Supercharging AI Development on RTX AI PCs: A Deep Dive into Microsoft and NVIDIA's Collaboration

Introduction:

The convergence of powerful hardware and streamlined software tools is rapidly democratizing Artificial Intelligence (AI) development. At Microsoft Ignite 2024, a significant leap forward was announced: a collaboration between Microsoft and NVIDIA aimed at empowering Windows developers to build and optimize AI applications directly on RTX AI PCs. This article delves into the purpose, features, installation, and usage of these new tools, providing a technical overview for developers eager to leverage the power of local AI processing.

Purpose:

The core purpose of this collaboration is to simplify and accelerate the AI development workflow for Windows developers. By providing optimized tools and libraries, Microsoft and NVIDIA aim to:

• Reduce Latency: Execute AI workloads locally on RTX AI PCs, eliminating the need for constant cloud connectivity and significantly reducing latency for real-time applications.
• Enhance Privacy: Process sensitive data locally, ensuring greater privacy and control over data security.
• Improve Accessibility: Enable AI development on a wider range of devices, making AI accessible to more developers and users.
• Streamline Development: Provide a unified and optimized development experience, reducing the complexity of building and deploying AI applications on Windows.
• Maximize Performance: Leverage the dedicated AI acceleration hardware (Tensor Cores) in NVIDIA RTX GPUs for optimal performance.

Key Features:

The collaboration introduces a suite of tools and libraries designed to enhance AI development on RTX AI PCs. Key features include:

• Optimized AI Libraries: Pre-built and optimized libraries for common AI tasks such as image recognition, natural language processing (NLP), and object detection. These libraries are designed to leverage the Tensor Cores in NVIDIA RTX GPUs for accelerated performance. Examples include:
  • ONNX Runtime (ORT) with NVIDIA Execution Provider: ORT provides a cross-platform framework for running trained AI models. The NVIDIA Execution Provider optimizes ORT for execution on NVIDIA GPUs, ensuring maximum performance.
  • NVIDIA TensorRT Integration: TensorRT is an SDK for high-performance deep learning inference. Integration with Windows allows developers to deploy optimized models directly on RTX AI PCs.
• Windows Subsystem for Linux (WSL) Integration: Improved integration between WSL and NVIDIA GPUs allows developers to seamlessly run Linux-based AI development environments within Windows.
• DirectML Enhancements: DirectML, Microsoft's hardware-accelerated machine learning API, is further optimized for NVIDIA RTX GPUs, providing a low-level interface for developers who require fine-grained control over their AI workloads.
• Simplified Deployment: Tools and documentation that simplify the process of deploying AI models and applications to Windows devices.
• Developer Tooling: Enhanced debugging and profiling tools specifically designed for AI workloads running on RTX AI PCs.

Code Example:

The following example demonstrates how to use ONNX Runtime with the NVIDIA Execution Provider to perform inference with a pre-trained image classification model:

import onnxruntime
import numpy as np
from PIL import Image

# 1. Load the ONNX model
model_path = "resnet50.onnx"  # Replace with your model path
session_options = onnxruntime.SessionOptions()

# 2. Configure the NVIDIA Execution Provider
session_options.providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']  # Prioritize CUDA

# 3. Create an inference session
session = onnxruntime.InferenceSession(model_path, sess_options=session_options)

# 4. Prepare the input data (e.g., resize and normalize an image)
def preprocess_image(image_path, input_shape=(224, 224)):
    img = Image.open(image_path).resize(input_shape)
    img_data = np.array(img).astype('float32')
    img_data = np.expand_dims(img_data, axis=0)
    img_data = img_data / 255.0  # Normalize
    return img_data

image_path = "cat.jpg" # Replace with your image path
input_data = preprocess_image(image_path)

# 5. Get input and output names
input_name = session.get_inputs()[0].name
output_name = session.get_outputs()[0].name

# 6. Run inference
results = session.run([output_name], {input_name: input_data})

# 7. Process the output (e.g., get the predicted class)
predictions = results[0]
predicted_class = np.argmax(predictions)

print(f"Predicted Class: {predicted_class}")

Explanation:

• onnxruntime.SessionOptions(): Creates an object to configure the ONNX Runtime session.
• session_options.providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']: Specifies the execution providers to use. CUDAExecutionProvider prioritizes the NVIDIA GPU. If CUDA is not available, it will fall back to the CPU.
• onnxruntime.InferenceSession(model_path, sess_options=session_options): Creates an inference session using the specified ONNX model and session options.
• preprocess_image(): A function to prepare the input image for the model. This typically involves resizing, normalization, and converting the image data to a NumPy array.
• session.run(): Runs the inference. It takes a list of output names and a dictionary of input names and data.
• np.argmax(predictions): Finds the index of the maximum value in the output array, representing the predicted class.

Installation:

To get started with AI development on RTX AI PCs, follow these steps:

1. Hardware Requirements: Ensure you have a Windows PC equipped with an NVIDIA RTX GPU.
2. NVIDIA Drivers: Install the latest NVIDIA drivers compatible with your RTX GPU. You can download them from the NVIDIA website.
3. Python Environment: Install Python (version 3.8 or later) using a distribution like Anaconda or Miniconda.

4. ONNX Runtime: Install the ONNX Runtime package with CUDA support:
   

   pip install onnxruntime-gpu
   

   If you don't have a CUDA-enabled GPU, you can install the CPU-only version:
   

   pip install onnxruntime
   

5. Optional: WSL Integration: If you plan to use WSL for development, ensure you have WSL 2 installed and configured. Refer to the official Microsoft documentation for instructions. Ensure your WSL distribution has the necessary CUDA drivers installed.

6. Other Libraries: Install any other required libraries based on your specific AI tasks (e.g., TensorFlow, PyTorch, scikit-learn).

Conclusion:

The collaboration between Microsoft and NVIDIA marks a significant advancement in AI development on Windows. By providing optimized tools, libraries, and hardware acceleration, they are empowering developers to build and deploy AI applications with greater efficiency, speed, and privacy. This article has provided a technical overview of these tools, including code examples and installation instructions, to help developers leverage the power of RTX AI PCs and unlock the full potential of local AI processing. As the AI landscape continues to evolve, this collaboration will undoubtedly play a crucial role in shaping the future of AI development on Windows.