lightning-hydra-template
nn-template
Our great sponsors
lightning-hydra-template | nn-template | |
---|---|---|
9 | 4 | |
3,645 | 612 | |
- | 2.6% | |
5.1 | 7.2 | |
about 2 months ago | 6 months ago | |
Python | Python | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
lightning-hydra-template
- User-friendly PyTorch Lightning and Hydra template for ML experimentation
-
Best practice for saving logits/activation values of model in PyTorch Lightning
I've been trying to learn PyTorch Lightning and Hydra in order to use/create my own custom deep learning template (e.g. like this) as it would greatly help with my research workflow. A lot of the work I do requires me to analyse metrics based on the logits/activations of the model.
-
[D] Is Pytorch Lightning + Wandb a good combination for research?
I can't say for sure whether it is the best combination for research in the long run, but if you do go down that route I have found this template very useful
-
How research scientists structure their code ?
lightning-hydra-template
-
[D] Any research specific PyTorch based boilerplate code?
This lightning + hydra template is quite complete. Great for learning best practices.
-
Typing and testing for torch
A good example is this project template https://github.com/ashleve/lightning-hydra-template. It uses a lot of cool things such as
-
Our template to kickstart your pytorch projects, with list of best practices. Minimal boilerplate code. Leverages Lightning + Hydra. Focused on scalability, reproducibility and fast experimentation.
and many more! (checkout the #Your Superpowers section of the readme)
-
General and feature-rich PyTorch/Hydra project template for rapid and scalable ML experimentation, with a list of best practices
I write a LightningDatamodule. I found it to be an intuitive way to encapsulate any dataset. LightningDatamodule is a simple abstraction providing methods for data download, split, transforms and exposing dataloaders. Would love to see more researchers try out this concept, even in projects which don't use pytorch lightning. Reading LightningDatamodule makes me immedietely see how the dataset is prepared, while it seems like most data science projects throw around data logic across different parts of the pipeline, making it hard to understand what's going on. You can see example of such datamodule here
-
[P] General and feature-rich PyTorch/Hydra template for rapid and scalable ML research/experimentation, with a list of best practices
I feel like most ML people don't use those tools because they simply don't realize all the advantages (especially Hydra seems like a very useful addition to any deep learning project). I focused on structuring the readme in a way, which (I hope) will give you a quick overview - my hope is it can help to spread the word about those frameworks in a broaded community. It incorporates best practices and tricks I gathered over the last couple of months of playing around with it.
nn-template
-
What libs/boiler plate/platforms do you use to abstract and optimize your workflow when starting a new project? [D]
If I was starting a new project, Iād like to try using this cookiecutter template: https://github.com/grok-ai/nn-template
-
MLOps stack using Pycaret
I would pick a project that interests you as that'll help you power through. If there's nothing that comes to mind, image classification is fairly standard. PyCaret does a lot but if you want to understand each of the tools you've listed, I'd recommend tackling each one separately. That being said, I don't think there's anything wrong starting with using a high level library and diving deeper as the need arises. If you do decide to build it piece by piece, it sometimes useful to have a library that'll help you start and remove the boilerplate of all of these tools. I came across a template repo which has a bunch of the tools you've listed which could be a good starting point: https://github.com/lucmos/nn-template
-
[P] Generic template to bootstrap your PyTorch project with PyTorch Lightning, Hydra, W&B, DVC, and Streamlit
Link: https://github.com/lucmos/nn-template
- Template to bootstrap your project with PyTorch Lightning, Hydra, W&B and DVC
What are some alternatives?
lightning-hydra-template - Deep Learning project template best practices with Pytorch Lightning, Hydra, Tensorboard.
pytorch_tempest - My repo for training neural nets using pytorch-lightning and hydra
garage - A toolkit for reproducible reinforcement learning research.
neptune-client - š The MLOps stack component for experiment tracking
lightning-transformers - Flexible components pairing š¤ Transformers with :zap: Pytorch Lightning
fds - Fast Data Science, AKA fds, is a CLI for Data Scientists to version control data and code at once, by conveniently wrapping git and dvc
traingenerator - š§ A web app to generate template code for machine learning
energy-forecasting - š š§šµš² ššš¹š¹ š¦šš®š°šø š³-š¦šš²š½š š šš¢š½š ššæš®šŗš²šš¼šæšø | šš²š®šæš» š šš & š šš¢š½š for free by designing, building and deploying an end-to-end ML batch system ~ š“š°š¶š³š¤š¦ š¤š°š„š¦ + 2.5 š©š°š¶š³š“ š°š§ š³š¦š¢š„šŖšÆšØ & š·šŖš„š¦š° š®š¢šµš¦š³šŖš¢šš“
neptune-contrib - This library is a location of the LegacyLogger for PyTorch Lightning.
mdx-net - KUIELAB-MDX-Net got the 2nd place on the Leaderboard A and the 3rd place on the Leaderboard B in the MDX-Challenge ISMIR 2021