Skip to main content

Intelligence Guidance Manager for AI.

Project description


PyPI PyPI - Python Version Loc Comments

Docs Deploy Code Test Badge Creation Package Release codecov

GitHub stars GitHub forks GitHub commit activity GitHub issues GitHub pulls Contributors GitHub license

IGM (Intelligence Guidance Manager for AI).

The ultimate purpose of AI is to serve science (as ai4science does), so let's call it sci-igm.


You can simply install it with pip command line from the official PyPI site.

pip install sci-igm

Or install from latest source code as follows:

git clone
cd igm
pip install . --user

Quick Start for IGM

Here is a simple example to create a hello world project:

igm new git+ helloword  # create helloworld project
cd helloword
igm run  # run the helloworld project

What Happened?

After you enter the igm new <template> <proj_dir> command to your terminal, igm operate as the following stages:

  1. Initialization Stage - Check the template, if remote url or repository detected, download it to local storage.
  2. Project Creation Stage
    1. Load Step - Load the template's meta information.
    2. Inquire Step - Ask the user to provide some necessary.
    3. Build Step - Build the project based on the template, the project will be placed at <proj_dir>.
  3. Project Use Stage
    • (Optional) Prerequisite Installation - run igm run install command to install the dependencies.
    • Code Run - run igm run command to run the main project code.
    • What Scripts Are Provided? - run igm run -h to see the list of provided scripts.
    • (Optional) Other custom scripts - you can use the other scripts provided by template, or custom the extra scripts in

How to Create A New Project Template

The detailed documentation is still preparing, but you can take a look at the following examples:

  • template-simple, a helloworld template example
  • template-linear-regression, a more advanced example of linear regression problem, with visualization example
  • template-resnet18, template for resnet18, including resource download and usage of tensorboard
  • IGM-di, example of usage of DI-engine, including custom complex generating of training code
  • Test Template, a test template for unittest of igm tools, more advanced usage can be found here.

For information on template syntax, see the following:

  • Jinja2, the template render framework we used in IGM.
  • potc, transform any object to readable python object, will be useful when render python source code. It is integrated into IGM with a filter named potc.


We appreciate all contributions to improve igm, both logic and system designs. Please refer to for more guides.


igm released under the Apache 2.0 license.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sci-igm-0.0.2.tar.gz (35.3 kB view hashes)

Uploaded source

Built Distribution

sci_igm-0.0.2-py3-none-any.whl (48.0 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page