Examples with MLPredictTaskRunner org.opensearch.ml.task.MLPredictTaskRunner used on opensource projects

Example 1 with MLPredictTaskRunner

use of org.opensearch.ml.task.MLPredictTaskRunner in project ml-commons by opensearch-project.

the class MachineLearningPlugin method createComponents.

@Override
public Collection<Object> createComponents(Client client, ClusterService clusterService, ThreadPool threadPool, ResourceWatcherService resourceWatcherService, ScriptService scriptService, NamedXContentRegistry xContentRegistry, Environment environment, NodeEnvironment nodeEnvironment, NamedWriteableRegistry namedWriteableRegistry, IndexNameExpressionResolver indexNameExpressionResolver, Supplier<RepositoriesService> repositoriesServiceSupplier) {
    this.client = client;
    this.threadPool = threadPool;
    this.clusterService = clusterService;
    Settings settings = environment.settings();
    JvmService jvmService = new JvmService(environment.settings());
    MLCircuitBreakerService mlCircuitBreakerService = new MLCircuitBreakerService(jvmService).init();
    Map<String, MLStat<?>> stats = new ConcurrentHashMap<>();
    stats.put(StatNames.ML_EXECUTING_TASK_COUNT, new MLStat<>(false, new CounterSupplier()));
    stats.put(StatNames.ML_TOTAL_REQUEST_COUNT, new MLStat<>(false, new CounterSupplier()));
    stats.put(StatNames.ML_TOTAL_FAILURE_COUNT, new MLStat<>(false, new CounterSupplier()));
    stats.put(StatNames.ML_TOTAL_MODEL_COUNT, new MLStat<>(false, new CounterSupplier()));
    this.mlStats = new MLStats(stats);
    mlIndicesHandler = new MLIndicesHandler(clusterService, client);
    mlTaskManager = new MLTaskManager(client, mlIndicesHandler);
    mlInputDatasetHandler = new MLInputDatasetHandler(client);
    MLTaskDispatcher mlTaskDispatcher = new MLTaskDispatcher(clusterService, client);
    mlTrainingTaskRunner = new MLTrainingTaskRunner(threadPool, clusterService, client, mlTaskManager, mlStats, mlIndicesHandler, mlInputDatasetHandler, mlTaskDispatcher, mlCircuitBreakerService);
    mlPredictTaskRunner = new MLPredictTaskRunner(threadPool, clusterService, client, mlTaskManager, mlStats, mlInputDatasetHandler, mlTaskDispatcher, mlCircuitBreakerService);
    mlTrainAndPredictTaskRunner = new MLTrainAndPredictTaskRunner(threadPool, clusterService, client, mlTaskManager, mlStats, mlInputDatasetHandler, mlTaskDispatcher, mlCircuitBreakerService);
    mlExecuteTaskRunner = new MLExecuteTaskRunner(threadPool, clusterService, client, mlTaskManager, mlStats, mlInputDatasetHandler, mlTaskDispatcher, mlCircuitBreakerService);
    // Register thread-safe ML objects here.
    LocalSampleCalculator localSampleCalculator = new LocalSampleCalculator(client, settings);
    MLEngineClassLoader.register(FunctionName.LOCAL_SAMPLE_CALCULATOR, localSampleCalculator);
    AnomalyLocalizerImpl anomalyLocalizer = new AnomalyLocalizerImpl(client, settings);
    MLEngineClassLoader.register(FunctionName.ANOMALY_LOCALIZATION, anomalyLocalizer);
    MLSearchHandler mlSearchHandler = new MLSearchHandler(client, xContentRegistry);
    return ImmutableList.of(mlStats, mlTaskManager, mlIndicesHandler, mlInputDatasetHandler, mlTrainingTaskRunner, mlPredictTaskRunner, mlTrainAndPredictTaskRunner, mlExecuteTaskRunner, mlSearchHandler);
}