Deep Learning on Amazon Sagemaker (July 2019)
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Talk at Floor 28, Tel Aviv. Built-in algorithms, built-in frameworks, Tensorflow, MXNet, Keras, Gluon
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Deep Learning on Amazon Sagemaker (July 2019)
- 1. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Deep Learning onAmazonSageMaker Julien Simon Global Evangelist, AI & Machine Learning, AWS @julsimon F l o o r 2 8 , T e l A v i v , J u l y 7 t h , 2 0 1 9
- 2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AmazonSageMaker 1 2 3
- 3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Model options Training code Factorization Machines Linear Learner Principal Component Analysis K-Means Clustering XGBoost And more Built-in Algorithms (17) No ML coding required No infrastructure work required Distributed training Pipe mode BringYour Own Container Full control, run anything! R, C++, etc. No infrastructure work required Built-in Frameworks Bring your own code: script mode Open source containers No infrastructure work required Distributed training Pipe mode
- 4. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. TheAmazonSageMakerAPI • Python SDK orchestrating all Amazon SageMaker activity • High-level objects for algorithm selection, training, deploying, automatic model tuning, etc. • Spark SDK (Python & Scala) • AWS SDK • For scripting and automation • CLI : ‘aws sagemaker’ • Language SDKs: boto3, etc.
- 5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Annotatingdataatscaleistime-consumingandexpensive
- 7. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AmazonSageMakerGroundTruth Buildscalableandcost-effectivelabelingworkflows Easily integrate human labelers Get accurate results K E Y F E A T U R E S Automatic labeling via machine learning Ready-made and custom workflows for image bounding box, segmentation, and text Quickly label training data Private and public human workforce Integrated with Deep Learning algorithms in Amazon SageMaker
- 8. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Demo: labeling images for object detection Toseehowlabeledimagescanbeeasilybeusedfortraining,pleaselookat:https://github.com/awslabs/amazon-sagemaker- examples/tree/master/ground_truth_labeling_jobs
- 9. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 10. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Built-in algorithms for Deep Learning [electric_guitar], with probability 0.671 Image classification Object Detection Semantic Segmentation Time-series (DeepAR) Machine translation (seq2seq) Word embeddings (BlazingText) General-purpose embeddings (Object2Vec)
- 11. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Demo: Built-in image classification with transfer learning https://gitlab.com/juliensimon/dlnotebooks/blob/master/sagemaker/06-Image-classification-deeplens.ipynb
- 12. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Demo: Text classification with BlazingText https://github.com/awslabs/amazon-sagemaker- examples/tree/master/introduction_to_amazon_algorithms/blazingtext_text_classification_dbpedia
- 13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 14. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Built-in frameworks: just add your code • Built-in containers for training and prediction. • Available on Github, e.g. https://github.com/aws/sagemaker-tensorflow-containers • Build them, run them on your own machine, customize them, etc. • Local mode: train and predict on your local machine • Script mode: use the same code as on your local machine
- 15. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS: the platform of choice to run Tensorflow 85% of all Tensorflow workloads in the cloud runs on AWS
- 16. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Training ResNet-50 with the ImageNet dataset using our optimized build ofTensorflow 1.11 on a c5.18xlarge instance type is 11x faster than training on the stock binaries. OptimizingTensorflow onAWS C5 instances (Intel Skylake) 65% 90% P3 instances (NVIDIAV100)
- 17. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Apache MXNet: Deep Learning for enterprise developers • Gluon CV Gluon NLP ONNX 2x faster Java Scala
- 18. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Demo: Image classification with Keras/Tensorflow and Keras/MXNet https://gitlab.com/juliensimon/dlnotebooks/tree/master/keras/05-keras-blog-post
- 19. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 20. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. GluonCV https://gluon-cv.mxnet.io https://github.com/dmlc/gluon-cv • State-of-the-art deep learning tools for computer vision • Pre-trained models • Training and fine-tuning scripts • Prototype products, validate new ideas and learn computer vision • Image classification: 50+ models • Object detection: Faster RCNN, SSD,Yolo-v3 • Semantic segmentation: FCN, PSP, DeepLab v3 • Instance segmentation: Mask RCNN • Pose estimation: Simple Pose • Person re-identification (Market1501 dataset) • GANs: Wasserstein GAN, Super Resolution GAN, CycleGAN
- 21. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. GluonNLP https://gluon-nlp.mxnet.io https://github.com/dmlc/gluon-nlp • State-of-the-art deep learning tools for natural language processing • Pre-trained models and embeddings • Training and fine-tuning scripts • Prototype products, validate new ideas and learn NLP • Word embeddings:Word2Vec, FastText, GloVE, BERT • Machine translation: GNMT,Transformer • Sentiment analysis:TextCNN • Text classification: FastText • Language models • Text generation • Natural language inference • Parsing
- 22. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. GluonTS https://gluon-ts.mxnet.io/ https://github.com/awslabs/gluon-ts • State-of-the-art deep learning tools for time-series forecasting • Real and artificial datasets • Loading and iterating over time series datasets • Models ready to be trained • Building blocks to define your own models
- 23. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Demo:GluonCV https://gitlab.com/juliensimon/dlnotebooks/tree/master/gluoncv
- 24. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Getting started http://aws.amazon.com/free https://ml.aws https://aws.amazon.com/sagemaker https://github.com/aws/sagemaker-python-sdk https://github.com/aws/sagemaker-spark https://github.com/awslabs/amazon-sagemaker-examples https://medium.com/@julsimon https://gitlab.com/juliensimon/dlnotebooks
- 25. Merci! © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Julien Simon Global Evangelist, AI & Machine Learning, AWS @julsimon
Notes de l'éditeur
- TODO: Tensorflow 2.0
- 1/ And each one of these frames must be labeled to build a dataset that is can be used for training 2/This means human labelers first need to evaluate each frame and label objects, such as traffic signals, pedestrians, other vehicles, and even the road, so that the model can learn to identify these objects on its own. 3/ Today, customers distribute the labeling tasks across as many as thousands of human labelers, adding significant overhead and cost because of the sheer scale and complexity of managing so many people, and even then the process takes months. 4/ Further, if the labelers incorrectly label objects, the system will learn from the bad information and make inaccurate predictions, leading to real-world consequences, such as a car’s inability to detect a stop sign. 5/ Customers try to filter out errors with audits and redundant reviews, but this increases the time and cost required. TRANSITION: Increasingly, the time, expense, and complexity required for accurate labeling of large datasets have become so prohibitive that customers abandon their efforts to train new types of models that solve sophisticated problems.