Feature group sample

samples/feature_group/README.md

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+# Feature Group Sample
+
+This sample demonstrates how to create a feature group using the Amazon AWS Controllers for Kubernetes (ACK) service controller for Amazon SageMaker.
+
+Inspiration for this sample was taken from the notebook on [Fraud Detection with Amazon SageMaker FeatureStore](https://sagemaker-examples.readthedocs.io/en/latest/sagemaker-featurestore/sagemaker_featurestore_fraud_detection_python_sdk.html).
+
+## Prerequisites
+
+This sample assumes that you have completed the [common prerequisites](https://github.com/aws-controllers-k8s/sagemaker-controller/blob/main/samples/README.md).
+
+### Create an S3 bucket:
+
+Since we are using the offline store in this example, you need to set up an s3 bucket. [Here are directions](https://docs.aws.amazon.com/AmazonS3/latest/userguide/create-bucket-overview.html) to set up your s3 bucket through the S3 Console, AWS SDK, or AWS CLI.
+
+### Updating the Feature Group Specification:
+
+In the `my-feature-group.yaml` file, modify the placeholder values with those associated with your account and feature group.
+
+## Creating your Feature Group
+
+### Create a Feature Group:
+
+To submit your prepared feature group specification, apply the specification to your Kubernetes cluster as such:
+
+```
+$ kubectl apply -f my-feature-group.yaml
+featuregroup.sagemaker.services.k8s.aws/my-feature-group created
+```
+
+### List Feature Groups:
+
+To list all feature groups created using the ACK controller use the following command:
+
+```
+$ kubectl get featuregroup
+```
+
+### Describe a Feature Group:
+
+To get more details about the feature group once it's submitted, like checking the status, errors or parameters of the feature group use the following command:
+
+```
+$ kubectl describe featuregroup my-feature-group
+```
+
+## Ingesting Data into your Feature Group
+
+Note that ingestion is **not** supported in the controller.
+To ingest data from the my-sample-data.csv file into your feature group, use the following command:
+
+```
+$ python3 data_ingestion.py -i my-sample-data.csv -fg my-feature-group
+```
+
+## Deleting your Feature Group
+
+To delete the feature group, use the following command:
+
+```
+$ kubectl delete featuregroup my-feature-group
+featuregroup.sagemaker.services.k8s.aws "my-feature-group" deleted
+```

samples/feature_group/data_ingestion.py

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+#!/usr/bin/python
+
+import argparse
+import boto3
+import csv
+
+sagemaker_featurestore_runtime_client = boto3.Session().client(
+  service_name="sagemaker-featurestore-runtime")
+
+# Initialize the parser.
+parser = argparse.ArgumentParser()
+parser.add_argument("-i", "--input_file", help = "Path to a csv file containing data for ingestion.")
+parser.add_argument("-fg", "--feature_group_name", help = "Name of the feature group to write data to.")
+
+# Read arguments from the command line.
+args = parser.parse_args()
+
+# Write records from the csv file to s3.
+with open(args.input_file) as file_handle:
+  for row in csv.DictReader(file_handle, skipinitialspace=True):
+    record=[]
+    for featureName, valueAsString in row.items():
+      record.append({
+          'FeatureName':featureName,
+          'ValueAsString':valueAsString
+      })
+    sagemaker_featurestore_runtime_client.put_record(
+      FeatureGroupName=args.feature_group_name,
+      Record=record)       

samples/feature_group/my-feature-group.yaml

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+apiVersion: sagemaker.services.k8s.aws/v1alpha1
+kind: FeatureGroup
+metadata:
+  name: <YOUR FEATURE GROUP NAME>
+  spec:
+    eventTimeFeatureName: EventTime
+    featureDefinitions:
+      - featureName: TransactionID
+        featureType: Integral
+      - featureName: EventTime
+        featureType: Fractional
+    featureGroupName: <YOUR FEATURE GROUP NAME>
+    recordIdentifierFeatureName: TransactionID
+    offlineStoreConfig:
+      s3StorageConfig:
+        s3URI: s3://<YOUR BUCKET>/feature-group-data
+    onlineStoreConfig:
+enableOnlineStore: True
+    roleARN: <YOUR SAGEMAKER ROLE ARN>

samples/feature_group/my-sample-data.csv

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+TransactionID,EventTime
+1,1623434915
+2,1623435267
+3,1623435284