Use Custom Algorithms for Model Training and Hosting on Amazon SageMaker with Apache Spark - Amazon SageMaker

Use Custom Algorithms for Model Training and Hosting on Amazon SageMaker with Apache Spark

In Example 1: Use Amazon SageMaker for Training and Inference with Apache Spark, you use the kMeansSageMakerEstimator because the example uses the k-means algorithm provided by Amazon SageMaker for model training. You might choose to use your own custom algorithm for model training instead. Assuming that you have already created a Docker image, you can create your own SageMakerEstimator and specify the Amazon Elastic Container Registry path for your custom image.

The following example shows how to create a KMeansSageMakerEstimator from the SageMakerEstimator. In the new estimator, you explicitly specify the Docker registry path to your training and inference code images.

import import import import val estimator = new SageMakerEstimator( trainingImage = "", modelImage = "", requestRowSerializer = new ProtobufRequestRowSerializer(), responseRowDeserializer = new KMeansProtobufResponseRowDeserializer(), hyperParameters = Map("k" -> "10", "feature_dim" -> "784"), sagemakerRole = IAMRole(roleArn), trainingInstanceType = "ml.p2.xlarge", trainingInstanceCount = 1, endpointInstanceType = "ml.c4.xlarge", endpointInitialInstanceCount = 1, trainingSparkDataFormat = "sagemaker")

In the code, the parameters in the SageMakerEstimator constructor include:

  • trainingImage —Identifies the Docker registry path to the training image containing your custom code.

  • modelImage —Identifies the Docker registry path to the image containing inference code.

  • requestRowSerializer —Implements

    This parameter serializes rows in the input DataFrame to send them to the model hosted in SageMaker for inference.

  • responseRowDeserializer —Implements

    This parameter deserializes responses from the model, hosted in SageMaker, back into a DataFrame.

  • trainingSparkDataFormat —Specifies the data format that Spark uses when uploading training data from a DataFrame to S3. For example, "sagemaker" for protobuf format, "csv" for comma-separated values, and "libsvm" for LibSVM format.

You can implement your own RequestRowSerializer and ResponseRowDeserializer to serialize and deserialize rows from a data format that your inference code supports, such as .libsvm or ..csv.