/torchctr

A small pytorch implementation for ctr prediction in recommendation system for small companies

Primary LanguagePython

Purpuse

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. While the model is straightforward, the data preprocessing pipeline is more complex due to a variety of inputs.

Supported features include:

  • Both numerical and categorical input features
    • Categorical: automatic vocabulary extraction, low-frequency filtering, dynamic embedding, hash embedding
    • Numerical: standard or 0-1 normalization, automatic discretization, automatic update of statistical number for standard or 0-1 normalization if new data is fed in
  • 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
  • Sequence features support weights by setting the weight column
  • Implemented DataFrameDataset for straightforward training with input data of pandas/polars DataFrame format
  • 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

Install

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

Example

  1. Using DataFrameDataset to load the raw data as pytorch Dataset format
  2. Create a model definition file in torchctr/models, and implement the model by inherit from nn.Module but with some extra member methods,
    • required:
      • training_step
      • validation_step
    • optional:
      • configure_optimizers
      • configure_lr_scheduler
  3. Using Trainer to train the model
  4. Serving the model by Model API Serving

Model API Serving

  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

Related Dataset