This repository contains example notebooks that show how to apply machine learning and deep learning in Amazon SageMaker
These examples provide quick walkthroughs to get you up and running with the labeling job workflow for Amazon SageMaker Ground Truth.
- From Unlabeled Data to a Deployed Machine Learning Model: A SageMaker Ground Truth Demonstration for Image Classification is an end-to-end example that starts with an unlabeled dataset, labels it using the Ground Truth API, analyzes the results, trains an image classification neural net using the annotated dataset, and finally uses the trained model to perform batch and online inference.
- Ground Truth Object Detection Tutorial is a similar end-to-end example but for an object detection task.
- Basic Data Analysis of an Image Classification Output Manifest presents charts to visualize the number of annotations for each class, differentiating between human annotations and automatic labels (if your job used auto-labeling). It also displays sample images in each class, and creates a pdf which concisely displays the full results.
- Training a Machine Learning Model Using an Output Manifest introduces the concept of an "augmented manifest" and demonstrates that the output file of a labeling job can be immediately used as the input file to train a SageMaker machine learning model.
These examples provide a gentle introduction to machine learning concepts as they are applied in practical use cases across a variety of sectors.
- Targeted Direct Marketing predicts potential customers that are most likely to convert based on customer and aggregate level metrics, using Amazon SageMaker's implementation of XGBoost.
- Predicting Customer Churn uses customer interaction and service usage data to find those most likely to churn, and then walks through the cost/benefit trade-offs of providing retention incentives. This uses Amazon SageMaker's implementation of XGBoost to create a highly predictive model.
- Time-series Forecasting generates a forecast for topline product demand using Amazon SageMaker's Linear Learner algorithm.
- Cancer Prediction predicts Breast Cancer based on features derived from images, using SageMaker's Linear Learner.
- Ensembling predicts income using two Amazon SageMaker models to show the advantages in ensembling.
- Video Game Sales develops a binary prediction model for the success of video games based on review scores.
- MXNet Gluon Recommender System uses neural network embeddings for non-linear matrix factorization to predict user movie ratings on Amazon digital reviews.
- Fair Linear Learner is an example of an effective way to create fair linear models with respect to sensitive features.
- Population Segmentation of US Census Data using PCA and Kmeans analyzes US census data and reduces dimensionality using PCA then clusters US counties using KMeans to identify segments of similar counties.
These examples introduce SageMaker's hyperparameter tuning functionality which helps deliver the best possible predictions by running a large number of training jobs to determine which hyperparameter values are the most impactful.
- XGBoost Tuning shows how to use SageMaker hyperparameter tuning to improve your model fits for the Targeted Direct Marketing task.
- TensorFlow Tuning shows how to use SageMaker hyperparameter tuning with the pre-built TensorFlow container and MNIST dataset.
- MXNet Tuning shows how to use SageMaker hyperparameter tuning with the pre-built MXNet container and MNIST dataset.
- Keras BYO Tuning shows how to use SageMaker hyperparameter tuning with a custom container running a Keras convolutional network on CIFAR-10 data.
- R BYO Tuning shows how to use SageMaker hyperparameter tuning with the custom container from the Bring Your Own R Algorithm example.
- Analyzing Results is a shared notebook that can be used after each of the above notebooks to provide analysis on how training jobs with different hyperparameters performed.
These examples provide quick walkthroughs to get you up and running with Amazon SageMaker's custom developed algorithms. Most of these algorithms can train on distributed hardware, scale incredibly well, and are faster and cheaper than popular alternatives.
- k-means is our introductory example for Amazon SageMaker. It walks through the process of clustering MNIST images of handwritten digits using Amazon SageMaker k-means.
- Factorization Machines showcases Amazon SageMaker's implementation of the algorithm to predict whether a handwritten digit from the MNIST dataset is a 0 or not using a binary classifier.
- Latent Dirichlet Allocation (LDA) introduces topic modeling using Amazon SageMaker Latent Dirichlet Allocation (LDA) on a synthetic dataset.
- Linear Learner predicts whether a handwritten digit from the MNIST dataset is a 0 or not using a binary classifier from Amazon SageMaker Linear Learner.
- Neural Topic Model (NTM) uses Amazon SageMaker Neural Topic Model (NTM) to uncover topics in documents from a synthetic data source, where topic distributions are known.
- Principal Components Analysis (PCA) uses Amazon SageMaker PCA to calculate eigendigits from MNIST.
- Seq2Seq uses the Amazon SageMaker Seq2Seq algorithm that's built on top of Sockeye, which is a sequence-to-sequence framework for Neural Machine Translation based on MXNet. Seq2Seq implements state-of-the-art encoder-decoder architectures which can also be used for tasks like Abstractive Summarization in addition to Machine Translation. This notebook shows translation from English to German text.
- Image Classification includes full training and transfer learning examples of Amazon SageMaker's Image Classification algorithm. This uses a ResNet deep convolutional neural network to classify images from the caltech dataset.
- XGBoost for regression predicts the age of abalone (Abalone dataset) using regression from Amazon SageMaker's implementation of XGBoost.
- XGBoost for multi-class classification uses Amazon SageMaker's implementation of XGBoost to classify handwritten digits from the MNIST dataset as one of the ten digits using a multi-class classifier. Both single machine and distributed use-cases are presented.
- DeepAR for time series forecasting illustrates how to use the Amazon SageMaker DeepAR algorithm for time series forecasting on a synthetically generated data set.
- BlazingText Word2Vec generates Word2Vec embeddings from a cleaned text dump of Wikipedia articles using SageMaker's fast and scalable BlazingText implementation.
- Object Detection illustrates how to train an object detector using the Amazon SageMaker Object Detection algorithm with different input formats (RecordIO and image). It uses the Pascal VOC dataset. A third notebook is provided to demonstrate the use of incremental training.
- Object detection for bird images demonstrates how to use the Amazon SageMaker Object Detection algorithm with a public dataset of Bird images.
- Object2Vec for movie recommendation demonstrates how Object2Vec can be used to model data consisting of pairs of singleton tokens using movie recommendation as a running example.
- Object2Vec for multi-label classification shows how ObjectToVec algorithm can train on data consisting of pairs of sequences and singleton tokens using the setting of genre prediction of movies based on their plot descriptions.
- Object2Vec for sentence similarity explains how to train Object2Vec using sequence pairs as input using sentence similarity analysis as the application.
- IP Insights for suspicious logins shows how to train IP Insights on a login events for a web server to identify suspicious login attempts.
- Semantic Segmentation shows how to train a semantic segmentation algorithm using the Amazon SageMaker Semantic Segmentation algorithm. It also demonstrates how to host the model and produce segmentaion masks and probability of segmentation.
The following provide examples demonstrating different capabilities of Amazon SageMaker RL.
- Cartpole using Coach demonstrates the simplest usecase of Amazon SageMaker RL using Intel's RL Coach.
- AWS DeepRacer demonstrates AWS DeepRacer trainig using RL Coach in the Gazebo environment.
- HVAC using EnergyPlus demonstrates the training of HVAC systems using the EnergyPlus environment.
- Knapsack Problem demonstrates how to solve the knapsack problem using a custom environment.
- Mountain Car Mountain car is a classic RL problem. This notebook explains how to solve this using the OpenAI Gym environment.
- Distributed Neural Network Compression This notebook explains how to compress ResNets using RL, using a custom environment and the RLLib toolkit.
- Turtlebot Tracker This notebook demonstrates object tracking using AWS Robomaker and RL Coach in the Gazebo environment.
- Portfolio Management This notebook uses a custom Gym environment to manage multiple financial investments.
- Autoscaling demonstrates how to adjust load depending on demand. This uses RL Coach and a custom environment.
- Roboschool is an open source physics simulator that is commonly used to train RL policies for robotic systems. This notebook demonstrates training a few agents using it.
- Stable Baselines In this notebook example, we will make the HalfCheetah agent learn to walk using the stable-baselines, which are a set of improved implementations of Reinforcement Learning (RL) algorithms based on OpenAI Baselines.
- Travelling Salesman is a classic NP hard problem, which this notebook solves with AWS SageMaker RL.
These examples provide more thorough mathematical treatment on a select group of algorithms.
- Streaming Median sequentially introduces concepts used in streaming algorithms, which many SageMaker algorithms rely on to deliver speed and scalability.
- Latent Dirichlet Allocation (LDA) dives into Amazon SageMaker's spectral decomposition approach to LDA.
- Linear Learner features 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
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.
- Data Distribution Types showcases the difference between two methods for sending data from S3 to Amazon SageMaker Training instances. This has particular implication for scalability and accuracy of distributed training.
- Encrypting Your Data shows how to use Server Side KMS encrypted data with Amazon SageMaker training. The IAM role used for S3 access needs to have permissions to encrypt and decrypt data with the KMS key.
- Using Parquet Data shows how to bring Parquet data sitting in S3 into an Amazon SageMaker Notebook and convert it into the recordIO-protobuf format that many SageMaker algorithms consume.
- Connecting to Redshift demonstrates how to copy data from Redshift to S3 and vice-versa without leaving Amazon SageMaker Notebooks.
- Bring Your Own XGBoost Model shows how to use Amazon SageMaker Algorithms containers to bring a pre-trained model to a realtime hosted endpoint without ever needing to think about REST APIs.
- Bring Your Own k-means Model shows how to take a model that's been fit elsewhere and use Amazon SageMaker Algorithms containers to host it.
- Bring Your Own R Algorithm shows how to bring your own algorithm container to Amazon SageMaker using the R language.
- Installing the R Kernel shows how to install the R kernel into an Amazon SageMaker Notebook Instance.
- Bring Your Own scikit Algorithm provides a detailed walkthrough on how to package a scikit learn algorithm for training and production-ready hosting.
- Bring Your Own MXNet Model shows how to bring a model trained anywhere using MXNet into Amazon SageMaker.
- Bring Your Own TensorFlow Model shows how to bring a model trained anywhere using TensorFlow into Amazon SageMaker.
- Inference Pipeline with SparkML and XGBoost 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 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 shows how to organize Training Jobs into projects, and track relationships between Models, Endpoints, and Training Jobs.
- Creating Algorithm and Model Package - Listing on AWS Marketplace provides a detailed walkthrough on how to package a scikit learn algorithm to create SageMaker Algorithm and SageMaker Model Package entities that can be used with the enhanced SageMaker Train/Transform/Hosting/Tuning APIs and listed on AWS Marketplace.
- Using Algorithm and Model Packages - From AWS Marketplace provides a detailed walkthrough on how to use Algorithm and Model Package entities with the enhanced SageMaker Train/Transform/Hosting/Tuning APIs by choosing a canonical product listed on AWS Marketplace.
These examples show you to write idiomatic TensorFlow or MXNet and then train or host in pre-built containers using SageMaker Python SDK.
- Chainer CIFAR-10 trains a VGG image classification network on CIFAR-10 using Chainer (both single machine and multi-machine versions are included)
- Chainer MNIST trains a basic neural network on MNIST using Chainer (shows how to use local mode)
- Chainer sentiment analysis trains a LSTM network with embeddings to predict text sentiment using Chainer
- IRIS with Scikit-learn trains a Scikit-learn classifier on IRIS data
- CIFAR-10 with MXNet Gluon trains a ResNet-34 image classification model using MXNet Gluon
- MNIST with MXNet Gluon trains a basic neural network on the MNIST handwritten digit dataset using MXNet Gluon
- MNIST with MXNet trains a basic neural network on the MNIST handwritten digit data using MXNet's symbolic syntax
- Sentiment Analysis with MXNet Gluon trains a text classifier using embeddings with MXNet Gluon
- TensorFlow Neural Networks with Layers trains a basic neural network on the abalone dataset using TensorFlow layers
- TensorFlow Networks with Keras trains a basic neural network on the abalone dataset using TensorFlow and Keras
- Introduction to Estimators in TensorFlow trains a DNN classifier estimator on the Iris dataset using TensorFlow
- TensorFlow and TensorBoard trains a ResNet image classification model on CIFAR-10 using TensorFlow and showcases how to track results using TensorBoard
- Distributed TensorFlow trains a simple convolutional neural network on MNIST using TensorFlow
These examples show you how to build Machine Learning models with frameworks like Apache Spark or Scikit-learn using SageMaker Python SDK.
- Inference with SparkML Serving shows how to build an ML model with Apache Spark using Amazon EMR on Abalone dataset and deploy in SageMaker with SageMaker SparkML Serving.
- Pipeline Inference with Scikit-learn and LinearLearner builds a ML pipeline using Scikit-learn preprocessing and LinearLearner algorithm in single endpoint
These examples show how to use Amazon SageMaker for model training, hosting, and inference through Apache Spark using SageMaker Spark. SageMaker Spark allows you to interleave Spark Pipeline stages with Pipeline stages that interact with Amazon SageMaker.
These Amazon SageMaker examples fully illustrate a concept, but may require some additional configuration on the users part to complete.
What do I need in order to get started?
- The quickest setup to run example notebooks includes:
- An AWS account
- Proper IAM User and Role setup
- An Amazon SageMaker Notebook Instance
- An S3 bucket
Will these examples work outside of Amazon SageMaker Notebook Instances?
- Although most examples utilize key Amazon SageMaker functionality like distributed, managed training or real-time hosted endpoints, these notebooks can be run outside of Amazon SageMaker Notebook Instances with minimal modification (updating IAM role definition and installing the necessary libraries).
How do I contribute my own example notebook?
- Although we're extremely excited to receive contributions from the community, we're still working on the best mechanism to take in examples from external sources. Please bear with us in the short-term if pull requests take longer than expected or are closed.