snorkel
grape
Our great sponsors
| snorkel | grape | |
|---|---|---|
| 5 | 3 | |
| 5,694 | 477 | |
| 0.6% | 4.4% | |
| 5.5 | 6.4 | |
| about 2 months ago | about 2 months ago | |
| Python | Jupyter Notebook | |
| Apache License 2.0 | 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.
snorkel
-
[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
The paid product came out of an open source tool: https://github.com/snorkel-team/snorkel
- [Discussion] - "data sourcing will be more important than model building in the era of foundational model fine-tuning"
-
Can't use load_data from utils
Actually, I referenced it in my issue as well. There seems to be different utils.py file in different folders under the snorkel-tutorials repo but the utils file we get after importing snorkel has a different [file](https://github.com/snorkel-team/snorkel/blob/master/snorkel/utils/core.py) ,i.e. the utils file is different in the main snorkel repo
- [D] A hand-picked selection of the best Python ML Libraries of 2021
grape
-
Zoomable, animated scatterplots in the browser that scales over a billion points
Ideally, you'd embed the graph into 2 or 3d first, then visualize it as a scatterplot.
Visualizing the edges at scale doesnt yield nice results in general.
The way to do it is to reduce the graph to some 300d or 500d embeddings, then use TSNE/UMAP/PACMAP to reduce that to 3d. Then visualize.
My prefered way is to use some first order embedding method like GGVec in this library [1] (disclaimer I wrote it). Node2Vec and ProNE don't yield great embeddings for visualization (the first is too filamented, the second too close to the unit ball).
Another great library to do this work is GRAPE [2]. Try first-order embedding methods, or short walks on second order methods to avoid the embeddings being too filamented by long random walk sampling.
-
[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
For graph embeddings, there's quite a few. I'd recommend this one, but there's also this one (disclaimer: I'm the author) or this one, more of a DGL library.
What are some alternatives?
skweak - skweak: A software toolkit for weak supervision applied to NLP tasks
argilla - Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
weasel - Weakly Supervised End-to-End Learning (NeurIPS 2021)
caer - High-performance Vision library in Python. Scale your research, not boilerplate.
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
snorkel-tutorials - A collection of tutorials for Snorkel
deodel - A mixed attributes predictive algorithm implemented in Python.
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.
awesome-production-machine-learning - A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
dcai-lab - Lab assignments for Introduction to Data-Centric AI, MIT IAP 2024 👩🏽💻