distributed-ml 0.1.4

A package for distributed scikit-learn tasks.

distributed-ml

distributed-ml is a Python-based machine learning package that facilitates model training, hyperparameter tuning, and data handling through a structured API. It supports various scikit-learn estimators, including classifiers, regressors, and hyperparameter search classes like GridSearchCV and RandomizedSearchCV.

This package allows users to:

• Create and manage API sessions
• Check and download datasets
• Train models with various configurations
• Monitor job status with progress bars
• Handle hyperparameter tuning efficiently

API Reference

Class

MLTaskManager()

• Instantiates class.
• Returns session id.

Methods

check_data(data_name)

• Checks if a dataset is available.
• Arguments:
  • data_name (str): Name of the dataset.
• Returns path where data was downloaded, otherwise 404 Error.

download_data(data_link, data_name, data_type)

• Downloads data from a specified source.
• Arguments:
  • data_link (str): URL or dataset identifier.
  • data_name (str): Name to save dataset as.
  • data_type (str): Source type (e.g., “kaggle”).
• Returns path where data was downloaded.

train(estimator, dataset_name, train_params=None, wait_for_completion=False)

• Submits a training job to the API.
• Arguments:
  • estimator: A scikit-learn model.
  • dataset_name (str): Name of dataset.
  • train_params (dict, optional): Training configurations.
  • wait_for_completion (bool, optional): Whether to wait for the job to complete.
• Returns training progress and job results (i.e. best results, best parameters)

check_job_status(job_id)

• Retrieves the status of a training job.
• Arguments:
  • job_id (str): Unique job identifier.
• Returns training progress and job results (i.e. best results, best parameters)