ds2 VS OmniBridge

Compare ds2 vs OmniBridge and see what are their differences.

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ds2 OmniBridge
5 4
48 22
- -
0.0 8.5
2 months ago 10 months ago
Python Python
Apache License 2.0 Apache License 2.0
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.

OmniBridge

Posts with mentions or reviews of OmniBridge. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing ds2 and OmniBridge you can also consider the following projects:

carefree-learn - Deep Learning ❤️ PyTorch

autoai - Python based framework for Automatic AI for Regression and Classification over numerical data. Performs model search, hyper-parameter tuning, and high-quality Jupyter Notebook code generation.

superduperdb - 🔮 SuperDuperDB: Bring AI to your database! Build, deploy and manage any AI application directly with your existing data infrastructure, without moving your data. Including streaming inference, scalable model training and vector search.

autoembedder - PyTorch autoencoder with additional embeddings layer for categorical data 🚘

cascade - Lightweight and modular MLOps library targeted at small teams or individuals

cerche - Experimental search engine for conversational AI such as parl.ai, large language models such as OpenAI GPT3, and humans (maybe).

only_train_once - OTOv1-v3, NeurIPS, ICLR, TMLR, DNN Training, Compression, Structured Pruning, Erasing Operators, CNN, Diffusion, LLM

nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

function-identification - This project demonstrates how a convolutional neural network can be used to detect the boundaries of a function in compiled code