CI corrections

end_to_end/music_recommendation/01_data_exploration.ipynb

@@ -147,7 +147,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pretrained_model_path = get_model('./model/model.tar.gz', bucket)"
+    "pretrained_model_path = get_model('./model/model.tar.gz', bucket, prefix)"
    ]
   },
   {

end_to_end/music_recommendation/03_train_deploy_debugger_explain_monitor_registry.ipynb

@@ -750,7 +750,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "local_explanations_out = pd.read_csv(explainability_output_path+'/explanations_shap/out.csv')\n",
+    "s3_client.download_file(bucket, f\"{prefix}/clarify-output/explainability/explanations_shap/out.csv\", f\"out.csv\")\n",
+    "local_explanations_out = pd.read_csv('out.csv')\n",
     "local_explanations_out.columns = feature_names\n",
     "\n",
     "print(\"Model prediction:\", playlist.iloc[0, 0])\n",

end_to_end/music_recommendation/code/demo_helpers.py

@@ -35,12 +35,12 @@ def get_data(s3_client, public_s3_data, to_bucket, to_prefix, sample_data=1):
     return new_paths
 
 
-def get_model(model_path, to_bucket):
+def get_model(model_path, to_bucket, to_prefix):
     # upload model to our default s3 bucket for SageMaker Studio
     filename = model_path.split('/')[-1]
-    print("Uploading {} to {}\n".format(model_path, os.path.join(to_bucket,prefix,filename)))
-    s3_client.upload_file(model_path, to_bucket, os.path.join(prefix,filename))
-    return "s://{}".format(os.path.join(to_bucket,prefix,filename))
+    print("Uploading {} to {}\n".format(model_path, os.path.join(to_bucket,to_prefix,filename)))
+    s3_client.upload_file(model_path, to_bucket, os.path.join(to_prefix,filename))
+    return "s://{}".format(os.path.join(to_bucket,to_prefix,filename))
 
 
 def update_data_sources(flow_path, tracks_data_source, ratings_data_source):

end_to_end/music_recommendation/end_to_end_pipeline.ipynb

@@ -116,6 +116,7 @@
     "\n",
     "account_id = boto3.client('sts').get_caller_identity()[\"Account\"]\n",
     "\n",
+    "sess = sagemaker.Session()\n",
     "bucket = sess.default_bucket()\n",
     "prefix='music-recommendation'"
    ]