Optimised Caffe with OpenCL supporting for less powerful devices such as mobile phones
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Updated
Mar 12, 2019 - C++
Optimised Caffe with OpenCL supporting for less powerful devices such as mobile phones
Let's train CIFAR 10 Pytorch with Half-Precision!
apextrainer is an open source toolbox for fp16 trainer based on Detectron2 and Apex
Simple Example of Pytorch -> TensorRT and Inference
Converts a floating-point number or hexadecimal representation of a floating-point numbers into various formats and displays them into binary/hexadecimal.
Round matrix elements to lower precision in MATLAB
Pytorch implementation of DreamerV2: Mastering Atari with Discrete World Models, based on the original implementation
IEEE 754-style floating-point converter
Export pytorch model to ONNX and convert ONNX from float32 to float 16
Deploy stable diffusion model with onnx/tenorrt + tritonserver
A flexible utility for converting tensor precision in PyTorch models and safetensors files, enabling efficient deployment across various platforms.
👀 Apply YOLOv8 exported with ONNX or TensorRT(FP16, INT8) to the Real-time camera
Transformer implementation in pytorch trained on NVIDIA A100 in fp16
InsightFace REST API for easy deployment of face recognition services with TensorRT in Docker.
llama 3.2 1b fp16 cpu inference in one file of pure VB.NET
Conversion to/from half-precision floating point formats
A reproducible GPU benchmarking lab that compares FP16 vs FP32 training on MNIST using PyTorch, CuPy, and Nsight profiling tools. This project blends performance engineering with cinematic storytelling—featuring NVTX-tagged training loops, fused CuPy kernels, and a profiler-driven README that narrates the GPU’s inner workings frame by frame.
🎬 Explore GPU training efficiency with FP32 vs FP16 in this modular lab, utilizing Tensor Core acceleration for deep learning insights.
Multimodal search engine using CLIP embeddings for bidirectional image-text retrieval.
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