Training Kinect

Mihai BudiuMicrosoft Research, Silicon Valley

UCSD CNS 2012 RESEARCH REVIEW February 8, 2012

2

Label body parts in depth map

Parallelizing the Training of the Kinect Body Parts Labeling AlgorithmMihai Budiu, Jamie Shotton, Derek G. Murray, and Mark FinocchioBig Learning: Algorithms, Systems and Tools for Learning at Scale, Sierra Nevada, Spain, December 16-17, 2011

3

Solution: Learn from Data

Classifier

Training examplesMachine learning

4

Big data

• 1M Training examples• 300,000 pixels/image• 100,000 features• <220 tree nodes/tree• 31 body parts• 3 trees

Dryad

DryadLINQ

Decision forest inference

Classifier

Execution

Application

Data-Parallel Computation

5

Storage

Language

ParallelDatabases

Map-Reduce

GFSBigTable

CosmosAzureHPC

Dryad

DryadLINQSawzall,FlumeJava

Hadoop

HDFSS3

Pig, HiveSQL ≈SQL LINQSawzall, Java

6

Dryad = 2-D Piping• Unix Pipes: 1-D

grep | sed | sort | awk | perl

• Dryad: 2-D grep1000 | sed500 | sort1000 | awk500 | perl50

7

Virtualized 2-D Pipelines

8

Virtualized 2-D Pipelines

9

Virtualized 2-D Pipelines

10

Virtualized 2-D Pipelines

11

Virtualized 2-D Pipelines• 2D DAG• multi-machine• virtualized

12

Fault Tolerance

13

LINQ

Dryad

=> DryadLINQ

14

LINQ = .Net+ Queries

Collection<T> collection;bool IsLegal(Key);string Hash(Key);

var results = from c in collection where IsLegal(c.key) select new { Hash(c.key), c.value};

15

DryadLINQ Data Model

Partition

Collection

.Net objects

16

Collection<T> collection;bool IsLegal(Key k);string Hash(Key);

var results = from c in collection where IsLegal(c.key) select new { Hash(c.key), c.value};

DryadLINQ = LINQ + Dryad

C#

collection

results

C# C# C#

Vertexcode

Queryplan(Dryad job)Data

17

Kinect Training Pipeline

20x

18

Partial tree ImagesFeatures

split

New partial tree

Query plan for one tree layer

Parallelize on:• Features• Images• Tree nodes

19

High cluster utilization

Time

Mac

hine

20

CONCLUSIONS

21

Huge Commercial Success

22

Tremendous Interest from Developers

23

Consumer Technologies Push The Envelope

Price: 6000$

Price: 150$

24

Unique Opportunity for Technology Transfer

25

I can finally explain to my sonwhat I do for a living…

26

BACKUP SLIDES

27

10 100 1000 10000 100000 10000000

0.05

0.1

0.158 core machine1000 core cluster

Number of training images (log scale)

core

* h

ours

/ im

age

Training efficiency

28

Cluster usage for one tree

Time (s)

Machine(235)

Prep

roce

ss

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 (f

aile

d)

19

18.3 hours, 137.2 CPU days, 107421 processes, 29.56 TB data, average parallelism=140

1440

0 pr

oces

ses

Nor

mal

izeTr

ee

29

DryadLINQ Language Summary

WhereSelectGroupByOrderByAggregateJoin