Boost Performance with ONNX Runtime for Training and Inference

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Boost Performance with ONNX Runtime for Training and Inference

Table of Contents

  1. Introduction
  2. Goals of the Demo
  3. Prerequisites
  4. Onyx Runtime and its Benefits
  5. Deep Speed and Hugging Face
  6. Code Walkthrough
  7. Containerizing Deep Learning Applications
  8. Training with PyTorch, Onyx Runtime, and Deep Speed
  9. Inferencing with PyTorch and Onyx Runtime
  10. Conclusion

Introduction

Welcome to this tutorial on Onyx runtime and how it can be used to accelerate deep learning applications. In this tutorial, we will cover various topics such as the goals of the demo, prerequisites, an overview of Onyx runtime, deep speed, and hugging face, as well as a code walkthrough on using Onyx runtime for training and inference.

Goals of the Demo

The main goal of this demo is to showcase how Onyx runtime training and inference can significantly improve performance. We will demonstrate how Onyx runtime composes well with deep speed, a deep learning optimizing library, and how it can be integrated into the Azure container for PyTorch. Additionally, we will build a question answering model using deep learning and showcase the benefits of leveraging Onyx runtime and deep speed.

Prerequisites

Before diving into the actual code, there are a few prerequisites that You need to be aware of. First, you will need access to an Azure Machine Learning (AML) workspace. This is where all the code will be run and hosted. You will also need an Azure CLI enabled dev machine and some knowledge of Docker. Optionally, if you are interested in the AI model itself, there is a link provided to a hugging face tutorial for further understanding.

Onyx Runtime and its Benefits

Onyx runtime offers two aspects: training and inference. In terms of training, Onyx runtime accelerates large transformer-Based PyTorch models. You can expect faster training and the ability to run larger models on the same GPU hardware due to memory optimizations. Onyx runtime is also well-integrated into the PyTorch ecosystem, making it easy for developers to include it in their deep learning applications.

For inference, Onyx runtime provides a high-performance engine for deploying models to production. It is optimized for both cloud and edge workloads and integrates well with various hardware accelerators. It can be used not only for PyTorch models but also for TensorFlow models and other compatible formats.

Deep Speed and Hugging Face

Deep speed is a deep learning optimizing library that allows for training and inference with billions of parameters distributed across multiple GPUs. It offers excellent throughput, reduced GPU constraints, unparalleled latency, and model size reduction. It is truly a powerful tool for accelerating deep learning models.

Hugging Face is an open-source collection of Python modules that allows developers to leverage large language transformer models easily. With Hugging Face, developers can train language models at Scale, take AdVantage of Onyx runtime natively, and access a centralized repository of state-of-the-art models.

Code Walkthrough

In this section, we will walk through the actual code for setting up and running the demo. We will cover containerizing deep learning applications, training with PyTorch, Onyx runtime, and deep speed, as well as inferencing with PyTorch and Onyx runtime.

Containerizing Deep Learning Applications

To containerize your deep learning application using Azure, you can use the Azure container for PyTorch. This allows for easy deployment and management of your application on Azure. We will guide you on how to set up and add the container to your Azure ML workspace.

Training with PyTorch, Onyx Runtime, and Deep Speed

We will demonstrate how to train a question answering model using PyTorch, Onyx runtime, and deep speed. The training script will cover various aspects such as data loading, model setup, Onyx runtime optimization, deep speed configuration, and uploading the trained model weights to Azure ML.

Inferencing with PyTorch and Onyx Runtime

Once the model is trained, we will perform inference using PyTorch and Onyx runtime. We will showcase the speed improvement achieved by leveraging Onyx runtime for inference compared to the baseline PyTorch approach. The inferencing script will demonstrate loading the trained weights, tokenizing the input data, and running inference using both PyTorch and Onyx runtime.

Conclusion

In conclusion, Onyx runtime offers significant performance benefits for both training and inference of deep learning models. By leveraging deep speed and integrating with hugging face, developers can accelerate their deep learning applications without sacrificing accuracy or convergence. Containerization using Azure container for PyTorch further simplifies the deployment process. We hope you find this tutorial informative and encourage you to explore the Onyx runtime training examples and experiment with the code on your own.

Highlights

  • Onyx runtime training provides faster training and memory optimization for large PyTorch models.
  • Onyx runtime inference offers high-performance engine deployment options with support for various hardware accelerators.
  • Deep speed library optimizes training by enabling training on distributed systems with billions of parameters.
  • Hugging face provides a collection of Python modules for leveraging large language transformer models.
  • Azure container for PyTorch simplifies the containerization process for deep learning applications on Azure.

FAQ

Q: What are the benefits of using Onyx runtime for training?

A: Onyx runtime accelerates training with faster training times, enhanced memory optimization, and the ability to run larger models on the same hardware.

Q: Can Onyx runtime be used for deployment in production?

A: Yes, Onyx runtime offers a high-performance engine for deploying models to production, with support for various hardware accelerators.

Q: Is deep speed compatible with Onyx runtime?

A: Yes, deep speed can be integrated with Onyx runtime for training deep learning models, providing further performance improvements.

Q: How can I containerize my deep learning application using Azure?

A: You can use the Azure container for PyTorch, which simplifies the process of deploying and managing your deep learning application on Azure.

Q: Are there any performance improvements when using Onyx runtime for inference?

A: Yes, Onyx runtime can significantly improve inference speed compared to the baseline PyTorch approach, without sacrificing accuracy or convergence.

Q: What are the prerequisites for running the demo?

A: The prerequisites include access to an AML workspace, an Azure CLI enabled dev machine, knowledge of Docker, and optionally, familiarity with the AI model used in the tutorial.

Q: Where can I find additional support or ask questions about Onyx runtime?

A: You can visit the issues page of the Onyx runtime training examples codebase and open a question for support from the Onyx runtime team.

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