MLOps Utils

mlops-utils is a collection of tools for managing MLOps infrastructure.

Installation

Install using via PyPi

pip install mlops-utils

Requirements

Python 3.10 or higher is required. For automatic tracking in PyTorch logging, versions between 1.9.0 and 2.1.1 (inclusive) are supported

Documentation

Documentation will be added soon. Stay tuned for updates!

Example

import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from mlops_utils.mlflow_logger.sklearn import Sklearn

# URL 주소 변경
sklearn = Sklearn("{url}", 5000)
experiment_name = "minjun_researcher"
run_name = "20240116-sklearn"


@sklearn.logger(experiment_name, run_name)
def ml_run():
    noise = np.random.rand(100, 1)
    X = sorted(10 * np.random.rand(100, 1)) + noise
    y = sorted(10 * np.random.rand(100))

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)
    pipe = Pipeline([("scaler", StandardScaler()), ("lr", LinearRegression())])
    pipe.fit(X_train, y_train)
    preds = pipe.predict(X_test)
    print(preds)

    sklearn.post_board(y_test, preds)

ml_run()