neptune-contrib
lightning-hydra-template
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| neptune-contrib | lightning-hydra-template | |
|---|---|---|
| - | 9 | |
| 27 | 3,658 | |
| - | - | |
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| over 1 year ago | about 2 months ago | |
| Python | Python | |
| MIT License | MIT License |
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lightning-hydra-template
- User-friendly PyTorch Lightning and Hydra template for ML experimentation
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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.
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[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
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How research scientists structure their code ?
lightning-hydra-template
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[D] Any research specific PyTorch based boilerplate code?
This lightning + hydra template is quite complete. Great for learning best practices.
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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
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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)
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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
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[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.
What are some alternatives?
scikit-learn - scikit-learn: machine learning in Python
lightning-hydra-template - Deep Learning project template best practices with Pytorch Lightning, Hydra, Tensorboard.
Keras - Deep Learning for humans
pytorch_tempest - My repo for training neural nets using pytorch-lightning and hydra
tensorflow - An Open Source Machine Learning Framework for Everyone
neptune-client - 📘 The MLOps stack component for experiment tracking
bodywork - ML pipeline orchestration and model deployments on Kubernetes.
lightning-transformers - Flexible components pairing 🤗 Transformers with :zap: Pytorch Lightning
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
traingenerator - 🧙 A web app to generate template code for machine learning
TFLearn - Deep learning library featuring a higher-level API for TensorFlow.
garage - A toolkit for reproducible reinforcement learning research.