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Hyperparameter Alternatives
Similar projects and alternatives to hyperparameter
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towhee
Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
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InfluxDB
InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.
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keras
Discontinued Deep Learning for humans [Moved to: https://github.com/keras-team/keras] (by fchollet)
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lance
Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, and PyTorch with more integrations coming..
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pytorch-lightning
Discontinued Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning] (by PyTorchLightning)
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better hyperparameter alternative or higher similarity.
hyperparameter discussion
hyperparameter reviews and mentions
Posts with mentions or reviews of hyperparameter.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-04-10.
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Hyper-parameter Optimization with Optuna and hyperparameter
the full tutorial: https://github.com/reiase/hyperparameter/tree/master/examples/optuna
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Pythonic configuration framework?
When I was working on my own configuration framework (HyperParameter, previous post), I suddenly realize that what I want is not another configuration framework with some fancy API. All I want is to change my ML experiments without modifying the code and get rid of the configuration handling codes. The right way of configuration is not writing configurable code and wasting time on different frameworks. The best solution is a tool that makes your code configurable.
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hyperparameter, a lightweight configuration framework
github: https://github.com/reiase/hyperparameter
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HyperParameter for ML Models and Systems
HyperParameter is a configuration and parameter management library for Python. HyperParameter provides the following features:
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What is the best practice for injecting configuration into a python application
you can take a look at https://github.com/reiase/hyperparameter, a scoped, thread-safe config object that is lightweight enough. There is no need to modify too much code:
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[P] Modify Hyperparameters Easily
I'm developing a Hyperparameter tuning toolbox for my machine learning projects. It maps keyword arguments to hyper-parameters, for example:
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A hyper-parameter toolbox for data-scientists and machine-learning engineers
I'm developing [a toolbox for managing hyper-parameters](https://github.com/reiase/hyperparameter) in my data science and machine learning projects. It provides object-style API for nested dict( which is very common for config files):
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A note from our sponsor - SaaSHub
www.saashub.com | 23 May 2025
Stats
Basic hyperparameter repo stats
7
23
6.2
11 months ago
reiase/hyperparameter is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of hyperparameter is Rust.
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