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Optimized Frameworks
The NVIDIA Optimized Frameworks such as Kaldi, MXNet, NVCaffe, PyTorch, and TensorFlow offer flexibility with designing and training custom deep neural networks (DNNs) for machine learning and AI applications.
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Deep Learning SDK
The NVIDIA Deep Learning SDK offers powerful libraries such as cuDNN and NCCL for training, TensorRT and Triton Inference Server for inference, and DALI for data loading. Together, these tools and libraries with the use of mixed precision and Tensor Cores can be used to design and deploy GPU-accelerated deep learning applications.
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Deep Learning Performance
GPUs accelerate machine learning operations by performing calculations in parallel. Many operations, especially those representable as matrix multiplies, will see good acceleration right out of the box. Even better performance can be achieved by tweaking operation parameters to efficiently use GPU resources. The performance documents presents the tips that we think are most widely useful.
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NVIDIA Neural Modules
Neural Modules is a flexible python toolkit enabling data scientists and researchers to build state of the art speech and language deep learning models composed of reusable building blocks that can be safely connected together for conversational AI applications.
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NVIDIA GPU Cloud
NVIDIA GPU Cloud empowers AI researchers with fast and easy access to performance-engineered deep learning framework containers, pre-integrated and optimized by NVIDIA.
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NVIDIA DIGITS
The NVIDIA Deep Learning GPU Training System (DIGITS) can be used to rapidly train highly accurate DNNs for image classification, segmentation and object detection tasks.
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DGX Systems
DGX Systems provide integrated hardware, software, and tools for running GPU-accelerated, HPC applications such as deep learning, AI analytics, and interactive visualization.
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