ML-Git Step-by-Step Guide

About

In order to facilitate the learning process of using the ML-Git API, we offer a series of step-by-step guides that emulate real situations in the learning context by applying the correct API command sequences that should be used in each scenario.

The guides were created using Jupyter notebooks, as they offer the possibility to run, step-by-step, code snippets in a format similar to an explanatory document, thus becoming ideal for teaching new users how to use the API in real case scenarios.

How execute notebooks:

1. To run notebooks more easily, a docker environment has been created that performs all the environment settings required by the user. So make sure that the procedures in the local environment configuration section have been performed.

Summary of existing notebooks:

• basic_flow

  • This notebook describes a basic execution flow of ML-Git with its API.
  • GitHub link

• clone_repository

  • This notebook describes how to clone an ML-Git repository.
  • GitHub link

• multiple_projects

  • This notebook describes how to work with multiple projects in the ML-Git API.
  • GitHub link

• relationship_api_commands

  • This notebook describes a basic flow in the context of relationships between entities with the API provided by ML-Git.
  • GitHub link

• mnist_random_forest_api

  • This notebook describes a basic execution flow of ML-Git with its API using the MNIST dataset.
  • GitHub link

• mnist_random_forest_cli

  • This notebook describes a basic execution flow of ML-Git with its CLI using the MNIST dataset.
  • GitHub link

• checkout_with_sample

  • This notebook describes how to perform a checkout operation with ML-Git using samples of a dataset.
  • GitHub link

• multiple_datasets

  • This notebook describes how to handle the scenario where the same file is present in more than one dataset.
  • GitHub link

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Last update: October 3, 2023