Add python script to upload sample model to S3 (#42)

Download and upload a xgboost-churn model to S3 for use in the model crd.

Author: ryansteakley

samples/model/README.md

@@ -6,6 +6,13 @@ This sample demonstrates how to start models/create a model using your own model
 
 This sample assumes that you have completed the [common prerequisites](/samples/README.md).
 
+### Upload S3 Data
+
+You will need a model uploaded to an S3 bucket. Make sure you have AWS credentials and and have the bucket in the same region where you plan to create SageMaker resources. Run the following python script to upload sample data to your S3 bucket.
+```
+python3 s3_sample_model.py $S3_BUCKET_NAME
+```
+
 ### Get an Image
 
 All SageMaker models are run from within a container with all necessary dependencies and modules pre-installed and with the model scripts referencing the acceptable input and output directories. Sample container images are [available](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html).

samples/model/my-model.yaml

@@ -7,10 +7,10 @@ spec:
   primaryContainer:
     containerHostname: xgboost
     # The source of the model data
-    modelDataURL: s3://<YOUR BUCKET/PATH>
+    modelDataURL: s3://<YOUR BUCKET>/sagemaker/xgboost/model/xgb-churn-prediction-model.tar.gz
     # The URL and tag of your ECR container  
     # If you are not on us-west-2 you can find an imageURI here https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html
-    image: 433757028032.dkr.ecr.us-west-2.amazonaws.com/xgboost:1
+    image: 246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:0.90-2-cpu-py3
     environment:
       my_var: my_value
       my_var2: my_value2

samples/model/s3_sample_model.py

@@ -0,0 +1,28 @@
+import urllib.request
+from urllib.parse import urlparse
+import sys
+
+# Gets your bucket name from command line
+try:
+    bucket = str(sys.argv[1])
+except Exception as error:
+    print("Please pass your bucket name as a commandline argument")
+    sys.exit(1)
+
+# Download model from pinned commit
+url = "https://github.com/aws/amazon-sagemaker-examples/raw/af6667bd0be3c9cdec23fecda7f0be6d0e3fa3ea/sagemaker_model_monitor/introduction/model/xgb-churn-prediction-model.tar.gz"
+urllib.request.urlretrieve(url, "xgb-churn-prediction-model.tar.gz")
+
+import os
+import boto3
+
+# Upload model to S3
+prefix = "sagemaker/xgboost/model"
+model_file = open("xgb-churn-prediction-model.tar.gz", "rb")
+s3_key = os.path.join(prefix, "xgb-churn-prediction-model.tar.gz")
+boto3.Session().resource("s3").Bucket(bucket).Object(s3_key).upload_fileobj(model_file)
+s3url = f"s3://{bucket}/{s3_key}"
+print(f"Writing to {s3url}")
+
+# Remove downloaded file
+os.remove("xgb-churn-prediction-model.tar.gz")