RNN service

Description

This project utilizes an LSTM (Long Short-Term Memory) model for making predictions. It involves time-series forecasting or sequential data analysis, where LSTM is well-suited due to its ability to retain information over long periods.

Installation

This project utilizes Poetry for package management. Ensure you have Poetry installed, then run:

poetry install

Usage

To run tests, execute:

poetry run pytest

Additionally, there are scripts provided:

• serve: For serving the model.
• data: For managing data.

Dependencies

This project has the following dependencies:

• Python >=3.9,<3.11
• pandas ^2.2.1
• tensorflow-io-gcs-filesystem 0.27.0
• tensorflow ~2.10
• numpy ^1.26.4
• matplotlib ^3.8.3
• joblib ^1.3.2
• flask ^3.0.2
• requests ^2.31.0
• scikit-learn ^1.4.1.post1
• datetime ^5.4

For development, it additionally requires:

• pytest ^8.1.1

GitHub Actions

This project is integrated with GitHub Actions. Upon push or pull request to the main branch, it automatically installs Python dependencies, runs tests, and lints the code.

License

This project is licensed under the MIT License.