tutel VS best-of-ml-python

Compare tutel vs best-of-ml-python and see what are their differences.

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tutel best-of-ml-python
1 16
658 15,364
3.0% 0.9%
6.5 7.8
16 days ago 3 days ago
Python Python
MIT License Creative Commons Attribution Share Alike 4.0
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.

tutel

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

best-of-ml-python

Posts with mentions or reviews of best-of-ml-python. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-12-10.

What are some alternatives?

When comparing tutel and best-of-ml-python you can also consider the following projects:

hivemind - Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.

Awesome-WAF - πŸ”₯ Web-application firewalls (WAFs) from security standpoint.

mixture-of-experts - PyTorch Re-Implementation of "The Sparsely-Gated Mixture-of-Experts Layer" by Noam Shazeer et al. https://arxiv.org/abs/1701.06538

ktrain - ktrain is a Python library that makes deep learning and AI more accessible and easier to apply

transformers - πŸ€— Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

dtale - Visualizer for pandas data structures

ModuleFormer - ModuleFormer is a MoE-based architecture that includes two different types of experts: stick-breaking attention heads and feedforward experts. We released a collection of ModuleFormer-based Language Models (MoLM) ranging in scale from 4 billion to 8 billion parameters.

ffcv - FFCV: Fast Forward Computer Vision (and other ML workloads!)

awesome-python - An opinionated list of awesome Python frameworks, libraries, software and resources.

kmodes - Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data

NBA-Machine-Learning-Sports-Betting - NBA sports betting using machine learning

livelossplot - Live training loss plot in Jupyter Notebook for Keras, PyTorch and others