Spark Based Distributed Deep Learning Framework For Big Data Applications

Thesis Topic:
Spark based Distributed Deep
Learning Framework for Big
Data Applications
SMCC
Lab
Social Media Cloud Computing
Research Center
Prof Lee Han-Ku

III Challenges in Distributed Computing
IV Apache Spark
V Deep Learning in Big Data
VI Proposed System
I Motivation
II Introduction
VII Experiments and Results
Conclusion
Outline

Solution: Cluster
Data Parallelism
(partitioning data)

Wait a minute!?
D << N
D (dimension/number of features) = 1,300
N (size of training data) = 5,000,000

What if : Feature size is almost
as huge as dataset
D ~ N
D = 1,134,000
N = 5,000,000

Further solution
Model Parallelism
CPU 1 CPU 2 CPU 3 CPU 4

 Computer Vision: Face Recognition
 Finance: Fraud Detection …
 Medicine: Medical Diagnosis …
 Data Mining: Prediction, Classification …
 Industry: Process Control …
 Operational Analysis: Cash Flow Forecasting …
 Sales and Marketing: Sales Forecasting …
 Science: Pattern Recognition …
 …
Introduction
Applications of Deep Learning

Map
ping
Mountain
River
City
Sun
Blue Cloud
Input Layer
Output LayerHidden
Layers
Some Examples

Map
ping
Input Layer Output Layer
The Face
Successfully
Recognized
Hidden
Layers
Some Examples

Map
ping
Hidden
Layers
Input Layer Output Layer
love
Romeo
kiss
hugs
…………
Happy End
Romance
Detective
Historical
Scientific
Technical
Some Examples

https://theclevermachine.wordpress.com/tag/backpropagation/
How it works?

Challenges
Distributed Computing Complexities
 Heterogeneity
 Openness
 Security
 Scalability
 Fault Handling
 Concurrency
 Transparency

Apache Spark
 Most Machine Learning algorithms are inherently iterative because
each iteration can improve the results
 With disk based approach each iteration’s output is written to disk
which makes reading back slow
 In Spark, the output can be cached in memory which makes reading
very fast (distributed cache)
Hadoop execution flow
Spark execution flow

 Initially started at UC Berkeley in 2009
 Fast and general purpose cluster computing system
 10x (on disk) – 100x (in-memory) faster than Hadoop
 Most popular for running Iterative Machine Learning Algorithms
 Provides high level API in
 Java
 Scala
 Python
 R
 Combine SQL, streaming, and complex analytics.
 Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can
access diverse data sources including HDFS, Cassandra, HBase, and
S3.
Apache Spark

Spark Stack
 Spark SQL
 For SQL and unstructured data processing
 Spark Streaming
 Stream processing of live data streams
 MLLib
 Machine Learning Algorithms
 GraphX
 Graph Processing
Apache Spark

 "Deep learning" is the new big trend in Machine Learning. It
promises general, powerful, and fast machine learning, moving us
one step closer to AI.
 An algorithm is deep if the input is passed through several non-linear
functions before being output. Most modern learning algorithms
(including Decision Trees and SVMs and Naive Bayes) are "shallow".
 Deep Learning is about learning multiple levels of representation and
abstraction that help to make sense of data such as images, sound,
and text.
Deep Learning in Big Data

 A key task associated with Big Data Analytics is information retrieval
 Instead of using raw input for data indexing, Deep Learning can be
utilized to generate high-level abstract data representations which will
be used for semantic indexing.
 These representations can reveal complex associations and factors
(especially if raw input is Big Data), leading to semantic knowledge
and understanding, for example by making search engines work more
quickly and efficiently.
 Deep Learning aids in providing a semantic and relational
understanding of the data.
Semantic Indexing

 The learnt complex data representations contain semantic and
relational information instead of just raw bit data, they can directly
be used for semantic indexing when each data point is presented by a
vector representation, allowing for a vector-based comparison which
is more efficient than comparing instances based directly on raw
data.
 The data instances that have similar vector representations are likely
to have similar semantic meaning.
 Thus, using vector representations of complex high-level data
abstractions for indexing the data makes semantic indexing feasible

Traditional methods for representing word vectors
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 … ]
[0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 … ]
[government debt problems turning into banking crisis as has happened]
[saying that Europe needs unified banking regulation to replace the old]
Motel Say Good Cat Main
Snake Award Business Cola Twitter
Google Save Money Florida Post
Great Success Today Amazon Hotel
…. …. …. …. ….
Keep word by its context

Word2Vec
(distributed representation of words)
•The cake was just good
(trained tweet)
Training
data
•The cake was just great
(new unseen tweet)Test data

Great ( 0.938401)
Awesome ( 0.8912334 )
Well ( 0.8242320 )
Fine ( 0.7943241 )
Outstanding ( 0.71239 )
Normal ( 0.640323 )
…. ( ….. )
Good ( 1.0 )
They are close in
vector space
Word2Vec
(distributed representation of words)
•The cake was just good
(trained tweet)
Training
data
•The cake was just great
(new unseen tweet)Test data

Proposed System should deal with:
 Concurrency
 Asynchrony
 Distributed Computing
 Parallelism
 model parallelism
 data parallelism
Proposed System

1 2 3 4 5 6
Data
Shard 1
Data
Shard 1
Data
Shard 1
Model
Replicas
Parameter Servers
Master
Spark
Driver
HDFS
data nodes
Architecture

Domain Entities
 Master
 Start
 Done
 JobDone
 DataShard
 ReadyToProcess
 FetchParameters
 ParameterShard
 ParameterRequest
 LatestParameters
 NeuralNetworkLayer
 DoneFetchingParameters
 Gradient
 ForwardPass
 BackwardPass
 ChildLayer

Backward Pass
Child Layer Gradient Fetching Parameters
Forward Pass
Ready To Process
MASTER
Deep Layer Worker
Parameter Shard
Worker
Job Done
Start
Data Shard Worker
Fetch Parameters
Parameter Request
Latest Parameters
Output
Proposed System
Class Hierarchy
Class Hierarchy

Data Shards (HDFS)
X1 𝑊11 𝑊12 𝑊12 𝑊14 𝑊15 𝑊16 …
X2 𝑊21 𝑊22 … … 𝑊26 …
X3 𝑊31 𝑊32 … … 𝑊36 …
… … … … … … … …
h1 𝑊11 𝑊12 𝑊12 𝑊14 𝑊15 𝑊16 …
h2 𝑊21 𝑊22 … … 𝑊26 …
h3 𝑊31 𝑊32 … … 𝑊36 …
… … … … … … … …
Corresponding
Model Replica
Input-to-hidden
parameters
Hidden-to-output
parameters
Data Shards

W W W W W W W W W W W W W W W W W W W W W W W W W W W W W . . . W W
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
.
.
.
X
X
Parameter Server

1.Start
Master
Client
Data Shards (HDFS)
Parameter Shards (HDFS)
Initialize
Parameters
Node1 Node2 Node3 Node4
Initialization
Workflow

Master
Client
Data Shards
Parameter Shards
2. Ready
To Process
Initialization
Initialize Neural
Network Layers
Initialize
Parameters
1.Start
Workflow

Master
Client
Data Shards
Parameter Shards
2.Ready
To Process
5.Parameter Request
4.FetchParams
1.Start
Workflow

Master
Client
Data Shards
Parameter Shards
2.Ready
To Process
Initial
Parameters
5.Parameter Request
4.FetchParams
6.Latest Parameters
1.Start
Workflow

Master
Client
Data Shards
Parameter Shards
2.Ready
To Process
7.DoneFetchingParams
5.Parameter Request
6.Latest Parameters
1.Start
Workflow

Master
Client
Data Shards
Parameter Shards
2.Ready
To Process
8.Forward
5.Parameter Request
6.Latest Parameters
Training Data
Examples
One by one
1.Start
Workflow

1.Start
Master
Client
Data Shards
Parameter Shards
2.Ready
To Process
9.Gradient
10.Latest Parameters
8.Forward
7.Backward
7.Backward
Logging
11. Output
Workflow

Master
Client
Data Shards
Parameter Shards
2.Gradient
5.Backward
5.Backward
Training(Learning) Phase
1.Forward
3.Latest Parameters
Logging 6. Output
Workflow

7.JobDone
Master
Client
Data Shards
Parameter Shards
6.Done
3.Backward
3.Backward
Training is Done
1.Gradient
2.Latest Parameters
Workflow
Logging 4. Output

Model Replica 1
Model Replica 2
Model Replica 3
Model Replica 4
Model Replica 5
Model Replica 6
Corresponding
Parameter
Shard
𝑥0
𝑥1
𝑥2
𝑥3
𝑥4
𝑥0𝑥1𝑥2𝑥3
𝑥4
Learning Process

Cluster Nodes Single Node
3D view of the Model (Convergence point is the global minimum)
Global minimum
is the target

procedure STARTASYNCHRONOUSLYFETCHINGPARAMETERS(parameters)
parameters ← GETPARAMETERSFROMPARAMSERVER()
procedure STARTASYNCHRONOUSLYPUSHINGGRADIENTS(accruedgradients)
SENDGRADIENTSTOPARAMSERVER(accruedgradients)
accruedgradients ← 0
main
global parameters, accruedgradients
step ← 0
accruedgradients ← 0
while true do
if (step mod 𝑁𝑓𝑒𝑡𝑐ℎ) == 0
then STARTASYNCHRONOUSLYFETCHINGPARAMETERS(parameters)
data ← GETNEXTMINIBATCH()
gradient ← COMPUTEGRADIENT(parameters, data)
accruedgradients ← accruedgradients + gradient
parameters ← parameters − α ∗ gradient
if (step mod npush) == 0
then STARTASYNCHRONOUSLYPUSHINGGRADIENTS(accruedgradients)
step ← step + 1
SGD Algorithm

Sentiment Analysis
Experiments &Results

•Training
Data
Tokenizer
•Tokenized
Data
Count
Vector •Word2Vec
(distributed
represent)
Output
•Nonlinear
classifier
Deep Net
Word2Vec - Deep Net

Assessment Cluster Specification (10 nodes)
CPU Intel Xeon 4 Core DP E5506 2.13GHz *2E
RAM 4GB Registered ECC DDR * 4EA
HDD 1TB SATA-2 7,200 RPM
OS Ubuntu 12.04 LTS 64bit
Spark Spark-1.6.0
Hadoop(HDFS) Hadoop 2.6.0
Java Oracle JDK 1.8.0_61 64 bit
Scala Scala-12.9.1
Python Python-2.7.9
Cluster Specs

0
5
10
15
20
25
30
2 nodes 4 nodes 6 nodes 8 nodes 10 nodes
Time Performance vs. Number of nodes
RunTime(mins)
Number of Nodes in Cluster
Performance

50
40
30
20
10
0
Iterations
ErrorRate
Accuracy

N p/n Sample from positive and negative tweets corpus
1 0 Very sad about Iran.
2 0 where is my picture i feel naked
3 1 the cake was just great!
4 1 had a WONDERFUL day G_D is GREAT!!!!!
5 1 I have passed 70-542 exam today
6 0 #3turnoffwords this shit sucks
7 1 @alexrauchman I am happy you are staying around here.
8 1 praise God for this beautiful day!!!
9 0 probably guna get off soon since no one is talkin no more
10 0 i still Feel like a Douchebag
11 1 Just another day in paradise. ;)
12 1 No no no. Tonight goes on the books as the worst SYTYCD results
show.
13 0 i couldnt even have one fairytale night
14 0 AFI are not at reading till sunday this sucks !!
Samples

 The main goal of this work was to build Distributed Deep Learning
Framework which is targeted for Big Data applications. We managed
to implement the proposed system on top of Apache Spark, well-
known general purpose data processing engine.
 Deep network training of proposed system depends on well-known
distributed Stochastic Gradient Descent method, namely Downpour
SGD.
 The system can be used in building Big Data application or can be
integrated to Big Data analytics pipeline as it showed satisfactory
performance in terms of both time and accuracy.
 However, there are a lot of room for further enhancement and new
features.
Conclusion

Spark Based Distributed Deep Learning Framework For Big Data Applications

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