Deep Learning Documentation

Optimized Frameworks
The NVIDIA Optimized Frameworks such as Kaldi, NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet, NVCaffe, PyTorch, and TensorFlow (which includes DLProf and TF-TRT) offer flexibility with designing and training custom deep neural networks (DNNs) for machine learning and AI applications.
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cuDNN
NVIDIA CUDA Deep Neural Network (cuDNN) library is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. Deep learning researchers and framework developers worldwide rely on cuDNN for high-performance GPU acceleration.
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TensorRT
NVIDIA TensorRT is an SDK for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high-throughput for deep learning inference applications. The core of NVIDIA TensorRT is a C++ library that facilitates high-performance inference on NVIDIA GPUs. TensorRT takes a trained network, which consists of a network definition and a set of trained parameters, and produces a highly optimized runtime engine which performs inference for that network.
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Triton Inference Server
NVIDIA Triton Inference Server (formerly TensorRT Inference Server) provides a cloud inferencing solution optimized for NVIDIA GPUs. The server provides an inference service via an HTTP or GRPC endpoint, allowing remote clients to request inferencing for any model being managed by the server.
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NCCL
The NVIDIA Collective Communications Library (NCCL) is a library of multi-GPU collective communication primitives that are topology-aware and can be easily integrated into applications. Collective communication algorithms employ many processors working in concert to aggregate data. NCCL is not a full-blown parallel programming framework; rather, it is a library focused on accelerating collective communication primitives.
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DALI
NVIDIA Data Loading Library (DALI) is a collection of highly optimized building blocks, and an execution engine, to accelerate the pre-processing of the input data for deep learning applications. DALI provides both the performance and the flexibility for accelerating different data pipelines as a single library. This single library can then be easily integrated into different deep learning training and inference 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 multiplier, 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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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. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real time with advanced visualizations, and selecting the best performing model from the results browser for deployment.
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NeMo
NVIDIA NeMo 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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Jarvis
NVIDIA Jarvis is an SDK for building multimodal conversational systems. Jarvis is used for building and deploying AI applications that fuse vision, speech, sensors, and services together to achieve conversational AI use cases that are specific to your domain of expertise. It offers a complete workflow to build, train and deploy AI systems that can use visual cues such as gestures and gaze along with speech in context.
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