change: adding reinvent notebook examples

README.md

@@ -91,6 +91,19 @@ These examples provide more thorough mathematical treatment on a select group of
 - [Latent Dirichlet Allocation (LDA)](scientific_details_of_algorithms/lda_topic_modeling) dives into Amazon SageMaker's spectral decomposition approach to LDA.
 - [Linear Learner features](scientific_details_of_algorithms/linear_learner_class_weights_loss_functions) shows how to use the class weights and loss functions features of the SageMaker Linear Learner algorithm to improve performance on a credit card fraud prediction task
 
+### Amazon SageMaker Debugger
+These examples provide and introduction to SageMaker Debugger which allows debugging and monitoring capabilities for training of machine learning and deep learning algorithms. Note that although these notebooks focus on a specific framework, the same approach works with all the frameworks that Amazon SageMaker Debugger supports. The notebooks below are listed in the order in which we recommend you review them.
+
+- [Using a built-in rule with TensorFlow](sagemaker-debugger/tensorflow_builtin_rule/)
+- [Using a custom rule with TensorFlow Keras](sagemaker-debugger/tensorflow_keras_custom_rule/)
+- [Interactive tensor analysis in notebook with MXNet](sagemaker-debugger/mnist_tensor_analysis/)
+- [Real-time analysis in notebook with MXNet](sagemaker-debugger/mxnet_realtime_analysis/)
+- [Using a built in rule with XGBoost](sagemaker-debugger/xgboost_builtin_rules/)
+- [Real-time analysis in notebook with XGBoost](sagemaker-debugger/xgboost_realtime_analysis/)
+- [Using SageMaker Debugger with Managed Spot Training and MXNet](sagemaker-debugger/mxnet_spot_training/)
+- [Reacting to CloudWatch Events from Rules to take an action based on status with TensorFlow](sagemaker-debugger/tensorflow_action_on_rule/)
+- [Using SageMaker Debugger with a custom PyTorch container](sagemaker-debugger/pytorch_custom_container/)
+
 ### Advanced Amazon SageMaker Functionality
 
 These examples that showcase unique functionality available in Amazon SageMaker.  They cover a broad range of topics and will utilize a variety of methods, but aim to provide the user with sufficient insight or inspiration to develop within Amazon SageMaker.
@@ -109,6 +122,9 @@ These examples that showcase unique functionality available in Amazon SageMaker.
 - [Inference Pipeline with SparkML and XGBoost](advanced_functionality/inference_pipeline_sparkml_xgboost_abalone) shows how to deploy an Inference Pipeline with SparkML for data pre-processing and XGBoost for training on the Abalone dataset. The pre-processing code is written once and used between training and inference.
 - [Inference Pipeline with SparkML and BlazingText](advanced_functionality/inference_pipeline_sparkml_blazingtext_dbpedia) shows how to deploy an Inference Pipeline with SparkML for data pre-processing and BlazingText for training on the DBPedia dataset. The pre-processing code is written once and used between training and inference.
 - [Experiment Management Capabilities with Search](advanced_functionality/search) shows how to organize Training Jobs into projects, and track relationships between Models, Endpoints, and Training Jobs.
+- [Host Multiple Models with Your Own Algorithm](advanced_functionality/multi_model_bring_your_own) shows how to deploy multiple models to a realtime hosted endpoint with your own custom algorithm.
+- [Host Multiple Models with XGBoost](advanced_functionality/multi_model_xgboost_home_value) shows how to deploy multiple models to a realtime hosted endpoint using a multi-model enabled XGBoost container.
+- [Host Multiple Models with SKLearn](advanced_functionality/multi_model_sklearn_home_value) shows how to deploy multiple models to a realtime hosted endpoint using a multi-model enabled SKLearn container.
 
 ### Amazon SageMaker Neo Compilation Jobs
 
@@ -120,6 +136,13 @@ These examples provide you an introduction to how to use Neo to optimizes deep l
 - [Distributed TensorFlow](sagemaker_neo_compilation_jobs/tensorflow_distributed_mnist) Adapts form [tensorflow mnist](sagemaker-python-sdk/tensorflow_distributed_mnist) including Neo API and comparsion between the baseline
 - [Predicting Customer Churn](sagemaker_neo_compilation_jobs/xgboost_customer_churn) Adapts form [xgboost customer churn](introduction_to_applying_machine_learning/xgboost_customer_churn) including Neo API and comparsion between the baseline
 
+### Amazon SageMaker Procesing
+
+These examples show you how to use SageMaker Processing jobs to run data processing workloads.
+
+- [Scikit-Learn Data Processing and Model Evaluation](sagemaker_processing/scikit_learn_data_processing_and_model_evaluation) shows how to use SageMaker Processing and the Scikit-Learn container to run data preprocessing and model evaluation workloads.
+- [Feature transformation with Amazon SageMaker Processing and SparkML](sagemaker_processing/feature_transformation_with_sagemaker_processing) shows how to use SageMaker Processing to run data processing workloads using SparkML prior to training.
+
 ### Amazon SageMaker Pre-Built Framework Containers and the Python SDK
 
 #### Pre-Built Deep Learning Framework Containers
@@ -173,6 +196,9 @@ These examples show you how to use model-packages and algorithms from AWS Market
 	- [Using models for extracting vehicle metadata](aws_marketplace/using_model_packages/auto_insurance) provides a detailed walkthrough on how to use pre-trained models from AWS Marketplace for extracting metadata for a sample use-case of auto-insurance claim processing.
 	- [Using models for identifying non-compliance at a workplace](aws_marketplace/using_model_packages/improving_industrial_workplace_safety) provides a detailed walkthrough on how to use pre-trained models from AWS Marketplace for extracting metadata for a sample use-case of generating summary reports for identifying non-compliance at a construction/industrial workplace.
 
+- [Using Data](aws_marketplace/using_data)
+	- [Using data and algorithm from AWS Marketplace for training a model](aws_marketplace/using_data/using_data_from_aws_data_exchange_to_predict_product_popularity) provides a detailed walkthrough on how to use data from AWS Marketplace for training a model that predicts popularity of a bath product.
+
 ### Under Development
 
 These Amazon SageMaker examples fully illustrate a concept, but may require some additional configuration on the users part to complete.

advanced_functionality/multi_model_bring_your_own/container/Dockerfile

@@ -0,0 +1,43 @@
+FROM ubuntu:16.04
+
+# Set a docker label to advertise multi-model support on the container
+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
+
+# Install necessary dependencies for MMS and SageMaker Inference Toolkit
+RUN apt-get update && \
+    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/* \
+    && curl -O https://bootstrap.pypa.io/get-pip.py \
+    && python3 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
+
+# Install MXNet, MMS, and SageMaker Inference Toolkit to set up MMS
+RUN pip3 --no-cache-dir install mxnet \
+                                multi-model-server \
+                                sagemaker-inference \
+                                retrying
+
+# Copy entrypoint script to the image
+COPY dockerd-entrypoint.py /usr/local/bin/dockerd-entrypoint.py
+RUN chmod +x /usr/local/bin/dockerd-entrypoint.py
+
+RUN mkdir -p /home/model-server/
+
+# Copy the default custom service file to handle incoming data and inference requests
+COPY model_handler.py /home/model-server/model_handler.py
+
+# Define an entrypoint script for the docker image
+ENTRYPOINT ["python", "/usr/local/bin/dockerd-entrypoint.py"]
+
+# Define command to be passed to the entrypoint
+CMD ["serve"]

advanced_functionality/multi_model_bring_your_own/container/dockerd-entrypoint.py

@@ -0,0 +1,29 @@
+import subprocess
+import sys
+import shlex
+import os
+from retrying import retry
+from subprocess import CalledProcessError
+from sagemaker_inference import model_server
+
+def _retry_if_error(exception):
+    return isinstance(exception, CalledProcessError or OSError)
+
+@retry(stop_max_delay=1000 * 50,
+       retry_on_exception=_retry_if_error)
+def _start_mms():
+    # by default the number of workers per model is 1, but we can configure it through the
+    # environment variable below if desired.
+    # os.environ['SAGEMAKER_MODEL_SERVER_WORKERS'] = '2'
+    model_server.start_model_server(handler_service='/home/model-server/model_handler.py:handle')
+
+def main():
+    if sys.argv[1] == 'serve':
+        _start_mms()
+    else:
+        subprocess.check_call(shlex.split(' '.join(sys.argv[1:])))
+
+    # prevent docker exit
+    subprocess.call(['tail', '-f', '/dev/null'])
+
+main()

advanced_functionality/multi_model_bring_your_own/container/model_handler.py

@@ -0,0 +1,167 @@
+"""
+ModelHandler defines an example model handler for load and inference requests for MXNet CPU models
+"""
+from collections import namedtuple
+import glob
+import json
+import logging
+import os
+import re
+
+import mxnet as mx
+import numpy as np
+
+class ModelHandler(object):
+    """
+    A sample Model handler implementation.
+    """
+
+    def __init__(self):
+        self.initialized = False
+        self.mx_model = None
+        self.shapes = None
+
+    def get_model_files_prefix(self, model_dir):
+        """
+        Get the model prefix name for the model artifacts (symbol and parameter file).
+        This assume model artifact directory contains a symbol file, parameter file, 
+        model shapes file and a synset file defining the labels
+
+        :param model_dir: Path to the directory with model artifacts
+        :return: prefix string for model artifact files
+        """
+        sym_file_suffix = "-symbol.json"
+        checkpoint_prefix_regex = "{}/*{}".format(model_dir, sym_file_suffix) # Ex output: /opt/ml/models/resnet-18/model/*-symbol.json
+        checkpoint_prefix_filename = glob.glob(checkpoint_prefix_regex)[0] # Ex output: /opt/ml/models/resnet-18/model/resnet18-symbol.json
+        checkpoint_prefix = os.path.basename(checkpoint_prefix_filename).split(sym_file_suffix)[0] # Ex output: resnet18
+        logging.info("Prefix for the model artifacts: {}".format(checkpoint_prefix))
+        return checkpoint_prefix
+
+    def get_input_data_shapes(self, model_dir, checkpoint_prefix):
+        """
+        Get the model input data shapes and return the list
+
+        :param model_dir: Path to the directory with model artifacts
+        :param checkpoint_prefix: Model files prefix name
+        :return: prefix string for model artifact files
+        """
+        shapes_file_path = os.path.join(model_dir, "{}-{}".format(checkpoint_prefix, "shapes.json"))
+        if not os.path.isfile(shapes_file_path):
+            raise RuntimeError("Missing {} file.".format(shapes_file_path))
+
+        with open(shapes_file_path) as f:
+            self.shapes = json.load(f)
+
+        data_shapes = []
+
+        for input_data in self.shapes:
+            data_name = input_data["name"]
+            data_shape = input_data["shape"]
+            data_shapes.append((data_name, tuple(data_shape)))
+
+        return data_shapes
+
+    def initialize(self, context):
+        """
+        Initialize model. This will be called during model loading time
+        :param context: Initial context contains model server system properties.
+        :return:
+        """
+        self.initialized = True
+        properties = context.system_properties
+        # Contains the url parameter passed to the load request
+        model_dir = properties.get("model_dir") 
+        gpu_id = properties.get("gpu_id")
+
+        checkpoint_prefix = self.get_model_files_prefix(model_dir)
+
+        # Read the model input data shapes
+        data_shapes = self.get_input_data_shapes(model_dir, checkpoint_prefix)
+
+        # Load MXNet model
+        try:
+            ctx = mx.cpu() # Set the context on CPU
+            sym, arg_params, aux_params = mx.model.load_checkpoint(checkpoint_prefix, 0)  # epoch set to 0
+            self.mx_model = mx.mod.Module(symbol=sym, context=ctx, label_names=None)
+            self.mx_model.bind(for_training=False, data_shapes=data_shapes, 
+                   label_shapes=self.mx_model._label_shapes)
+            self.mx_model.set_params(arg_params, aux_params, allow_missing=True)
+            with open("synset.txt", 'r') as f:
+                self.labels = [l.rstrip() for l in f]
+        except (mx.base.MXNetError, RuntimeError) as memerr:
+            if re.search('Failed to allocate (.*) Memory', str(memerr), re.IGNORECASE):
+                logging.error("Memory allocation exception: {}".format(memerr))
+                raise MemoryError
+            raise           
+
+    def preprocess(self, request):
+        """
+        Transform raw input into model input data.
+        :param request: list of raw requests
+        :return: list of preprocessed model input data
+        """
+        # Take the input data and pre-process it make it inference ready
+
+        img_list = []
+        for idx, data in enumerate(request):
+            # Read the bytearray of the image from the input
+            img_arr = data.get('body')  
+
+            # Input image is in bytearray, convert it to MXNet NDArray
+            img = mx.img.imdecode(img_arr) 
+            if img is None:
+                return None
+
+            # convert into format (batch, RGB, width, height)
+            img = mx.image.imresize(img, 224, 224) # resize
+            img = img.transpose((2, 0, 1)) # Channel first
+            img = img.expand_dims(axis=0) # batchify
+            img_list.append(img)
+
+        return img_list
+
+    def inference(self, model_input):
+        """
+        Internal inference methods
+        :param model_input: transformed model input data list
+        :return: list of inference output in NDArray
+        """
+        # Do some inference call to engine here and return output
+        Batch = namedtuple('Batch', ['data'])
+        self.mx_model.forward(Batch(model_input))
+        prob = self.mx_model.get_outputs()[0].asnumpy()
+        return prob
+
+    def postprocess(self, inference_output):
+        """
+        Return predict result in as list.
+        :param inference_output: list of inference output
+        :return: list of predict results
+        """
+        # Take output from network and post-process to desired format
+        prob = np.squeeze(inference_output)
+        a = np.argsort(prob)[::-1]
+        return [['probability=%f, class=%s' %(prob[i], self.labels[i]) for i in a[0:5]]]
+
+    def handle(self, data, context):
+        """
+        Call preprocess, inference and post-process functions
+        :param data: input data
+        :param context: mms context
+        """
+
+        model_input = self.preprocess(data)
+        model_out = self.inference(model_input)
+        return self.postprocess(model_out)
+
+_service = ModelHandler()
+
+
+def handle(data, context):
+    if not _service.initialized:
+        _service.initialize(context)
+
+    if data is None:
+        return None
+
+    return _service.handle(data, context)