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About

I primarily used TensorFlow for large-scale recommendation tasks when in big company, but PyTorch could be more efficient for smaller tasks in a smaller company.

This directory aims to train a Click-Through Rate (CTR) model using PyTorch. It's a simple example, seeking to keep everything minimal.

Tips:

  1. Apply polars instead of pandas to process data. I first implemented the pandas version of FeatureTransformer, but it's very slow when datasize> 20 millon.
  2. Apply parquets instead of pickle files to save data samples. It's really saved my memory.

Features:

  • Fast FeatureTransformer for DNN (tested on >100millon rows on single machine). Which support:
    • Both numerical and categorical input features
      • Categorical: automatic vocabulary extraction, low-frequency filtering, dynamic embedding, hash embedding
      • Numerical: standard/0-1 normalization, automatic discretization, automatic update of statistical number for standard/0-1 normalization when multiple times of fitting.
    • Variable-length sequence feature support, if there's order in the sequence, please put the latest data before the oldest data as it may pads at the end of the sequence
  • Implemented a common Trainer for training pytorch models, and save/load the results
  • Basic FastAPI for Model API Serving

Not supported:

  • Distribution training, as target of this tool is for small companies. And I used polars to speed up the data processing.

Install

pip install git+https://github.com/xiahouzuoxin/torchctr

And follow the example to use.

Model API Serving

An simple serving without latency considered.

  1. [Optional] According to your model and data processing, maybe need create a new ServingModel like BaseServingModel

  2. Set up the service:

    • Debuging: Given service name and model path from command line 
      cd $torchctr_root
python -m torchctr.serving.serve --name [name] --path [path/to/model or path/to/ckpt] --serving_class BaseServingModel
    • Production: write the command line parameters to serving_models variable in torchctr/serving/serve.py

  3. Test the service: reference test_predict in example