DirectML: A Comprehensive Introduction
DirectML is a high-performance, hardware-accelerated library designed specifically for machine learning tasks on DirectX 12. It provides a seamless way to leverage the power of GPUs across a wide variety of hardware, making tasks like machine learning faster and more efficient. DirectML is compatible with all DirectX 12-capable GPUs, including those from major vendors like AMD, Intel, NVIDIA, and Qualcomm.
Getting Started with DirectML
DirectML is included with Windows 10 as of version 1903, and is also offered as a standalone package for applications requiring a specific version or those operating on earlier Windows 10 versions. This flexibility allows developers to utilize DirectML for a variety of applications ranging from games to real-time machine learning models.
Hardware Requirements
To use DirectML, a DirectX 12-capable device is necessary. Most modern graphics cards support DirectX 12, including:
- AMD GCN 1st Gen (Radeon HD 7000 series) and newer
- Intel Haswell (4th-gen core) HD Integrated Graphics and newer
- NVIDIA Kepler (GTX 600 series) and newer
- Qualcomm Adreno 600 and newer
For Application Developers
Application developers can access DirectML via a native C++ DirectX 12 API. The required headers and libraries are available through the Windows 10 SDK or a NuGet package, making it straightforward to integrate DirectML into their applications.
For Users, Data Scientists, and Researchers
DirectML serves as a backend to many machine learning frameworks, including Windows ML, ONNX Runtime, and TensorFlow. This integration means that users and researchers can benefit from DirectML's acceleration capabilities without needing to interact directly with the library itself.
DirectML Samples
Numerous sample projects are available to help developers understand and leverage DirectML in their applications. These samples cover a broad range of use cases, including simple introductions like "HelloDirectML", super-resolution scaling, object detection with YOLOv4, and more.
Windows ML on DirectML
Windows ML, powered by DirectML, offers a high-performance API for deploying machine learning tasks on Windows devices. It ensures that applications can run efficiently regardless of the underlying hardware, thanks to DirectML's GPU acceleration capabilities.
ONNX Runtime on DirectML
For those using multiple machine learning frameworks, ONNX Runtime provides a cross-platform solution that can incorporate DirectML as an execution provider. This allows for enhanced hardware acceleration across different systems, although it requires Windows 10.
PyTorch with DirectML
Through torch-directml, PyTorch models can train and infer on a broad spectrum of DirectX 12-compatible hardware. This support extends to native Windows environments as well as the Windows Subsystem for Linux, bringing enhanced versatility for model deployment.
TensorFlow with DirectML
TensorFlow users can also benefit from DirectML’s hardware acceleration. DirectML support is available for TensorFlow 1.15 and enables efficient model training and inference, providing significant performance improvements on compatible hardware.
Feedback and Contributing
DirectML encourages feedback and contributions from the community. Whether related to TensorFlow, PyTorch, Windows ML, or ONNX Runtime, users can provide feedback through dedicated issue-tracking systems or direct contact points.
This project follows Microsoft's open-source code of conduct, welcoming contributions while ensuring interactions remain respectful and constructive.
In summary, DirectML offers a powerful solution for developers and researchers looking to leverage machine learning technologies efficiently across a variety of hardware platforms. Whether integrated into existing machine learning frameworks or used directly in applications, DirectML provides robust support for high-performance machine learning tasks.