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Add auto_tool_repos #38

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merged 2 commits into from
Dec 11, 2024
Merged

Add auto_tool_repos #38

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0475e62
added descriptions for each tool
qchiujunhao Dec 11, 2024
627deae
Merge pull request #37 from goeckslab/change_shed_description
qchiujunhao Dec 11, 2024
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26 changes: 23 additions & 3 deletions tools/.shed.yml
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Original file line number Diff line number Diff line change
@@ -1,8 +1,28 @@
categories:
- Machine Learning
description: Tools for machine learning with pycaret in a simple, powerful and robust way
description: A powerful and robust low-code library for simplifying machine learning workflows with PyCaret.
name: galaxy_pycaret
owner: goeckslab
long_description: Tools for machine learning with pycaret in a simple, powerful and robust way
long_description: |
PyCaret is a low-code machine learning library that simplifies the process of building, training, and deploying machine learning models.
With its comprehensive suite of tools, PyCaret enables users to perform complex tasks such as data preprocessing,
model selection, hyperparameter tuning, and evaluation, all within a single, intuitive interface.
It is designed to be powerful and robust, making it suitable for both beginners and advanced users.
Whether you are exploring data for insights, building predictive models, or deploying them into production,
PyCaret provides a streamlined workflow that saves time and reduces code complexity.
remote_repository_url: https://github.com/goeckslab/Galaxy-Pycaret
homepage_url: https://github.com/goeckslab/Galaxy-Pycaret
homepage_url: https://github.com/goeckslab/Galaxy-Pycaret

auto_tool_repositories:
name_template: "{{ tool_id }}"
description_template: "Wrapper for pycaret tool: {{ tool_name }}"
suite:
name: suite_pycaret
description: A powerful and robust low-code library for simplifying machine learning workflows with PyCaret.
long_description: |
PyCaret is a low-code machine learning library that simplifies the process of building, training, and deploying machine learning models.
With its comprehensive suite of tools, PyCaret enables users to perform complex tasks such as data preprocessing,
model selection, hyperparameter tuning, and evaluation, all within a single, intuitive interface.
It is designed to be powerful and robust, making it suitable for both beginners and advanced users.
Whether you are exploring data for insights, building predictive models, or deploying them into production,
PyCaret provides a streamlined workflow that saves time and reduces code complexity.
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