bagua VS nni

Compare bagua vs nni and see what are their differences.

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bagua nni
6 5
865 13,765
0.2% 0.6%
4.8 6.7
9 months ago 2 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.

bagua

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

nni

Posts with mentions or reviews of nni. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-10-04.

What are some alternatives?

When comparing bagua and nni you can also consider the following projects:

machin - Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...

optuna - A hyperparameter optimization framework

Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]

FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.

autogluon - Fast and Accurate ML in 3 Lines of Code

PERSIA - High performance distributed framework for training deep learning recommendation models based on PyTorch.

AutoML - This is a collection of our NAS and Vision Transformer work. [Moved to: https://github.com/microsoft/Cream]

modin - Modin: Scale your Pandas workflows by changing a single line of code

hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python

Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

archai - Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.