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This chapter explores deep learning frameworks with GPU support, which is essential for building efficient and scalable AI models. It introduces the evolution of deep learning frameworks and highlights the critical role of GPU acceleration. A comparative overview of TensorFlow, PyTorch, JAX, and MXNet is provided, emphasizing architectural, usability, and performance differences. Readers are guided through setting up GPU support, including installing CUDA, cuDNN, and verifying GPU availability. Practical sections on TensorFlow and PyTorch explain GPU-compatible tensors, device placement, mixed-precision training, and multi-GPU strategies like MirroredStrategy and DDP, with hands-on examples such as CNN training on CIFAR-10. The discussion also covers frameworks like JAX, MXNet, and ONNX Runtime. Advanced topics include multi-GPU training, model, and data parallelism, GPU memory optimization, TensorRT integration, and deployment on edge devices and containers. The chapter concludes with techniques for benchmarking and tuning performance using tools like Nsight Systems, nvidia-smi, and TensorBoard.

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Advanced Deep Learning Architectures for GPU Workloads

  • 章节

  • 发表日期：

    2025年

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  • Ramchandra S Mangrulkar,
  • Pallavi Vijay Chavan

摘要

This chapter explores deep learning frameworks with GPU support, which is essential for building efficient and scalable AI models. It introduces the evolution of deep learning frameworks and highlights the critical role of GPU acceleration. A comparative overview of TensorFlow, PyTorch, JAX, and MXNet is provided, emphasizing architectural, usability, and performance differences. Readers are guided through setting up GPU support, including installing CUDA, cuDNN, and verifying GPU availability. Practical sections on TensorFlow and PyTorch explain GPU-compatible tensors, device placement, mixed-precision training, and multi-GPU strategies like MirroredStrategy and DDP, with hands-on examples such as CNN training on CIFAR-10. The discussion also covers frameworks like JAX, MXNet, and ONNX Runtime. Advanced topics include multi-GPU training, model, and data parallelism, GPU memory optimization, TensorRT integration, and deployment on edge devices and containers. The chapter concludes with techniques for benchmarking and tuning performance using tools like Nsight Systems, nvidia-smi, and TensorBoard.