optuna
awesome-production-machine-learning
optuna | awesome-production-machine-learning | |
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34 | 9 | |
9,714 | 16,136 | |
2.2% | 1.6% | |
9.9 | 7.5 | |
about 6 hours ago | 8 days ago | |
Python | ||
GNU General Public License v3.0 or later | MIT License |
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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
awesome-production-machine-learning
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
One trove of treasures is the awesome-production-machine-learning repository on GitHub. This curated list provides a multitude of frameworks, libraries, and software designed to facilitate various stages of the ML lifecycle.
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
There is a cool, gigantic list for MLOps that I can recommend: https://github.com/EthicalML/awesome-production-machine-learning
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How much of a full DS project pipeline can I do for free?
There are a lot of frameworks and specific tools out there that try to make production ML projects viable; from specific like Airflow (orchestrating jobs) and MLflow (experiment tracking) to more complex ones like Kubeflow. You can have a grasp here.
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Sqldiff: SQLite Database Difference Utility
https://github.com/EthicalML/awesome-production-machine-lear...
- [D] What are the best resources to crack M L system design interviews?
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I'm looking for a tool that let's you visualize the models architecture like this. Any idea what it is called?
https://github.com/EthicalML/awesome-production-machine-learning I think you will find most of the tools to visualize the model on this link.
- Awesome production machine learning - curated list of awesome open source libraries that will help you deploy, monitor, version, scale, and secure your production machine learning [free] [website] [@all]
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Crucial differences in MLOps for deep learning
2/ https://github.com/EthicalML/awesome-production-machine-learning
What are some alternatives?
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.
shap - A game theoretic approach to explain the output of any machine learning model.
hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python
awesome-jax - JAX - A curated list of resources https://github.com/google/jax
rl-baselines3-zoo - A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
netron - Visualizer for neural network, deep learning and machine learning models
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
awesome-mlops - :sunglasses: A curated list of awesome MLOps tools
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
awesome-ml-for-cybersecurity - :octocat: Machine Learning for Cyber Security
pyGAM - [HELP REQUESTED] Generalized Additive Models in Python
datascience - Curated list of Python resources for data science.