Build, train, and deploy Machine Learning models at scale (May 2018)
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Talk @ AWS Summit Stockholm, 16/05/2018
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Build, train, and deploy Machine Learning models at scale (May 2018)
- 1. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Julien Simon Principal Technical Evangelist, AI and Machine Learning @julsimon Build, train, and deploy machine learning models at scale
- 2. 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
- 3. Amazon SageMaker Collect and prepare training data Choose and optimize your ML algorithm Set up and manage environments for training Deploy model in production Scale and manage the production environment Easily build, train, and deploy Machine Learning models Train and tune model (trial and error)
- 4. 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 TensorFlow Caffe2, CNTK, PyTorch, 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
- 5. 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
- 6. 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
- 7. Amazon ECR 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 Amazon SageMaker
- 8. Open Source Containers for TF and MXNet https://github.com/aws/sagemaker-tensorflow-containers https://github.com/aws/sagemaker-mxnet-containers • Customize them • Run them locally for development and testing • Run them on SageMaker for training and prediction at scale
- 9. Bring your own container https://github.com/aws/sagemaker-container-support • Integration with SageMaker Python SDK Estimators, including: • Downloading user-provided Python code • Deserializing hyperparameters (preserving their Python types) • bin/entry.py, the Docker entrypoint required by SageMaker • Reading in the metadata files provided to the container during training • nginx + Gunicorn HTTP server for serving inference requests https://github.com/awslabs/amazon-sagemaker-examples/tree/master/advanced_functionality/scikit_bring_your_own https://github.com/awslabs/amazon-sagemaker-examples/tree/master/advanced_functionality/r_bring_your_own
- 10. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Amazon EC2 C5 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 : Ne xt G e n e ra t ion Co m p ut e -O pt imize d I n st a n ces wit h In te l® Xe o n ® Sca la b le P ro ce sso r AW S Co m p u t e o p t im ize d in st a n ces su p p o rt t h e n e w I n t e l® AVX-5 1 2 a d va n ced in stru ctio n se t , e n a b ling yo u t o m o re e ff icient ly ru n ve ct o r p ro ce ssing wo rklo a d s wit h sin gle a n d d o u ble f lo a t ing p o in t p re cisio n, su ch a s A I / m a ch ine le a rn ing o r vid e o p ro ce ssing. 25% improvement in price/performance over C4
- 11. 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/
- 12. Amazon EC2 P3 Instances • P3.2xlarge, P3.8xlarge, P3.16xlarge • Up to eight NVIDIA Tesla V100 GPUs in a single instance • 40,960 CUDA cores, 5120 Tensor 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
- 13. Digital Globe https://aws.amazon.com/solutions/case-studies/digitalglobe-machine-learning/ • Operating Earth imaging satellites and providing image analysis services. • Over 100 PB of imagery. • Extensive use of Machine Learning on SageMaker to extract information from images. • Working with the AWS ML Lab, built a predictive model reducing cloud storage costs by 50%.
- 14. DEMOS Linear Learner (built-in) – binary classification of MNIST (0 vs 1-9) Image Classification (built-in) – classifying Caltech-256 TensorFlow – classifying MNIST with a CNN Spark on EMR + XGBoost (built-in) – classifying spam Bonus: invoking a SageMaker endpoint with AWS Chalice
- 15. Thank you! Julien Simon PrincipalTechnical Evangelist, AI and Machine Learning @julsimon https://aws.amazon.com/sagemaker https://github.com/awslabs/amazon-sagemaker-examples https://github.com/aws/sagemaker-python-sdk https://github.com/aws/sagemaker-spark https://medium.com/@julsimon https://youtube.com/juliensimonfr
Notes de l'éditeur
- First, you need to collect and prepare your training data to discover which elements of your data set are important. Then, you need to select which algorithm and framework you’ll use. After deciding on your approach, you need to teach the model how to make predictions by training, which requires a lot of compute. Then, you need to tune the model so it delivers the best possible predictions, which is often a tedious and manual effort. After you’ve developed a fully trained model, you need to integrate the model with your application and deploy this application on infrastructure that will scale. All of this takes a lot of specialized expertise, access to large amounts of compute and storage, and a lot of time to experiment and optimize every part of the process. In the end, it's not a surprise that the whole thing feels out of reach for most developers.
- Amazon SageMaker removes the complexity that holds back developer success with each of these steps. Amazon SageMaker includes modules that can be used together or independently to build, train, and deploy your machine learning models.
- SageMaker makes it easy to build ML models and get them ready for training by providing everything you need to quickly connect to your training data, and to select and optimize the best algorithm and framework for your application. Amazon SageMaker includes hosted Jupyter notebooks that make it is easy to explore and visualize your training data stored in Amazon S3. You can connect directly to data in S3, or use AWS Glue to move data from Amazon RDS, Amazon DynamoDB, and Amazon Redshift into S3 for analysis in your notebook. To help you select your algorithm, Amazon SageMaker includes the 10 most common machine learning algorithms which have been pre-installed and optimized to deliver up to 10 times the performance you’ll find running these algorithms anywhere else. Amazon SageMaker also comes pre-configured to run TensorFlow and Apache MXNet, two of the most popular open source frameworks, or you have the option of using your own framework.
- You can begin training your model with a single click in the Amazon SageMaker console. The service manages all of the underlying infrastructure for you and can easily scale to train models at petabyte scale. To make the training process even faster and easier, Amazon SageMaker can automatically tune your model to achieve the highest possible accuracy.
- Once your model is trained and tuned, SageMaker makes it easy to deploy in production so you can start generating predictions on new data (a process called inference). Amazon SageMaker deploys your model on an auto-scaling cluster of Amazon EC2 instances that are spread across multiple availability zones to deliver both high performance and high availability. It also includes built-in A/B testing capabilities to help you test your model and experiment with different versions to achieve the best results. For maximum versatility, we designed Amazon SageMaker in three modules – Build, Train, and Deploy – that can be used together or independently as part of any existing ML workflow you might already have in place.