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Introduction to reusing
models across languages
Thierry Janssens
Blog: https://www.dataT.be
April 18th, 2018
Ordina, Mechelen
VISIONWORKS
part of
Machine Learning Model Intro
Different algorithms & programming languages
Algorithms
A Model
“a trained model is an artefact
produced by a machine
learning algorithm as part of
training which can be used for
inference or prediction.”
Training a supervised model
train run
software
hardware
train run
Programming languages
Languages
Frameworks (libraries)
What about .net?
• CNTK, now Microsoft Cognitive toolkit
• Januari 2017
• Linux and Windows 64bit
• Python & C#
• ONNX support (early 2018)
• Focus on NN
• Accord.net
• API
• C#
• C. R. Souza, "The Accord.NET Framework,"
• http://accord-framework.net .
• Brazil. Dec 2014.
Facts
It is nontrivial to switch from one AI framework to another
• Differences among the frontend and backend implementations.
• Developers often use more than one framework.
Developers need to maintain multiple backends to guarantee
performance on hardware
• ranging from smartphone chips to data center GPUs.
Chip vendors need to support multiple AI frameworks for every
new chip they build.
The model, independent of soft & hardware
• Type of model
• Parameters
• Target platforms
• Hardware
• Software
train run
platform
hardware
train
Model Process Trained ModelData
platform platform platform
hardwarehardware
Enter the
standards
or not…
PMML
NNVM
ONNX
NNEF
Predictive Model Markup Language
• First appearance in 1997
• Last update in 2014 – version 4.3
• XML-based predictive model interchange format
• Widely accepted
• Over 30 organisations support it
• http://dmg.org/pmml/products.html
• Producers <-> consumers !
• Similar standard = PFA
Sample PMML file
Neural Network Exchange Format
• December 2017, version 1.0
• The Krhonos Group
• Open Specification
• Descriptive model and data
• Text based
• Detailed
• no native support yet
• exporter in C++ on Github
10/04/2018
Sample NNEF file
Open Neural Network eXchange
• December, 2017 version 1.0
• Amazon, Facebook & Microsoft
• Many supporters are joining
• Descriptive, open format.
• ‘binary’ -> protobuf (Google - serializing structured data. )
• Working versions in Python, C# and R
10/04/2018
NNVM
• Amazon and University of Washingthon, first appearance oct
2017
• Non descriptive
• A compiler that translates models to a ‘uniform’ underlying
intermediate representation (IR code).
• For specific hardware (ARM, Nvidia)
A note on Brainscript
• Microsoft
• Is a declarative language to write models
• Part of CNTK
• Descriptive, but in a ‘binary’ format
• Future not clear…
Conclusion
PMML
• for all models
• broad support in different languages.
• New version on its way
ONNX and NNEF only for Neural Networks
• They both need the underlying language to be NNEF or ONNX aware
• NNEF
• More options in its current form
• Backed by a lot of companies
• But no native support yet
• ONNX
• Focusses on ease of use
• Working version in Python & R
• ... C# - requires Early adopters Win 10
Sample
ONNX -> resnet model -> Python ONNX -> R
Netron viewer
Questions?
Thierry Janssens@ordina.be
Blog: https://www.dataT.be
VISIONWORKS
part of

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Machine Learning Standards

  • 1. Introduction to reusing models across languages Thierry Janssens Blog: https://www.dataT.be April 18th, 2018 Ordina, Mechelen VISIONWORKS part of
  • 2. Machine Learning Model Intro Different algorithms & programming languages
  • 4. A Model “a trained model is an artefact produced by a machine learning algorithm as part of training which can be used for inference or prediction.”
  • 5. Training a supervised model train run software hardware train run
  • 8. What about .net? • CNTK, now Microsoft Cognitive toolkit • Januari 2017 • Linux and Windows 64bit • Python & C# • ONNX support (early 2018) • Focus on NN • Accord.net • API • C# • C. R. Souza, "The Accord.NET Framework," • http://accord-framework.net . • Brazil. Dec 2014.
  • 9. Facts It is nontrivial to switch from one AI framework to another • Differences among the frontend and backend implementations. • Developers often use more than one framework. Developers need to maintain multiple backends to guarantee performance on hardware • ranging from smartphone chips to data center GPUs. Chip vendors need to support multiple AI frameworks for every new chip they build.
  • 10. The model, independent of soft & hardware • Type of model • Parameters • Target platforms • Hardware • Software train run platform hardware train Model Process Trained ModelData platform platform platform hardwarehardware
  • 12. Predictive Model Markup Language • First appearance in 1997 • Last update in 2014 – version 4.3 • XML-based predictive model interchange format • Widely accepted • Over 30 organisations support it • http://dmg.org/pmml/products.html • Producers <-> consumers ! • Similar standard = PFA
  • 14. Neural Network Exchange Format • December 2017, version 1.0 • The Krhonos Group • Open Specification • Descriptive model and data • Text based • Detailed • no native support yet • exporter in C++ on Github 10/04/2018
  • 16. Open Neural Network eXchange • December, 2017 version 1.0 • Amazon, Facebook & Microsoft • Many supporters are joining • Descriptive, open format. • ‘binary’ -> protobuf (Google - serializing structured data. ) • Working versions in Python, C# and R 10/04/2018
  • 17. NNVM • Amazon and University of Washingthon, first appearance oct 2017 • Non descriptive • A compiler that translates models to a ‘uniform’ underlying intermediate representation (IR code). • For specific hardware (ARM, Nvidia)
  • 18. A note on Brainscript • Microsoft • Is a declarative language to write models • Part of CNTK • Descriptive, but in a ‘binary’ format • Future not clear…
  • 19. Conclusion PMML • for all models • broad support in different languages. • New version on its way ONNX and NNEF only for Neural Networks • They both need the underlying language to be NNEF or ONNX aware • NNEF • More options in its current form • Backed by a lot of companies • But no native support yet • ONNX • Focusses on ease of use • Working version in Python & R • ... C# - requires Early adopters Win 10
  • 20. Sample ONNX -> resnet model -> Python ONNX -> R Netron viewer