Update Inference Pipeline with Scikit-learn and Linear Learner notebook for SageMaker v2 API. Addresses #1891 (#1892)

Author: danielsiwiec

sagemaker-python-sdk/scikit_learn_inference_pipeline/Inference Pipeline with Scikit-learn and Linear Learner.ipynb

@@ -333,7 +333,7 @@
     "    entry_point=script_path,\n",
     "    role=role,\n",
     "    framework_version=FRAMEWORK_VERSION,\n",
-    "    train_instance_type=\"ml.c4.xlarge\",\n",
+    "    instance_type=\"ml.c4.xlarge\",\n",
     "    sagemaker_session=sagemaker_session)\n"
    ]
   },
@@ -398,8 +398,8 @@
    "outputs": [],
    "source": [
     "import boto3\n",
-    "from sagemaker.amazon.amazon_estimator import get_image_uri\n",
-    "ll_image = get_image_uri(boto3.Session().region_name, 'linear-learner')"
+    "from sagemaker.image_uris import retrieve\n",
+    "ll_image = retrieve('linear-learner', boto3.Session().region_name)"
    ]
   },
   {
@@ -414,17 +414,17 @@
     "ll_estimator = sagemaker.estimator.Estimator(\n",
     "    ll_image,\n",
     "    role, \n",
-    "    train_instance_count=1, \n",
-    "    train_instance_type='ml.m4.2xlarge',\n",
-    "    train_volume_size = 20,\n",
-    "    train_max_run = 3600,\n",
+    "    instance_count=1, \n",
+    "    instance_type='ml.m4.2xlarge',\n",
+    "    volume_size = 20,\n",
+    "    max_run = 3600,\n",
     "    input_mode= 'File',\n",
     "    output_path=s3_ll_output_location,\n",
     "    sagemaker_session=sagemaker_session)\n",
     "\n",
     "ll_estimator.set_hyperparameters(feature_dim=10, predictor_type='regressor', mini_batch_size=32)\n",
     "\n",
-    "ll_train_data = sagemaker.session.s3_input(\n",
+    "ll_train_data = sagemaker.inputs.TrainingInput(\n",
     "    preprocessed_train, \n",
     "    distribution='FullyReplicated',\n",
     "    content_type='text/csv', \n",
@@ -494,16 +494,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sagemaker.predictor import json_serializer, csv_serializer, json_deserializer, RealTimePredictor\n",
-    "from sagemaker.content_types import CONTENT_TYPE_CSV, CONTENT_TYPE_JSON\n",
+    "from sagemaker.predictor import Predictor\n",
+    "from sagemaker.serializers import CSVSerializer\n",
+    "\n",
     "payload = 'M, 0.44, 0.365, 0.125, 0.516, 0.2155, 0.114, 0.155'\n",
     "actual_rings = 10\n",
-    "predictor = RealTimePredictor(\n",
-    "    endpoint=endpoint_name,\n",
+    "predictor = Predictor(\n",
+    "    endpoint_name=endpoint_name,\n",
     "    sagemaker_session=sagemaker_session,\n",
-    "    serializer=csv_serializer,\n",
-    "    content_type=CONTENT_TYPE_CSV,\n",
-    "    accept=CONTENT_TYPE_JSON)\n",
+    "    serializer=CSVSerializer())\n",
     "\n",
     "print(predictor.predict(payload))"
    ]
@@ -544,7 +543,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,