Scaling XGBoost to large scale clusters with fault tolerance and recovery

Aug 21, 2019Download as pptx, pdf1 like206 views

Scaling XGBoost to large scale clusters with fault tolerance and recovery on https://www.meetup.com/XGBoost-Meetup-Group/events/263446497/

Scaling XGBoost to Large-Scale
Clusters with Fault Tolerance and
Recovery
Aug 20, 2019
Chen Qin Big Data - ML @ Uber
1

01 Production ML with Spark / XGBoost at Uber
02 Fault Recovery AllReduce on MapReduce
03 Results
Agenda
2

Typical XGB Pipeline at Uber
USE CASES:
● Uber Map
● Uber Eats
● Uber Freight
● Uber Driver Safety
3

Distributed XGBoost - AllReduce
AllReduce
● Widely used in iterative distributed ML
● All-OR-Nothing Fail-All in ring/tree graph
● Checkpoint-restart
4

Distributed XGBoost - MapReduce
MapReduce
● De facto standard in data processing
● Retryable parallel tasks in bipartite graph
5

AllReduce on MapReduce
“I will suggest to somehow build some
fault tolerance in the job/framework as you
can not guarantee all the machines in the
cluster are healthy … broadly we need to
have fault tolerance built into the job.”
6

Goals
● Improve AllReduce fault tolerance running on top of MapReduce frameworks
● Improve stability of long running XGB jobs on preemptive cluster
● Minimize failure caused additional data reshuffle on large dataset
7

Rabit - Retryable XGB Synchronization
Rabit was initially designed and implemented by Tianqi Chen, Ignacio Cano,
and Tianyi Zhou to solve iterative ML synchronization problem.
Rabit consists two major parts to enable retryable XGB trainer
● AllReduce Consensus Protocol
○ Reach agreement on next transaction
● Peer-to-Peer Recovery
○ Backfill failed trainer catch up
We recently contributed bootstrap cache feature, debugging tools, fixes, etc
8

AllReduce Consensus Protocol
Every node has to be alive and
connected, it sends proposal when it calls
the Rabit API
Allreduce were executed to run reduce
function on those proposals, share same ack
to every node with possible different proposals
Certain proposal(s) were accepted, routing and
message passing were executed
Eventually, every node runs on same phase
where all proposals were accepted at the
same time and executed collectively
9

Failure Recovery - P2P
Allreduce/Broadcasts results were cached up to
one iteration
Backfill latest checkpoint / results from nearest
neighbour
Failed trainer retry (not entire job)
11

Better AllReduce on MapReduce
Recall our goals
Improve AllReduce fault tolerance running on top
of MapReduce frameworks
Improve stability of long running XGB jobs on
preemptive cluster
Minimize failure caused additional data
reshuffle on large dataset
12

Results
● Support very large dataset
● Resilient towards series of failures/preemptions
● Also runs much faster 1? - 4x
● Production roll out: alpha customer tests run
13

Proprietary and confidential ? 2019 Uber Technologies, Inc. All rights reserved. No part of this document may be reproduced or utilized in any
form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage or retrieval systems, without
permission in writing from Uber. This document is intended only for the use of the individual or entity to whom it is addressed and contains
information that is privileged, confidential or otherwise exempt from disclosure under applicable law. All recipients of this document are notified
that the information contained herein includes proprietary and confidential information of Uber, and recipient may not make use of, disseminate,
or in any way disclose this document or any of the enclosed information to any person other than employees of addressee to the extent
necessary for consultations with authorized personnel of Uber.
14

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Scaling XGBoost to large scale clusters with fault tolerance and recovery

1. Scaling XGBoost to Large-Scale Clusters with Fault Tolerance and Recovery Aug 20, 2019 Chen Qin Big Data - ML @ Uber 1

2. 01 Production ML with Spark / XGBoost at Uber 02 Fault Recovery AllReduce on MapReduce 03 Results Agenda 2

3. Typical XGB Pipeline at Uber USE CASES: ● Uber Map ● Uber Eats ● Uber Freight ● Uber Driver Safety 3

4. Distributed XGBoost - AllReduce AllReduce ● Widely used in iterative distributed ML ● All-OR-Nothing Fail-All in ring/tree graph ● Checkpoint-restart 4

5. Distributed XGBoost - MapReduce MapReduce ● De facto standard in data processing ● Retryable parallel tasks in bipartite graph 5

6. AllReduce on MapReduce “I will suggest to somehow build some fault tolerance in the job/framework as you can not guarantee all the machines in the cluster are healthy … broadly we need to have fault tolerance built into the job.” 6

7. Goals ● Improve AllReduce fault tolerance running on top of MapReduce frameworks ● Improve stability of long running XGB jobs on preemptive cluster ● Minimize failure caused additional data reshuffle on large dataset 7

8. Rabit - Retryable XGB Synchronization Rabit was initially designed and implemented by Tianqi Chen, Ignacio Cano, and Tianyi Zhou to solve iterative ML synchronization problem. Rabit consists two major parts to enable retryable XGB trainer ● AllReduce Consensus Protocol ○ Reach agreement on next transaction ● Peer-to-Peer Recovery ○ Backfill failed trainer catch up We recently contributed bootstrap cache feature, debugging tools, fixes, etc 8

9. AllReduce Consensus Protocol Every node has to be alive and connected, it sends proposal when it calls the Rabit API Allreduce were executed to run reduce function on those proposals, share same ack to every node with possible different proposals Certain proposal(s) were accepted, routing and message passing were executed Eventually, every node runs on same phase where all proposals were accepted at the same time and executed collectively 9

10. 10

11. Failure Recovery - P2P Allreduce/Broadcasts results were cached up to one iteration Backfill latest checkpoint / results from nearest neighbour Failed trainer retry (not entire job) 11

12. Better AllReduce on MapReduce Recall our goals Improve AllReduce fault tolerance running on top of MapReduce frameworks Improve stability of long running XGB jobs on preemptive cluster Minimize failure caused additional data reshuffle on large dataset 12

13. Results ● Support very large dataset ● Resilient towards series of failures/preemptions ● Also runs much faster 1? - 4x ● Production roll out: alpha customer tests run 13

14. Proprietary and confidential ? 2019 Uber Technologies, Inc. All rights reserved. No part of this document may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage or retrieval systems, without permission in writing from Uber. This document is intended only for the use of the individual or entity to whom it is addressed and contains information that is privileged, confidential or otherwise exempt from disclosure under applicable law. All recipients of this document are notified that the information contained herein includes proprietary and confidential information of Uber, and recipient may not make use of, disseminate, or in any way disclose this document or any of the enclosed information to any person other than employees of addressee to the extent necessary for consultations with authorized personnel of Uber. 14

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