inclusionAI/AReaL
Nov 2025 – Nov 2025
1 Month active
Languages Used
Python
Technical Skills
API developmentasynchronous programmingbackend developmentdistributed systemsmachine learningscheduler design
daihaowz
PROFILE
Daihaowz
During November 2025, Aihao Wang developed foundational distributed training orchestration for the inclusionAI/AReaL repository, focusing on scalable machine learning workflows. He introduced a local scheduler for single-controller mode and a train controller to coordinate multi-worker distributed training, leveraging Python and asynchronous programming. By implementing a set_env API endpoint, he enabled per-worker environment configuration, ensuring consistent RANK and WORLD_SIZE provisioning. Updates to the CLI, API, and documentation improved developer experience and reliability, including dependency migrations. This work reduced setup complexity and enhanced scalability, providing a robust backend foundation for repeatable, efficient experimentation in distributed systems and scheduler design contexts.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
5,211
Activity Months1
Your Network
55 peopleWork History
November 2025
2 Commits • 1 Features
Nov 1, 2025Month: 2025-11. Delivered foundational Distributed Training Orchestration for inclusionAI/AReaL, introducing a local scheduler for single-controller mode and a train controller to orchestrate multi-worker distributed training. Implemented per-worker environment configuration via a new set_env endpoint, enabling consistent RANK/WORLD_SIZE provisioning across workers. Updated CLI/API and documentation to reflect new scheduling capabilities and improved developer experience (including dependency adjustments and numpy-style docstrings). These changes reduce setup complexity, improve scalability, and accelerate experimentation across distributed environments.
2 Commits • 1 Features
Nov 1, 2025Month: 2025-11. Delivered foundational Distributed Training Orchestration for inclusionAI/AReaL, introducing a local scheduler for single-controller mode and a train controller to orchestrate multi-worker distributed training. Implemented per-worker environment configuration via a new set_env endpoint, enabling consistent RANK/WORLD_SIZE provisioning across workers. Updated CLI/API and documentation to reflect new scheduling capabilities and improved developer experience (including dependency adjustments and numpy-style docstrings). These changes reduce setup complexity, improve scalability, and accelerate experimentation across distributed environments.
November 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage70.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
API developmentasynchronous programmingbackend developmentdistributed systemsmachine learningscheduler design
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