Learning ONNX for trading - page 2

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An Overview of the PyTorch-ONNX Converter

An Overview of the PyTorch-ONNX Converter

In this overview, Bowen Bao from Microsoft introduces the PyTorch-ONNX Converter, explaining its architecture and flow to convert a PyTorch model to the torch intermediate representation (IR) and then to the ONNX IR, and finally, to a Protobuf file. The converter handles exporting models with dynamic shapes, quantitated models, and captures loops, if statements, and other operations through ONNX loop and ONNX if nodes. Additionally, users can create custom operators or use existing custom PyTorch operators to export an ONNX model or register a custom symbolic function to tell the exporter how to export it. Bowen also explains the ONNX local function, addresses mixed precision through autocast, and concludes with plans to continue supporting more models while improving failure diagnosis.

• 00:00:00 In this section, Bowen, a software engineer at Microsoft, provides an overview of the PyTorch-ONNX Converter, including its architecture and flow. The converter, which is part of the PyTorch package, utilizes the torch tracer and torch script to convert a PyTorch model to the torch intermediate representation (IR) and then to the ONNX IR. The final step involves serializing it to an ONNX model in a Protobuf file. The converter supports exporting models with dynamic input and output shapes, as well as quantitated models, and it can capture loops, if statements, and other operations through ONNX loop and ONNX if nodes. Additionally, users can create custom operators or use existing custom PyTorch
  operators to export an ONNX model or register a custom symbolic function to tell the exporter how to export it. Lastly, Bowen explains the motivation behind the ONNX local function.
  
  
• 00:05:00 In this section, the focus is on the PyTorch-ONNX converter and some common complaints surrounding the exported ONNX model, such as flattened nodes. To address this issue, a local function feature has been added which allows users to specify a Torch module class or node type function. Mixed precision is also supported through the use of Torch autocast and NVIDIA Apex mp. The video concludes with a discussion on improved failure diagnosis and the goal of continuing to support more models. The links provided offer helpful resources and tools for those using the PyTorch-ONNX converter.

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Introduction to ONNX | Tutorial-1 | Open Neural Network Exchange | ONNX

Introduction to ONNX | Tutorial-1 | Open Neural Network Exchange | ONNX

This tutorial provides an introduction to ONNX, which is an intermediary machine learning framework that converts existing models from one framework to another. The course covers the challenges faced in deep learning, an overview of ONNX, and a discussion on ONNX runtime. The instructor demonstrates practical examples of converting models with ONNX and shares a real-world use case of converting a Python model to a TensorFlow model. Familiarity with Python programming, machine learning basics, and frameworks like PyTorch, TensorFlow, and Scikit-learn is required.

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Challenges in Deep Learning | Tutorial-2 | Open Neural Network Exchange | ONNX

Challenges in Deep Learning | Tutorial-2 | Open Neural Network Exchange | ONNX

The challenges of using different deep learning frameworks and hardware accelerators during inferencing are discussed in this video segment. ONNX is presented as an intermediary model type that is compatible with various deep learning frameworks and hardware accelerators, allowing for seamless transfer of models between them. Converting ONNX models to specific frameworks when needed enables greater flexibility when using trained models across different systems. Understanding these challenges and solutions can help in creating an effective pipeline for working with deep learning.

• 00:00:00 In this section of the video, the challenges in deep learning are discussed. The various popular frameworks used in deep learning include Cafe, TensorFlow, Keras, and MXNet. For high computational power, hardware accelerators are required, which are optimized for deep learning solutions, such as NVIDIA GPU, Intel GPU, and Google TPU. Inferencing also requires an optimized model to ensure efficiency, and there are various inferencing hardware options available such as NVIDIA T4, CPUs, and S devices like Google Coral, Raspberry Pi, and Jetson VR. It is crucial to consider software and hardware factors while selecting a device for deep learning purposes. ONNX is preferred for its compatibility with various deep learning frameworks and hardware accelerators, facilitating the seamless transfer of models between them.
  
  
• 00:05:00 In this section, the speaker explains the challenge of using a trained model with a different framework or hardware in the inferencing stage. This issue can be addressed with the Open Neural Network Exchange (ONNX), an intermediary model type that can be used with different hardware and frameworks. The ONNX model can be converted to any specific framework if needed. This solution allows for greater flexibility in using trained models across different systems. The speaker concludes by saying that understanding these problems and solutions will help in creating a pipeline and working effectively with deep learning.

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All about ONNX | Tutorial-3 | Open Neural Network Exchange | ONNX

All about ONNX | Tutorial-3 | Open Neural Network Exchange | ONNX

The video discusses ONNX, an intermediary machine learning framework that allows for the conversion of models into different framework model types and provides optimization capabilities. First introduced in 2017 by AWS, Microsoft, and Facebook, ONNX has gained popularity and contributions from other companies, including IBM, Intel, and Huawei. Many companies are currently dedicating themselves to working on the ONNX ecosystem.

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Design principles | Tutorial-4 | Open Neural Network Exchange | ONNX

Design principles | Tutorial-4 | Open Neural Network Exchange | ONNX

In this video, the speaker explains the design principles of the Open Neural Network Exchange (ONNX). Initially developed for deep learning, the ecosystem has expanded to support traditional machine learning as well. ONNX is adaptable with updates from other frameworks, standardized with well-defined operations from practical applications, and able to export/import models with ease. These features make it a convenient choice for end-users looking for a flexible and efficient solution.

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ONNX file format | Tutorial-5 | Open Neural Network Exchange | ONNX

ONNX file format | Tutorial-5 | Open Neural Network Exchange | ONNX

This tutorial video covers the ONNX file format for machine learning models, which contains input and output lists, computational nodes and operators, and operator parameters along with metadata and version of the model. The ONNX file format is not a black box and can be visualized. The speaker provides examples of operators such as ReLU and PReLU, and demonstrates a deep learning model graph and compares it with the ONNX file format graph. Custom operators can also be mapped using ONNX, making it a popular choice for neural networks due to its flexibility and functionality.

• 00:00:00 In this section, the speaker discusses the ONNX file format for machine learning models, which is not a black box and can be visualized. The ONNX file format contains input and output lists, a list of computational nodes and operators, and operator parameters, as well as metadata and the version of the model. The speaker demonstrates a deep learning model graph, which is a sequence of computational nodes, and compares it to the ONNX file format graph. The ONNX file format contains operator schemas that map operators from frameworks like Keras, TensorFlow, and PyTorch. The speaker shows examples of operators like ReLU and PReLU.
  
  
• 00:05:00 In this section, the speaker explains how when converting an actual model to ONNX file format, the framework is just mapping the operators onto the model. Custom operators can also be created and mapped using ONNX. ONNX is a flexible platform that provides lots of functionality, which is why it has become popular in the field of neural networks.

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ONNX Data Type | Tutorial-6 | Open Neural Network Exchange | ONNX

ONNX Data Type | Tutorial-6 | Open Neural Network Exchange | ONNX

The video explains that ONNX has two types of specifications: deep neural networks and machine learning. The former uses tensor data types like integers, floats, booleans, strings, and complex types, which are also used in Python and TensorFlow. Meanwhile, the latter uses non-tensor data types like sequences and maps due to statistical-based learning not typically utilizing tensors.

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Machine Learning Example | Tutorial-7 | Open Neural Network Exchange | ONNX

Machine Learning Example | Tutorial-7 | Open Neural Network Exchange | ONNX

This video tutorial explains how to convert a model saved in pickle format into an Open Neural Network Exchange (ONNX) model file format for practical example of machine learning using ONNX. The video provides a requirement file specifying necessary packages, and the speaker gives a simple code to import data, split, and train the model before converting to ONNX format using the skl2onnx package. A conversion script is provided, and instructions for visualizing the resulting graph with Netron tool and performing inference on the ONNX model are shared. The speaker highlights the portability and optimization of ONNX format and encourages practice with the conversion process.

• 00:00:00 In this section, the video discusses a practical example of machine learning using ONNX. The example involves training a model and saving it in pickle format, and then converting it into ONNX model file format. The video provides a requirement.txt file where all the requirement packages are specified, including sklearn and skl2onnx, which is the converter. The video then shows a simple code of importing the is dataset, performing a train-test split, and training the model. Finally, a conversion script is provided to convert the saved model to ONNX format using the skl2onnx package.
  
  
• 00:05:00 In this section, the speaker explains how to convert a pickle model to an ONNX model using a simple script. The script involves loading the pickle model, defining the data type, calling the convert_scalar function, and passing the class object before saving the model as an ONNX file. The speaker also demonstrates how to visualize the resulting graph using the Netron tool and perform inference using the ONNX model by passing a sample input. The ONNX format is described as more portable and optimized than the pickle format, as it can be used in any ONNX ecosystem. The speaker recommends practicing the conversion process to remember how to do it effectively.

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ONNX Runtime | Tutorial-8 | Open Neural Network Exchange | ONNX

ONNX Runtime | Tutorial-8 | Open Neural Network Exchange | ONNX

The speaker discusses the ONNX runtime and its importance in deep learning. The ONNX Runtime is a high-performance engine that is fast and founded by Microsoft. It is an extensible and modular framework that is open source and ships with Windows 10. Microsoft prefers this runtime because it is fast and efficient for deep learning, unlike the default runtime, which can lag. Additionally, the ONNX runtime diagram shows how the ONNX Runtime is used to convert an existing model to an ONNX file format, and then the ONNX runtime is used to execute the model without worrying about the hardware or framework. The speaker suggests that the audience can deep dive into the ONNX runtime on the official GitHub of ONNX.

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ONNX Model Zoo | Tutorial-9 | Open Neural Network Exchange | ONNX

ONNX Model Zoo | Tutorial-9 | Open Neural Network Exchange | ONNX

The ONNX Model Zoo is a collection of pre-trained models for different tasks such as image classification, object detection, and speech and audio processing. The pre-trained models are available for download as ONNX files and can be used with any framework or the ONNX runtime for inference. Additionally, cloud platforms such as Azure ML offer similar functionality where users can upload their own data and train models to download as ONNX files. The next video will showcase how to use a pre-trained model from the ONNX Model Zoo for handwritten digit recognition.