gpu technology conference - digits deep …...2015 using digits deep learning gpu training system...
TRANSCRIPT
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DIGITS DEEP LEARNING GPU TRAINING SYSTEM
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1 Introduction to Deep Learning
2 What is DIGITS
3 How to use DIGITS AGENDA
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Practical DEEP LEARNING Examples
Image Classification, Object Detection, Localization, Action Recognition, Scene Understanding
Speech Recognition, Speech Translation, Natural Language Processing
Pedestrian Detection, Traffic Sign Recognition Breast Cancer Cell Mitosis Detection, Volumetric Brain Image Segmentation
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What is DEEP LEARNING?
Input Result
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Image Classification with DNNs
Training
Inference
cars buses trucks motorcycles
truck
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Image Classification with DNNs
Training
cars buses trucks motorcycles
Typical training run
Pick a DNN design
Input 100 million training images
spanning 1,000 categories
One week of computation
Test accuracy
If bad: modify DNN, fix training
set or update training parameters
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Why are GPUs good for deep learning?
GPUs deliver --
same or better prediction accuracy
faster results
smaller footprint
lower power
Neural Networks GPUs
Inherently
Parallel Matrix
Operations
FLOPS
0 0 4
60
110 28%
26%
16%
12%
7%
2010 2011 2012 2013 2014
bird
frog
person
dog
chair
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Deep Learning Acceleration with GPUs
CPU is 16 core Haswell E5-2698 at 2.3 GHz, with 3.6 GHz Turbo
GPU is NVIDIA Titan X
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Image Recognition
Face Recognition
Gesture Recognition
Video Search & Analytics
Speech Recognition & Translation
Recommendation Engines
Indexing & Search
Use Cases Industry
Image Analytics for Creative
Cloud
Image Classification
Speech/Image Recognition
Recommendation
Hadoop
Search Rankings
Research
WHO IS USING DEEP Learning?
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What is DIGITS
Deep Learning GPU Training System
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DIGITS
Deep Learning GPU Training System
Visualization tool for DNN training
Use default network, import one, or
design your own
Import your training data from disk or
web
Monitor multiple trainings in parallel
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DIGITS
Deep Learning GPU Training System
Who it is for
Deep learning researchers
Automotive
Medical Researchers
Defense
Intelligent Video Analytics
Web Companies
Startups
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DIGITS
Deep Learning GPU Training System
Available at
developer.nvidia.com/digits
Free to use
v1.0 supports classification on images
Future versions: More problem types
and data formats (video, speech)
(Also available on Github for
advanced developers)
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NVIDIA® DIGITS Roadmap
Support for image
classification networks
Visualize layer-wise
responses
Run locally, manage single-
GPU jobs
Framework Support
Features
Version 1 March 2015
Caffe
Additional image analysis
network types
Richer visual analysis tools
Run locally, more job
management options
Continued improvement to
visualization tools
Front end to cluster task
scheduler
Additional frameworks
Version 3 Version 2
API for easy framework
integration
2015
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Using DIGITS
Deep Learning GPU Training System
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How to start DIGITS
Two options
“digits-devserver”
Starts a development server that listens on port 5000
“digits-server”
Gunicorn application that listens on port 8080. You can configure this with nginx,
and access DIGITS @ http://localhost/
DIGITS
Main Console
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DIGITS Workflow
Create your database
Configure your model
Choose a default network, modify
one, or create your own
Main Console
Create your dataset
Configure your Network
Start Training
Choose your database
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DIGITS Workflow
Create your database
Configure your model
Choose a default network, modify
one, or create your own
Main Console
Create your dataset
Configure your Network
Start Training
Choose your database
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Create the Database
Image parameter
options
Create your dataset
Insert the path to your train
and validation set
DIGITS can automatically create your
training and validation set
OR
OR use a URL list
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Create the Database
Image directory
on host machine
images
truck cars
images
person house
planes dogs
images
DIGITS creates your training
and validation set for you.
Insert the path to your images here
cats bikes
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Create the Database
Create Training and Validation Set
Validation
images
truck cars
images
person house
planes dogs
Training
bikes cats
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Create the Database
train
val
Apply rotation, color distortion,
noise to training set
planes cats
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Creating the Database
Training and validation data set information
Job status
information displays
here – progress,
completion, and
errors
Category data information is
posted
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DIGITS Workflow
Create your database
Configure your model
Choose a default network, modify
one, or create your own
Main Console
Create your dataset
Configure your Network
Start Training
Choose your database
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Network Configuration
Insert your network here
Choose a preconfigured network
OR choose a previous configuration
OR add it here
Start training
Select training dataset
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Network Configuration
Select a standard network and start training
OR
Customize a Standard Network
Choose a preconfigured network
Select training dataset
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Network Configuration
Select a standard network and start training
OR
Customize a Standard Network
Choose a preconfigured network
Select training dataset
Make network changes here
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Network Configuration
Select a standard network and start training
OR
Customize a Standard Network
Choose a preconfigured network
Select training dataset
Make network changes here
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Network Configuration
Select a standard network and start training
OR
Customize a Standard Network
Visualize your network Choose a preconfigured network
Select training dataset
Make network changes here
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Network Configuration
Choose a preconfigured network
Start training
Select training dataset
Make network changes here
Select a standard network and start training
OR
Customize a Standard Network
Visualize your network
Start training
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DIGITS
Download
network files
Training status
Accuracy and
loss values
during training
Learning rate
Classification on
the with the
network
snapshots
Classification
Visualize DNN performance in real time
Compare networks
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DIGITS Compare Networks
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DIGITS
Classify Multiple Images
Upload a text file with URLs or
images on the host machine
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Thank you!
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NVIDIA Resources
• Try out GPU Computing: developer.nvidia.com/cuda-education-training
• Subscribe to Parallel Forall blog: devblogs.nvidia.com/parallelforall
• CUDACasts at bit.ly/cudacasts
• Self-paced labs: nvidia.qwiklab.com
• 90-minute labs, simply need a supported web browser
• Sign up as a Registered Developer -www.nvidia.com/paralleldeveloper
• Technical Questions: • NVIDIA Developer forums devtalk.nvidia.com
• Search or ask on stackoverflow.com/tags/cuda
• GPU Technology Conference www.gputechconf.com