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An Introduction to Amazon SageMaker (October 2018)
This document introduces Amazon SageMaker, a fully managed platform for building, training, and deploying machine learning models. It provides several key services: built-in algorithms for common ML tasks, the ability to bring your own algorithms or scripts for model training, hyperparameter tuning to optimize models, and managed hosting for deploying trained models. The document outlines the ML workflow that SageMaker aims to simplify and provides demos of using XGBoost and BlazingText for text classification tasks on SageMaker.
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An Introduction to Amazon SageMaker (October 2018)
- 1. An Introduction to AmazonSageMaker Julien Simon Principal Technical Evangelist, AI and Machine Learning, AWS @julsimon October 2018
- 2. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Put Machine Learning in the hands of every developer and data scientist Our mission
- 3. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Application Services Platform Services Frameworks & Infrastructure API-driven services:Vision, Language & Speech Services, Chatbots AWS ML Stack Deploy machine learning models with high-performance machine learning algorithms, broad framework support, and one-click training, tuning, and inference. Develop sophisticated models with any framework, create managed, auto-scaling clusters of GPUs for large scale training, or run prediction on trained models.
- 4. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Application Services Platform Services Frameworks & Infrastructure API-driven services:Vision, Language & Speech Services, Chatbots AWS ML Stack Deploy machine learning models with high-performance machine learning algorithms, broad framework support, and one-click training, tuning, and inference. Develop sophisticated models with any framework, create managed, auto-scaling clusters of GPUs for large scale training, or run prediction on trained models.
- 5. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Data Visualization & Analysis Business Problem ML problem framing Data Collection Data Integration Data Preparation & Cleaning Feature Engineering Model Training & Parameter Tuning Model Evaluation Are Business Goals met? Model Deployment Monitoring & Debugging YesNo DataAugmentation Feature Augmentation The Machine Learning Process Re-training Predictions
- 6. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. ML is still too complicated for everyday developers Collect and prepare training data Choose and optimize your ML algorithm Set up and manage environments for training Train and tune model (trial and error) Deploy model in production Scale and manage the production environment
- 7. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Amazon SageMaker Pre-built notebooks for common problems K-MeansClustering Principal Component Analysis Neural TopicModelling FactorizationMachines Linear Learner XGBoost Latent Dirichlet Allocation ImageClassification Seq2Seq, And more! ALGORITHMS Apache MXNet, Chainer TensorFlow, PyTorch Caffe2, CNTK, Torch FRAMEWORKS Set up and manage environments for training Train and tune model (trial and error) Deploy model in production Scale and manage the production environment Built-in, high- performance algorithms Build
- 8. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Amazon SageMaker Pre-built notebooks for common problems Built-in, high- performance algorithms One-click training Hyperparameter optimization Build Train Deploy model in production Scale and manage the production environment
- 9. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Amazon SageMaker Fully managed hosting with auto- scaling One-click deployment Pre-built notebooks for common problems Built-in, high- performance algorithms One-click training Hyperparameter optimization Build Train Deploy
- 10. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Selected Amazon SageMaker customers
- 11. Working with AmazonSageMaker
- 12. TheAmazon SageMakerAPI • PythonSDK orchestrating all Amazon SageMaker activity • High-level objects for algorithm selection, training, deploying, automatic model tuning, etc. • There’s also a Spark SDK (Python & Scala) which we won’t cover today. • AWS CLI: ‘aws sagemaker’ • AWS SDK: boto3, etc.
- 13. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Model Training (on EC2) Model Hosting (on EC2) Trainingdata Modelartifacts Training code Helper code Helper codeInference code GroundTruth Client application Inference code Training code Inference requestInference response Inference Endpoint
- 14. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Training code Factorization Machines Linear Learner PrincipalComponent Analysis K-Means Clustering XGBoost And more Built-inAlgorithms BringYour Own ContainerBringYour Own Script Model options
- 15. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Amazon EC2 C5 / C5d instances AVX 512 72 vCPUs “Skylake” 144 GiB memory C5 12 Gbps to EBS 2X vCPUs 2X performance 3X throughput 2.4X memory C4 36 vCPUs “Haswell” 4 Gbps to EBS 60 GiB memory C5: N e x t G e n e r at ion Compu t e -Opt imize d I n st an ces wit h I n t e l® Xe on ® Sc alab le Pr oc e ssor AWS Compu t e opt imize d in st an c e s su ppor t t he n e w I n t e l® AV X -51 2 ad van ced in st r uct ion set , en ab lin g you t o mor e e f f ic ie n t ly r u n ve c t or pr oc e ssin g wor k load s wit h sin gle an d d ou b le f loat in g poin t pr e c ision , su c h as A I /mac hin e le ar n in g or vid e o pr oc e ssin g. 25% improvement in price/performance over C4
- 16. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. FasterTensorFlow training on C5 https://aws.amazon.com/blogs/machine-learning/faster-training-with-optimized-tensorflow-1-6-on- amazon-ec2-c5-and-p3-instances/
- 17. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Amazon EC2 P3 Instances • P3.2xlarge, P3.8xlarge, P3.16xlarge • Up to eight NVIDIATeslaV100 GPUs in a single instance • 40,960 CUDA cores, 5120Tensor cores • 128GB of GPU memory • 1 PetaFLOPs of computational performance – 14x better than P2 • 300 GB/s GPU-to-GPU communication (NVLink) – 9x better than P2 T h e f a s t e s t , m o s t p o w e r f u l G P U i n s t a n c e s i n t h e c l o u d
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- 19. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Scalable algorithms implemented by Amazon orange: supervised, yellow: unsupervised Linear Learner: regression, classification Image Classification: Deep Learning (ResNet) Factorization Machines: regression, classification, recommendation Object Detection: Deep Learning (VGG or ResNet) K-Nearest Neighbors: non-parametric regression and classification NeuralTopic Model: topic modeling XGBoost: regression, classification, ranking https://github.com/dmlc/xgboost Latent Dirichlet Allocation: topic modeling (mostly) K-Means: clustering BlazingText: GPU-based Word2Vec, and text classification Principal Component Analysis: reduction Sequence to Sequence: machine translation, speech to text and more Random Cut Forest: anomaly detection DeepAR: time-series forecasting (RNN)
- 20. XGBoost • Very populartree-based algorithm for regression, classification and ranking • Handles missing values automatically • Works well on sparse data • Trains fast • “Easy” to inspect and control https://github.com/dmlc/xgboost https://xgboost.readthedocs.io/en/latest/ https://arxiv.org/abs/1603.02754
- 21. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Demo: Customer classification with XGBoost
- 22. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. Blazing Text https://dl.acm.org/citation.cfm?id=3146354
- 23. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Demo: Text Classification with BlazingText
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- 25. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Automatic Model Tuning Finding the optimal set of hyper parameters 1. Manual Search (”I know what I’m doing”) 2. Random Search (“Spray and pray”) 3. Grid Search (“X marks the spot”) • Typically training hundreds of models • Slow and expensive 4. HPO: use Machine Learning • Training fewer models • Gaussian Process Regression and Bayesian Optimization, https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html
- 26. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Demo: Customer classification with XGBoost
- 27. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Amazon SageMaker Fully managed hosting with auto- scaling One-click deployment Pre-built notebooks for common problems Built-in, high- performance algorithms One-click training Hyperparameter optimization Build Train Deploy
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- 29. Thank you! Julien Simon PrincipalTechnicalEvangelist, AI and Machine Learning @julsimon