microsoft / qlib

Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.
https://qlib.readthedocs.io/en/latest/
MIT License
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Enhance README with LightGBM Installation Guidance for Mac M1 Users #1766

Closed Xisen-Wang closed 3 months ago

Xisen-Wang commented 3 months ago

Description

This update introduces additional guidance in the README for Mac users with M1 chips who encounter issues installing LightGBM, a dependency of PyQlib. A new section under installation tips provides a workaround for the common wheel building error related to missing OpenMP dependencies.

Motivation and Context

During the installation of PyQlib on Mac systems with M1 chips, users may face difficulties building LightGBM due to missing OpenMP dependencies. This issue has been highlighted in several community discussions. The added instructions aim to streamline the installation process, enhancing user experience and supporting broader adoption of PyQlib on Mac platforms.

How Has This Been Tested?

The provided solution has been manually tested on a Mac with an M1 chip. Following the updated README instructions resulted in a successful installation of PyQlib, including the correct setup of LightGBM.

Screenshots of Test Results (if appropriate):

Not entirely applicable as this update involves documentation only. The results for installation are shown

Screenshot 2024-03-20 at 12 23 14 PM Screenshot 2024-03-20 at 12 24 02 PM

Types of changes

I've added a specific tip for Mac M1 users in the README, outlining the need to install libomp using Homebrew before attempting to install PyQlib. This step addresses a common issue where the installation process fails due to missing OpenMP dependencies required by LightGBM. The aim is to make the setup process smoother for Mac users and minimize installation-related inquiries.