Examples with ML_MODEL_INDEX org.opensearch.ml.indices.MLIndicesHandler.ML_MODEL_INDEX used on opensource projects

Example 1 with ML_MODEL_INDEX

use of org.opensearch.ml.indices.MLIndicesHandler.ML_MODEL_INDEX in project ml-commons by opensearch-project.

the class MLTrainingTaskRunner method train.

private void train(MLTask mlTask, MLInput mlInput, ActionListener<MLTaskResponse> actionListener) {
    ActionListener<MLTaskResponse> listener = ActionListener.wrap(r -> actionListener.onResponse(r), e -> {
        mlStats.createCounterStatIfAbsent(failureCountStat(mlTask.getFunctionName(), ActionName.TRAIN)).increment();
        mlStats.getStat(ML_TOTAL_FAILURE_COUNT).increment();
        actionListener.onFailure(e);
    });
    try {
        // run training
        mlTaskManager.updateTaskState(mlTask.getTaskId(), MLTaskState.RUNNING, mlTask.isAsync());
        Model model = MLEngine.train(mlInput);
        mlIndicesHandler.initModelIndexIfAbsent(ActionListener.wrap(indexCreated -> {
            if (!indexCreated) {
                listener.onFailure(new RuntimeException("No response to create ML task index"));
                return;
            }
            // TODO: put the user into model for backend role based access control.
            MLModel mlModel = new MLModel(mlInput.getAlgorithm(), model);
            try (ThreadContext.StoredContext context = client.threadPool().getThreadContext().stashContext()) {
                ActionListener<IndexResponse> indexResponseListener = ActionListener.wrap(r -> {
                    log.info("Model data indexing done, result:{}, model id: {}", r.getResult(), r.getId());
                    mlStats.getStat(ML_TOTAL_MODEL_COUNT).increment();
                    mlStats.createCounterStatIfAbsent(modelCountStat(mlTask.getFunctionName())).increment();
                    String returnedTaskId = mlTask.isAsync() ? mlTask.getTaskId() : null;
                    MLTrainingOutput output = new MLTrainingOutput(r.getId(), returnedTaskId, MLTaskState.COMPLETED.name());
                    listener.onResponse(MLTaskResponse.builder().output(output).build());
                }, e -> {
                    listener.onFailure(e);
                });
                IndexRequest indexRequest = new IndexRequest(ML_MODEL_INDEX);
                indexRequest.source(mlModel.toXContent(XContentBuilder.builder(XContentType.JSON.xContent()), ToXContent.EMPTY_PARAMS));
                indexRequest.setRefreshPolicy(WriteRequest.RefreshPolicy.IMMEDIATE);
                client.index(indexRequest, ActionListener.runBefore(indexResponseListener, () -> context.restore()));
            } catch (Exception e) {
                log.error("Failed to save ML model", e);
                listener.onFailure(e);
            }
        }, e -> {
            log.error("Failed to init ML model index", e);
            listener.onFailure(e);
        }));
    } catch (Exception e) {
        // todo need to specify what exception
        log.error("Failed to train " + mlInput.getAlgorithm(), e);
        listener.onFailure(e);
    }
}

Example 2 with ML_MODEL_INDEX

use of org.opensearch.ml.indices.MLIndicesHandler.ML_MODEL_INDEX in project ml-commons by opensearch-project.

the class GetModelTransportAction method doExecute.

@Override
protected void doExecute(Task task, ActionRequest request, ActionListener<MLModelGetResponse> actionListener) {
    MLModelGetRequest mlModelGetRequest = MLModelGetRequest.fromActionRequest(request);
    String modelId = mlModelGetRequest.getModelId();
    GetRequest getRequest = new GetRequest(ML_MODEL_INDEX).id(modelId);
    try (ThreadContext.StoredContext context = client.threadPool().getThreadContext().stashContext()) {
        client.get(getRequest, ActionListener.wrap(r -> {
            log.info("Completed Get Model Request, id:{}", modelId);
            if (r != null && r.isExists()) {
                try (XContentParser parser = createXContentParserFromRegistry(xContentRegistry, r.getSourceAsBytesRef())) {
                    ensureExpectedToken(XContentParser.Token.START_OBJECT, parser.nextToken(), parser);
                    MLModel mlModel = MLModel.parse(parser);
                    actionListener.onResponse(MLModelGetResponse.builder().mlModel(mlModel).build());
                } catch (Exception e) {
                    log.error("Failed to parse ml model" + r.getId(), e);
                    actionListener.onFailure(e);
                }
            } else {
                actionListener.onFailure(new MLResourceNotFoundException("Fail to find model"));
            }
        }, e -> {
            if (e instanceof IndexNotFoundException) {
                actionListener.onFailure(new MLResourceNotFoundException("Fail to find model"));
            } else {
                log.error("Failed to get ML model " + modelId, e);
                actionListener.onFailure(e);
            }
        }));
    } catch (Exception e) {
        log.error("Failed to get ML model " + modelId, e);
        actionListener.onFailure(e);
    }
}