Merge branch 'main' into atqy/refactor-predictive-maintenance

advanced_functionality/multi_model_bring_your_own/container/Dockerfile

@@ -5,18 +5,30 @@ LABEL com.amazonaws.sagemaker.capabilities.multi-models=true
 # Set a docker label to enable container to use SAGEMAKER_BIND_TO_PORT environment variable if present
 LABEL com.amazonaws.sagemaker.capabilities.accept-bind-to-port=true
 
+
+# Upgrade installed packages
+RUN apt-get update && apt-get upgrade -y && apt-get clean
+
+# Python package management and basic dependencies
+RUN apt-get install -y curl python3.7 python3.7-dev python3.7-distutils
+
+# Register the version in alternatives
+RUN update-alternatives --install /usr/bin/python python /usr/bin/python3.7 1
+
+# Set python 3 as the default python
+RUN update-alternatives --set python /usr/bin/python3.7
+
 # Install necessary dependencies for MMS and SageMaker Inference Toolkit
-RUN apt-get update && \
-    apt-get -y install --no-install-recommends \
+RUN apt-get -y install --no-install-recommends \
     build-essential \
     ca-certificates \
     openjdk-8-jdk-headless \
-    python3-dev \
     curl \
     vim \
     && rm -rf /var/lib/apt/lists/* \
+    && python --version \
     && curl -O https://bootstrap.pypa.io/get-pip.py \
-    && python3 get-pip.py
+    && python get-pip.py
 
 RUN update-alternatives --install /usr/bin/python python /usr/bin/python3 1
 RUN update-alternatives --install /usr/local/bin/pip pip /usr/local/bin/pip3 1
@@ -40,4 +52,4 @@ COPY model_handler.py /home/model-server/model_handler.py
 ENTRYPOINT ["python", "/usr/local/bin/dockerd-entrypoint.py"]
 
 # Define command to be passed to the entrypoint
-CMD ["serve"]
+CMD ["serve"]

index.rst

@@ -27,7 +27,7 @@ On SageMaker Studio, you will need to open a terminal, go to your home folder, t
 
    intro.rst
 
-We recommend the following 20 notebooks as a broad introduction to the capabilities that SageMaker offers. To explore in even more depth, we provide additional notebooks covering even more use cases and frameworks.
+We recommend the following notebooks as a broad introduction to the capabilities that SageMaker offers. To explore in even more depth, we provide additional notebooks covering even more use cases and frameworks.
 
 .. toctree::
    :maxdepth: 1
@@ -39,9 +39,11 @@ We recommend the following 20 notebooks as a broad introduction to the capabilit
    :maxdepth: 1
    :caption: Prepare data
 
+   sagemaker_processing/spark_distributed_data_processing/sagemaker-spark-processing_outputs
    sagemaker_processing/basic_sagemaker_data_processing/basic_sagemaker_processing_outputs
 
 
+
 .. toctree::
    :maxdepth: 1
    :caption: Train and tune models

sagemaker-inference-recommender/inference-recommender.ipynb

@@ -145,7 +145,8 @@
    "source": [
     "if download_the_model:\n",
     "    tf.keras.backend.set_learning_phase(0)\n",
-    "    model = tf.keras.applications.ResNet50()\n",
+    "    input_tensor = tf.keras.Input(name=\"input_1\", shape=(224, 224, 3))\n",
+    "    model = tf.keras.applications.resnet50.ResNet50(input_tensor=input_tensor)\n",
     "\n",
     "    # Creating the directory strcture\n",
     "    model_version = \"1\"\n",

sagemaker-inference-recommender/xgboost/xgboost-inference-recommender.ipynb

@@ -435,7 +435,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If you specify a set of instance types below (i.e. non-empty list), then Inference Recommender will only support recommendations within the set of instances below. For this example, we provide a list of common CPU instance types used with XGBoost."
+    "If you specify a set of instance types below (i.e. non-empty list), then Inference Recommender will only support recommendations within the set of instances below. For this example, we provide a list of common CPU instance types used with XGBoost. Note that, if you want to try to compile your XGboost model with Amazon SageMaker Neo, then the `ModelInput` field is required for your model's input shape. Currently, it supports Images list here: [Inference Container Images](https://docs.aws.amazon.com/sagemaker/latest/dg/neo-deployment-hosting-services-container-images.html) or SageMaker XGboost containers. And you need to make sure the xgboost version is 1.0 to 1.3."
    ]
   },
   {
@@ -466,6 +466,9 @@
     "                \"Framework\": framework.upper(),\n",
     "                \"FrameworkVersion\": framework_version,\n",
     "                \"NearestModelName\": model_name,\n",
+    "                \"ModelInput\": {\n",
+    "                    \"DataInputConfig\": '{\"data\": [1, 50]}'  # this is required for compilation\n",
+    "                },\n",
     "            }\n",
     "        ],\n",
     "        \"SupportedContentTypes\": input_mime_types,  # required, must be non-null\n",