Top 23 Python hyperparameter-optimization Projects
An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.Project mention: Is it normal to have a negative and near-zero explained variance in PPO? | reddit.com/r/reinforcementlearning | 2021-12-25
I guess I did, as I directly use the PPO agent provided by the RLlib.
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.Project mention: Automated Machine Learning (AutoML) - 9 Different Ways with Microsoft AI | dev.to | 2021-10-04
For a complete tutorial, navigate to this Jupyter Notebook: https://github.com/microsoft/nni/blob/master/examples/notebooks/tabular_data_classification_in_AML.ipynb
OPS - Build and Run Open Source Unikernels. Quickly and easily build and deploy open source unikernels in tens of seconds. Deploy in any language to any cloud.
Automated Machine Learning with scikit-learnProject mention: Why not AutoML every tabular data? | reddit.com/r/datascience | 2021-07-26
Efficiency Ignoring the feature engineering aspects aside, a typical data scientist workflow involves trying out the different models. Some of the AutoML modules like H2O AutoML, AutoSklearn does this for you, and allow you to interpret your models. All these save so much time experimenting with the standard models.
A hyperparameter optimization frameworkProject mention: Trading Algos - 5 Key Metrics and How to Implement Them in Python | dev.to | 2022-01-08
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!
AutoGluon: AutoML for Text, Image, and Tabular DataProject mention: What will the data science job market be like in 5 years? | reddit.com/r/datascience | 2021-08-14
Some AutoML is getting pretty good, AutoGluon is very solid for tabular data. That being said you still need to have your data in tabular format and deployment still requires some effort.
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic DocumentationProject mention: I'm Looking to Help Contribute, I am very confident with my skills | reddit.com/r/Python | 2021-12-02
Automated Machine Learning (AutoML) Python package https://github.com/mljar/mljar-supervised You can check list of open issues. Or I can recommend some just tell me your preferences (Im the main contributor)
Determined: Deep Learning Training PlatformProject mention: How to train large deep learning models as a startup | news.ycombinator.com | 2021-10-07
Check out Determined https://github.com/determined-ai/determined to help manage this kind of work at scale: Determined leverages Horovod under the hood, automatically manages cloud resources and can get you up on spot instances, T4's, etc. and will work on your local cluster as well. Gives you additional features like experiment management, scheduling, profiling, model registry, advanced hyperparameter tuning, etc.
Full disclosure: I'm a founder of the project.
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A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.Project mention: [N][R] A Brief Tutorial for Developing AutoML Tools with Hypernets | reddit.com/r/MachineLearning | 2021-06-28
Please see here for the Hypernets library.
A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.Project mention: How do I convert zoo / gym trained models to TensorFlow Lite or PyTorch TorchScript? | reddit.com/r/learnmachinelearning | 2021-03-22
https://github.com/araffin/rl-baselines-zoo (TensorFlow based, using https://github.com/hill-a/stable-baselines)
Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.Project mention: Gradient-Free-Optimizers A collection of modern optimization methods in Python | reddit.com/r/patient_hackernews | 2021-02-28
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.Project mention: Easily load and upload Stable-baselines3 models from the Hugging Face Hub 🤗 | reddit.com/r/reinforcementlearning | 2022-01-21
An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.Project mention: Hyperactive: An optimization and data collection toolbox for AutoML | news.ycombinator.com | 2021-05-15
Automated modeling and machine learning framework FEDOTProject mention: Winning a Flood-Forecasting Hackathon with Hydrology and AutoML | reddit.com/r/hackathon | 2022-01-14
Hi to everyone! I am a developer of the FEDOT framework, and our team and I (NSS_lab team) recently won a hackathon EmergencyDataHack (rus). There was a recent post on TowardsDataSciense based on our competition things: Winning a Flood-Forecasting Hackathon with Hydrology and AutoML.
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)Project mention: (NLP) Best practices for topic modeling and generating interesting topics? | reddit.com/r/MLQuestions | 2021-05-31
My team and I have recently released a python library called OCTIS (https://github.com/mind-Lab/octis) that allows you to automatically optimize the hyperparameters of a topic model according to a given evaluation metric (not log-likelihood). I guess, in your case, you might be interested in topic coherence. So you will get good quality topics with a low effort on the choice of the hyperparameters. Also, we included some state-of-the-art topic models, e.g. contextualized topic models (https://github.com/MilaNLProc/contextualized-topic-models).
Bayesian Optimization using GPflowProject mention: [D] Choosing best parameters from an optimization | reddit.com/r/MachineLearning | 2021-06-05
1- Hyperparameter optimization as already suggested by u/sener87 but I think your validation does not have to be change as it tests generalization as far as I understand you right. If you have more parameter/larger search space, you may look into Bayesian optimization for this task as implemented e.g. with tensorflow, torch or numpy frameworks.
Asynchronous Distributed Hyperparameter Optimization. (by Epistimio)
Time Series Cross-Validation -- an extension for scikit-learnProject mention: [P] First release candidate of tscv v0.0.5 | reddit.com/r/MachineLearning | 2021-03-19
The wheel binary can be downloaded from my GitHub repo for early testing. If you notice any bug, please open a ticket in the repo. The final version is expected to be released by the end of this month, and users will be able to pip install it.
Examples for https://github.com/optuna/optunaProject mention: Data Scientists are dying out | reddit.com/r/dataengineering | 2022-01-18
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
Hyperparameters tuning and feature selection, using evolutionary algorithms.Project mention: GitHub - rodrigo-arenas/Sklearn-genetic-opt: Hyperparameters tuning and feature selection, using evolutionary algorithms. | reddit.com/r/Python | 2021-12-19
Intelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning modelsProject mention: Klines/candles: What's the real difference between long intervals and short intervals? | reddit.com/r/algotrading | 2021-10-18
You can try https://github.com/jmrichardson/tuneta which uses a clustered center for parameter selection to avoid peaks and can also constrain to time ranges
Python Automated Machine Learning library for tabular data.Project mention: Introducing my AutoML library | reddit.com/r/datascience | 2021-06-18
I'm excited to introduce you my (and another great developer) school diploma project. Fully open-source, Automated Machine Learning Library! We are beating built-in AutoML in SAP famous product. GitHub repository (waiting for your stars): https://github.com/dan0nchik/SAP-HANA-AutoML Web-application for users who don't want to code: https://share.streamlit.io/dan0nchik/sap-hana-automl/main/web.py
The MLOps platform for innovators 🚀Project mention: DS2.ai Release: End-to-End MLOps Platform | reddit.com/r/programming | 2021-07-13
zero-code hyperparameters optimization frameworkProject mention: Easyopt: Zero-Code Hyperparameter Optimization | news.ycombinator.com | 2021-09-16
Python hyperparameter-optimization related posts
Discussion: the feasubility of using open source hyperparameter optimization tools and SQLAlchemy to automatically tune database performance
3 projects | reddit.com/r/PostgreSQL | 8 Jan 2022
[P] optimization of Hugging Face Transformer models to get Inference < 1 Millisecond Latency + deployment on production ready inference server
3 projects | reddit.com/r/MachineLearning | 5 Nov 2021
Best practices for hyperparameters tuning (code/framework)
1 project | reddit.com/r/deeplearning | 18 Sep 2021
Easyopt: Zero-Code Hyperparameter Optimization
1 project | news.ycombinator.com | 16 Sep 2021
How to improve the performance of a machine learning (ML) model
1 project | dev.to | 16 Sep 2021
[P] easyopt: zero-code hyperparameters optimization framework
2 projects | reddit.com/r/MachineLearning | 16 Sep 2021
Git token, how to I encourage git to have me put in a username and password?
1 project | reddit.com/r/git | 1 Sep 2021
What are some of the best open-source hyperparameter-optimization projects in Python? This list will help you:
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