Learning ONNX for trading - page 8

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Importing Neural Networks with ONNX

Importing Neural Networks with ONNX

This video explores the importance of the Open Neural Network Exchange (ONNX) project in machine learning and its benefits in model conversion across various tools. The speaker discusses the challenges of loading models manually or using automated tools and how ONNX eliminates this problem through its graph-based computational model. The speaker also highlights the advantages of ONNX in hand conversion of complex models and its compatibility with different frameworks. The video touches on parameterized net models, the structure of ONNX, and potential challenges that may arise when using the project. Despite these challenges, the speaker believes ONNX will thrive due to its substantial backing from various companies.

• 00:00:00 In this section, the speaker discusses the importance of ONNX in machine learning and the difficulties faced when loading models manually or by automated tools. The speaker explains that with the introduction of PyTorch and TensorFlow, most models are developed in those frameworks, making it difficult to get external models into other frameworks. The manual process of converting models is time-consuming and tedious since the process involves linking weights to the architecture, which is not a fun thing to do. The speaker then discusses the growth of the neural net repository and how they added several models to the repository since version 12. They also introduce the MobileNet, an image classification network that was introduced in 2018, which reduces the size of image classification networks and runs faster.
  
  
• 00:05:00 section focuses on the importance of parameterized net models and why they are necessary in the neural net repository. Without parameterization, the repository would become too long and difficult to navigate. It also explains how to pick a non-default model and gives an example of the BPE model, which tokenizes segments into words or sub-words depending on the language. The section also highlights the need for ONNX in hand conversion, as manual conversion of complex models like the famous model released on February 14, 2019, can be challenging.
  
  
• 00:10:00 In this section, the speaker describes the details of the net encoder function used in the GPD2 model. This function has all the pre-processing that the model needs, and it converts a string into a list of tokens. Additionally, it gives the positional index and has a vector of size 768, depending on the size of the model being used. The speaker explains that creating the architecture for the 800-1000 layer network by hand was not feasible, and that's why they needed ONNX. The advantage of using ONNX is that it eliminates the need for maintaining a single converter for every kind, and it has a model zoo that users can leverage to import different models effectively.
  
  
• 00:15:00 In this section, the YouTuber discusses the benefits and specifications of the Open Neural Network Exchange (ONNX) project. The project enables model conversion from state-of-the-art tools, making it easier to choose the best tool for model creation. ONNX has a vast list of community partners, including large companies, making it easy to move models between various tools. The project provides hardware optimization tools and visualization tools, which are missing in many other frameworks. The YouTuber believes that since the project has considerable backing from various companies, it will continue to thrive.
  
  
• 00:20:00 In this section, the speaker discusses the structure of ONNX, a graph-based computational model that has operators implemented externally to the graph. Each node in the graph is referenced as an operator and has one or more inputs and outputs that must be portable across frameworks. The speaker provides an example of how ONNX works, demonstrating with an internal function that takes the model in the protocol format and converts it into a mathematical Association. The graph contains the metadata related to the model. The speaker then explains how the graph has different nodes, and he spends time discussing the properties of a convolution layer, which is one of the nodes of the graph. Lastly, he shows how to get information about the dimensions of the layer.
  
  
• 00:25:00 In this section, the speaker discusses some of the issues that may arise when working with ONNX. There are certain errors that can occur with the reshape operator when it does not have badge dimensions, so the operator coverage needs to be improved to support these types of cases. The speaker mentions that there is future work to be done in terms of adding support for RNNs and static hyper networks, as well as hooking up other tools like version and shape inference and model checkers. Additionally, improving operator coverage remains a work in progress. However, the speaker points out that ONNX is actively being developed and has support from major companies.

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Importing and Exporting Neural Networks with ONNX

Importing and Exporting Neural Networks with ONNX

The video demonstrates the use of ONNX as a cross-platform specification and file format for machine learning models to exchange models between different neural network frameworks. The speakers show how to import and export neural networks using ONNX through Mathematica and Keras, and how to inspect and import metadata, as well as set metadata when exporting. They also discuss the exporting and importing of models between Core ML, PyTorch, and Wolfram Language, and the importance of using the correct offset during conversion. The speakers discuss the future of ONNX, including expanding support for import and export, improving challenging cases for the importer, and allowing exporting to multiple operator set versions. Additionally, the speaker explains the difference between ONNX and MXNet, and provides information on how to check which functions can be exported to ONNX using internal utilities.

• 00:00:00 In this section, Matteo Salvarezza and Maria Sarksyan introduce ONNX, a cross-platform specification and file format for machine learning models that allows for different neural network frameworks to exchange models with each other. They demonstrate how to import and export neural networks using ONNX through Mathematica and Keras, showing how to inspect and import metadata and how to set metadata when exporting. They then showcase the conversion of a Fortnite model from Mathematica to Keras and then to ONNX before importing it back into Mathematica.
  
  
• 00:05:00 In this section, the speaker discusses the segmentation model which segments an image into white matter and gray matter to signify different anatomical tissues of the human brain. The architecture of this model consists of a shared unit type encoder and distinct decoders. The speaker outlines the process of exporting a brain image into a JPEG file, importing it into Python, running the model to evaluate the image, and exporting the segmented image into an NS file for importing back to Mathematica. The process involves using the OneNext library and performing an additional checker, followed by importing the ONNX export, setting up an input for the Keras model, making the prediction, and exporting the segmented image into an NS file. Finally, the speaker notes the importance of ensuring that the image's imported range is from zero to one and divided by 255.
  
  
• 00:10:00 In this section of the video, the speaker discusses importing and exporting neural networks using ONNX. They mention the possibility of exporting models from Keras to ONNX and then importing them back into Mathematica. The speaker emphasizes the importance of using the correct offset in the conversion process and notes that certain operations may need to be replaced using net surgery operators. They then demonstrate how to move a model from Core ML to ONNX using the ONNX ML tools package and how to export a pretrained model and its classes to Core ML. The video emphasizes that there are many steps involved in the process and provides examples for users to evaluate on their own.
  
  
• 00:15:00 In this section, the speaker explains how to export from ONNX to Core ML, including mean and variance correction, using the proper functions. They also cover converting a net from PyTorch to ONNX and then to full Wolfram language. The process involves necessary surgery to make the final format usable, and the speaker shows how they were able to get a usable format and evaluate it. Finally, the speaker demonstrates the final step of going from Wolfram language to PyTorch via ONNX by taking the pre-trained net and classes and exporting them.
  
  
• 00:20:00 In this section of the video, the speakers discuss importing and exporting neural networks with ONNX. They explain that once the ONNX model is loaded, it can be converted and evaluated in PyTorch, and transformations such as resize, center cropping, and normalizing are used. The model is evaluated to get the three classes with the highest probabilities. The speakers then move on to discuss the future of ONNX import and export, including expanding support for import and export, improving challenging cases for the importer, and allowing exporting to multiple operator set versions.
  
  
• 00:25:00 In this section, the speaker explains the difference between ONNX and MXNet. MXNet is a complete neural network framework, while ONNX is just a file format used to export and import neural networks. The speaker also mentions that there is a separate software called ONNX runtime, which acts as an actual framework. The speaker also provides information on how to check what functions can be exported to ONNX using internal utilities.

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Converting Tensorflow model to ONNX format - Human emotions detection

Converting Tensorflow model to Onnx format - Human emotions detection

The video discusses the benefits of converting pre-trained TensorFlow models to ONNX format, which provides a common format for representing machine learning models that can be interpreted across different hardware platforms using the ONNX runtime. By converting models, developers can run them more efficiently in different frameworks or use them more easily with other practitioners. The video demonstrates the process of converting TensorFlow and Keras models to ONNX format using tools and specifications provided on the ONNX GitHub repo, and highlights how the ONNX format optimizes the model and reduces runtime for predictions. The ONNX model also outperforms the TensorFlow model for human emotions detection on a CPU.

• 00:00:00 In this section of the video, the presenter discusses the ONNX format, which stands for open neural network exchange. The ONNX standard, co-developed by Microsoft, Facebook, and AWS, provides a common format for representing machine learning models that can be interpreted across different hardware platforms using the ONNX runtime. By converting pre-trained TensorFlow models to ONNX format, developers can then convert them to models in other frameworks such as PyTorch, enabling models to run even more efficiently in a different framework or be used by other practitioners. The ONNX runtime is a lightweight and modular inference engine that enables developers to run ONNX models on any hardware platform they choose. Overall, the presenter emphasizes the flexibility and ease of converting models to the ONNX format, making it easier for developers to work with any framework they choose while ensuring their models can run on any hardware platform as well.
  
  
• 00:05:00 In this section, the video covers the process of converting a TensorFlow model to the ONNX format using TensorFlow to ONNX tool and ONNX runtime. The video explains that while the initial TensorFlow model was almost one gigabyte in size and requires the same amount of space for deployment, the optimized ONNX version is only 327 megabytes. Additionally, the video shows how to convert a Keras model to the ONNX format using the specifications provided on the ONNX GitHub repo. Finally, the video ends by mentioning that the converted model can be tested for inference.
  
  
• 00:10:00 In this section, the speaker explains how to run an ONNX model using the ONNX runtime, which eliminates the need for TensorFlow, by demonstrating an emotion detection model running on a CPU. First, the speaker shows how to specify the CPU execution provider and import the ONNX runtime. Next, the speaker highlights the importance of specifying the output names correctly and passes in an input image using NumPy instead of TensorFlow. The ONNX model, which provides probabilities for emotions, is able to detect the emotion in the input image and produce the same probabilities much faster since it does not require TensorFlow. By importing the time library, the speaker demonstrates the difference in speed between running the emotion detection model with TensorFlow versus the ONNX runtime.
  
  
• 00:15:00 In this section of the video, the presenter demonstrates how to measure the inference time for the TensorFlow and ONNX models using both a CPU and GPU. The TensorFlow model with a GPU takes 0.15 seconds to run, while the ONNX model with a CPU takes 0.5 seconds. The presenter then installs the ONNX runtime with a GPU and restarts the runtime to consider the GPU version. Finally, the inference time for the ONNX model with a GPU is measured and compared to the TensorFlow model.
  
  
• 00:20:00 In this section, the video shows the process of converting a TensorFlow model to an ONNX format and how it can optimize the initial TensorFlow model. The ONNX format allows for optimizing the model and reducing the runtime for predictions. The video demonstrates running the original TensorFlow model with GPU and CPU, then the ONNX format with GPU, followed by testing both models with 100 predictions to measure the average time per prediction. The ONNX model resulted in an average time taken of 23 milliseconds for a single prediction, six times faster than the initial TensorFlow model.
  
  
• 00:25:00 In this section, the speaker discusses the performance of the ONNX model compared to the Tensorflow model for human emotions detection. Using a CPU, the ONNX model runs about twice as fast as the Tensorflow model. The speaker provides a specific example, stating that 0.8 divided by 0.35 was executed with both models and the Onnx model outperformed Tensorflow.

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How to convert almost any PyTorch model to ONNX and serve it using flask

How to convert almost any PyTorch model to ONNX and serve it using flask

The video tutorial demonstrates how to convert a PyTorch model to ONNX format and serve it using Flask. The presenter starts with importing the dataset and defining the model using data parallel, followed by loading the model weights and exporting it to ONNX. The video showcases how to create a Flask endpoint to serve the ONNX model, followed by converting tensors to numpy arrays and obtaining the output from the model. The speaker also applies the sigmoid function to the model output to convert it to a probability between 0 and 1. Finally, they switch the device to CPU for fair comparison and demonstrate the faster response time of the API. The video concludes by noting that there are many ways to optimize ONNX models for improved performance and inviting viewers to share their feedback in the comments section.

• 00:00:00 In this section, the video discusses how to convert PyTorch models to ONNX format and how to create a Flask endpoint to serve it. The video uses the bird sentiment model as a base model, with slight modifications to the max length and batch size parameters. The video then walks through importing the necessary dependencies and writing the main function for the conversion, including specifying the input shape. The video also covers how to save the converted model and how to create a Flask endpoint to serve the model.
  
  
• 00:05:00 In this section of the video, the presenter discusses how to convert a PyTorch model to ONNX and serve it using Flask. They start by importing the dataset and then defining the model using data parallel. Next, they load the model weights and put the model in evaluation mode. They show how to get the inputs by looking at the model and determining that the inputs are IDs, mask, and token type IDs. The presenter then shows how to export the model to ONNX format, specifying the three input names and output name. They also define dynamic axis, which is a dictionary that specifies which inputs or outputs have dynamic shape.
  
  
• 00:10:00 In this section, the video shows how to convert a PyTorch model to ONNX format using the "convert to ONNX" code to run the model using Flask. The PyTorch model is first converted to ONNX, and then a new file is created to make predictions from the ONNX model. The video shows how to specify a session for the ONNX model, demonstrating that ONNX runtime can be imported, and then the model can be loaded into the inference session. The video explains that the output and sending the model to the device is not needed and that there will be something returned.
  
  
• 00:15:00 In this section of the video tutorial, the speaker demonstrates how to create a simple dictionary for ONNX input in PyTorch, which is crucial because ONNX doesn't accept all tensors. The dictionary consists of keys as names and values as PyTorch tensors. To convert tensors to numpy errors, a to numpy function is created using the 'detach' and 'requires grad' commands. The speaker then showcases how to obtain the output from the model by calling the model.run function with pertinent arguments. Finally, the output can be printed directly or returned for use in Flask.
  
  
• 00:20:00 In this section, the presenter applies a sigmoid function to the output of the PyTorch model in order to convert the output to a probability between 0 and 1. They demonstrate how to write a simple sigmoid function and then incorporate it into the Flask API for the ONNX model. They also show how to start the API on a local host and port and demonstrate how to test the API using a curl request. Finally, the presenter switches the device to CPU for fairness in comparison with the old API, which was running on GPU, and runs the API again to demonstrate the faster response time.
  
  
• 00:25:00 In this section of the video, the speaker concludes the tutorial on how to convert PyTorch models to ONNX and serve them using Flask. They compare the timing of the old and new requests, which appear to be similar, and note that there are many more optimizations that users can apply to ONNX models to further improve their performance. The speaker encourages viewers to experiment with the possibilities that ONNX has to offer for distributing models to different environments and concludes by thanking viewers for watching and inviting them to share their suggestions in the comments section.

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How to convert PyTorch model to Tensorflow | onnx.ai | Machine Learning | Data Magic

How to convert PyTorch model to Tensorflow | onnx.ai | Machine Learning | Data Magic

In this video, the presenter demonstrates how to use the Open Neural Network Exchange (ONNX) library to convert a PyTorch model into a TensorFlow model. The benefits and usage of the ONNX library are discussed in detail, with a PyTorch model created to identify handwritten numbers used as an example. The process of training the model and converting it into the ONNX format is shown, before loading it into TensorFlow for prediction on sample images. The resulting TensorFlow model is saved as a .pb file, showcasing how the ONNX library can be used to convert any PyTorch model into TensorFlow.

• 00:00:00 In this section, the presenter discusses how to convert a PyTorch model into a TensorFlow model with the help of the Open Neural Network Exchange (ONNX) library. The ONNX library is an open source library that aims to increase compatibility across different machine learning libraries in the industry. The presenter explains the benefits and usage of the library and demonstrates how to install it and its dependent libraries. A PyTorch model is created to identify handwritten numbers, and the presenter shows how to train and test the model with the ONNX library. The code for the PyTorch model is not discussed in detail as it is not the focus of the video.
  
  
• 00:05:00 In this section, the transcript discusses the process of converting a trained PyTorch model into a TensorFlow model using the Neural Network Exchange (ONNX) library. Firstly, the PyTorch model is trained and saved as an mnist.pth file. The model is then converted into the ONNX format and loaded into TensorFlow to make predictions on sample images. Finally, the TensorFlow model is saved as a .pb file. The process demonstrates how to use the ONNX library to convert any PyTorch model into TensorFlow.
  
  
• 00:10:00 This excerpt does not provide any relevant content for summarization as it only consists of concluding remarks by the speaker, including a thank you message and a call to action for viewers to like and subscribe. The speaker also invites viewers to ask questions and provides links to the code and resources used in the video.

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How to convert Tensorflow model/tflite models to ONNX

How to convert Tensorflow model/tflite models to ONNX for importing it into unity

tf2onnx converts TensorFlow (tf-1.x or tf-2.x), tf.keras and tflite models to ONNX via command line or Python API.

https://github.com/onnx/tensorflow-onnx

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Convert Pytorch (pytorch lightning) model to onnx model with variable batch size

Convert Pytorch (pytorch lightning) model to ONNX model with variable batch size

In this tutorial we will learn how Convert Pytorch (pytorch lightning) model to ONNX model with variable/dynamic batch size.

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PyTorch ONNX Export Support - Lara Haidar, Microsoft

PyTorch ONNX Export Support - Lara Haidar, Microsoft

Lara Haidar from Microsoft explains the advantages of the PyTorch ONNX model export feature, allowing models to be moved from research to production and run on various hardware. She states that the ONNX runtime has become very popular, with millions of devices now using it and achieving notable performance gains. Moreover, the ONNX Export Support now includes improvements in model coverage, performance optimization, and backend support to ensure models can run on various versions with different backends. Finally, Lara encourages users to test the exported models and share feedback to enhance the feature further.

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296 - Converting keras trained model to ONNX format - Image Classification example

296 - Converting keras trained model to ONNX format - Image Classification example

The video tutorial covers the process of converting a Keras trained image classification model to ONNX format for deployment. The speaker shows how to create a model using Keras, compile it, and save it as an H5 file before converting it to ONNX format. They provide a step-by-step guide on how to import the necessary libraries for ONNX conversion, how to load the saved H5 model, and how to convert it to ONNX format using a single line of code. The presenter then demonstrates how to use the resulting ONNX model in an ONNX runtime session, shows how to predict classes in an image classification example using ONNX, and compares the probabilities of the predictions using ONNX and Keras. The speaker emphasizes the effectiveness and advantages of using ONNX for deployment and notes the simplicity of converting an existing HDF file to ONNX.

• 00:00:00 In this section, the speaker discusses the process of converting a Keras trained model to ONNX format, specifically for image classification. They explain that ONNX is an intermediary format that can convert models saved in various file formats such as H5, to work on different runtimes such as ONNX runtime. The speaker guides the audience through creating a simple image classification model with Keras, saving it as an H5 file, and then converting it to ONNX format. They then compare the accuracy of the two formats and discuss the various optimizations that can be added to ONNX models. The speaker also emphasizes installing the necessary libraries for Keras, in addition to the ONNX runtime and H5 Pi, for performing inferences.
  
  
• 00:05:00 In this section, the presenter discusses the dataset used for training and testing the model, which includes 50,000 small images assigned for training and 10,000 for testing. The images are 32 by 32 by 3, and he normalizes them by scaling the values between 0 to 1. Next, the presenter converts the integer-encoded values into categorical values and defines a model with convolutional layers, batch normalization, dropout, and softmax activation to return 10 values representing the probabilities of each class. Finally, he compiles the model using stochastic gradient descent optimizer and tracks the accuracy metrics while minimizing the loss function of categorical cross entropy. The presenter also demonstrates using the early stopping callback to terminate the training process after ten epochs.
  
  
• 00:10:00 In this section, the speaker explains how they trained a model using Keras and saved it in H5 format before moving on to converting it to ONNX format. They show how to load the saved model, import the necessary libraries for ONNX conversion, and use the single line of code required to convert the Keras model to ONNX format. They then show how to use the resulting ONNX model in an ONNX runtime session and compare speeds to demonstrate the advantages of using ONNX for deployment.
  
  
• 00:15:00 In this section, the speaker explains how to use ONNX to predict classes in an image classification example. First, they show how to get the input and output names from the model. Then, they demonstrate how to expand dimensions to make the image ready for inference. After defining the ground truth, they run the prediction and compute the predicted class using argmax. Finally, they plot the results and compare the predictions using ONNX and Keras. The probabilities are almost identical, demonstrating the effectiveness of ONNX for image classification tasks.
  
  
• 00:20:00 In this section, the speaker demonstrates the successful conversion of a trained Keras model to the ONNX format for image classification. Probability results are shown for various test images, with the model achieving excellent results after being trained for 50 epochs. The speaker notes that converting an existing HDF file to ONNX is a simple process and hints at a forthcoming tutorial on image segmentation.

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297 - Converting keras trained model to ONNX format​ - Semantic Segmentation

297 - Converting keras trained model to ONNX format​ - Semantic Segmentation

This video focuses on converting a keras trained model to ONNX format for semantic segmentation of electron microscopy images of mitochondria. The presenter provides detailed steps on how to crop and load images, use data augmentation techniques, define generators for training and validation, train and save the model. The video also covers converting the model to ONNX format using the tf2onnx.convert library and using the ONNX model for prediction. The presenter highlights best practices for training and conversion and provides links to their previous videos on multi-class segmentation. The tutorial concludes with the presenter stating that this is the end of the ONNX series and they will focus on other topics in the next video.

• 00:00:00 In this section, the presenter builds on the previous videos where they demonstrated how to save the weights of a Keras trained model to ONNX and perform inference using the converted model. This time, they provide steps on how to train and save a semantic segmentation model to ONNX. They use an electron microscopy dataset of mitochondria, where they have raw and labeled images. The presenter shows how to crop the images into appropriate sizes, load them into the neural network, and use the simple unit model to perform binary segmentation. They explain how the simple unit model works and provide links to their previous videos where they explained more complex neural networks for multi-class semantic segmentation.
  
  
• 00:05:00 In this section, the author discusses the process of dividing a small dataset of 165 images into 12 images for training purposes. They apply the patchify library to divide the images and masks into smaller patches of 256x256 pixels. They also discuss a few random images as a "sanity check" to ensure that the images and masks line up accurately. The author uses data augmentation techniques such as height shift range, shear range, and zoom range to generalize the results. They also mention how to ensure that random transformations are not truly random and that the same seed is used for generating transformations in both images and masks to maintain the same randomness. Finally, the author defines the generators that will be used for training and validation, and seeds are fixed for consistency.
  
  
• 00:10:00 In this section, the speaker is preparing to train a unit model for semantic segmentation using best practices, and combines the image and mask generator into one. They go on to define their batch size and steps per epoch before moving onto training the model, applying a threshold of 0.5 to convert the probability output to binary output. Though the model could use more training images, it is doing a moderately good job of segmenting images. The speaker then saves the H5 file and proceeds to convert it into onnx using the TF2 to onnx library.
  
  
• 00:15:00 In this section, the video explains how to convert a Keras trained model to ONNX format using tf2onnx.convert. After loading the model, optimizations can be added before saving it as a .onnx file. In addition, the video demonstrates using the ONNX model in a runtime session for prediction, showing identical results to the Keras model. The video concludes by stating that this tutorial is the end of the ONNX series, and the presenter will focus on other topics in the next video.