Mlse20190208

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1. AE is a domain-agnostic platform for adaptive experimentation developed by Facebook. It allows using techniques like Bayesian optimization and A/B testing to iteratively improve systems based on experimental results. 2. AE supports sequential experimentation techniques including Bayesian optimization to automatically suggest promising parameter configurations. It also allows for human experts to manually select configurations. 3. The core API and backend services of AE are designed to handle large-scale experimentation workloads from companies like Facebook to help optimize systems like the DNN models and web interfaces.

Technology

AE: A domain-agnostic
platform for adaptive
experimentation
2019-02-08@ MLSE NuerIPS2018 Sys for ML

Yuta Kashino ( )
BakFoo, Inc. CEO
Astro Physics /Observational Cosmology
Zope / Python
Realtime Data Platform for Enterprise
PoC/Prototyping

AE: A domain-agnostic platform
for adaptive experimentation
Eytan Bakshy, Lili Dworkin, Brian Karrer, Konstantin Kashin, Benjamin Letham,
Ashwin Murthy, Shaun Singh
Facebook

AE:
• Google Vizier / Ray.Tune
ML + Python
• Vizier: http://bit.ly/2DZoh4I
• Ray.Tune: http://bit.ly/2DZ4D8U
• FB (OSS
• FB Learner Flow http://bit.ly/2DXHGD5 …
• sequential experiment: BO,

AE:
1.
1. DNN Web UI A/B / HHVM JIT
2. HHVM JIT: PHP C++/LLVM http://bit.ly/2DYOqQO
2. human-in-the-loop
3. A/B
4.

1. : BO
• Bayesian Optimization
•
• Spearmint http://bit.ly/2E0pM2p
• BayesOpt http://bit.ly/2DZ1mGB
• MOE http://bit.ly/2DZOCiS
• Hyperopt http://bit.ly/2DYIGXx
• Optuna https://optuna.org/
• Ray.Tune http://bit.ly/2DZ4D8U
• SigOpt https://sigopt.com/
• Vizier http://bit.ly/2DZoh4I
• Kaggle
Hyperopt

1.seqential expriment
• sequential expriment:
• box 1992 http://bit.ly/2E0qPiR
• Bayesian Optimization
Bayesian Optimization

1. :BO
•
• Gaussian
Process(GP)
• A/B
• x: parameter conﬁg.
• x*: param.
• f:
• Expected Improvement(EI)
• EI
http://bit.ly/2GA7Opj
EI(x) =
∫
∞
−∞
max(0,y − y*)p(y|x)dy
y = f(x), y* = f(x*)

1.Hyperopt
• BO : Tree Parzen Estimator (TPE)
• PFN http://bit.ly/2GtAm3s
•
•
• fmin

3.AE:
• Bayesian Optimization
• Constrained Bayesian Optimization with Noisy Experiments http://bit.ly/2E1pJTZ
• Expected Improvement
• botorch
• PyTorch/GPyTorch
•

4.AE:
• Runner Batch
• Online/Ofﬂine
• MySQL
• AnalysisPlan Batch
• AnalysisPlan
• AnalysisPlan

AE: FB
• HHVM JIT
• PlanOut UI A/B
• DNN
•
• FB Video
• FB
http://bit.ly/2GwMAbI

• AE Runner: Python shell
• Ray + Tune
• DNN Kubernetes
• Kubeﬂow + Seldon
• Polyaxon
• 2018 ML
http://bit.ly/2GwocH8
http://bit.ly/2GuCxEa

BakFoo, Inc.
• Software 2.0 http://bit.ly/2DZ4z96
• Sofware 2.0 = ML + Software 1.0

BakFoo, Inc.
• Software
• Kubernetes
• FaaS +
• FaaS: AWS Lambda, Google Functions …
• kubeﬂow polyaxon
• DB/
• Distributed Functions ( )
•

Questions
kashino@bakfoo.com
@yutakashino

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The rise of deep learning over the last decade has led to profound changes in the landscape of the machine learning software stack both for research and production. In this talk we will provide a comprehensive overview of the open-source deep learning frameworks landscape with both a theoretical and hands-on approach. After a brief introduction and historical contextualization, we will highlight common features and distinctions of their recent developments. Finally, we will take at deeper look into three of the most used deep learning frameworks today: Caffe, Tensorflow, PyTorch; with practical examples and considerations worth reckoning in the choice of such libraries.

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Mlse20190208

1. AE: A domain-agnostic platform for adaptive experimentation 2019-02-08@ MLSE NuerIPS2018 Sys for ML

2. Yuta Kashino ( ) BakFoo, Inc. CEO Astro Physics /Observational Cosmology Zope / Python Realtime Data Platform for Enterprise PoC/Prototyping

3. AE: A domain-agnostic platform for adaptive experimentation Eytan Bakshy, Lili Dworkin, Brian Karrer, Konstantin Kashin, Benjamin Letham, Ashwin Murthy, Shaun Singh Facebook

4. AE: • Google Vizier / Ray.Tune ML + Python • Vizier: http://bit.ly/2DZoh4I • Ray.Tune: http://bit.ly/2DZ4D8U • FB (OSS • FB Learner Flow http://bit.ly/2DXHGD5 … • sequential experiment: BO,

5. AE: 1. 1. DNN Web UI A/B / HHVM JIT 2. HHVM JIT: PHP C++/LLVM http://bit.ly/2DYOqQO 2. human-in-the-loop 3. A/B 4.

6. 1. : BO • Bayesian Optimization • • Spearmint http://bit.ly/2E0pM2p • BayesOpt http://bit.ly/2DZ1mGB • MOE http://bit.ly/2DZOCiS • Hyperopt http://bit.ly/2DYIGXx • Optuna https://optuna.org/ • Ray.Tune http://bit.ly/2DZ4D8U • SigOpt https://sigopt.com/ • Vizier http://bit.ly/2DZoh4I • Kaggle Hyperopt

7. 1.seqential expriment • sequential expriment: • box 1992 http://bit.ly/2E0qPiR • Bayesian Optimization Bayesian Optimization

8. 1. :BO • • Gaussian Process(GP) • A/B • x: parameter conﬁg. • x*: param. • f: • Expected Improvement(EI) • EI http://bit.ly/2GA7Opj EI(x) = ∫ ∞ −∞ max(0,y − y*)p(y|x)dy y = f(x), y* = f(x*)

9. 1.Hyperopt • BO : Tree Parzen Estimator (TPE) • PFN http://bit.ly/2GtAm3s • • • fmin

10. 1.AE: Core API

11. 2.AE: human-in-the-loop

12. 3.AE: • Bayesian Optimization • Constrained Bayesian Optimization with Noisy Experiments http://bit.ly/2E1pJTZ • Expected Improvement • botorch • PyTorch/GPyTorch •

13. 4.AE: • Runner Batch • Online/Ofﬂine • MySQL • AnalysisPlan Batch • AnalysisPlan • AnalysisPlan

14. AE: FB • HHVM JIT • PlanOut UI A/B • DNN • • FB Video • FB http://bit.ly/2GwMAbI

15. • AE Facebook

16. • AE Runner: Python shell • Ray + Tune • DNN Kubernetes • Kubeﬂow + Seldon • Polyaxon • 2018 ML http://bit.ly/2GwocH8 http://bit.ly/2GuCxEa

17. BakFoo, Inc. • Software 2.0 http://bit.ly/2DZ4z96 • Sofware 2.0 = ML + Software 1.0

18. BakFoo, Inc. • Software • Kubernetes • FaaS + • FaaS: AWS Lambda, Google Functions … • kubeﬂow polyaxon • DB/ • Distributed Functions ( ) •

19. Questions kashino@bakfoo.com @yutakashino

20. BakFoo, Inc. NHK NMAPS: +

21. BakFoo, Inc. PyConJP 2015 Python

22. BakFoo, Inc.

23. BakFoo, Inc. : SNS +

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