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

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Deep Learning Neural Nets

Source: https://www.slideshare.net/roelofp/deep-learning-as-a-catdog-detector

1 Develop 2 Train 3 DeployGPU

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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

Bitfusion Docker Containers

Ping me:tim@bitfusion.io

Coming Soon!

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.

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Elastic GPU attachment

Partial GPU attachmentExpose finalized models for production inference.

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Inference Server

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