A high-throughput and memory-efficient inference and serving engine for LLMs
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Updated
Jun 6, 2024 - Python
CUDA® is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs.
A high-throughput and memory-efficient inference and serving engine for LLMs
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Containers for machine learning
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PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT
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label-smooth, amsoftmax, partial-fc, focal-loss, triplet-loss, lovasz-softmax. Maybe useful
CUDA integration for Python, plus shiny features
Pytorch domain library for recommendation systems
🤖 A Python library for learning and evaluating knowledge graph embeddings
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an implementation of 3D Ken Burns Effect from a Single Image using PyTorch
Self-hosted, local only NVR and AI Computer Vision software. With features such as object detection, motion detection, face recognition and more, it gives you the power to keep an eye on your home, office or any other place you want to monitor.
PPL Quantization Tool (PPQ) is a powerful offline neural network quantization tool.
Created by Nvidia
Released June 23, 2007