Addestrare il modello

Imposta i valori seguenti:

aws lookoutvision create-model --project-name "project name"\
  --output-config '{ "S3Location": { "Bucket": "output bucket", "Prefix":  "output folder" } }'\
  --tags '[{"Key":"Key","Value":"Value"}]' \
  --profile lookoutvision-access

Questo codice è tratto dal GitHub repository AWS Documentation SDK examples. Guarda l'esempio completo qui.

    @staticmethod
    def create_model(
        lookoutvision_client,
        project_name,
        training_results,
        tag_key=None,
        tag_key_value=None,
    ):
        """
        Creates a version of a Lookout for Vision model.

        :param lookoutvision_client: A Boto3 Lookout for Vision client.
        :param project_name: The name of the project in which you want to create a
                             model.
        :param training_results: The Amazon S3 location where training results are stored.
        :param tag_key: The key for a tag to add to the model.
        :param tag_key_value - A value associated with the tag_key.
        return: The model status and version.
        """
        try:
            logger.info("Training model...")
            output_bucket, output_folder = training_results.replace("s3://", "").split(
                "/", 1
            )
            output_config = {
                "S3Location": {"Bucket": output_bucket, "Prefix": output_folder}
            }
            tags = []
            if tag_key is not None:
                tags = [{"Key": tag_key, "Value": tag_key_value}]

            response = lookoutvision_client.create_model(
                ProjectName=project_name, OutputConfig=output_config, Tags=tags
            )

            logger.info("ARN: %s", response["ModelMetadata"]["ModelArn"])
            logger.info("Version: %s", response["ModelMetadata"]["ModelVersion"])
            logger.info("Started training...")

            print("Training started. Training might take several hours to complete.")

            # Wait until training completes.
            finished = False
            status = "UNKNOWN"
            while finished is False:
                model_description = lookoutvision_client.describe_model(
                    ProjectName=project_name,
                    ModelVersion=response["ModelMetadata"]["ModelVersion"],
                )
                status = model_description["ModelDescription"]["Status"]

                if status == "TRAINING":
                    logger.info("Model training in progress...")
                    time.sleep(600)
                    continue

                if status == "TRAINED":
                    logger.info("Model was successfully trained.")
                else:
                    logger.info(
                        "Model training failed: %s ",
                        model_description["ModelDescription"]["StatusMessage"],
                    )
                finished = True
        except ClientError:
            logger.exception("Couldn't train model.")
            raise
        else:
            return status, response["ModelMetadata"]["ModelVersion"]

Questo codice è tratto dal GitHub repository AWS Documentation SDK examples. Guarda l'esempio completo qui.

/**
 * Creates an Amazon Lookout for Vision model. The function returns after model
 * training completes. Model training can take multiple hours to complete.
 * You are charged for the amount of time it takes to successfully train a model.
 * Returns after Lookout for Vision creates the dataset.
 * 
 * @param lfvClient   An Amazon Lookout for Vision client.
 * @param projectName The name of the project in which you want to create a
 *                    model.
 * @param description A description for the model.
 * @param bucket      The S3 bucket in which Lookout for Vision stores the
 *                    training results.
 * @param folder      The location of the training results within the S3
 *                    bucket.
 * @return ModelDescription The description of the created model.
 */
public static ModelDescription createModel(LookoutVisionClient lfvClient, String projectName,
                String description, String bucket, String folder)
                throws LookoutVisionException, InterruptedException {

        logger.log(Level.INFO, "Creating model for project: {0}.", new Object[] { projectName });

        // Setup input parameters.
        S3Location s3Location = S3Location.builder()
                        .bucket(bucket)
                        .prefix(folder)
                        .build();

        OutputConfig config = OutputConfig.builder()
                        .s3Location(s3Location)
                        .build();

        CreateModelRequest createModelRequest = CreateModelRequest.builder()
                        .projectName(projectName)
                        .description(description)
                        .outputConfig(config)
                        .build();

        // Create and train the model.
        CreateModelResponse response = lfvClient.createModel(createModelRequest);

        String modelVersion = response.modelMetadata().modelVersion();
        boolean finished = false;
        DescribeModelResponse descriptionResponse = null;

        // Wait until training finishes or fails.

        do {
                DescribeModelRequest describeModelRequest = DescribeModelRequest.builder()
                                .projectName(projectName)
                                .modelVersion(modelVersion)
                                .build();

                descriptionResponse = lfvClient.describeModel(describeModelRequest);

                switch (descriptionResponse.modelDescription().status()) {
                        case TRAINED:
                                logger.log(Level.INFO, "Model training completed for project {0} version {1}.",
                                                new Object[] { projectName, modelVersion });
                                finished = true;
                                break;

                        case TRAINING:
                                logger.log(Level.INFO,
                                                "Model training in progress for project {0} version {1}.",
                                                new Object[] { projectName, modelVersion });
                                TimeUnit.SECONDS.sleep(60);

                                break;

                        case TRAINING_FAILED:
                                logger.log(Level.SEVERE,
                                                "Model training failed for for project {0} version {1}.",
                                                new Object[] { projectName, modelVersion });
                                finished = true;
                                break;

                        default:
                                logger.log(Level.SEVERE,
                                                "Unexpected error when training model project {0} version {1}: {2}.",
                                                new Object[] { projectName, modelVersion,
                                                                descriptionResponse.modelDescription()
                                                                                .status() });
                                finished = true;
                                break;

                }
        } while (!finished);

        return descriptionResponse.modelDescription();

}