pg_plan_advsr
optuna
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| pg_plan_advsr | optuna | |
|---|---|---|
| 1 | 34 | |
| 87 | 9,640 | |
| - | 3.4% | |
| 4.8 | 9.9 | |
| almost 3 years ago | 2 days ago | |
| C | Python | |
| - | GNU General Public License v3.0 or later |
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pg_plan_advsr
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Discussion: the feasubility of using open source hyperparameter optimization tools and SQLAlchemy to automatically tune database performance
Something along the lines of https://github.com/ossc-db/pg_plan_advsr sounds more promising.
optuna
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Optuna – A Hyperparameter Optimization Framework
I didn’t even know WandB did hyperparameter optimization, I figured it was a neural network visualizer based on 2 minute papers. Didn’t seem like many alternatives out there to Optuna with TPE + persistence in conditional continuous & discrete spaces.
Anyway, it’s doable to make a multi objective decide_to_prune function with Optuna, here’s an example https://github.com/optuna/optuna/issues/3450#issuecomment-19...
- How to test optimal parameters
- FOSS hyperparameter optimization framework to automate hyperparameter search
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How did you make that?!
The network configuration process is usually not particularly scientific and mostly relies on empirical observation. For some cases, tools like Optuna can be used to automatically find the optimal parameters. In others, on others, you can look for modern studies which explore the effect of this parameter on performance, such as this study (2022), but these are typically very specific to one particular architecture.
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
Keras Tuner, Optuna : https://github.com/optuna/optuna ?
- How to tune more than 2 hyperparameters in Grid Search in Python?
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Suggestion to optimize algo
I have used OpenTuner, but I don't think it is maintained anymore. I hear tell that Optuna is what to use now, but have not used it myself. https://optuna.org Optuna - A hyperparameter optimization framework
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Best practices for training PyTorch model
Research the type of model to get an idea of what hyper parameters to use. I recommend using a hyper parameter optimization library like Optuna to get the best configuration
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[D]How to optimize an ANN?
You can use Optuna, SMAC or hyperopt
What are some alternatives?
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.
orafce - The "orafce" project implements in Postgres some of the functions from the Oracle database that are missing (or behaving differently).Those functions were verified on Oracle 10g, and the module is useful for production work.
pg_show_plans - Show query plans of all currently running SQL statements
rl-baselines3-zoo - A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
libfirm - graph based intermediate representation and backend for optimising compilers
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
plpgsql_check - plpgsql_check is a linter tool (does source code static analyze) for the PostgreSQL language plpgsql (the native language for PostgreSQL store procedures).
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
postgresql-unit - SI Units for PostgreSQL
pyGAM - [HELP REQUESTED] Generalized Additive Models in Python