nn-template
Generic template to bootstrap your PyTorch project. (by grok-ai)
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 (by DagsHub)
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nn-template | fds | |
---|---|---|
4 | 3 | |
613 | 382 | |
2.8% | -0.3% | |
7.2 | 3.7 | |
7 months ago | 4 months ago | |
Python | Python | |
MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
nn-template
Posts with mentions or reviews of nn-template.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-01-17.
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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, I’d like to try using this cookiecutter template: https://github.com/grok-ai/nn-template
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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
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[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
fds
Posts with mentions or reviews of fds.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-05-28.
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I reviewed 50+ open-source MLOps tools. Here’s the result
Also fds, it's an open source command line wrapper around Git and DVC.
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Data Science Workflows — Notebook to Production
At DagsHub, we’re integrated with DVC, which I love using. First and foremost, it’s open-source. It provides pipeline capabilities and supports many cloud providers for remote storage. Also, DVC acts as an extension to Git, which allows you to keep using the standard Git flow in your work. If you don’t want to use both tools, I recommend using FDS, an open-source tool that makes version control for machine learning fast & easy. It combines Git and DVC under one roof and takes care of code, data, and model versioning. (Bias alert: DagsHub developed FDS)
- Show HN: FastDS – Open-Source Machine Learning Version Control. Fast and Easy
What are some alternatives?
When comparing nn-template and fds you can also consider the following projects:
lightning-hydra-template - Deep Learning project template best practices with Pytorch Lightning, Hydra, Tensorboard.
PyDrive2 - Google Drive API Python wrapper library. Maintained fork of PyDrive.
lightning-hydra-template - PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ⚡🔥⚡
dvc - 🦉 ML Experiments and Data Management with Git