malib
optuna-examples
| malib | optuna-examples | |
|---|---|---|
| 2 | 2 | |
| 464 | 599 | |
| 1.7% | 4.0% | |
| 3.4 | 8.7 | |
| 5 months ago | 4 days ago | |
| Python | Python | |
| MIT License | MIT License |
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malib
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MALib: A parallel framework for population-based multi-agent reinforcement learning
Code for https://arxiv.org/abs/2106.07551 found: https://github.com/sjtu-marl/malib
optuna-examples
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[D]How to optimize an ANN?
Check out the examples for Optuna, a popular hyper parameter tuning package. It has examples for most popular ML frameworks including Xgboost, so you can see how it compares to an ANN framework like Keras or PyTorch.
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Data Scientists are dying out
That's still regular ML because you are in charge of the features. Optuna might make your life easier though: https://github.com/optuna/optuna-examples/blob/main/xgboost/xgboost_simple.py
What are some alternatives?
Python-Raytracer - A basic Ray Tracer that exploits numpy arrays and functions to work fast.
tqdm - :zap: A Fast, Extensible Progress Bar for Python and CLI
modin - Modin: Scale your Pandas workflows by changing a single line of code
Hyperactive - An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
IC3Net - Code for ICLR 2019 paper: Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks
optuna - A hyperparameter optimization framework
hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python
SMAC3 - SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
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.
spaceopt - Hyperparameter optimization via gradient boosting regression
Sklearn-genetic-opt - ML hyperparameters tuning and features selection, using evolutionary algorithms.