How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
PyTorch layer-by-layer model profiler
The main features of awwong1/torchprof are: Developer Tools, Training and Utilities, PyTorch Utilities.
Open-source alternatives to awwong1/torchprof include: lanpa/tensorboard-pytorch — This project is a machine learning experiment tracker and event file generator that enables the recording of scalars,… ncullen93/torchsample — Train AI models efficiently on medical images using any framework. ecs-vlc/torchbearer — torchbearer: A model fitting library for PyTorch. henryre/pytorch-fitmodule — Super simple fit method for PyTorch Modules. catalyst-team/catalyst — Accelerated deep learning R&D. pytorch/ignite — Ignite is a high-level training framework for PyTorch neural networks that serves as a training engine and deep…
This project is a machine learning experiment tracker and event file generator that enables the recording of scalars, images, and histograms to monitor model performance. It functions as an integration bridge that allows training metrics from PyTorch to be logged into files compatible with the TensorBoard dashboard. The system includes a remote log synchronizer designed to stream experiment data to cloud services. This allows for the remote management and analysis of training results and the comparison of datasets across different training runs. The utility covers a broad range of monitoring
