Fast composition of Machine Learning Pipelines
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Modular Pipelines is a lightweight and extensible library to create complex multi-model and multi-modal pipelines.
You can easily build:
Ensemble Learning → Ensemble learning is a general meta approach to machine learning that seeks better predictive performance by combining the predictions from multiple models. See more on wikipedia Wiki - Ensemble Learning or A Gentle Introduction to Ensemble Learning Algorithms
Meta Learning → in machine learning refers to learning algorithms that learn from other learning algorithms. Most commonly, this means the use of machine learning algorithms that learn how to best combine the predictions from other machine learning algorithms in the field of ensemble learning. Meta Modelling and What Is Meta-Learning in Machine Learning?
The project was entire built in python
conda
,python >= 3.7
andpip
pytorch
andwandb
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Clone the project by running
git clone https://github.com/applied-exploration/modular-pipelines.git
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Navigate to the project root directory
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Build the project by executing
pip install -e .
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The repo comes with a conda env file, enter
mamba env create
to install all dependencies -
We use isort + black for formatting
To be able to use Huggingface
and Wandb
:
- Enter your
Wandb
andHuggingface
API in and.env
fileHF_HUB_TOKEN = [ENTER YOUR API] WANDB_API_KEY = [ENTER YOUR API]
Project Link: https://github.com/applied-exploration/modular-pipelines