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