Power AI introduction
- 1. PowerAI Mandie Quartly Ph.D. WW lead, Machine Learning & High Performance Analytics, OpenPOWER ISV Ecosystem @mandieq
- 2. Artificial Intelligence & Cognitive Applications Big Data Machine Learning Deep Learning (Neural Nets) Cognitive landscape: terms and relationship
- 3. Core concepts: Training & Inference Training • Data intensive: historical data sets • Compute intensive: 100% accelerated • Develop a model for use on the edge as inference Inference • Enables the computer to act in real time • Low Power • Out at the edge
- 6. Industry / Function AI Revolution Automotive Assisted / Autonomous Driving, Navigation, Voice Interface Defense Computer Vision for Drones, Surveillance, Satellites Manufacturing and Robotics Computer Vision for Manufacturing, Data Streams for Instrumented Tools Healthcare Diagnostic Image Recognition (CAT Scan/MRI), Predictive Diagnostics Finance Fraud Detection, Risk Analytics, Improved UX (Natural Language Processing) Energy Seismic Resource Analytics Education Adaptive Learning, Real Time Translation Media and Entertainment Augmented Reality, Intelligent Game Agents, Precision Recommendation Supply Chain Dynamic Routing Optimization Consumer / Mobile App Speech Recognition, Natural Language, Content Tagging and Recognition Retail Computer Vision for Shopping Carts, Precision Recommendation Pharma / Life Sciences Drug Discovery, Genome Analytics, Image Classification (molecules / whole systems)
- 7. Cross-industry benefits of PowerAI • For Marketing executives: Better understanding of client perspectives, recommendations, improve interaction • For IT Executives: Enterprise class support, better price performance and a shared infrastructure • For Data Scientists and Developers: NVLink means new opportunities for innovation, faster access to larger memory, execute larger training models and more iteration … refine, improve • Research Scientists: Explore new ways to understand existing datasets
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- 10. Introducing PowerAI: Get Started Fast with Deep Learning Enabled by High Performance Computing Infrastructure Package of Pre-Compiled Major Deep Learning Frameworks Easy to install & get started with Deep Learning with Enterprise-Class Support Optimised for performance to take advantage of NVLink
- 11. Machine Learning / Deep Learning Software Stack Healthcare Diagnosis / Treatment Banking Fraud analysis Pattern Recognition Self-driving Cars Retail Customer Service Agents Vision / Image processing NLP On-Prem & Cloud Scale-out servers Cloud Deployment DL Frameworks CAFFE, Torch, TensorFlow, … Industry Solutions MLDL Building Blocks Software Libraries Frameworks, Services Infrastructure ... Knowledge Representation Data Layer Accelerated DBsNoSQL DBs Streams Machine Learning Hadoop / HDFS Accelerators GPUs, FPGAs, CAPI Flash, DL Accelerators Unique Power Technologies NVLink, CAPI ML Libraries Spark ML, Spark R, SciKit, Numpy, SystemML, Mahout Cognitive Platforms Watson Cloud Services Amazon, Microsoft, Google, IBM Cloud Distributed Computing Hadoop, Spark, MPI ... Legacy DBs Math Libraries: OpenBLAS/MASS, ATLAS, ESSL, LAPACK, cuDNN, cuBLAS, cuSparse, … PowerAI
- 13. PowerAI platform Deep Learning Frameworks Accelerated Servers and Infrastructure for Scaling Spectrum Scale: High-Speed Parallel File System Scale to Cloud Cluster of NVLink Servers Coming Soon CAFFE NVCaffe TorchIBMCaffe TensorFlow OpenBLAS Bazel Theano Chainer Distributed Frameworks Supporting libraries DL4J NCCL DIGITS
- 14. Who is using PowerAI?
- 15. PowerAI takes advantage of NVLink between POWER8 & Tesla P100 to increase system bandwidth P100 GPU POWER8 CPU GPU Memory System Memory P100 GPU 80 GB/s GPU Memory NVLink 115 GB/s P100 GPU POWER8 CPU GPU Memory System Memory P100 GPU 80 GB/s GPU Memory NVLink 115 GB/s
- 16. S822LC for HPC: recommended configuration for PowerAI 2 Socket, 4 GPU System with NVLink Required: • 2 POWER8 10 Core CPUs • 4 NVIDIA P100 ”Pascal” GPUs • 256 GB System Memory • 2 SSD storage devices • High-speed interconnect (IB or Ethernet, depending on infrastructure) Optional: • Up to 1 TB System Memory • PCIe attached NVMe storage (aka Minsky)
- 17. TensorFlow on Tesla P100: PowerAI is 30% faster (larger is better) S822LC - Optimized E5-2640v4 0 50 100 150 200 250 300 Images Processed (Images/Sec) IBM S822LC 20-cores 2.86GHz 512GB memory / 4 NVIDIA Tesla P100 GPUs / Ubuntu 16.04 / CUDA 8.0.44 / cuDNN 5.1 / TensorFlow 0.12.0 / Inception v3 Benchmark (64 image minbatch) Intel Broadwell E5-2640v4 20-core 2.6 GHz 512GB memory / 4 NVIDIA Tesla P100 GPUs/ Ubuntu 16.04 / CUDA 8.0.44 / cuDNN 5.1 / TensorFlow 0.12.0 / Inception v3 Benchmark (64 image minbatch)
- 18. Deep Learning on Minsky x86 with 4x M40 / PCIe Power8 with 4x P100 / NVLink 0 20 40 60 80 100 120 140 AlexNet using Caffe Time to top-1, 50% Accuracy (Lower is better) x86 with 8x M40 / PCIe Power8 with 4x P100 / NVLink 0:00 1:12 2:24 3:36 4:48 6:00 7:12 8:24 VGGNet using BVLC Caffe vs IBM Caffe Time to Top-1 50% accuracy: (Lower is better) S822LC/HPC with 4 Tesla P100 Tesla GPUs is 24% Faster than 8x Tesla M40 GPUs S822LC/HPC with 4 Tesla P100 GPUs is 2.2x Faster than 4x Tesla M40 GPUs IBM S822LC 20-cores 2.86GHz 512GB memory / 4 NVIDIA Tesla P100 GPUs / Ubuntu 16.04 / CUDA 8.0.44 / cuDNN 5.1 / IBM Caffe 1.0.0-rc3 / Imagenet Data Intel Broadwell E5-2640v4 20-core 2.6 GHz 512GB memory / 4 or 8 NVIDIA Tesla M40 GPUs / Ubuntu 16.04 / CUDA 8.0.44 / cuDNN 5.1 / BVLC Caffe 1.0.0-rc3 / Imagenet Data Trainingtimes(mins)
- 19. NVLink and P100 advantage: reducing communication time, incorporating the fastest GPU for deep learning • NVLink reduces communication time and overhead • Data gets from GPU-GPU, Memory-GPU faster, for shorter training times x86 based GPU system POWER8 + Tesla P100+NVLink ImageNet / Alexnet: Minibatch size = 128 170 ms 78 ms IBM advantage: data communication and GPU performance
- 21. PowerAI Simplifies Access and Installation • Tested, binary builds of common Deep Learning frameworks for ease of implementation • Simple, complete installation process documented on ibm.biz/powerai • Future focus on optimizing specific packages for POWER: OpenBLAS, NVIDIA Caffe, TensorFlow, and Torch PowerAI OS Ubuntu 16.04 CUDA 8.0 cuDNN 5.1 Built w/ MASS Yes OpenBLAS 0.2.19 Caffe 1.0 rc3 NVIDIA Caffe 0.14.5 + 0.15.3 IBM Caffe 1.0 rc3 Chainer 1.18 NVIDIA DIGITS 5 Torch 7 Theano 0.8.2 TensorFlow 0.12.0 GPU 4 x P100 Base System S822LC/HPC
- 22. Easy Installation… Up and running... Software repository Setup The Deep Learning packages are published as an Ubuntu package that sets up an installation repository on the local machine. The repository can be enabled as follows: Download the latest mldl-repo-local .deb file from https://download.boulder.ibm.com/ibmdl/pub/software/server/mldl/ Install the repository package: $ sudo dpkg -i mldl-repo-local*.deb Update the package cache $ sudo apt-get update Installing all frameworks at once All the Deep Learning frameworks can be installed at once using the power-mldl meta-package: $ sudo apt-get install power-mldl Installing frameworks individually The Deep Learning frameworks can be installed individually if preferred. The framework packages are: ● caffe-bvlc - Berkeley Vision and Learning Center (BVLC) upstream Caffe, v1.0.0rc3 ● caffe-ibm - IBM Optimized version of BVLC Caffe, v1.0.0rc3 ● caffe-nv - NVIDIA fork of Caffe, v0.15.13 and v0.14.5 ● chainer - Chainer, v1.18.0 ● digits - DIGITS, v5.0.0-rc.1 ● tensorflow - Google TensorFlow, v0.12.0 ● theano - Theano, v0.8.2 ● torch - Torch, v7 Each can be installed with: $ sudo apt-get install <framework> Tuning recommendations Recommended settings for optimal Deep Learning performance on the S822LC for High Performance Computing are: Enable Performance Governor $ sudo apt-get install linux-tools-common cpufrequtils $ sudo cpupower -c all frequency-set -g performance Enable GPU persistence mode Use nvidia-persistenced ( http://docs.nvidia.com/deploy/driver-persistence/index .html ) or $ sudo nvidia-smi -pm ENABLED Set GPU memory and graphics clocks $ sudo nvidia-smi -ac 715,1480 For TensorFlow, set the SMT mode $ sudo ppc64_64 --smt=2
- 23. PowerAI in NIMBIX Cloud https://power.jarvice.com/
- 24. Get started today ● Things are moving VERY quickly in this space ● Where could you take advantage of AI capabilities? ● Find out more at: ibm.biz/powerai Mandie Quartly @mandieq
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