{"repo":"aws/sagemaker-spark","free":true,"listed":false,"github":"https://github.com/aws/sagemaker-spark","clone":"git clone https://github.com/aws/sagemaker-spark.git","description":"A Spark library for Amazon SageMaker.","language":"Scala","stars":301,"topics":["aws","machine-learning","spark","amazon-sagemaker","scala","python","sagemaker"],"license":"Apache-2.0","category":"machine-learning","readme_excerpt":"SageMaker Spark SageMaker Spark is an open source Spark library for Amazon SageMaker. With SageMaker Spark you construct Spark ML Pipeline s using Amazon SageMaker stages. These pipelines interleave native Spark ML stages and stages that interact with SageMaker training and model hosting. With SageMaker Spark, you can train on Amazon SageMaker from Spark DataFrame s using Amazon-provided ML algorithms like K-Means clustering or XGBoost, and make predictions on DataFrame s against SageMaker endpoints hosting your trained models, and, if you have your own ML algorithms built into SageMaker compatible Docker containers, you can use SageMaker Spark to train and infer on DataFrame s with your own algorithms -- all at Spark scale. Table of Contents Getting SageMaker Spark Scala Running SageMaker Spark Running SageMaker Spark Applications with spark-shell or spark-submit Running SageMaker Spark Applications on EMR Python S3 FileSystem Schemes API Documentation Getting Started: K-Means Clustering on SageMaker with SageMaker Spark SDK Example: Using SageMaker Spark with Any SageMaker Algorithm Example: Using SageMakerEstimator and SageMakerModel in a Spark Pipeline Example: Using Multiple SageMakerEstimators and SageMakerModels in a Spark Pipeline Example: Creating a SageMakerModel SageMakerModel From an Endpoint SageMakerModel From Model Data in S3 SageMakerModel From a Previously Completed Training Job Example: Tearing Down Amazon SageMaker Endpoints Configuring an IAM Role SageMake","default_branch":null,"files":null,"tree":[],"storefront":"/r/aws","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/aws/sagemaker-spark/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}