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Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Apr 24, 2024 · Artificial Intelligence

Training MNIST with Burn on wgpu: From PyTorch to Rust Backend

This tutorial demonstrates how to train a MNIST digit‑recognition model using the Rust‑based Burn framework on top of the cross‑platform wgpu API, covering model export from PyTorch to ONNX, code generation, data loading, training loops, and performance comparison across CPU, GPU, and other backends.

BurnDeep LearningGPU
0 likes · 13 min read
Training MNIST with Burn on wgpu: From PyTorch to Rust Backend
Python Programming Learning Circle
Python Programming Learning Circle
Nov 8, 2021 · Artificial Intelligence

YOLOv5 Tutorial: From YOLOv3 to YOLOv5, Code Walkthrough, Model Export (JIT & ONNX) and Usage

This article provides a comprehensive guide on YOLOv5, covering its background from YOLOv3, detailed code analysis of the model architecture, step‑by‑step instructions for running detect.py, configuring yolov5s.yaml, exporting the model to TorchScript JIT and ONNX formats, and practical inference examples using PyTorch and ONNX Runtime.

JITONNXPyTorch
0 likes · 16 min read
YOLOv5 Tutorial: From YOLOv3 to YOLOv5, Code Walkthrough, Model Export (JIT & ONNX) and Usage
DataFunSummit
DataFunSummit
Mar 28, 2021 · Artificial Intelligence

Deploying Scikit‑learn and HMMlearn Models as High‑Performance Online Prediction Services Using ONNX

This article demonstrates how to convert traditional scikit‑learn and hmmlearn machine‑learning models into ONNX format and integrate them into a C++ gRPC service for fast online inference, covering environment setup, model conversion, custom operators, performance testing, and end‑to‑end pipeline construction.

C++Model DeploymentONNX
0 likes · 22 min read
Deploying Scikit‑learn and HMMlearn Models as High‑Performance Online Prediction Services Using ONNX