ONNX Overview and MLflow

1. ONNX and MLflow
Andre Mesarovic
February 26, 2020
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2. Open Neural Network Exchange
Lingua Franca of Deep Learning
Hagay Lupesko, Engineering Leader, AI and ML at Facebook
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4. Agenda
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• What is ONNX?
• Who is using ONNX?
• ONNX and MLflow

5. ONNX
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• Open interoperable format to represent ML models
• Model portability across frameworks
• Decouple training and scoring
• Interoperability between frameworks, compilers, runtimes,
and hardware accelerators
• Accelerated inferencing on cloud and edge (IOT)

6. ONNX
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• Wide support by ML industry vendors
• Bringing the worlds of AI research and products closer
together so that they innovate and deploy faster
• Train in one framework and score in another
• Optimize models for deployments on multiple platforms
• Originally for DL models, but now covers non-DL (sklearn)

7. ONNX Pillars
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• ONNX format standard - Linux Foundation - Nov. 2019
• ONNX converters
○ TensorFlow to ONNX, ONNX to TensorFlow, etc.
• ONNX Runtime
○ MSFT ONNX Runtime - open-source
○ Nvidia TensorRT

8. ONNX Timelines
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• Founded in Sep. 2017 by Microsoft and Facebook
• AWS, Intel, AMD and NVIDIA support by Dec. 2017
• Enhanced Facebook F8 support in May 2018
• ONNX Runtime open-sourced by MSFT in Dec. 2018
• ONNX joins Linux Foundation

9. ONNX Community
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10. ONNX Training Frameworks
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11. ONNX
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12. ONNX Sample Optimizing Pipeline
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13. ONNX Model Zoo
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• Collection of pre-trained state-of-the-art DL models
• Central repository of reusable models
• Python Jupyter notebooks
• Image classification, natural language, vision, etc.
• https://github.com/onnx/models

14. Intermediate Representation (IR)
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• IR is common concept in compilers and virtual machines
• Two key features of an IR:
○ Capture source code without loss of information
○ Be independent of target platform
• Common representation for source tensor formats
• Providers optimize IR for target hardware devices

15. ONNX IR
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• 116 operators
• Acos, BatchNormalization, HardSigmoid, Relu, Softmax, etc.
• Export models is reasonably robust
• Import is less robust due to unimplemented ops
• https://github.com/onnx/onnx/blob/master/docs/Operators.
md

16. Optimizer Processing
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• Fusion - Fuse multiple ops
• Data layout abstraction
• Data reuse - reuse for subgraphs
• Graph scheduling - run similar subgraphs in parallel
• Graph partitioning - Partition subgraphs to run on different
devices
• Memory management

17. Computation Graph
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import numpy as np
a = np.ones(10)
b = np.ones(10) * 2
c = b * a
d = c + 1

18. ONNX and MLeap
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• Think of ONNX as MLeap on steroids
• Both address ML model interoperability but...
• MLeap focuses on real-time scoring with Spark ML
• ONNX support for Spark ML is weak
• MLeap: 2 person company Combust no longer supporting
• ONNX backed by large number of big ML vendors

19. Microsoft
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• Focus on WinML and ML.net
• Office, Windows, Cognitive Services, Skype, Bing Ads, PowerBI
• 100s millions of devices, serving billions of requests
• 2019 MSFT announced that Windows 10 will have ONNX
embedded in the OS to include the ability to run ML models
natively with hardware acceleration

20. Microsoft ONNX Runtime Usage
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• Used in millions of Windows devices and powers core models
across Office, Bing, and Azure
• Average of 2x performance gains
• Office - 14.6x reduction in latency
• Bing QnA - 2.8x reduction in latency
• Azure Cognitive Services - 3.5x reduction in latency for OCR

21. Microsoft - BERT - ONNX
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• Bidirectional Encoder Representations from Transformers
• Google’s state-of-the-art NLP model
• BERT is widely used in Bing
• MSFT just open-sources BERT in Jan. 2020
• 17x BERT inference acceleration with ONNX Runtime
• Scores with Nvidia V100 GPU in 1.7 milliseconds
• https://cloudblogs.microsoft.com/opensource/2020/01/21/microsoft-onnx-open-
source-optimizations-transformer-inference-gpu-cpu

22. Microsoft Raven- SQL Server + ONNX
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• Extending relational query processing with ML inference
○ http://cidrdb.org/cidr2020/papers/p24-karanasos-cidr20.pdf
• Project Raven
○ Raven, deep native integration of ONNX runtimes with SQL Server
○ and a unified IR
○ advanced cross-optimizations between ML and database operators
• Can in-RDBMS scoring outperform dedicated frameworks?

23. Raven Overview
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24. Raven Concepts
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• Introduces IR that includes both ML and relational operators.
• Optimize inference query that includes both data and ML
operations in a holistic manner
• Leverage relational operator and data properties to optimize
ML part of query

25. Raven Operator Sets
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• Relation algebra (RA)
• Linear Algebra (LA)
• Other ML operators and data featurizers (MLD) - classical non-
NN frameworks such as sklearn
• UDFs - Used to wrap the non-optimizable code as a black box

26. Raven Inference Engine
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• Inference execution modes
○ In-process execution (Raven)
○ Out-of-process execution (Raven Ext)
○ Containerized execution
• For small data sets Raven slower than ONNX runtime
• For large data sets Raven is 5x faster

27. Microsoft - SQL Database Edge
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• Deploy and make predictions with an ONNX model in SQL
Database Edge Preview - 2019-11-04
○ https://docs.microsoft.com/en-us/azure/sql-database-edge/deploy-onnx
• Machine learning and AI with ONNX in SQL Database Edge
Preview - 2019-11-07
○ https://docs.microsoft.com/en-us/azure/sql-database-edge/onnx-overview

28. AWS
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• ONNX is already integrated with MXNet
• ONNX installed on AWS Deep Learning AMIs (DLAMI)
• New Inferentia chip supports ONNX
• Amazon Elastic Inference supports ONNX
• Model Server for Apache MXNet (MMS)
• Score with ONNX.js using Lambda and Serverless

29. Facebook
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• PyTorch 1.0 has native ONNX export format since May 2018
• Has not been nearly as active recently as MIcrosoft
• But is quietly contributing to ONNX github
• https://www.facebook.com/onnxai
• https://ai.facebook.com/blog/onnx-expansion-speeds-ai-
development-

30. Nvidia
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• TensorRT - SDK for high-performance DL inferencing
• Nvidia GPU Cloud ONNX support for TensorRT in Dec. 2017
• ONNX Runtime support for TensorRT in Dec. 2018
• TensorRT backend for ONNX
○ https://github.com/onnx/onnx-tensorrt
• Jetson NANO

31. Nvidia TensorRT
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32. Intel
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33. Apple
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• Production-grade Core ML to ONNX conversion
• https://github.com/onnx/onnx-coreml
• https://apple.github.io/coremltools/

34. ONNX and Spark ML
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• Spark ML is not advertised as ONNX supported
• Conversion project does exist:
○ https://github.com/onnx/onnxmltools
• Very few examples
• Preliminary testing reveals problems
• Opportunity to contribute!

35. ONNX and MLflow
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• ONNX support introduced in MLflow 1.5.0
• Convert model to ONNX format
• Save ONNX model as ONNX flavor
• No automatic ONNX model logging like MLeap
• Scoring: use ONNX Runtime or convert to native flavor

36. ONNX MLflow spin around the block
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37. ONNX and MLflow Test Coverage
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38. MLflow and ONNX Sample Code
Github
• https://github.com/amesar/mlflow-examples/tree/master/python/sklearn
• https://github.com/amesar/mlflow-examples/tree/master/python/keras
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39. MLflow and ONNX Keras Example
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import mlflow.onnx
import onnxmltools
onnx_model = onnxmltools.convert_keras(model)
mlflow.onnx.log_model(onnx_model, "onnx-model")
Log Model
Read and Score Model
import onnxruntime
session = onnxruntime.InferenceSession(model.SerializeToString())
input_name = session.get_inputs()[0].name
predictions = session.run(None, {input_name:
data_np.astype(np.float32)})[0]

40. Thank you
Have a nice day
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