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1 . STARBUCKS
TECHNOLOGY
Simplifying Deep Learning
with HorovodRunner at Starbucks
2 . About the presenters
Denny Lee is a Technology Vishwanath Subramanian is a
Evangelist with Databricks; he Director of Data and Analytics
is a hands-on data sciences Engineering at Starbucks.
engineer with more than 15
Vishwanath has over 15 years of
years of experience experience with a background in
developing internet-scale distributed systems, product
infrastructure, data platforms, management, software
and distributed systems for
Denny Lee Vishwanath Subramanian engineering and Analytics.
both on-premises and cloud.
His key focuses surround At Starbucks, his key focus is on
solving complex large scale providing Next Generation
data problems – providing not Analytics platforms and enabling
only architectural direction large scale data processing and
but the hands-on
machine learning to enable
implementation of these Business Intelligence and Data
systems. Services across Starbucks.
3 .Scenarios
• Smarter checkout experiences
• Predicting customer traffic • On-Demand one click Provisioning
of Seamlessly integrated
Infrastructure Bill of Material for
Data Science and Intelligent Apps.
• Planogram Analysis
• Secured Connectivity to Enterprise
Data Platform completely
abstracted from Analytics teams.
• And more…
• Solution template containing
organization of deployments to
enable Adhoc experiments, shared
data engineering and Intelligent
App Development
4 .Current State
• Solving complex / streaming image and video analytics is
hard
• It also typically involves distributing the problem to multiple
nodes
• But how do I perform Keras+TensorFlow on a distributed
environment?
5 .Convolutional Neural Networks
6 .Convolutional Neural Networks
28 x 28 28 x 28 14 x 14
0
1
Fully Connected
Dropout
Convolution Convolution Subsampling 8
32 filters 64 filters Stride (2,2) 9
Feature Extraction Classification
7 . DEMO
Running Keras CNNs Standalone
Keras, TensorFlow, HorovodRunner, and MLflow: https://dbricks.co/2D58PDw
8 .Introducing HorovodRunner
• HorovodRunner is a general API to run distributed learning workloads
on Databricks using Uber’s Horovod framework
• On-Demand one click Provisioning
of Seamlessly integrated
• Combining Horovod with Apache Spark’s barrier mode allows longer-
Infrastructure Bill of Material for
running deep learning training jobs Data Science and Intelligent Apps.
• Secured Connectivity to Enterprise
Data Platform completely
• A Horovod MPI job is embedded as a Spark job using barrier
abstracted from Analytics teams.
execution mode • Solution template containing
organization of deployments to
enable Adhoc experiments, shared
data engineering and Intelligent
App Development
9 .HorovodRunner
• HorovodRunner takes a Python
method that contains DL training code
with Horovod hooks
• The first executor collects the IP
address of all of the task executors
using BarrierTaskContext
• Then it triggers a Horovod job using
mpirun.
• Each Python MPI process loads the
pickled program back, deserializes it,
and runs it.
10 .HorovodRunner
driver
workers
11 .HorovodRunner
driver
runCNN():
model.add(Conv2D(32, …))
model.add(Conv2D(64, …))
model.add(MaxPooling2D(…))
model.add(Dense(128, …)
model.add(Dense(10, ’softmax’) workers
optimizer = keras.optimizers \
.Adadelta(1.0)
In standalone or hvd local mode, the code is running on the driver
12 .HorovodRunner
variables driver
runCNN_hvd():
hvd.init()
config.tf.ConfigProto()
workers
# Original code
runCNN()
callbacks = []
With HorovodRunner, we wrap the original code and
code and variables are pushed to the workers
13 .HorovodRunner
driver
workers
With HorovodRunner, we wrap the original code and
code and variables are pushed to the workers
14 .HorovodRunner
driver
workers
With HorovodRunner, we wrap the original code and
code and variables are pushed to the workers
15 .HorovodRunner
driver
workers
With HorovodRunner, we wrap the original code and
code and variables are pushed to the workers
16 .HorovodRunner
driver
workers
Variables are transferred from driver to workers
Code is executed at the workers
17 .Migrate to HorovodRunner
# Primary code differences are noted below
+ hvd.init()
+ config.tfConfigProto()
• On-Demand one click Provisioning
+ config.gpu_options.allow_growth of=Seamlessly
True integrated
Infrastructure
+ config.gpu_options.visible_device_list = Bill of Material for
str(hvd.local_rank())
Data Science and Intelligent Apps.
+ epochs = int(math.ceil(12.0 / hvd.size()))
• Secured Connectivity to Enterprise
+ callbacks = [
Data Platform completely
+ abstracted from Analytics teams.
hvd.callbacks.BroadcastGlobalVariablesCallback(0),
+ ] • Solution template containing
organization of deployments to
enable Adhoc experiments, shared
data engineering and Intelligent
App Development
18 .Comparing the runs using MLflow
• On-Demand one click Provisioning
of Seamlessly integrated
Infrastructure Bill of Material for
Data Science and Intelligent Apps.
• Secured Connectivity to Enterprise
Data Platform completely
abstracted from Analytics teams.
• Solution template containing
organization of deployments to
enable Adhoc experiments, shared
data engineering and Intelligent
App Development
19 . DEMO
Object Detection
Keras, TensorFlow, HorovodRunner, and MLflow
20 . Object Detection Approaches
RCNN (2012)
• Region proposal algorithms - give you a set of regions in the image that are likely
to contain objects.
• Run those images in the bounding boxes to a pre-trained alexnet to compute
the features for that bounding box.
• Support vector machine, to classify what the object in the image is of.
• Run the box through a linear regression model to output tighter coordinates
for the box.
• RCNN -> Fast RCNN ->Faster RCNN
Rich feature hierarchies for accurate object detection and semantic segmentation - Girshick, Donahue, Darrell, Malik
Fast R-CNN - Girshick
Faster R-CNN: Towards Real-Time ObjectDetection with Region Proposal Networks - Ren, He, Girshick, Su
21 .Object Detection Approaches (contd.)
• YOLO – detection as a regression problem
• Not a traditional classifier
• Divide image into grid, each cell is responsible for predicting n bounding boxes
• Output confidence score that predicted bounding box
• Gives a probability distribution of all the classes its trained on
• Confidence score and class prediction is combined is combined into a score for
object classification
• Based on threshold, we determine relevant boxes.
• All the boxes fed to the neural network all at once.
You Only Look Once: Unified, Real-Time Object Detection - Redmon, Divvala, Girshick, Farhadi
22 . https://www.starbucks.com/careers/
TALENTED TECHNOLOGISTS
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