hyperparameter
Hyperparameter, Make configurable AI applications.Build for Python hackers. (by reiase)
Dependency Injector
Dependency injection framework for Python (by ets-labs)
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hyperparameter | Dependency Injector | |
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7 | 7 | |
23 | 3,590 | |
- | 1.9% | |
6.9 | 0.0 | |
about 1 month ago | about 2 months ago | |
Rust | Python | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
hyperparameter
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):
Dependency Injector
Posts with mentions or reviews of Dependency Injector.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-09-16.
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Java 21 makes me like Java again
Nothing to do with the nature of the language, but with the nature of the program.
If you're writing a few line script, you don't need a DI container. Once your program gets large, it becomes extremely messy without one. It's no surprise projects like [1] exist.
[1] https://github.com/ets-labs/python-dependency-injector
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Do You Use Singletons?
Totally agree with this. And I’ve found this pattern pairs really well with https://python-dependency-injector.ets-labs.org/
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Compclasses: prefer composition over inheritance
dependency_injector: https://github.com/ets-labs/python-dependency-injector
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Loosely coupled Python code with Dependency Injection
As projects continue to grow, its recommended to utilise a dependency injection framework to “inject” these dependencies, such as Dependency Injector, to inject dependency arguments automatically ✨.
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What is the best practice for injecting configuration into a python application
One approach is to pass this config as a variable to every class it is required, which I dont prefer. Another option is to annotate the config class as singleton and create the config object at every place where I need them. I also came across this library called Dependency_Injector. https://python-dependency-injector.ets-labs.org/ This seems a bit heavy weight for my use case though. I am looking forward to know how other solve this problem
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Dependency Injection and Python
Dependency Injector
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Introduction to Dependency Injection in Python
dependency-injector (docs) is python library that provides a framework which enables you to implement DI and IoC in Python.