (earlier `tf.distribute.experimental.TPUStrategy`).

*   [TF Profiler](https://www.tensorflow.org/guide/profiler) introduces two new
    tools: a memory profiler to visualize your model’s memory usage over time

### Concurrence + Parallélisme : Web + Machine Learning

Avec **FastAPI** vous pouvez bénéficier de la concurrence qui est très courante en développement web (c'est l'attrait principal de NodeJS).

Mais vous pouvez aussi profiter du parallélisme et multiprocessing afin de gérer des charges **CPU bound** qui sont récurrentes dans les systèmes de *Machine Learning*.

Il nécessite une configuration un peu plus verbeuse. Et comme il est basé sur WSGI (au lieu dASGI), il n'est pas
conçu pour profiter des hautes performances fournies par des outils comme Uvicorn, Starlette et Sanic.