nni
hyperopt
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| nni | hyperopt | |
|---|---|---|
| 5 | 14 | |
| 13,726 | 7,081 | |
| 0.9% | 0.9% | |
| 6.7 | 6.0 | |
| about 2 months ago | 11 days ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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.
nni
- Filter Pruning for PyTorch
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Automated Machine Learning (AutoML) - 9 Different Ways with Microsoft AI
For a complete tutorial, navigate to this Jupyter Notebook: https://github.com/microsoft/nni/blob/master/examples/notebooks/tabular_data_classification_in_AML.ipynb
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[D] Efficient ways of choosing number of layers/neurons in a neural network
optuna, hyperopt, nni, plenty of less-known tools too.
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Top 10 Developer Trends, Sun Oct 18 2020
microsoft / nni
hyperopt
- Hyperopt: Distributed Asynchronous Hyper-Parameter Optimization
- Hyperopt: Distributed Hyperparameter Optimization
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[D]How to optimize an ANN?
You can use Optuna, SMAC or hyperopt
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How should one go about tuning hyper parameters?
Hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python: https://github.com/hyperopt/hyperopt
- Hyperparameter tuning sklearn model using scripts and configs
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Finding the optimal parameter
Apart from the aforementioned comments noting that this is an optimization problem, ready-to-use python libraries for this kind of problem (accounting for evaluation time) include http://hyperopt.github.io/hyperopt/, https://github.com/automl/SMAC3, or https://www.ray.io/ray-tune
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Trading Algos - 5 Key Metrics and How to Implement Them in Python
Nothing can beat iteration and rapid optimization. Try running things like grid experiments, batch optimizations, and parameter searches. Take a look at various packages like hyperopt or optuna as packages that might be able to help you here!
- Discussion: the feasubility of using open source hyperparameter optimization tools and SQLAlchemy to automatically tune database performance
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How to automate hyperparameter tuning?
I suggest hyperopt
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How to use an optimizer in tensorflow 2.5?
Look into hyperopt they have a good documentation about optimization.
What are some alternatives?
optuna - A hyperparameter optimization framework
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
SMAC3 - SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
autogluon - AutoGluon: Fast and Accurate ML in 3 Lines of Code
pg_plan_advsr - PostgreSQL extension for automated execution plan tuning
AutoML - This is a collection of our NAS and Vision Transformer work. [Moved to: https://github.com/microsoft/Cream]
optuna-examples - Examples for https://github.com/optuna/optuna
archai - Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
automlbenchmark - OpenML AutoML Benchmarking Framework
StoRM - A neural network hyper parameter tuner