Accelerating the Machine Learning Pipeline on Very Large Datasets with the GPU Data Frame | QCon.AI

Veda Shankar (Senior Developer Advocate) and Wamsi Viswanath (Data Scientist) demonstrates on a very large dataset how to manage a full Machine Learning Pipeline with minimal data exchange overhead between MapD’s SQL engine and H2O’s generalized linear model library (GLM)..

Transcript

1. © MapD 2018 Accelerating the Machine Learning Pipeline on Very Large Datasets with the GPU Data Frame Veda Shankar & Wamsi Viswanath | April 11, 2018
2. © MapD 2018 Veda Shankar Sr Developer Advocate , MapD Community veda.shankar@mapd.com slides: https://speakerdeck.com/mapd Wamsi Viswanath Data Scientist, MapD wamsi@mapd.com
3. © MapD 2018 MapD is the analytics platform created for GPUs
4. © MapD 2018 MapD: software optimized for the fastest hardware
5. © MapD 2018 MapD Core: Query Compilation with LLVM
6. © MapD 2018 MapD Immerse: Hybrid Rendering
7. © MapD 2018 © MapD 2018 7 QUICK DEMO
8. © MapD 2018 Advanced memory management Three-tier caching to GPU RAM for speed and to SSDs for persistent storage 8 SSD or NVRAM STORAGE (L3) 250GB to 20TB 1-2 GB/sec CPU RAM (L2) 32GB to 3TB 70-120 GB/sec GPU RAM (L1) 24GB to 256GB 1000-6000 GB/sec Hot Data Speedup = 1500x to 5000x Over Cold Data Warm Data Speedup = 35x to 120x Over Cold Data Cold Data COMPUTE LAYER STORAGE LAYER Data Lake/Data Warehouse/System Of Record
9. © MapD 2018 The GPU Open Analytics Initiative (GOAI) Seamless data interchange framework in GPU memory 9
10. © MapD 2018 The GPU Open Analytics Initiative (GOAI) Creating common data frameworks to accelerate data science on GPUs 1 0 /mapd/pymapd /gpuopenanalytics/pygdf
11. © MapD 2018 Machine Learning Pipeline 11 Personas in Analytics Lifecycle (Illustrative) Business Analyst Data Scientist Data Engineer IT Systems Admin Data Scientist / Business Analyst Data Preparation Data Discovery & Feature Engineering Model & Validate Predict Operationalize Monitoring & Refinement Evaluate & Decide GPUs
12. © MapD 2018 • We’ve published a few notebooks showing how to connect to a MapD database and use an ML algorithm to make predictions 12 Github ML Examples /gpuopenanalytics/demo-docker /mapd/mapd-ml-demo
13. © MapD 2018 Quick Code Walkthrough
14. © MapD 2018 © MapD 2018 • community.mapd.com Ask questions and share your experiences • mapd.com/cloud Try 14-day free trial, no credit card needed • mapd.com/demos Play with our demos • mapd.com/platform/download-community/ Get our free Community Edition and start playing 14 Next Steps
15. © MapD 2018 THANK YOU Any questions?