Master oneDNN: A Comprehensive Guide

Table of Contents:

1. Introduction
2. Getting Started with oneDNN
3. Installing the oneAPI Base Toolkit
4. Finding the Source Code on GitHub
5. Exploring Examples and Tutorials
6. Trying out oneDNN on the Intel Dev-Cloud
7. Using Jupyter Notebooks for the Getting Started Tutorial
8. Building an Implementation Step by Step
9. Understanding the Programming Model and Concepts of oneDNN
10. Using vTune Analyzer for Performance Analysis
11. Engaging with the Design Team and Additional Resources

Getting Started with oneDNN

oneDNN, also known as the oneAPI Deep Neural Network Library, is a powerful library that provides optimized functions for deep learning workloads. In this article, we will guide you through the process of getting started with oneDNN, from installation to building your first implementation. Whether you are new to oneDNN or looking to enhance your existing knowledge, this guide will provide you with the necessary steps to begin using this library effectively. So, let's dive in and explore the world of oneDNN!

1. Introduction

Before we begin, let's understand what oneDNN is and how it can benefit your deep learning projects. oneDNN is a library that offers highly optimized functions for deep neural networks. It provides a range of primitives and operations that are essential for training and inference tasks. By utilizing oneDNN, you can enhance the performance and efficiency of your deep learning models, ultimately achieving faster and more accurate results.

2. Installing the oneAPI Base Toolkit

To get started with oneDNN, you will need to install the oneAPI Base Toolkit. This toolkit includes all the necessary components and dependencies to work with oneDNN effectively. You can download the toolkit from the official oneAPI website. Once you have downloaded the toolkit, follow the installation instructions provided to set it up on your system.

3. Finding the Source Code on GitHub

If you prefer to access the source code of oneDNN, you can find it on GitHub. By visiting the official oneDNN repository on GitHub, you can explore the source code and gain a deeper understanding of the library's implementation. The repository also contains examples, tutorials, and code snippets that can help you learn and experiment with oneDNN.

4. Exploring Examples and Tutorials

To gain hands-on experience with oneDNN, it is recommended to explore the examples and tutorials available. These resources provide step-by-step guidance on how to use different functionalities of the library. By following along with the examples and tutorials, you can familiarize yourself with the programming techniques and best practices for utilizing oneDNN effectively.

5. Trying out oneDNN on the Intel Dev-Cloud

If you want to try out oneDNN without installing it on your local system, you can utilize the Intel Dev-Cloud. The Intel Dev-Cloud provides access to a variety of Intel hardware, allowing you to run and test your oneDNN implementations on different devices. To get started, simply search for the Intel Dev-Cloud and apply for access. Once you receive the email with the link, you can start using the dev-cloud, open a Linux terminal, and run benchmarks on the provided samples.

6. Using Jupyter Notebooks for the Getting Started Tutorial

For a more interactive learning experience, you can use Jupyter notebooks to follow the getting started tutorial. By browsing to the oneAPI-Samples/libraries/oneDNN/tutorials directory, you can find a Jupyter notebook that walks you through the implementation of a simple model using oneDNN. Just open the notebook, follow the instructions, and run the samples step by step. This approach is an excellent way to understand the programming model of oneDNN.

7. Building an Implementation Step by Step

To build your own oneDNN implementation, you need to define the structure for the input and output tensors. The use of memory types is crucial in defining the input and output data, and in many cases, the weights are also included as input. Once the input and output tensors are defined, you can create the specific operation you want to perform using oneDNN's primitives. These primitives are real mathematical or logical functions that can be executed multiple times. By using post-ops, you can concatenate or fuse certain operations, enhancing performance. To execute the primitives, you need to work with a stream, which operates on an engine. The engine represents the target device on which the stream will run, such as an Intel CPU or an NVIDIA GPU.

8. Understanding the Programming Model and Concepts of oneDNN

To fully utilize the capabilities of oneDNN, it is essential to understand its programming model and concepts. This includes familiarizing yourself with the engine, stream, memory formats, and descriptors. By grasping these concepts, you can optimize your oneDNN implementations and make the most of the library's features. The documentation provides detailed information about the programming model and concepts, ensuring you have all the necessary knowledge to utilize oneDNN effectively.

9. Using vTune Analyzer for Performance Analysis

To analyze and optimize the performance of your oneDNN applications, you can leverage vTune Analyzer. vTune Analyzer automatically detects the just-in-time (JIT) code of oneDNN and provides valuable statistics for tracing and debugging performance bottlenecks. By utilizing the insights provided by vTune Analyzer, you can fine-tune your implementations and achieve maximum performance on your target devices.

10. Engaging with the Design Team and Additional Resources

As you explore oneDNN further, you may have questions, request enhancements, or want to provide feedback. The official oneDNN GitHub repository is an excellent platform to communicate with the design team and Seek answers to your queries. Additionally, the provided links in this article can be used to access further information and resources related to oneDNN, ensuring you are always up-to-date with the latest developments and best practices.