Merge branch 'main' into atqy/tensorflow2-california-housing

Author: atqy

introduction_to_applying_machine_learning/gluon_recommender_system/gluon_recommender_system.ipynb

@@ -684,13 +684,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "boto3.client(\"s3\").copy(\n",
-    "    {\n",
-    "        \"Bucket\": \"amazon-reviews-pds\",\n",
-    "        \"Key\": \"tsv/amazon_reviews_us_Digital_Video_Download_v1_00.tsv.gz\",\n",
-    "    },\n",
-    "    bucket,\n",
-    "    prefix + \"/train/amazon_reviews_us_Digital_Video_Download_v1_00.tsv.gz\",\n",
+    "s3 = boto3.client(\"s3\")\n",
+    "s3.download_file(\n",
+    "    f\"amazon-reviews-pds\",\n",
+    "    \"tsv/amazon_reviews_us_Digital_Video_Download_v1_00.tsv.gz\",\n",
+    "    \"amazon_reviews_us_Digital_Video_Download_v1_00.tsv.gz\",\n",
+    ")\n",
+    "\n",
+    "sagemaker.Session().upload_data(\n",
+    "    \"amazon_reviews_us_Digital_Video_Download_v1_00.tsv.gz\", key_prefix=prefix + \"/train\"\n",
     ")"
    ]
   },
@@ -753,8 +755,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "predictor = m.deploy(initial_instance_count=1, instance_type=\"ml.m4.xlarge\")\n",
-    "predictor.serializer = None"
+    "predictor = m.deploy(initial_instance_count=1, instance_type=\"ml.m4.xlarge\")"
    ]
   },
   {
@@ -773,14 +774,10 @@
    "outputs": [],
    "source": [
     "predictor.predict(\n",
-    "    json.dumps(\n",
-    "        {\n",
-    "            \"customer_id\": customer_index[customer_index[\"user\"] == 6][\n",
-    "                \"customer_id\"\n",
-    "            ].values.tolist(),\n",
-    "            \"product_id\": [\"B00KH1O9HW\", \"B00M5KODWO\"],\n",
-    "        }\n",
-    "    )\n",
+    "    {\n",
+    "        \"customer_id\": customer_index[customer_index[\"user\"] == 6][\"customer_id\"].values.tolist(),\n",
+    "        \"product_id\": [\"B00KH1O9HW\", \"B00M5KODWO\"],\n",
+    "    }\n",
     ")"
    ]
   },
@@ -829,7 +826,7 @@
     "test_preds = []\n",
     "for array in np.array_split(test_df[[\"customer_id\", \"product_id\"]].values, 40):\n",
     "    test_preds += predictor.predict(\n",
-    "        json.dumps({\"customer_id\": array[:, 0].tolist(), \"product_id\": array[:, 1].tolist()})\n",
+    "        {\"customer_id\": array[:, 0].tolist(), \"product_id\": array[:, 1].tolist()}\n",
     "    )\n",
     "\n",
     "test_preds = np.array(test_preds)\n",
@@ -872,15 +869,13 @@
     "predictions = []\n",
     "for array in np.array_split(product_index[\"product_id\"].values, 40):\n",
     "    predictions += predictor.predict(\n",
-    "        json.dumps(\n",
-    "            {\n",
-    "                \"customer_id\": customer_index[customer_index[\"user\"] == 6][\n",
-    "                    \"customer_id\"\n",
-    "                ].values.tolist()\n",
-    "                * array.shape[0],\n",
-    "                \"product_id\": array.tolist(),\n",
-    "            }\n",
-    "        )\n",
+    "        {\n",
+    "            \"customer_id\": customer_index[customer_index[\"user\"] == 6][\n",
+    "                \"customer_id\"\n",
+    "            ].values.tolist()\n",
+    "            * array.shape[0],\n",
+    "            \"product_id\": array.tolist(),\n",
+    "        }\n",
     "    )\n",
     "\n",
     "predictions = pd.DataFrame({\"product_id\": product_index[\"product_id\"], \"prediction\": predictions})"
@@ -917,15 +912,13 @@
     "predictions_user7 = []\n",
     "for array in np.array_split(product_index[\"product_id\"].values, 40):\n",
     "    predictions_user7 += predictor.predict(\n",
-    "        json.dumps(\n",
-    "            {\n",
-    "                \"customer_id\": customer_index[customer_index[\"user\"] == 7][\n",
-    "                    \"customer_id\"\n",
-    "                ].values.tolist()\n",
-    "                * array.shape[0],\n",
-    "                \"product_id\": array.tolist(),\n",
-    "            }\n",
-    "        )\n",
+    "        {\n",
+    "            \"customer_id\": customer_index[customer_index[\"user\"] == 7][\n",
+    "                \"customer_id\"\n",
+    "            ].values.tolist()\n",
+    "            * array.shape[0],\n",
+    "            \"product_id\": array.tolist(),\n",
+    "        }\n",
     "    )\n",
     "plt.scatter(predictions[\"prediction\"], np.array(predictions_user7))\n",
     "plt.show()"
@@ -964,16 +957,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sagemaker.Session().delete_endpoint(predictor.endpoint)"
+    "predictor.delete_model()\n",
+    "predictor.delete_endpoint()"
    ]
   }
  ],
  "metadata": {
   "celltoolbar": "Tags",
+  "instance_type": "ml.g4dn.xlarge",
   "kernelspec": {
-   "display_name": "conda_mxnet_p36",
+   "display_name": "Python 3 (MXNet 1.6 Python 3.6 GPU Optimized)",
    "language": "python",
-   "name": "conda_mxnet_p36"
+   "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/mxnet-1.6-gpu-py36"
   },
   "language_info": {
    "codemirror_mode": {
@@ -985,7 +980,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.6.13"
   },
   "notice": "Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.  Licensed under the Apache License, Version 2.0 (the \"License\"). You may not use this file except in compliance with the License. A copy of the License is located at http://aws.amazon.com/apache2.0/ or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
  },

sagemaker-debugger/tensorflow_profiling/tf-resnet-profiling-single-gpu-single-node.ipynb

@@ -29,8 +29,7 @@
     "if install_needed:\n",
     "    print(\"installing deps and restarting kernel\")\n",
     "    !{sys.executable} -m pip install -U sagemaker\n",
-    "    !{sys.executable} -m pip install -U smdebug\n",
-    "    IPython.Application.instance().kernel.do_shutdown(True)"
+    "    !{sys.executable} -m pip install -U smdebug"
    ]
   },
   {