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TensorRT is a deep learning inference engine and software development kit designed to optimize and deploy neural networks for high-performance execution on NVIDIA GPUs. It functions as a GPU acceleration framework that reduces latency and increases throughput for trained models during production deployment. The toolkit imports models from the Open Neural Network Exchange format and transforms them into optimized engines. It utilizes graph-based model optimization, layer-fusion kernel generation, and precision-based quantization to convert floating point weights into lower precision formats.
Paddle is a deep learning framework designed for building, training, and deploying neural networks. It provides a platform for constructing models using tensor-based computations and supports both dynamic and static execution graphs to facilitate research and production workflows. The platform functions as a distributed machine learning system, enabling the scaling of training workloads across multiple nodes and hardware clusters. It includes a comprehensive toolkit for model deployment and optimization, allowing users to convert external model formats, compress trained models for resource-co
This project is a comprehensive collection of educational examples and reference implementations for building vision and language models using PyTorch. It serves as a deep learning tutorial covering the end-to-end process of developing neural networks, from initial architecture definition to final production deployment. The repository provides detailed guides on implementing a wide range of domain-specific models, including convolutional neural networks for object detection and segmentation, as well as transformer and recurrent architectures for natural language processing. It emphasizes gene
This project is a comprehensive instructional resource and course for building neural networks using PyTorch. It covers the fundamental building blocks of deep learning, including tensor manipulation, automatic differentiation, and the construction of modular neural network components. The repository serves as a technical guide for several specialized domains. It provides implementation details for computer vision tasks such as image classification, object detection, and semantic segmentation, as well as natural language processing workflows involving transformers, recurrent networks, and gen
onnxsim is a deep learning graph optimizer and model simplifier designed to reduce the complexity of ONNX computation graphs. It functions as a model compressor that replaces complex operator sequences with simplified constant outputs to decrease operational overhead.
The main features of onnxsim/onnxsim are: Model Graph Optimizers, ONNX Model Optimizers, Operator, Model Compression, ONNX Intermediate Representation Manipulators, Constant Folding, Graph Pattern Rewriters, Deep Learning Optimization.
Projects with overlapping indexed features include: nvidia/tensorrt — TensorRT is a deep learning inference engine and software development kit designed to optimize and deploy neural… paddlepaddle/paddle — Paddle is a deep learning framework designed for building, training, and deploying neural networks. It provides a… tingsongyu/pytorch_tutorial — This project is a comprehensive collection of educational examples and reference implementations for building vision… tingsongyu/pytorch-tutorial-2nd — This project is a comprehensive instructional resource and course for building neural networks using PyTorch. It… pytorch/executorch — ExecuTorch is a lightweight C++ runtime for deploying PyTorch models on mobile, embedded, and edge hardware. It… apple/coremltools — coremltools is a conversion toolkit and translator designed to transform machine learning models from various…