fds
nn-template
| fds | nn-template | |
|---|---|---|
| 3 | 4 | |
| 382 | 619 | |
| -0.3% | 2.4% | |
| 3.7 | 7.2 | |
| 5 months ago | 7 months ago | |
| Python | Python | |
| MIT License | MIT License |
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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.
fds
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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
nn-template
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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
What are some alternatives?
PyDrive2 - Google Drive API Python wrapper library. Maintained fork of PyDrive.
lightning-hydra-template - Deep Learning project template best practices with Pytorch Lightning, Hydra, Tensorboard.
dvc - 🦉 ML Experiments and Data Management with Git
lightning-hydra-template - PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ⚡🔥⚡
Keras - Deep Learning for humans
garage - A toolkit for reproducible reinforcement learning research.
scikit-learn - scikit-learn: machine learning in Python
pytorch_tempest - My repo for training neural nets using pytorch-lightning and hydra
delta - An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs
energy-forecasting - 🌀 𝗧𝗵𝗲 𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝟳-𝗦𝘁𝗲𝗽𝘀 𝗠𝗟𝗢𝗽𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 | 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟𝗘 & 𝗠𝗟𝗢𝗽𝘀 for free by designing, building and deploying an end-to-end ML batch system ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 2.5 𝘩𝘰𝘶𝘳𝘴 𝘰𝘧 𝘳𝘦𝘢𝘥𝘪𝘯𝘨 & 𝘷𝘪𝘥𝘦𝘰 𝘮𝘢𝘵𝘦𝘳𝘪𝘢𝘭𝘴
lakeFS - lakeFS - Data version control for your data lake | Git for data
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