DirectML Tutorials
Welcome to the DirectML tutorials! This section provides step-by-step guides to help you leverage the power of DirectML for hardware-accelerated machine learning on Windows.
Getting Started
Begin your journey with DirectML by setting up your development environment and understanding the core concepts.
- A Windows 10/11 PC with a DirectX 12 compatible GPU.
- Visual Studio 2022 or later.
- Windows SDK version 10.0.19041.0 or later.
In this tutorial, you will learn:
- How to install the necessary SDKs and tools.
- Understanding the DirectML programming model.
- Initializing DirectML devices and command queues.
- Creating and managing tensors.
Learn more in "Your First DirectML Application"
Basic Operations
Explore fundamental DirectML operations and how to implement them in your applications.
This section covers:
- Element-wise operations (addition, multiplication, etc.).
- Matrix multiplication.
- Activation functions.
Example code snippet for element-wise addition:
// Assume tensorA and tensorB are already created and populated
IDMLOperatorX32* addOperator;
DML_OPERATOR_DESC addDesc = { DML_OPERATOR_ELEMENT_WISE_ADD, /* ... */ };
dmlDevice->CreateOperator(&addDesc, &addOperator);
// ... execution code ...
Explore "Implementing Basic ML Kernels"
Convolutional Networks
Dive into implementing convolutional layers, a cornerstone of many deep learning models.
Topics include:
- Understanding convolution operations.
- Efficiently implementing convolution using DirectML.
- Handling different kernel sizes and strides.
Mastering "Convolutional Layers with DirectML"
Tensor Operations
Deepen your understanding of tensor manipulation and data flow within DirectML.
Learn about:
- Tensor creation, reshaping, and broadcasting.
- Data layout and memory management.
- Interoperability with other graphics APIs.
Advanced "Tensor Management in DirectML"
Performance Optimization
Unlock the full potential of DirectML by optimizing your ML workloads for maximum performance.
Discover techniques for:
- Batching operations.
- Utilizing GPU features effectively.
- Profiling and identifying bottlenecks.
"Optimizing DirectML Workloads"
Continue exploring the documentation for more in-depth guides and API references.