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![POST-TRAINING OPTIMIZATIONS
§ Prepare Model for Serving
§ Simplify Network
§ Reduce Model Size
§ Quantize for Fast Matrix Math
§ Some Tools
§ Graph Transform Tool (GTT)
§ tfcompile
After Training
After
Optimizing!
pipeline optimize --optimization-list=[quantize_weights, tfcompile]
--model-type=tensorflow
--model-name=mnist
--model-tag=A
--model-path=./tensorflow/mnist/model
--output-path=./tensorflow/mnist/optimized_model
Linear
Regression](https://image.slidesharecdn.com/pipelineaioverview-171212020249/75/PipelineAI-AWS-SageMaker-Distributed-TensorFlow-AI-Model-Training-and-Serving-December-2017-NIPS-Conference-LA-Big-Data-and-Python-Meetups-14-2048.jpg)
![LIVE, ADAPTIVE TRAFFIC ROUTING
§ A/B Tests
§ Inflexible and Boring
§ Multi-Armed Bandits
§ Adaptive and Exciting!
pipeline traffic-router-split --model-type=tensorflow
--model-name=mnist
--model-tag-list=[A,B,C]
--model-weight-list=[1,2,97]
Adjust
Traffic Routing
Dynamically](https://image.slidesharecdn.com/pipelineaioverview-171212020249/75/PipelineAI-AWS-SageMaker-Distributed-TensorFlow-AI-Model-Training-and-Serving-December-2017-NIPS-Conference-LA-Big-Data-and-Python-Meetups-23-2048.jpg)
Uploaded byChris Fregly
PipelineAI + AWS SageMaker + Distributed TensorFlow + AI Model Training and Serving - December 2017 - NIPS Conference - LA Big Data and Python Meetups
Pipeline.AI is a platform for deploying and optimizing machine learning models at scale. It allows users to package models with their runtime dependencies, perform load testing and optimizations, deploy models to production safely using techniques like canary deployments, and monitor models both offline and online. The platform aims to enable live, continuous model training directly in production environments.
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PipelineAI + AWS SageMaker + Distributed TensorFlow + AI Model Training and Serving - December 2017 - NIPS Conference - LA Big Data and Python Meetups
- 1. PIPELINE.AI: HIGH PERFORMANCEMODEL TRAINING & SERVING WITH GPUS… …AND AWS SAGEMAKER, GOOGLE CLOUD ML, AZURE ML & KUBERNETES! CHRIS FREGLY FOUNDER @ PIPELINE.AI
- 2. RECENT PIPELINE.AI NEWS Sept2017 Dec 2017
- 3. INTRODUCTIONS: ME § ChrisFregly, Founder & Engineer @PipelineAI § Formerly Netflix, Databricks, IBM Spark Tech § Advanced Spark and TensorFlow Meetup § Please Join Our 60,000+ Global Members!! Contact Me chris@pipeline.ai @cfregly Global Locations * San Francisco * Chicago * Austin * Washington DC * Dusseldorf * London
- 4. INTRODUCTIONS: YOU § SoftwareEngineer, Data Scientist, Data Engineer, Data Analyst § Interested in Optimizing and Deploying TF Models to Production § Nice to Have a Working Knowledge of TensorFlow (Not Required)
- 5. PIPELINE.AI IS 100%OPEN SOURCE § https://github.com/PipelineAI/pipeline/ § Please Star 🌟 this GitHub Repo! § Some VC’s Value GitHub Stars @ $15,000 Each (?!)
- 6. PIPELINE.AI OVERVIEW 450,000 DockerDownloads 60,000 Users Registered for GA 60,000 Meetup Members 40,000 LinkedIn Followers 2,200 GitHub Stars 12 Enterprise Beta Users
- 7. WHY HEAVY FOCUSON MODEL SERVING? Model Training Batch & Boring Offline in Research Lab Pipeline Ends at Training No Insight into Live Production Small Number of Data Scientists Optimizations Very Well-Known Real-Time & Exciting!! Online in Live Production Pipeline Extends into Production Continuous Insight into Live Production Huuuuuuge Number of Application Users **Many Optimizations Not Yet Utilized <<< Model Serving 100’s Training Jobs per Day 1,000,000’s Predictions per Sec
- 8. AGENDA § Deploy andTune Models + Runtimes Safely in Prod § Compare Models Both Offline and Online § Auto-Shift Traffic to Winning Model or Cloud § Live, Continuous Model Training in Production
- 9. PACKAGE MODEL +RUNTIME AS ONE § Build Model with Runtime into Immutable Docker Image § Emphasize Immutable Deployment and Infrastructure § Same Runtime Dependencies in All Environments § Local, Development, Staging, Production § No Library or Dependency Surprises § Deploy and Tune Model + Runtime Together pipeline predict-server-build --model-type=tensorflow --model-name=mnist --model-tag=A --model-path=./models/tensorflow/mnist/ Build Local Model Server A
- 10. LOAD TEST LOCALMODEL + RUNTIME § Perform Mini-Load Test on Local Model Server § Immediate, Local Prediction Performance Metrics § Compare to Previous Model + Runtime Variations pipeline predict-server-start --model-type=tensorflow --model-name=mnist --model-tag=A pipeline predict --model-endpoint-url=http://localhost:8080 --test-request-path=test_request.json --test-request-concurrency=1000 Load Test Local Model Server A Start Local Model Server A
- 11. PUSH IMAGE TODOCKER REGISTRY § Supports All Public + Private Docker Registries § DockerHub, Artifactory, Quay, AWS, Google, … § Or Self-Hosted, Private Docker Registry pipeline predict-server-push --image-registry-url=<your-registry> --image-registry-repo=<your-repo> --model-type=tensorflow --model-name=mnist --model-tag=A Push Image To Docker Registry
- 12. CLOUD-BASED OPTIONS § AWSSageMaker § Released Nov 2017 @ Re-invent § Custom Docker Images for Training & Serving ie. PipelineAI Images § Distributed TensorFlow Training through Estimator API § Traffic Splitting for A/B Model Testing § Google Cloud ML Engine § Mostly Command-Line Based § Driving TensorFlow Open Source API (ie. Experiment API) § Azure ML
- 13. TUNE MODEL +RUNTIME AS SINGLE UNIT § Model Training Optimizations § Model Hyper-Parameters (ie. Learning Rate) § Reduced Precision (ie. FP16 Half Precision) § Post-Training Model Optimizations § Quantize Model Weights + Activations From 32-bit to 8-bit § Fuse Neural Network Layers Together § Model Runtime Optimizations § Runtime Configs (ie. Request Batch Size) § Different Runtimes (ie. TensorFlow Lite, Nvidia TensorRT)
- 14. POST-TRAINING OPTIMIZATIONS § PrepareModel for Serving § Simplify Network § Reduce Model Size § Quantize for Fast Matrix Math § Some Tools § Graph Transform Tool (GTT) § tfcompile After Training After Optimizing! pipeline optimize --optimization-list=[quantize_weights, tfcompile] --model-type=tensorflow --model-name=mnist --model-tag=A --model-path=./tensorflow/mnist/model --output-path=./tensorflow/mnist/optimized_model Linear Regression
- 15. RUNTIME OPTION: TENSORFLOWLITE § Post-Training Model Optimizations § Currently Supports iOS and Android § On-Device Prediction Runtime § Low-Latency, Fast Startup § Selective Operator Loading § 70KB Min - 300KB Max Runtime Footprint § Supports Accelerators (GPU, TPU) § Falls Back to CPU without Accelerator § Java and C++ APIs
- 16. RUNTIME OPTION: NVIDIATENSOR-RT § Post-Training Model Optimizations § Specific to Nvidia GPU § GPU-Optimized Prediction Runtime § Alternative to TensorFlow Serving § PipelineAI Supports TensorRT!
- 17. DEPLOY MODELS SAFELYTO PROD § Deploy from CLI or Jupyter Notebook § Tear-Down or Rollback Models Quickly § Shadow Canary Deploy: ie.20% Live Traffic § Split Canary Deploy: ie. 97-2-1% Live Traffic pipeline predict-cluster-start --model-runtime=tflite --model-type=tensorflow --model-name=mnist --model-tag=B --traffic-split=2 Start Production Model Cluster B pipeline predict-cluster-start --model-runtime=tensorrt --model-type=tensorflow --model-name=mnist --model-tag=C --traffic-split=1 Start Production Model Cluster C pipeline predict-cluster-start --model-runtime=tfserving_gpu --model-type=tensorflow --model-name=mnist --model-tag=A --traffic-split=97 Start Production Model Cluster A
- 18. AGENDA § Deploy andTune Models + Runtimes Safely in Prod § Compare Models Both Offline and Online § Auto-Shift Traffic to Winning Model or Cloud § Live, Continuous Model Training in Production
- 19. COMPARE MODELS OFFLINE& ONLINE § Offline, Batch Metrics § Validation + Training Accuracy § CPU + GPU Utilization § Live Prediction Values § Compare Relative Precision § Newly-Seen, Streaming Data § Online, Real-Time Metrics § Response Time, Throughput § Cost ($) Per Prediction
- 20. VIEW REAL-TIME PREDICTIONSTREAM § Visually Compare Real-Time Predictions Prediction Inputs Prediction Results & Confidences Model B Model CModel A
- 21. PREDICTION PROFILING ANDTUNING § Pinpoint Performance Bottlenecks § Fine-Grained Prediction Metrics § 3 Steps in Real-Time Prediction 1. transform_request() 2. predict() 3. transform_response()
- 22. AGENDA § Deploy andTune Models + Runtimes Safely in Prod § Compare Models Both Offline and Online § Auto-Shift Traffic to Winning Model or Cloud § Live, Continuous Model Training in Production
- 23. LIVE, ADAPTIVE TRAFFICROUTING § A/B Tests § Inflexible and Boring § Multi-Armed Bandits § Adaptive and Exciting! pipeline traffic-router-split --model-type=tensorflow --model-name=mnist --model-tag-list=[A,B,C] --model-weight-list=[1,2,97] Adjust Traffic Routing Dynamically
- 24. SHIFT TRAFFIC TOMAX(REVENUE) § Shift Traffic to Winning Model using AI Bandit Algos
- 25. SHIFT TRAFFIC TOMIN(CLOUD CO$T) § Based on Cost ($) Per Prediction § Cost Changes Throughout Day § Lose AWS Spot Instances § Google Cloud Becomes Cheaper § Shift Across Clouds & On-Prem
- 26. AGENDA § Deploy andTune Models + Runtimes Safely in Prod § Compare Models Both Offline and Online § Auto-Shift Traffic to Winning Model or Cloud § Live, Continuous Model Training in Production
- 27. LIVE, CONTINUOUS MODELTRAINING § The Holy Grail of Machine Learning § Q1 2018: PipelineAI Supports Continuous Model Training! § Kafka, Kinesis § Spark Streaming
- 28. PSEUDO-CONTINUOUS TRAINING § Identifyand Fix Borderline Predictions (~50-50% Confidence) § Fix Along Class Boundaries § Retrain Newly-Labeled Data § Game-ify Labeling Process § Enable Crowd Sourcing
- 29. DEMO: TRAIN, DEPLOY,TEST MODEL § https://github.com/PipelineAI/pipeline/ § Please Star 🌟 this GitHub Repo! pipeline predict-server-build --model-type=tensorflow --model-name=mnist --model-tag=A --model-path=./models/tensorflow/mnist/
- 30. THANK YOU!! § https://github.com/PipelineAI/pipeline/ §Please Star 🌟 this GitHub Repo! § Reminder: VC’s Value GitHub Stars @ $15,000 Each (!!) Contact Me chris@pipeline.ai @cfregly