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OpenPOWER and IBM AI overview
- 1. Sivakumar Krishnasamy &Josiah Samuel IBM Cognitive Systems February 2019 PowerAI: Open-Source Based Enterprise AI Platform
- 2. 2 Artificial Intelligence Mimic Humans Machine Learning Learn with Experience DeepLearning (Neural Networks) Self-Learn with More Data
- 3. 3 Transform & Prep Data(ETL) AI Infrastructure Stack Applications Cognitive APIs (Eg: Watson) In-House APIs Machine & Deep Learning Libraries & Frameworks Distributed Computing Data Lake & Data Stores Segment Specific: Finance, Retail, Healthcare Speech, Vision, NLP, Sentiment TensorFlow, Caffe, SparkML Spark, MPI Hadoop HDFS, NoSQL DBs Accelerated Infrastructure Accelerated Servers Storage PowerAI + Watson Studio
- 4. 4 PowerAI Open-Source Based Enterprise AIPlatform Open Source Frameworks: Supported Distribution Developer Ease-of-Use Tools Faster Training Times via HW & SW Performance Optimizations Integrated & Supported AI Platform 3-4x Speedup for AI Training Ease of Use Tools for Data Scientists GPU-Accelerated Power Servers Storage Caffe SnapML
- 5. 5 AI Changes theCompute Architecture 5x Faster Data Communication with Unique CPU-GPU NVLink High-Speed Connection 1 TB Memory Power 9 CPU V100 GPU V100 GPU 170GB/s NVLink 150 GB/s 1 TB Memory Power 9 CPU V100 GPU V100 GPU 170GB/s NVLink 150 GB/s IBM AC922 Power System Deep Learning Server (4-GPU Config) Store Large Models in System Memory Operate on One Layer at a Time Fast Transfer via NVLink
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- 7. Large AI ModelsTrain ~4 Times Faster POWER9 Servers with NVLink to GPUs vs x86 Servers with PCIe to GPUs 7 3.1 Hours 49 Mins 0 2000 4000 6000 8000 10000 12000 Xeon x86 2640v4 w/ 4x V100 GPUs Power AC922 w/ 4x V100 GPUs Time(secs) Caffe with LMS (Large Model Support) Runtime of 1000 Iterations 3.8x Faster GoogleNet model on Enlarged ImageNet Dataset (2240x2240) Detailed Benchmark Information in Back
- 8. 8 Snap ML -Distributed GPU-Accelerated Machine Learning Library libGLM (C++ / CUDA Optimized Primitive Lib) Distributed Training Logistic Regression Linear Regression Support Vector Machines (SVM) Distributed Hyper- Parameter Optimization More Coming Soon Snap Machine Learning (ML) Library
- 9. Distributed Deep Learning(DDL): Reduce training time: Days to Hours Deep Learning has limited scaling to multiple servers: IBM DDL solves this limitation 1 2 4 8 16 32 64 128 256 4 16 64 256 Speedup Number of GPUs Ideal Scaling DDL Actual Scaling 95%Scaling with 256 GPUS ResNet-50, ImageNet-1K Caffe with PowerAI DDL, Running on Minsky (S822Lc) Power8 System 16 Days 7 Hours PowerAI Distributed Deep Leaning (DDL)9
- 10. PowerAI Enterprise 10 Deep Learning Impact (DLI)Module Data & Model Management, ETL, Visualize, Advise IBM Spectrum Conductor Cluster Virtualization, Auto Hyper-Parameter Optimization, Spark for Data Transformation and Preparation PowerAI: Open Source ML Frameworks Large Model Support (LMS) Distributed Deep Learning (DDL) Auto ML Auto-ML for Images & Video PowerAI Vision Label Train Deploy Accelerated Infrastructure Accelerated Servers Storage
- 11. PowerAI Vision: Auto-DeepLearning for Images & Video 11 Label Image or Video Data Auto-Train AI Model Package & Deploy AI Model
- 12. Semi-Automatic Labeling usingPowerAI Vision 12 Train DL Model Define Labels Manually Label Some Images / Video Frames Manually Label Use Trained DL Model Run Trained DL Model on Entire Input Data to Generate Labels Correct Labels on Some Data Manually Correct Labels on Some Data Repeat Till Labels Achieve Desired Accuracy
- 13. 13 Simplicity: Integrated Platform thatJust Works Curate, Test, and Support Fast Moving Open Source Provide Enterprise Distribution on RedHat Easy to deploy Enterprise AI Platform Ease of Use, Unique Capabilities Faster Model Training Time Large data & model support due to NVLink Acceleration of Analytics & ML AutoML: PowerAI Vision Elastic Training: Scale GPUs as Required Faster Training Times in Single Server Scalability to 100s of Servers (Cluster level Integration) Leads to Faster Insights and Better Economics Platform that Partners can build on Software Partners: H2O, Anaconda SIs, Solution Vendors & Accelerator Partners Open AI Platform w/ Ecosystem Partners Power9 CPU GPU PowerAI IBM SW ISV SW Solution SIs Top Reasons to Choose PowerAI
- 14. Get Started Todaywith Machine & Deep Learning 14 Build a Data Science Team Your Developers Can Learn http://cognitiveclass.ai Identify a Low Hanging Use Case Figure Out Data Strategy Consider Pre-Built AI APIs Hire Consulting Services Get Started Today at www.ibm.biz/poweraideveloper Docker hub https://hub.docker.com/r/ibmcom/powerai/