Deep Learning on AWS with Bitfusion
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Why deep learning, the deep learning frameworks, and how Bitfusion can help on AWS.
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Deep Learning on AWS with Bitfusion
- 1. Making Managing and Using Deep Learning and AI Infrastructure Easy, Elastic, and Efficient @timgasper @pspitler3
- 2. Amazon AI Enabled Services DIY Deep Learning
- 3. *
- 4. Deep Learning Neural Nets Source: https://www.slideshare.net/roelofp/deep-learning-as-a-catdog-detector
- 5. 1 Develop 2 Train 3 Deploy GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU Deep Learning Application Lifecycle Locally or in the cloud On CPUs or GPUs (Single, Multi-GPU, or Distributed) To “inference” endpoint API or edge device (mobile app, drone, car, robot, etc.)
- 6. Deep Learning Training vs Inference Source: NVIDIA
- 7. ROBOTICS SOFTWARE ADS & PUBLISHING ENTERTAINMENT AUTOMOTIVE FINANCE PHARMA HEALTHCARE ENERGY EDUCATION SALES CUSTOMER SERVICE MAINTENANCE SECURITY COMPUTER VISION & SPEECH, DRONES, DROIDS PERSONAL ASSISTANTS, CODE GENERATION AUTO-CONTENT CREATION, ADAPTIVE AD TARGETING INTERACTIVE VIRTUAL & MIXED REALITY SELF-DRIVING CARS, CO-PILOT ADVISOR PREDICTIVE PRICE ANALYSIS, DYNAMIC DECISION SUPPORT DRUG DISCOVERY, PROTEIN SIMULATION PREDICTIVE DIAGNOSIS, WEARABLE INTELLIGENCE GEO-SEISMIC RESOURCE DISCOVERY ADAPTIVE LEARNING COURSES ADAPTIVE PRODUCT RECOMMENDATIONS BOTS AND FULLY-AUTOMATED SERVICE DYNAMIC RISK MITIGATION AND YIELD OPTIMIZATION ADAPTIVE THREAT IDENTIFICATION & EARLY DISCOVERY INDUSTRY / FUNCTION AI REVOLUTION
- 9. Deep Learning Frameworks - Popularity
- 10. Deep Learning Frameworks – Pros/Cons Ease to Start Speed Audio/Video Images Scale Out Keras* Intel/Nervana If forced to segment…
- 11. Deep Learning Frameworks – Pros/Cons Deep Learning Benchmarks: https://arxiv.org/abs/1608.07249
- 12. Bitfusion AMIs AWS Marketplace Bitfusion Boost Any Datacenter or Cloud GPU Applications No Code Changes Bitfusion GPU Virtualization • Elastic GPUs • Partial GPUs • GPU Scale Out • GPU HA/Failover Heterogeneous Infrastructure
- 13. Bitfusion Docker Containers Ping me: tim@bitfusion.io Coming Soon!
- 14. CPU Instances Get started quickly with our pre-built deep learning AMIs or containers. Develop locally or on shared CPU nodes with Elastic GPUs. 1 Train and optimize models on shared resources at scale, either with many Elastic GPUs or directly on the GPU cluster. 3 GPU Instances Boost Enabled Environment Local Environment GPU Scale Out AMIs or Docker Containers Leverage Partial GPUs early in the development process for maximum utilization and efficiency. 2 Elastic GPU attachment Partial GPU attachment Expose finalized models for production inference. 4 Inference Server
- 15. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Thank You!