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Igel

Igel is a Python machine-learning library and command-line tool intended to train, test and use models without writing code. It is also associated with automated machine learning and data analysis.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Igel — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Igel when you want a simplified, no-code-oriented workflow for experimenting with machine-learning models, particularly in automated machine-learning or data-analysis projects.

Who it is for

  • Python users exploring machine-learning workflows
  • Analysts and learners seeking simplified model experimentation
  • Developers interested in a no-code machine-learning tool

Documented uses

  • Run machine-learning experiments without writing model-training code
  • Train and test machine-learning models
  • Use models in data-analysis workflows
Before you choose

Requirements & limits

The project is identified as an active, publicly visible open-source repository under the MIT license. It is presented as a Python library with a CLI platform.

  • The main distinction is its stated no-code approach to training, testing and using models.
  • Python is the identified technical language, while CLI is the listed platform.
  • The project is licensed under MIT.
  • The available project description establishes the intended workflow but does not document detailed installation or execution steps here.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install igel and create a starter configuration

Install igel and generate a basic YAML configuration file in the current working directory.

Before you start

  • Python package installer pip.
  • A terminal.

Steps

  1. Install igel with the command below.

    Documented command
    pip install igel

  2. Run igel init in the terminal to generate the starter configuration.

  3. Inspect or edit the created igel.yaml file if needed.

Expected outcome

igel is installed and a starter `igel.yaml` configuration is created in the current working directory.

Guide limitations

  • The starter configuration alone does not train a model; training requires dataset and configuration paths.
  • The documentation describes the igel init command as creating a basic config but does not specify its contents.
Documentation 1 ↗Documentation 2 ↗
Source-based practical guide

Train an image-classification model from a folder of images

Start igel's automatic image-classification training using an existing image folder.

Before you start

  • igel installed and available in the terminal.
  • A dataset of raw images stored in a folder.

Steps

  1. Prepare a folder containing the raw image dataset.

  2. Run igel auto-train --data_path 'path_to_your_images_folder' --task ImageClassification.

  3. Allow igel to process the images and start training and optimization.

Expected outcome

igel processes the image folder and starts training and optimizing a model for image classification.

Guide limitations

  • The documentation describes this workflow as computationally expensive because igel tries multiple models and compares their performance.
  • The source describes training and optimization, but does not establish a particular accuracy or best-model result.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
30 September 2026
Original website title
nidhaloff/igel · Official homepage title · retained in its source language
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