Deep Learning on AWS with Bitfusion
15
Making Managing and Using Deep Learning and AI Infrastructure Easy, Elastic, and Efficient @timgaspe r @pspitler 3
-
Upload
tim-gasper -
Category
Technology
-
view
134 -
download
0
Transcript of Deep Learning on AWS with Bitfusion
Making Managing and Using Deep Learning andAI Infrastructure Easy, Elastic, and Efficient
@timgasper @pspitler3
Amazon AI Enabled Services DIY Deep Learning
*
Deep Learning Neural Nets
Source: https://www.slideshare.net/roelofp/deep-learning-as-a-catdog-detector
1 Develop 2 Train 3 DeployGPU
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.)
Deep Learning Training vs Inference
Source: NVIDIA
ROBOTICSSOFTWAREADS & PUBLISHINGENTERTAINMENTAUTOMOTIVEFINANCEPHARMAHEALTHCAREENERGYEDUCATIONSALESCUSTOMER SERVICEMAINTENANCESECURITY
COMPUTER VISION & SPEECH, DRONES, DROIDSPERSONAL ASSISTANTS, CODE GENERATION
AUTO-CONTENT CREATION, ADAPTIVE AD TARGETING INTERACTIVE VIRTUAL & MIXED REALITYSELF-DRIVING CARS, CO-PILOT ADVISOR
PREDICTIVE PRICE ANALYSIS, DYNAMIC DECISION SUPPORTDRUG DISCOVERY, PROTEIN SIMULATION
PREDICTIVE DIAGNOSIS, WEARABLE INTELLIGENCEGEO-SEISMIC RESOURCE DISCOVERY
ADAPTIVE LEARNING COURSESADAPTIVE PRODUCT RECOMMENDATIONS
BOTS AND FULLY-AUTOMATED SERVICEDYNAMIC RISK MITIGATION AND YIELD OPTIMIZATION
ADAPTIVE THREAT IDENTIFICATION & EARLY DISCOVERY
INDUSTRY / FUNCTION AI REVOLUTION
Source: Bored Panda
Deep Learning Frameworks - Popularity
Deep Learning Frameworks – Pros/Cons
Ease to Start
Speed
Audio/Video
Images
Scale Out
Keras*
Intel/Nervana
If forcedto segment…
Deep Learning Frameworks – Pros/Cons
Deep Learning Benchmarks: https://arxiv.org/abs/1608.07249
Bitfusion AMIsAWS Marketplace
Bitfusion BoostAny Datacenter or Cloud
GPU ApplicationsNo Code Changes
Bitfusion GPUVirtualization• Elastic GPUs• Partial GPUs• GPU Scale Out• GPU HA/Failover
HeterogeneousInfrastructure
CPUInstances
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.
3GPUInstances
Boost Enabled Environment
LocalEnvironment
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 attachmentExpose finalized models for production inference.
4
Inference Server
© 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Thank You!
