Simple Example of Pytorch -> TensorRT and Inference
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Updated
Mar 20, 2021 - Jupyter Notebook
Simple Example of Pytorch -> TensorRT and Inference
A flexible utility for converting tensor precision in PyTorch models and safetensors files, enabling efficient deployment across various platforms.
Converts a floating-point number or hexadecimal representation of a floating-point numbers into various formats and displays them into binary/hexadecimal.
apextrainer is an open source toolbox for fp16 trainer based on Detectron2 and Apex
IEEE 754-style floating-point converter
Export pytorch model to ONNX and convert ONNX from float32 to float 16
Let's train CIFAR 10 Pytorch with Half-Precision!
Optimised Caffe with OpenCL supporting for less powerful devices such as mobile phones
Pytorch implementation of DreamerV2: Mastering Atari with Discrete World Models, based on the original implementation
CPP20 implementation of a 16-bit floating-point type mimicking most of the IEEE 754 behavior. Single file and header-only.
Round matrix elements to lower precision in MATLAB
👀 Apply YOLOv8 exported with ONNX or TensorRT(FP16, INT8) to the Real-time camera
Stage 3 IEEE 754 half-precision floating-point ponyfill
Deploy stable diffusion model with onnx/tenorrt + tritonserver
Conversion to/from half-precision floating point formats
InsightFace REST API for easy deployment of face recognition services with TensorRT in Docker.
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