mle-project 0.1.1

No project description provided

example_project

Tools used in this project

• Poetry: Dependency management - article
• hydra: Manage configuration files - article
• pre-commit plugins: Automate code reviewing formatting - article
• DVC: Data version control - article
• pdoc: Automatically create an API documentation for your project

Project structure

.
├───.buildkite                              # Code to run on buildkite platform
│   ├───hooks                               # buildkite pre-command
│   ├───plugins                             # plugins for multiple customers, regions, sites
│   │   ├───get-environments-definition
│   │   │   └───hooks
│   │   └───get-python-comm                 # plugins for common python packages
│   │       └───hooks
│   └───scripts                             # scripts to check the difference between projects
├───api
│   └───migrations                          # scripts for API
├───batch
│   └───batch_linear_regression_scheduler   # script for batch scheduler - processor - writer
│       ├───.buildkite
│       │   └───scripts
│       └───terraform
│           ├───backend                     # backend infrastructure for terraform
│           ├───templates                   # user-data: install ssm, Inspector, and ECS config
│           └───vars                        # variables for each region
├───config
│   ├───model                               # config for each model
│   └───process                             # config for each process
├───dist
├───docs                                    # DOCUMENTATION
├───http_server                             # Http_server for inferencing
│   └───.buildkite
│       └───scripts
├───models                                  # models output 
├───notebooks                               # notebook for inference
├───src
│   └───__pycache__
└───tests

1. Installation

pip install mle-project

2. Test with python

from src import SimpleLinearRegression, evaluate, generate_data

X_train, y_train, X_test, y_test = generate_data()
model = SimpleLinearRegression()
model.fit(X_train, y_train)
predicted = model.predict(X_test)
evaluate(model, X_test, y_test, predicted)