Learning ONNX for trading - page 11

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Neural Net in your Phone: From Training to Deployment through ONNX

Neural Net in your Phone: From Training to Deployment through ONNX

In this video on "Neural Net in your Phone: From Training to Deployment through ONNX", the presenter demonstrates how to train a neural network using the iNaturalist community API to identify different mushroom species based on whether they are toxic or edible. They then explain how to deploy the model on an iPhone using the Core ML package from Apple. The speaker also points out the importance of formatting the trained model in the ONNX file format before importing it into Core ML. The presenter highlights that the EfficientNet will be the future model for image classification, with care required in model selection, and suggests building classifiers for plants, animals or birds.

• 00:00:00 In this section, the presenter explains how to train a mushroom image classifier using the iNaturalist community's API to obtain hundreds of different images of all kinds of mushroom species. Using Mathematica, they stored the images and classified them according to whether they were toxic or edible, based on 11 toxic and 11 edible mushroom species common in their region. The images were cropped and resized before later training of the neural network. The presenter demonstrates with both the Fly Agarik and the Death Cap, a deadly mushroom that was also classified effectively through the same method.
  
  
• 00:05:00 In this section, the speaker discusses the process of training a neural network to identify different species of mushrooms, using a pre-trained net model from the net repository. They describe how they created class labels and training and testing sets, and used transfer learning to train the model with a stochastic gradient descent method. They also mention the importance of exporting the trained model in the ONNX file format, which is an open neural network exchange format created a few years ago by industry leaders in machine learning.
  
  
• 00:10:00 In this section, the speaker explains how neural networks can be deployed to an iOS operating system device using the Core ML package from Apple. To convert the model into the Core ML format, the speaker shows how to use CoreML tools to import various types of net models, including ONNX, and how to specify preprocessing arguments and class labels for the mushrooms dataset used as an example. The speaker also notes that the Core ML models have a similar way of working as the natural language models, with an encoder and decoder, and highlights some differences between the two formats in terms of pixel values and color bias.
  
  
• 00:15:00 In this section, the speaker explains the steps to take to deploy a Core ML model to an iPhone. They demonstrate how to replace the pre-existing MobileNet model on an Xcode project with their own mushroom species model. The speaker demonstrates that the model works correctly by testing it on various mushrooms they found in the woods. They encourage the audience to check out their Jupyter notebook for more information.
  
  
• 00:20:00 In this section, the speaker mentions that the best performing model for image classification is the EfficientNet, which will be available in the future. However, the user must be careful not to choose an EfficientNet model that is too heavy in memory. The speaker warns against using the classifier for cooking without expert consultation, as some types of mushrooms can be deadly. In the future, the speaker plans to create a workflow for the presentation and offer blog posts on the topic. They also plan to include examples for audio, such as identifying bird songs. The speaker suggests starting a study group for such applications and topics, and highlights that ONNX point is up to Android's usage.
  
  
• 00:25:00 In this section, the speaker discusses different options for importing species observations and other helpful functions, such as importing gpif search, gpif import, and global data, that can be used to build classifiers on animals or plants. The speaker also thanks the audience for their attention and invites them to ask more questions in the machine learning group in the community.

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ONNX on MCUs

ONNX on MCUs

Rohit Sharma talks about the challenges and opportunities of running ONNX models on microcontrollers. He emphasizes that while these devices lack the resources of high-performance servers, there has been a growing number of machine learning applications for tiny devices due to the improvement in hardware resources and the AI community's efforts to reduce model size. Sharma discusses two tools for implementing machine learning on microcontrollers with ease: DeepSea, an open-source ahead-of-time compiler that supports Python and enables developers to create custom ML algorithms, and Canvas, a no-code/low-code platform providing over 70 tiny ML applications that can be customized to suit the user's data set. He provides two use cases for these tools, including a wearable glove that translates sign gestures into words and weak word detection for speech-assisted devices like Amazon Echo.

• 00:00:00 In this section, Rohit Sharma discusses the challenges and opportunities of running ONNX models on microcontrollers, which are tiny devices operating on batteries for months. While these devices do not have the compute resources of high-performance servers with accelerators or single board computers, the increasing number of machine learning applications running on tiny devices is due to the fact that MCU providers continue to improve hardware resources while the AI research community is working to reduce model size. Sharma explains that all tiny ML apps are edge AI apps, but not all edge AI apps are tiny ML apps, as the difference is rooted in power consumption. He then goes on to discuss the process of compiling ONNX models with DeepSea, an open source, vendor-independent deep learning library compiler and inference framework that is designed for small form factor devices including microcontrollers, IoT, and edge devices.
  
  
• 00:05:00 In this section, the speaker describes two tools for implementing machine learning on microcontrollers with ease. The first tool is DeepSea, an open-source ahead-of-time (AOT) compiler that supports Python and enables developers to create custom machine learning algorithms. The second tool is Canvas, a no-code/low-code platform providing a gallery of over 70 tiny machine learning applications. Canvas allows for customization of these applications to create a tiny machine learning model suited to the user's data set. The speaker also provided two use cases for these tools- a wearable glove that converts sign gestures into spoken words and weak word detection for enabling speech-assisted devices such as Amazon Echo.

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Leverage the power of Machine Learning with ONNX - Ron Dagdag

Leverage the power of Machine Learning with ONNX - Ron Dagdag

In this video, Ron Dagdag delves into the importance of machine learning frameworks, particularly ONNX, which facilitates interoperability between deep learning frameworks and deployment. He outlines the ways to obtain ONNX models, including converting existing models, training models with Azure's automated machine learning, and using Azure's custom vision service. Dagdag emphasizes the decision of whether to deploy machine learning models in the cloud or on the edge, and he suggests leveraging ONNX to make the process more seamless. Moreover, he walks through the process of using Microsoft's ML.NET to create a machine learning model, and demonstrates how to incorporate the ONNX model into an application using the ONNX runtime for inferencing. Dagdag also explores ONNX as an open standard for machine learning, its various platforms and languages, and tools to make the models smaller in size.

• 00:00:00 In this section, the speaker discusses how traditional programming differs from machine learning, as machine learning focuses on training computers to learn algorithms while traditional programming focuses on input and calculations. Training data is important in machine learning and this data is used to train machines to learn algorithms as a part of the process. The speaker emphasizes the importance of machine learning frameworks, such as ONNX (Open Neural Network Exchange), that serves as a bridge between machine learning frameworks and deployment. ONNX helps to move models between deep learning frameworks, ensuring interoperability between frameworks.
  
  
• 00:05:00 In this section, Ron Dagdag discusses the different ways to obtain an ONNX model. Data scientists, similar to chefs, are responsible for creating and refining the recipe to improve a company's model, while ONNX models are like PDFs for neural representation of a graph of operations. There are four ways to attain ONNX models, including through GitHub, the Azure custom vision service, converting existing models, and training them using Azure's automated machine learning. The process to convert models to ONNX is straightforward, as there are converters available to change models from TensorFlow, Keras or PyTorch to ONNX, with important steps including loading the existing model, doing the conversion, and saving it. Overall, ONNX can help companies integrate machine learning into their applications more seamlessly.
  
  
• 00:10:00 In this section, the speaker discusses the usage of ONNX for machine learning models. ONNX allows developers to identify what the input and output data of their models are when visualizing them. It can be used through command line conversion and can quickly scale with GPU clusters in the cloud. ONNX also offers a model registry that can help with versioning models and assisting with deployment. The speaker emphasizes the difference between data scientists and software engineers, where the former generates a secret recipe while the latter figures out how to operationalize it by integrating it with various sources, sourcing data, and creating pipelines. ONNX can be deployed on various devices, including Windows devices and IoT endpoints.
  
  
• 00:15:00 In this section, the speaker discusses the decision of whether to deploy machine learning models in the cloud or on the edge, which refers to processing that is closer to the user. He explains that deploying to the edge can be more cost-effective, flexible, and have lower latency, which is ideal when processing video or images. Additionally, deploying to the edge can be necessary when rules and regulations dictate that the data should not leave a particular network or country. The speaker suggests using ONNX, an intermediary format that can convert models from different machine learning frameworks, to make the deployment process more seamless.
  
  
• 00:20:00 In this section, the speaker discusses the ONNX framework and its potential applications. ONNX allows the conversion of models to different formats, including TensorFlow, Core ML, and transfer learning, and has a high-performance runtime for executing the models, called ONNX Runtime. ONNX Runtime is cross-platform and supports traditional machine learning operations. It also has a GPU version and a C# API. Overall, ONNX is a powerful tool for developers, and users can get started with it through the ONNX ecosystem by using the docker container instance. In his demo, the speaker shows how to download and use different ONNX packages in C# to manipulate data using data frames.
  
  
• 00:25:00 In this section, the speaker demonstrates the process of using Microsoft's ML.NET to create a simple machine learning model that predicts salary based on years of experience. He first splits the data into training and testing sets, creates a pipeline using the ML context, and trains the model using the training set. He then evaluates the model's metrics and saves it to an ONNX model. Afterward, he shows how to incorporate the ONNX model into an application using the ONNX runtime for inferencing. Finally, he creates an input container for the application and runs the model to get the score.
  
  
• 00:30:00 In this section, the speaker discusses the benefits of using ONNX runtime, the different Microsoft teams that have used it in the past, and how it improves their processes. He also talks about using Windows ML if you're deploying to Windows devices, which is available across Windows family devices in win32 and WinRT applications, and connecting ONNX runtime using APIs. The speaker then explores Direct ML, which is ideal for creating real-time high-control machine learning API, and is best for gaming. He also highlights ONNX JS for running ONNX models in the browser or in nodejs, and Embedded Learning Library, which allows you to start using devices that do not have a whole operating system or Linux Mac.
  
  
• 00:35:00 In this section, Ron Dagdag discusses the use of ONNX as an open standard for machine learning, and how it can efficiently convert to different platforms. The ONNX models can be created in several languages such as dotnet, JavaScript, and Python. The performance can be achieved through deployment to the cloud or the edge. The audience asked questions such as, can you import your ONNX model in C#, what is the memory footprint of ONNX runtime, and how can you convert a large image model to a smaller ONNX model suitable for smaller devices. Dagdag suggested using pruning or quantization to compress the models and reduce its size. He also highlighted that the slides and demo codes are available at the GitHub repository, along with the binder to try out the code.
  
  
• 00:40:00 In this section, the speaker discusses the process of making ONNX models smaller in terms of size. The ONNX model is composed of graphs of operations that identify what operations it can perform. While there may not be a specific way to make an ONNX model smaller, there is a utility that can compress it. As it is an open-source software, it is possible that a new feature could be released in the future. Viewers are encouraged to submit any further questions to Ron via the chat or Q&A website.

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Leverage Power of Machine Learning with ONNX | Ron Lyle Dagdag | Conf42 Machine Learning 2021

Leverage Power of Machine Learning with ONNX | Ron Lyle Dagdag | Conf42 Machine Learning 2021

In this video, Ron Dagdag discusses the benefits of using ONNX (Open Neural Network Exchange) as an open format for machine learning models, particularly when deploying models to different endpoints such as phones or cloud infrastructure. He covers the scenarios in which converting a model to ONNX may be useful, such as low performance or combining models trained on different frameworks, and describes how popular models such as RestNet can be downloaded in the ONNX format. Additionally, he discusses the benefits of running machine learning models on the edge, as well as the importance of managing models by registering them in the cloud and versioning them. He demonstrates how to convert a model to ONNX and how to use the ONNX runtime in Python for inferencing, and concludes by emphasizing ONNX's role in enabling data scientists and software engineers to work together effectively.

• 00:00:00 In this section, Ron Dagdag from Spacy introduces ONNX (Open Neural Network Exchange) as an open format for machine learning models that is capable of handling traditional machine learning models in addition to neural networks. He emphasizes that this format bridges the gap between the training phase of machine learning and where to deploy the learned model, which can be a variety of endpoints from phones to cloud infrastructure. ONNX has gained partnerships with a growing number of organizations, most notably Microsoft and Facebook.
  
  
• 00:05:00 In this section, the speaker describes the growing popularity of ONNX, a framework that allows for machine learning models trained in one programming language to be deployed in another language or on different hardware, as well as the situations in which it may be useful to convert a model to ONNX. These include scenarios where there is high latency or low performance, when deploying to IoT or edge devices, or when combining models trained on different frameworks. The speaker likens ONNX to a PDF in that it allows for models to be displayed on different types of devices, and goes on to discuss how ONNX models can be created, including exporting from the ONNX model zoo or using Azure Custom Vision.
  
  
• 00:10:00 In this section, the speaker discusses how popular machine learning models, such as RestNet, are already converted into ONNX formats that can be easily downloaded for use. He also mentions how neural network models can be converted to ONNX using tools such as Torch and SKL or through command line. Furthermore, he talks about Netron, a tool that visualizes ONNX models by showing the inputs and outputs of the operations graph without the need for the data scientist's original code. Finally, the speaker highlights the importance of managing machine learning models by registering them in the cloud and versioning them.
  
  
• 00:15:00 In this section, Ron Lyle Dagdag discusses the importance of where to deploy machine learning models and the different factors to consider for deployment. He explains that deployment can be done in various ways, such as deploying to the cloud or running inferencing at the edge, closer to the user. Additionally, he mentions the importance of building an image and creating a pipeline for deployment, which can be done through a service or a Docker container, and talks about the availability of ONNX Docker images that can be used to incorporate ONNX into an application.
  
  
• 00:20:00 In this section, the speaker discusses the benefits of running machine learning models on the edge instead of in the cloud. One major advantage is low latency, as running the model locally on the device can provide faster inference times. Another advantage is scalability, as it can be more efficient to deploy the model to millions or billions of devices rather than shipping data to the cloud. The ONNX ecosystem is introduced as a solution for converting existing models to a format that can run on the edge using different hardware accelerators. The ONNX runtime, a high-performance inference engine for ONNX models, is also discussed as an open-source solution for running models on the edge.
  
  
• 00:25:00 In this section, the speaker discusses the ONNX runtime and how it can be used with the Windows AI platform, such as with the WinML API, for practical and simple model-based API inferencing. Additionally, there is DirectML API for creating games, a JavaScript library called ONNX.js for running models in a browser, and several robust driver models depending on the system's capabilities. The speaker then proceeds to demo how to convert a trained model in ml.net into ONNX using nuget packages and the C# application.
  
  
• 00:30:00 In this section, Ron Lyle Dagdag demonstrates a simple example of how to create a machine learning model that predicts salary based on the input of a reviewer's years of experience using ml.net. Once the model is trained, it can be converted into an ONNX model using the `context.model.convert_to_onnx` function. The ONNX model can then be verified and used for inferencing in a Python notebook using the ONNX runtime library. The input and output of the model are displayed using `netron.app`.
  
  
• 00:35:00 In this section, the speaker demonstrates how to use ONNX runtime in Python for inferencing a model created in ML.NET and exported to an ONNX file. The speaker shows how to get the name, shape, and type of the inputs and outputs of the ONNX model and pass input values to the model for inferencing. The speaker also emphasizes the importance of using ONNX as an open standard for integrating machine learning models into applications and how ONNX enables data scientists and software engineers to work together effectively. Finally, the speaker provides a recap of the key takeaways from the discussion, including how to create and deploy an ONNX model and the various platforms that support ONNX deployment.
  
  
• 00:40:00 In this section, Ron Dagdag, a lead software engineer at Spacy and a Microsoft MVP, concludes the video by thanking the audience and sharing ways to contact him if they want to geek out about ONNX runtime, Jupyter notebooks, bakeries, bakers, and breads.

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Inference in JavaScript with ONNX Runtime Web!

Inference in JavaScript with ONNX Runtime Web!

The video covers using ONNX Runtime Web in the browser through a Next.js template which offers a UI for running inferencing on pre-selected images. The process of converting image data to a tensor using RGB values and dimension creation is demonstrated. The model helper function is explored, which passes pre-processed data to the ONNX inference session using the path to the model, execution provider, and session options. Feeds for the model are created using the input name and tensor object and are passed to the session.run function to obtain the top five results. The first result populates the image display while the webpack configuration and instructions for server-side inferencing using ONNX Runtime Node are provided.

• 00:00:00 In this section, we learn about using ONNX Runtime Web with JavaScript in the browser using a template that provides all the necessary pre-processing needed to do inferencing. The template is built on Next.js, a React framework for building production-ready apps, and offers a simple UI for running inferencing on pre-selected sample images. The author takes us through the code which sets up an HTML canvas element to display images and reports various statistics about the inferencing. The image is then converted into a tensor using the image helper utility and then passed through the predict function in the model helper that calls on the ONNX Runtime Web API to perform the inferencing.
  
  
• 00:05:00 In this section, the video describes the process of converting image data to a tensor for inferencing using RGB values, reshaping, and creating a tensor with data and dimensions using the tensor object in ONNX Runtime Web. The video also explores the model helper function, which passes pre-processed data to the ONNX inference session by giving it the path to the model, execution provider (WebGL or WebAssembly), and session options. The input name and ORT tensor object are needed to create feeds for the model, which are passed into the session.run function to obtain the result. The top five results are returned, and the first result is used to populate the image display. Additionally, a webpack configuration is provided, along with instructions for using ONNX Runtime Node to do inferencing on the server side with an API framework.

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Ron Dagdag - Making Neural Networks in the Browser with ONNX

Ron Dagdag - Making Neural Networks in the Browser with ONNX

In this video, Ron Dagdag explains how the ONNX machine learning framework can be used to run neural networks in a browser. He discusses the basics of machine learning, the creation and deployment of ONNX models, and the ONNX runtime environment. Dagdag demonstrates the use of ONNX with various examples, including predicting salaries based on work experience and detecting emotions in images. He also covers the deployment of ONNX models to different platforms, such as Android and iOS, and highlights available resources and demos for experimenting with ONNX. Dagdag encourages experimentation with ONNX and emphasizes the importance of efficient inferencing on target platforms using the ONNX runtime.

• 00:00:00 In this section, Ron Dagdag, Director of Software Engineering and Microsoft MVP, discusses the basics of machine learning and the difference between traditional programming and machine learning. He explains that, in machine learning, the input is followed by providing a bunch of examples or answers and the goal is to train the computer to create an algorithm for you. He also discusses ONNX , a machine learning framework that is gaining popularity among JavaScript developers as it allows them to make neural networks run in the browser.
  
  
• 00:05:00 In this section, Ron Dagdag discusses ONNX, an open format for machine learning models that serves as a bridge between training and incorporating models into applications. ONNX is not limited to neural networks but also includes traditional machine learning, and it is available on GitHub and has numerous contributors. It is production-ready and optimized for production use, making it suitable for resource-constrained edge devices or IoT devices. Additionally, ONNX allows combining models built in different frameworks, making it an excellent tool for collaborating with multiple teams that use different machine learning frameworks.
  
  
• 00:10:00 In this section, Ron Dagdag discusses the process of creating an ONNX model and deploying it. He compares the process to baking bread, stating that just like bread-making requires a secret recipe, data scientists experiment with different combinations to create the correct model that fits the specific dataset. Dagdag explains that an ONNX model is a graph of operations that can be visualized using the Netron app. He mentions three ways to create the model, including using the ONNX model zoo, the Azure custom vision service for image classification, and converting an existing model. Dagdag suggests that the easiest way to experiment with a model is by using the custom vision service, which allows for a dataset to be uploaded, labeled, and used to build a customized ONNX model.
  
  
• 00:15:00 In this section, Ron Dagdag explains how to convert existing machine learning models into ONNX and save them to ONNX files. He provides simple examples using PyTorch and Keras, as well as command line tools for TensorFlow and Scikit-learn. He also talks about the importance of managing and registering the models to ensure the right version is used in the deployment process. Dagdag emphasizes that software engineers can empower data scientists by providing the necessary skills to integrate machine learning models with existing applications, thus making them useful for organizations.
  
  
• 00:20:00 In this section, the speaker discusses deployment options for neural networks, including deploying to a VM or device like iOS or Android. The difference between cloud and edge deployment is also highlighted, using the analogy of McDonald's baking their bread in a factory versus Subway baking it at the restaurant. The ONNX runtime is introduced as an open source tool that can be used for inferencing on ONNX models in JavaScript, Node, and React Native. The speaker then provides a demo of how to visualize an ONNX model in a node application, and briefly touches on how they trained and converted the model to ONNX using .NET interactive and a Python notebook.
  
  
• 00:25:00 In this section, Ron Dagdag explains how he creates an ONNX model from his NuGet packages to train a model using Microsoft ML. He uses data provided by Microsoft that predicts an output based on one input factor – for this case, user experience and salary based on years of experience. Once he has trained the model, it generates an ONNX file, which he then integrates into a JavaScript application by creating a session and passing in the feeds. Once the feeds are set up in the session, he runs it and uses the score. output to display the prediction results. Dagdag uses ONNX Runtime Node for the runtime environment to execute the JavaScript application.
  
  
• 00:30:00 In this section, Ron Dagdag explains how to run machine learning models in the browser using ONNX runtime web, which is a JavaScript running library that uses web assembly and webGL technologies. Running models in the browser is faster, safer, and cheaper since it's running locally and doesn't rely on an internet connection. Ron also explains that you should not run large models in the browser, and he showcases how to run an existing ONNX model in a neural network using a simple example where he uses input to pass it to the model and gets an output of prediction.
  
  
• 00:35:00 In this section, Dagdag demonstrates the process of creating and integrating an ONNX model in a browser. He uses an ONNX model capable of distinguishing emotions from a 64x64 grayscale image as an example. To use the model on an image, Dagdag first resizes and preprocesses the image, then converts it to a tensor. He loads the ONNX model in three steps, creates a session, feeds the input to the model and processes the output to display the emotions detected in the image. Dagdag notes that the process of integrating an ONNX with a browser application involves creating a session, passing it to session.run and processing the output.
  
  
• 00:40:00 In this section, Ron Dagdag discusses the different resources and demos available for experimenting with ONNX, such as the MNIST demo which allows users to draw a number to see if the model can accurately classify it. He also mentions that ONNX can be used with React Native on Android and iOS platforms, but it needs to be converted to a format optimized for mobile. ONNX is compatible with various platforms such as Windows 10, Mac, Ubuntu, iOS, Android, and can be used with webassembly or webgl. Ron emphasizes the importance of using ONNX runtime to run the model efficiently on the target platform and to separate what is used for training and inferencing. He also mentions that ONNX can be deployed using Windows ML, Azure ML, and can be used with JavaScript and Python. Ron concludes by saying that ONNX is capable of running on different devices such as Snapchat AR glasses and encourages viewers to experiment with ONNX using the resources and demos available.

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Making neural networks run in browser with ONNX - Ron Dagdag - NDC Melbourne 2022

Making neural networks run in browser with ONNX - Ron Dagdag - NDC Melbourne 2022

Ron Dagdag shares his expertise on making neural networks run in browsers with ONNX. He discusses the basics of programming and how it differs from machine learning, the availability of JavaScript and machine learning frameworks, and how machine learning models can run on different devices, including phones, IoTs, and the cloud. He introduces ONNX, which is an open format for machine learning models that can integrate models created in different frameworks with existing applications in different programming languages. Dagdag demonstrates how to create, manage, and deploy ONNX models, incorporating ONNX runtime, web assembly, and web GL technologies to run ONNX models in browsers while optimizing performance, safety, and cost. The video also covers the scoring of pre-trained models on mobile devices, cost considerations, and the benefits of running object detection closer to the edge for local processing of large amounts of data.

• 00:00:00 In this section, Ron Dagdag explains the basics of programming and how it differs from machine learning. He discusses the training data and framework required to create a model, which is used in inferencing. He also points out the availability of various JavaScript and machine learning frameworks. Finally, he emphasizes that machine learning models can run on different devices, including phones, IoTs, and the cloud, and can also become a feedback loop to improve the model.
  
  
• 00:05:00 In this section, the speaker introduces ONNX (Open Neural Network Exchange), which is an open format for machine learning models that is open source on GitHub. ONNX allows for the integration of machine learning models created in different frameworks, such as PyTorch and Keras, with existing applications in different programming languages such as C#, Java, and JavaScript. Using ONNX is particularly useful when you need high inferencing latency and fast results, especially when running on IoT or edge devices. Additionally, ONNX enables combining different models and training locally instead of sending it remote. The agenda for the session includes creating ONNX models and deploying them.
  
  
• 00:10:00 In this section, the speaker discusses different ways to create an ONNX model for deployment. One way is through the ONNX model Zoo on GitHub, where existing models built on image classification, among others, are already available for free download. Another way is through Microsoft's Custom Vision service, where an image or dataset can be uploaded, tagged, and trained to create a custom model that can be exported to an ONNX model. Converting existing models is another option, which can be done using libraries or tools such as the PyTorch library or the ONNX ML tools. The speaker emphasizes the importance of a data scientist in experimenting and figuring out the best approach to create the most effective model for a company's data.
  
  
• 00:15:00 In this section, Ron Dagdag explains the process of creating and managing machine learning models using Azure Machine Learning, where models can be registered and managed like a Github repository for code changes. He also demonstrates how to create an ML model using ml.net in Visual Studio Code and how to export it to ONNX using a command-line interface, which generates a model.onnx file that can be opened in the ONNX netron app.
  
  
• 00:20:00 In this section, Ron Dagdag discusses how to deploy ONNX models once they are created. He explains that deployment is crucial in integrating the models with applications, and developers need to consider whether to run the model in the cloud or at the edge. When running in the cloud, developers need to decide which data center to deploy it to, while deploying at the edge means the model runs closer to the user, such as on a phone or browser. Dagdag notes the advantages of running the model at the edge, such as flexibility, and the importance of building systems to collect money or pass data to process and create business rules.
  
  
• 00:25:00 In this section, the speaker talks about ONNX runtime, a high-performance inference engine for ONNX models that is open-source and fully compatible with ONNX ML spec. ONNX runtime allows developers to select various platforms and architectures to run their models, including the web browser, iOS, Android, Mac, and different APIs. The speaker demonstrates how to use ONNX runtime with node.js and webassembly to load an ONNX model into memory, pass input data, and get the output. They also explain how ONNX runtime allows for efficient processing by passing through only necessary data for calculation while ignoring the rest.
  
  
• 00:30:00 In this section, Ron Dagdag explains how to incorporate a node, created using ml.net and exported to ONNX, into a JavaScript application running on the server side. By utilizing webassembly and webgl technologies, the ONNX model can run on both the CPU and GPU, resulting in faster performance, increased safety, offline usage, and decreased costs. While incorporating the model into the browser has many benefits, large model sizes and hardware requirements can impact the user experience, so simplification and consideration of the device are necessary. A react template is also available for react developers to utilize.
  
  
• 00:35:00 In this section, Ron Dagdag demonstrates how to run a neural network in the browser using ONNX. He shows a demo of an ONNX model he downloaded from the ONNX model zoo that detects emotions in an image. The ONNX runtime web is used to upload the image and process it. The model requires an input image of 64x64 size, so Ron resizes and converts the image to grayscale before converting it to a tensor using ort.tensor. The output is a tensor of 1x8 containing the emotions detected in the image.
  
  
• 00:40:00 In this section, the speaker discusses how to make neural networks run in a browser with ONNX. He explains that the three steps to loading an ONNX model into memory are loading it, creating input parameters based on the model, and running the session to get results. He also discusses the process of scoring a pre-trained model on mobile devices using React Native, which involves converting the ONNX model to an optimized mobile model called mobile.ort. The ONNX runtime is compatible with various platforms, including Windows 10, Mac OS, Ubuntu, iOS, Android, Chrome, Edge, Safari, and Electron. The speaker emphasizes that understanding the use cases for the different pipelines of inferencing and model training is crucial, and he provides a link to his GitHub for those interested in learning more.
  
  
• 00:45:00 In this section, the speaker talks about using ONNX to run machine learning on Snapchat's spectacles, which have object detection and segmentation capabilities. He also discusses how to incorporate PyTorch and TensorFlow models into applications using ONNX, as it serves as a middle ground for conversion between different frameworks. He suggests considering the cost of processing data when deciding whether to use a device or a smart device for IoT applications, and notes that sending large amounts of data can become expensive. The speaker recommends optimization through the use of models that can convert to ONNX, and mentions the need for extra work when custom operators are not yet mapped.
  
  
• 00:50:00 In this section of the video, Ron Dagdag explains the benefits of running object detection closer to the edge, rather than in the cloud. Processing happens locally, which is ideal when working with large amounts of data. Sending the results of the inferencing to your event hub or stream, instead of the raw data itself, can also help to optimize the process.

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Linux Foundation Artificial Intelligence & Data Day - ONNX Community Meeting - October 21, 2021

Emma Ning (Microsoft) ONNX Runtime Web for In Browser Inference

001 ONNX 20211021 Ning ONNX Runtime Web for In Browser Inference

Microsoft AI Framework team's product manager Emma introduces ONNX Runtime Web, a new feature in ONNX Runtime that enables JavaScript developers to run and deploy machine learning models in a browser, with two backends, including web assembly for CPU and WebGL for GPU. The web assembly backend can run any ONNX model, leverage multi-threading and SIMD, and support most functionality native ONNX Runtime supports, while the WebGL backend is a pure JavaScript-based implementation with WebGL APIs. The speaker also discusses the compatibility of ONNX operators with both the backends, provides code snippets for creating an inference session and running a model, and showcases a demo website featuring several in-browser image models scenarios powered by MobileNet model. However, the speaker also acknowledges that there is still room for improvement in enhancing ONNX runtime web's performance and memory consumption and expanding the supported ONNX operators.

• 00:00:00 In this section, Emma, a product manager from Microsoft AI Framework team, introduces ONNX Runtime Web, a new solution for in-browser inference. The idea of in-browser machine learning has been gaining traction, as it enables cross-platform portability with system implementation through the browser, protects user privacy, and accelerates performance without sending data to the server. ONNX Runtime Web is a new feature in ONNX Runtime that enables JavaScript developers to run and deploy machine learning models in a browser, with improved inference performance, model coverage, and development experience. The architecture of ONNX Runtime Web comprises two backends, including web assembly for CPU and WebGL for GPU, that allow ONNX Runtime Web to accelerate the inference on both CPUs and GPUs. The web assembly backend can run any ONNX model, leverage multi-threading and SIMD, and support most functionality native ONNX Runtime supports. The WebGL backend, on the other hand, is a pure JavaScript-based implementation with WebGL APIs that provide direct access to the computer's GPU, enabling many optimization techniques to further push the performance to the maximum.
  
  
• 00:05:00 In this section, the speaker discusses the compatibility of ONNX operators with both the WebAssembly and WebGL backends, which support the most popular platforms in the web world. They provide a link to a table that shows the compatible platforms and which ONNX operators are supported. They also provide code snippets that demonstrate how to create an inference session and run a model with the ONNX runtime web, which enables a consistent development experience for server-side and client-side influencing. The speaker then shares a demo website featuring several interesting in-browser vision scenarios powered by image models, such as running the MobileNet model in a browser with the option to choose different backends. The speaker acknowledges that there is still room for improvement in terms of adding more ONNX operators and optimizing ONNX runtime web performance and memory consumption, as well as working on more demos to showcase its capabilities.

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Web and Machine Learning W3C Workshop Summer 2020

ONNX.js - A Javascript library to run ONNX models in browsers and Node.js

ONNX.js - A Javascript library to run ONNX models in browsers and Node.js

ONNX.js is a JavaScript library that allows users to run ONNX models in browsers and Node.js. It optimizes the model on both CPU and GPU with various techniques and supports profiling, logging, and debugging for easy analysis. The library supports all major browsers and platforms and enables parallelization using web workers for better performance on multicore machines. Using WebGL to access GPU capabilities, it provides significant performance improvements and reduces data transfer between the CPU and GPU. Although further optimization and operator support is needed, the speaker encourages community contributions to improve ONNX.js.

• 00:00:00 In this section, Emma from Microsoft talks about ONNX.js, which is a JavaScript library used to run ONNX models in browsers and Node.js. JavaScript is a very important language used by 95% of websites and is the most popular client-side language used for electron apps such as GitHub Desktop and VS Code. Despite the perception that JavaScript isn't designed for high-performance computing, there are techniques available to make JavaScript and machine learning work well together. A benefit of using client-side machine learning includes privacy protection while allowing real-time analysis, enabling consistent AI experience cross-platform, and accelerating performance by utilizing GPUs without requiring libraries or drivers installation. ONNX.js is similar to TensorFlow.js and provides machine learning models in the ONNX format, which is a standard framework.
  
  
• 00:05:00 In this section, we learn about the ONNX community, which was established in 2017 by Microsoft and Facebook to provide a window-neutral, open-format standard. ONNX.js is a pure JavaScript implementation of ONNX that allows users to run ONNX models in a browser and load JS. It optimizes the model on both CPU and GPU with several advanced technology techniques, and has three back-ends enabled, two for CPU using JavaScript and WebAssembly, and one for GPU using WebGL. ONNX.js also provides a profiler, logger, and other utilities for easy debugging and analysis, and supports all browsers on major platforms to easily build AI applications across platforms. Finally, the use of web workers enables parallelization within heavy operators that significantly improves performance on machines with multicore.
  
  
• 00:10:00 In this section, the speaker discusses the benefits of using WebGL, a popular standard API for accessing GPU capabilities, to accelerate graphics creation in JavaScript. The use of WebGL enables many optimizations for reducing data transfer between the CPU and GPU, as well as reducing GPU processing cycles, resulting in significant performance improvements. The speaker also provides an end-to-end flow example of using ONNX.js to run a model and demonstrates how to use ONNX.js with an HTML example and npm and boundary tools. Additionally, the speaker discusses the need for further optimization and support for more ONNX operators and encourages community contributions to improve ONNX.js.

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How to Run PyTorch Models in the Browser With ONNX.js

How to Run PyTorch Models in the Browser With ONNX.js

The video explains the advantages of running a PyTorch model in a browser using JavaScript and ONNX.js, including better response time, scalability, offline availability, and enhanced user privacy. The video also walks through the process of converting a PyTorch model to an ONNX model, loading it into an ONNX.js session and running inference in the browser. Data preparation, debugging and augmentations are also discussed, and the speaker demonstrates how to make the model more robust using data augmentation techniques. The video provides sample code and a demo website for users to try out the model for themselves.

• 00:00:00 In this section, Eliot Wait discusses the benefits of running a PyTorch model in a browser using JavaScript. Firstly, running the model in the browser provides better response time and avoids the latency of sending data to and from a server. Secondly, setting up a website with just static files makes it easier to scale and handle more users. Thirdly, the model will work offline, so as long as the JavaScript files are already installed, they can still be used without internet access. Fourthly, hosting the model in the browser enhances user privacy as data is not shared with any server. However, if the model is too large or takes too much time to calculate on user devices, it is recommended to host it on a server. Finally, Eliot illustrates how to easily convert PyTorch models into JavaScript using an Mnest model for handwritten digit recognition.
  
  
• 00:05:00 In this section, the video explains the difference between using TensorFlow.js and ONNX.js, and suggests using TensorFlow.js for training and ONNX.js for inference. ONNX stands for "open neural network exchange", and it defines a common file format for machine learning models. The video then walks through the process of converting a PyTorch model to an ONNX model using the torch.onnx.export method, and shows how to load the model into an ONNX.js inference session to run inference on it in the browser. The video provides sample code for creating the session, loading the model, and running inference on a dummy input, which returns a read-only output map.
  
  
• 00:10:00 In this section, the video discusses how to address an error that occurs when attempting to run the PyTorch model in the browser using ONNX.js. Specifically, the error message states that the log-softmax operator is not currently supported by ONNX.js, but the video presenter shows that the softmax operator is supported instead. The video also introduces a demo website where users can draw numbers and see the output predictions of the PyTorch model. However, the presenter notes that there is an issue with loading the model, which is corrected by ensuring that the model has loaded before running data through it. Finally, the video presents an updated version of the model code that reshapes an image data list into a 280x280x4 tensor, allowing the model to predict digit values based on pixel input.
  
  
• 00:15:00 In this section of the video, the speaker explains how to prepare data for a PyTorch model that will run in the browser using ONNX.js. They extract the fourth channel of a drawn image to reshape it into the expected shape for PyTorch images. They also apply the average pool operator and divide the tensor by 255 to adjust the image values within the expected range. Additionally, they explain how to normalize the data by subtracting the dataset's mean and dividing it by the standard deviation. The speaker identifies an error due to the old shape of the dummy input and goes over how to fix it. They also explain how to debug and apply data augmentation to make the model more accurate, rotating and translating the image data before passing it through the model.
  
  
• 00:20:00 In this section, the speaker demonstrates how to make the model more robust by adding data augmentation to the training script. These augmentations include translations, scaling, and shearing of the digits, producing tougher samples for the model to learn from. The speaker then retrains the model from scratch and tests it, noting that while it could still improve, the added data augmentation has made it more robust overall. The speaker invites viewers to try out the model for themselves using the link in the video description.