Revert "Update XBoost Notebooks To Use Sagemaker SDK V2" (aws#1522)

This reverts commit ea39ff5.

Author: mchoi8739

sagemaker-debugger/xgboost_builtin_rules/xgboost-regression-debugger-rules.ipynb

@@ -56,7 +56,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Amazon SageMaker Debugger is available in Amazon SageMaker XGBoost container version 0.90-2 or later. If you want to use XGBoost with Amazon SageMaker Debugger, you have to specify `repo_version='0.90-2'` in the `retrieve` function."
+    "Amazon SageMaker Debugger is available in Amazon SageMaker XGBoost container version 0.90-2 or later. If you want to use XGBoost with Amazon SageMaker Debugger, you have to specify `repo_version='0.90-2'` in the `get_image_uri` function."
    ]
   },
   {
@@ -65,12 +65,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sagemaker.image_uris import retrieve\n",
+    "from sagemaker.amazon.amazon_estimator import get_image_uri\n",
     "\n",
     "# Below changes the Region to be one where this notebook is running\n",
     "region = boto3.Session().region_name\n",
     "\n",
-    "container = retrieve(region=region, framework=\"xgboost\", version=\"0.90-2\")"
+    "container = get_image_uri(region, \"xgboost\", repo_version=\"0.90-2\")"
    ]
   },
   {
@@ -237,11 +237,11 @@
     "algorithm_mode_default_estimator = Estimator(\n",
     "    role=role,\n",
     "    base_job_name=base_job_name,\n",
-    "    instance_count=1,\n",
-    "    instance_type='ml.m5.xlarge',\n",
-    "    image_uri=container,\n",
+    "    train_instance_count=1,\n",
+    "    train_instance_type='ml.m5.xlarge',\n",
+    "    image_name=container,\n",
     "    hyperparameters=hyperparameters,\n",
-    "    max_run=1800,\n",
+    "    train_max_run=1800,\n",
     "\n",
     "    debugger_hook_config=DebuggerHookConfig(\n",
     "        s3_output_path=bucket_path,  # Required\n",
@@ -292,10 +292,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sagemaker.inputs import TrainingInput\n",
+    "from sagemaker.session import s3_input\n",
     "\n",
-    "train_s3_input = TrainingInput(\"s3://{}/{}/{}\".format(bucket, prefix, \"train\"), content_type=\"libsvm\")\n",
-    "validation_s3_input = TrainingInput( \"s3://{}/{}/{}\".format(bucket, prefix, \"validation\"), content_type=\"libsvm\")\n",
+    "train_s3_input = s3_input(\"s3://{}/{}/{}\".format(bucket, prefix, \"train\"), content_type=\"libsvm\")\n",
+    "validation_s3_input = s3_input( \"s3://{}/{}/{}\".format(bucket, prefix, \"validation\"), content_type=\"libsvm\")\n",
     "algorithm_mode_default_estimator.fit(\n",
     "    {\"train\": train_s3_input, \"validation\": validation_s3_input},\n",
     "    # This is a fire and forget event. By setting wait=False, you just submit the job to run in the background.\n",
@@ -640,5 +640,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 2
 }

sagemaker-debugger/xgboost_census_explanations/xgboost-census-debugger-rules.ipynb

@@ -130,7 +130,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Amazon SageMaker Debugger is available in Amazon SageMaker XGBoost container version 0.90-2 or later. If you want to use XGBoost with Amazon SageMaker Debugger, you have to specify `repo_version='0.90-2'` in the `retrieve` function."
+    "Amazon SageMaker Debugger is available in Amazon SageMaker XGBoost container version 0.90-2 or later. If you want to use XGBoost with Amazon SageMaker Debugger, you have to specify `repo_version='0.90-2'` in the `get_image_uri` function."
    ]
   },
   {
@@ -139,8 +139,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sagemaker.image_uris import retrieve\n",
-    "container = retrieve(region=region, framework=\"xgboost\", version=\"0.90-2\")"
+    "from sagemaker.amazon.amazon_estimator import get_image_uri\n",
+    "container = get_image_uri(region, \"xgboost\", repo_version=\"0.90-2\")"
    ]
   },
   {
@@ -261,8 +261,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sagemaker.amazon.amazon_estimator import retrieve\n",
-    "container = retrieve(region=region, framework=\"xgboost\", version=\"0.90-2\")"
+    "from sagemaker.amazon.amazon_estimator import get_image_uri\n",
+    "container = get_image_uri(region, \"xgboost\", repo_version=\"0.90-2\")"
    ]
   },
   {
@@ -377,11 +377,11 @@
     "xgboost_estimator = Estimator(\n",
     "    role=role,\n",
     "    base_job_name=base_job_name,\n",
-    "    instance_count=1,\n",
-    "    instance_type='ml.m5.4xlarge',\n",
-    "    image_uri=container,\n",
+    "    train_instance_count=1,\n",
+    "    train_instance_type='ml.m5.4xlarge',\n",
+    "    image_name=container,\n",
     "    hyperparameters=hyperparameters,\n",
-    "    max_run=1800,\n",
+    "    train_max_run=1800,\n",
     "\n",
     "    debugger_hook_config=DebuggerHookConfig(\n",
     "        s3_output_path=bucket_path,  # Required\n",
@@ -438,11 +438,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sagemaker.session import TrainingInput\n",
+    "from sagemaker.session import s3_input\n",
     "\n",
-    "train_input = TrainingInput(\"s3://{}/{}/{}\".format(bucket,\n",
+    "train_input = s3_input(\"s3://{}/{}/{}\".format(bucket,\n",
     "                                              prefix, \"data/train.csv\"), content_type=\"csv\")\n",
-    "validation_input = TrainingInput(\n",
+    "validation_input = s3_input(\n",
     "    \"s3://{}/{}/{}\".format(bucket, prefix, \"data/validation.csv\"), content_type=\"csv\")\n",
     "xgboost_estimator.fit(\n",
     "    {\"train\": train_input, \"validation\": validation_input},\n",
@@ -740,7 +740,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [],
    "source": [
     "shap.summary_plot(shap_no_base, X_train)"
@@ -929,5 +931,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 2
 }

sagemaker-debugger/xgboost_realtime_analysis/xgboost-realtime-analysis.ipynb

@@ -76,11 +76,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sagemaker.amazon.amazon_estimator import retrieve\n",
+    "from sagemaker.amazon.amazon_estimator import get_image_uri\n",
     "\n",
     "# Below changes the region to be one where this notebook is running\n",
     "region = boto3.Session().region_name\n",
-    "container = retrieve(region=region, framework=\"xgboost\", version=\"0.90-2\")"
+    "container = get_image_uri(region, \"xgboost\", repo_version=\"0.90-2\")"
    ]
   },
   {
@@ -207,11 +207,11 @@
     "xgboost_algorithm_mode_estimator = Estimator(\n",
     "    role=role,\n",
     "    base_job_name=base_job_name,\n",
-    "    instance_count=1,\n",
-    "    instance_type='ml.m5.xlarge',\n",
-    "    image_uri=container,\n",
+    "    train_instance_count=1,\n",
+    "    train_instance_type='ml.m5.xlarge',\n",
+    "    image_name=container,\n",
     "    hyperparameters=hyperparameters,\n",
-    "    max_run=1800,\n",
+    "    train_max_run=1800,\n",
     "\n",
     "    debugger_hook_config = DebuggerHookConfig(\n",
     "        s3_output_path=bucket_path,  # Required\n",
@@ -256,10 +256,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sagemaker.session import TrainingInput\n",
+    "from sagemaker.session import s3_input\n",
     "\n",
-    "train_s3_input = TrainingInput(\"s3://{}/{}/{}\".format(bucket, prefix, \"train\"), content_type=\"libsvm\")\n",
-    "validation_s3_input = TrainingInput(\"s3://{}/{}/{}\".format(bucket, prefix, \"validation\"), content_type=\"libsvm\")\n",
+    "train_s3_input = s3_input(\"s3://{}/{}/{}\".format(bucket, prefix, \"train\"), content_type=\"libsvm\")\n",
+    "validation_s3_input = s3_input(\"s3://{}/{}/{}\".format(bucket, prefix, \"validation\"), content_type=\"libsvm\")\n",
     "\n",
     "# This is a fire and forget event. By setting wait=False, you just submit the job to run in the background.\n",
     "# Amazon SageMaker will start one training job and release control to next cells in the notebook.\n",