Dependency Injector
hyperparameter
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Dependency Injector | hyperparameter | |
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7 | 7 | |
3,590 | 23 | |
2.2% | - | |
0.0 | 6.9 | |
about 2 months ago | about 1 month ago | |
Python | Rust | |
BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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Dependency Injector
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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.
hyperparameter
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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):
What are some alternatives?
django-rest-framework - Web APIs for Django. 🎸
towhee - Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
kink - Dependency injection container made for Python
pytorch-lightning - 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]
flask-restful - Simple framework for creating REST APIs
streamlit - Streamlit — A faster way to build and share data apps.
falcon - The no-magic web data plane API and microservices framework for Python developers, with a focus on reliability, correctness, and performance at scale.
keras - Deep Learning for humans [Moved to: https://github.com/keras-team/keras]
connexion - Connexion is a modern Python web framework that makes spec-first and api-first development easy.
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, with more integrations coming..
flask-api - Browsable web APIs for Flask.
Keras - Deep Learning for humans