前言本文总结了的NVIDIA企业的GPU、AI/SOC等产品的硬件、软件栈&工具链,可作为自动驾驶计算&域控平台学习、研发的参考资料。 1. NV GPU凭借着高性能的算力和完善的软件工具生态,英伟达GPU在自动驾驶、云…
Every major deep learning framework such as PyTorch, TensorFlow, and JAX rely on Deep Learning SDK libraries to deliver high-performance multi-GPU accelerated training. As a framework user, it’s as simple as downloading a framework and instructing it to use GPUs for training. Learn more about...
DPX instructions comparison NVIDIA HGX™ H100 4-GPU vs dual socket 32-core IceLake. Accelerated Data Analytics Data analytics often consumes the majority of time in AI application development. Since large datasets are scattered across multiple servers, scale-out solutions with commodity CPU-only ser...
Deep learning frameworks offer building blocks for designing, training and validating deep neural networks, through a high level programming interface. Every major deep learning framework such as PyTorch, TensorFlow, and JAX rely on Deep Learning SDK libraries to deliver high-performance multi-GPU accel...
另外,我们现在有以下GitHub项目(GitHub - NVIDIA-AI-IOT/jetson_dla_tutorial: A tutorial for getting started with the Deep Learning Accelerator (DLA) on NVIDIA Jetson),可以帮助您开始使用DLA。它涵盖了定义和分析模型、调整模型以提高DLA兼容性以及执行INT8校准的内容。这可能有助于您了解与DLA一起工作相关的...
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人工智能与深度学习 (Artificial Intelligence and Deep Learning) 近年来,NVIDIA在人工智能和深度学习领域取得了显著进展。其CUDA架构允许开发者利用GPU进行并行计算,大大加速了深度学习模型的训练过程。NVIDIA的Tensor Core技术更是专为深度学习优化,提升了计算效率。
主要用于在多个GPU之间进行数据传输和通信。它被设计为在异构计算环境中（包括NVIDIA GPU和CPU）高效地...
Deep Learning GPU Training System. Contribute to NVIDIA/DIGITS development by creating an account on GitHub.
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