Open harthur opened 6 years ago
hi @harthur, thanks for your interest! Unfortunately, we're not sure at the moment if it's something that we'll add anytime soon, but we're always reprioritizing our backlog based on customer feedback.
Hi @knakad, Thanks for a quick response. But this isn't related to auto model tuning. I was referring to something similar to the highlighted in below example from sagemaker-spark github page but with the XGBoostSageMakerEstimator
Instead of creating a KMeansSageMakerEstimator, you can create an equivalent SageMakerEstimator:
val estimator = new SageMakerEstimator( trainingImage = "382416733822.dkr.ecr.us-east-1.amazonaws.com/kmeans:1", modelImage = "382416733822.dkr.ecr.us-east-1.amazonaws.com/kmeans:1", 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")
It looks like there's a nice Python API for "Automatic Model Tuning": https://aws.amazon.com/blogs/aws/sagemaker-automatic-model-tuning/. Would something like
HyperparameterTuner
be added to this Scala Spark library?