generate unique endpoint names for SKLearn E2E Notebook (#1046)

Author: knakad

sagemaker-python-sdk/scikit_learn_randomforest/Sklearn_on_SageMaker_end2end.ipynb

@@ -268,6 +268,7 @@
     "    train_instance_count=1,\n",
     "    train_instance_type='ml.c5.xlarge',\n",
     "    framework_version='0.20.0',\n",
+    "    base_job_name='rf-scikit',\n",
     "    metric_definitions=[\n",
     "        {'Name': 'median-AE',\n",
     "         'Regex': \"AE-at-50th-percentile: ([0-9.]+).*$\"}],\n",
@@ -519,12 +520,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "endpoint_name = 'rf-scikit-endpoint'\n",
-    "\n",
-    "model.deploy(\n",
+    "predictor = model.deploy(\n",
     "    instance_type='ml.c5.large',\n",
-    "    initial_instance_count=1,\n",
-    "    endpoint_name=endpoint_name)"
+    "    initial_instance_count=1)"
    ]
   },
   {
@@ -534,16 +532,6 @@
     "### Invoke with the Python SDK"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# we use the SklearnPredictor from the python SDK\n",
-    "predictor = sagemaker.sklearn.model.SKLearnPredictor(endpoint_name=endpoint_name)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -585,7 +573,7 @@
    "source": [
     "# csv serialization\n",
     "response = runtime.invoke_endpoint(\n",
-    "    EndpointName=endpoint_name,\n",
+    "    EndpointName=predictor.endpoint,\n",
     "    Body=testX[data.feature_names].to_csv(header=False, index=False).encode('utf-8'),\n",
     "    ContentType='text/csv')\n",
     "\n",
@@ -615,7 +603,7 @@
     "np.save(buffer, testX[data.feature_names].values)\n",
     "\n",
     "response = runtime.invoke_endpoint(\n",
-    "    EndpointName=endpoint_name,\n",
+    "    EndpointName=predictor.endpoint,\n",
     "    Body=buffer.getvalue(),\n",
     "    ContentType='application/x-npy')\n",
     "\n",
@@ -635,15 +623,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sm_boto3.delete_endpoint(EndpointName=endpoint_name)"
+    "sm_boto3.delete_endpoint(EndpointName=predictor.endpoint)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {