Carefree-learn
A minimal Automatic Machine Learning (AutoML) solution for tabular datasets based on PyTorch.
automl tabular pytorch tabular-data library code video

carefree-learn is a minimal Automatic Machine Learning (AutoML) solution for tabular datasets based on PyTorch.

Why carefree-learn?

carefree-learn

  • Provides a scikit-learn-like interface with much more 'carefree' usages, including:
    • Automatically deals with data pre-processing.
    • Automatically handles datasets saved in files (.txt, .csv).
    • Supports Distributed Training, which means hyper-parameter tuning can be very efficient in carefree-learn.
  • Includes some brand new techniques which may boost vanilla Neural Network (NN) performances on tabular datasets, including:
  • Supports many convenient functionality in deep learning, including:
    • Early stopping.
    • Model persistence.
    • Learning rate schedulers.
    • And more...
  • Full utilization of the WIP ecosystem cf*, such as:
    • carefree-toolkit: provides a lot of utility classes & functions which are 'stand alone' and can be leveraged in your own projects.
    • carefree-data: a lightweight tool to read -> convert -> process ANY tabular datasets. It also utilizes cython to accelerate critical procedures.

From the above, it comes out that carefree-learn could be treated as a minimal Automatic Machine Learning (AutoML) solution for tabular datasets when it is fully utilized. However, this is not built on the sacrifice of flexibility. In fact, the functionality we've mentioned are all wrapped into individual modules in carefree-learn and allow users to customize them easily.

Don't forget to tag @carefree0910 in your comment, otherwise they may not be notified.

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一个啥都想学的浮莲子
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