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Deep Learning using OpenPOWER
- 1. Chekuri S. Choudary CognitiveSystems Specialist, IBM Chekuri.Choudary@ibm.com November 10, 2018 Deep Learning On OpenPOWER Academic Discussion Group Workshop 2018 Dallas TX
- 2. Deep Learning InOcean Engineering Use Case 1: Whale Conservation Using AIS and Acoustic Data Use Case 2: Satellite Automatic Identification System (S-AIS Data) HPC Infrastructure Design for AIS Applications Cognitive Applications of AIS Data
- 3. Deep Learning forWhale Conservation • Fewer than 500 Atlantic Right Whales are alive • 17 right whales died last summer in Atlantic Ocean • Acoustic sensors installed in ocean waters • Whales sounds are manually classified by the marine biologists A dead North Atlantic right whale, spotted off the coast of Virginia on Jan. 22, 2018.
- 4. Deep Learning forWhale Conservation Segmentation and Frequency Domain Conversion Raw Acoustic Files Ocean Sound Classification Algorithm AIS DatabaseOcean Acoustics Database Ship Collision Detection, Whale Conservation Strategies • CNNs on Spectrograms • LSTM for Unclassified Whales
- 5. Satellite Automatic IdentificationSystem (S-AIS) Data Reference: MEOPAR – exactEarth – Dalhousie S-AIS Data Initiative, Casey Hilliard, Dr. Stan Matwin, and Dr. Ron Pelot • Tracking system on ships • Most ships transmit data to coast and satellites at fixed time intervals • Unique identity, type, position, speed etc. • S-AIS provide a holistic view of the oceans • Incredibly useful for ocean management and monitoring
- 6. HPC Infrastructure DesignFor S-AIS Applications Local storage IBM PowerAI, IBM Spectrum Conductor (Spark applications with GPU Scheduling) 1) Populating Database using one or more nodes 2) Querying Database. One node if PostGres and MapD. Multiple nodes if using Kinetica. Optionally outputs HDF5, LMDB, and TFRecord files that can be used by Caffe, Tensorflow, and other distributed deep learning applications. The query results can also be fed directly to applications. 3) Ocean data Analytics based on Spark, MapReduce, MPI etc. IBM Spectrum LSF (MPI applications) IBM ESS Storage PostGres with POST-STROM GPU plugin (5-10 TB) (single node) MapD (single node) (5 – 10 TB) Kinetica (Distributed) (5 – 10 TB) SOLR (Distributed) (10 – 20 TB) HDF5 files LMDB files TFRecords files POWER8 GPUs Local storage POWER8 GPUs Local storage POWER8 GPUs Local storage POWER8 GPUs
- 7. 7 Enterprises Embracing Open- SourceAI Software Enterprises building machine learning teams Most using Open-Source software: TensorFlow is most popular Today: proofs of concept/technology Tomorrow: congratulations! You’re in production 57% AI Developed On-Premise 42% Cloud Hosted Modeling 57% Developed using on-prem resources
- 8. 8 Original design Simplifythe process of installing and running optimized deep learning on Power Integrated AI Platform Caffe
- 9. 9 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
- 10. 10 PowerAI Features • TraditionalMachine Learning at Scale (SnapML) • Hyperparameter Search & Optimization • Distributed Training Across Multiple GPUs & Servers • Large Model Support • Handling Multiple Training Jobs Concurrently • Data Preparation Tools • Training Visualization • Model Selection
- 11. Distributed Deep Learning(DDL) Using the Power of 100s of Servers 16 Days Down to 7 Hours: Near Ideal Scaling to 256 GPUs and Beyond 1 System 64 Systems 16 Days 7 Hours ResNet-101, ImageNet-22K, Caffe with PowerAI DDL, Running on Minsky (S822Lc) Power System 58x Faster
- 12. What is SnapMachine Learning? IBM Research - Zurich / Introduction to Snap Machine Learning / May 2018 / © 2018 IBM Corporation 12 Framework Models GPU Acceleration Distributed Training Sparse Data Support scikit-learn ML No No Yes Apache Spark* MLlib ML No Yes Yes TensorFlow** ML/{DL} Yes Yes Limited Snap ML GLMs Yes Yes Yes Machine Learning (ML) Deep Learning (DL) GLMs Snap ML: A new framework for fast training of GLMs * The Apache Software Foundation (ASF) owns all Apache-related trademarks, service marks, and graphic logos on behalf of our Apache project communities, and the names of all Apache projects are trademarks of the ASF. **TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.
- 13. Example: snap-ml-mpi IBM Research- Zurich / Introduction to Snap Machine Learning / May 2018 / © 2018 IBM Corporation Describe application using high-level Python code. Launch application on 4 nodes using mpirun (4 GPUs per node):
- 14. Data DNN Model Monitor &Prune Select Best Hyperparameters Job n Job 2 Job 1 Auto Hyper-Parameter Tuning in PowerAI IBM Spectrum Conductor with Spark GPU-Accelerated Power9 Servers • Data scientists run 100s of jobs with different Hyper-parameters • Learning rate, Decay rate, Batch size, Optimizers (GradientDecedent, Adadelta, Momentum, RMSProp, ..) • Auto-Tuner searches for good hyper-parameters by launching 10s of jobs & selecting the best ones • 3 search approaches: Random, Tree-based Parzen Estimator (TPE), Bayesian PowerAI Auto-Tuner (DL Insight)
- 15. Runtime Training Visualization Monitor,Analyze,& Optimize 15
- 16. Data Preparation forDeep Learning 16 Import from different formats Transform, split and shuffle
- 17. IBM Model Library ibmcws-deep-learning-caffe-samplesibmcws-deep-learning-tensorflow-samples Talk to us if you need an new optimized model @ our Slack Channel http://ibm.biz/joinslackcws
- 18. Training deep learning workloads Elastic Distributed Multi-tenantlarge scale distributed
- 19. Train Larger MoreComplex Models 19 Large Model SupportTraditional Model Support Limited memory on GPU forces tradeoff in model size / data resolution Use system memory and GPU to support more complex and higher resolution data CPUDDR4 GPU PCIe Graphics Memory System Bottleneck Here POWER CPU DDR4 GPU NVLink Graphics Memory POWER NVLink Data Pipe Enterprise