jo2lxq/wafl
Mar 2025 – Mar 2025
1 Month active
Languages Used
MarkdownPythonShell
Technical Skills
Data LoadingData PreprocessingDataset ManagementDeep LearningDocumentationFederated Learning
ueda0913
PROFILE
Ueda0913
Worked on the jo2lxq/wafl repository to enhance data preprocessing and dataset management for federated learning experiments. Developed GPU-accelerated dataset loading by migrating from ImageFolder to MyGPUdataset, enabling dynamic node counts and parameterized dataset attributes for scalable training. Implemented per-node mean and standard deviation computation during non-IID filter creation, improving analysis fidelity and supporting robust model training. Refactored preprocessing by removing pre_transform, streamlining the data pipeline. Addressed documentation accuracy by correcting data distribution tables in the README. Utilized Python, PyTorch, and Shell scripting to optimize data loading, preprocessing, and documentation, preparing the codebase for future experimental flexibility.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
5Total
Bugs
1
Commits
5
Features
2
Lines of code
129
Activity Months1
Your Network
8 peopleShared Repositories
8
jo2lxqMember
yagiryo0219Member
amura870Member
takarataniguchiMember
KT220mMember
yagiryo0219Member
Ryuhei YamaguchiMember
taniguchitakaraMember
Work History
March 2025
5 Commits • 2 Features
Mar 1, 2025Monthly summary for 2025-03 (jo2lxq/wafl): Key features delivered: - Non-IID Data Preprocessing Statistics: Compute and save per-node mean and standard deviation during non-IID filter creation to support analysis and model training. - GPU-Accelerated Dataset Loading and Dynamic Configuration: Migrate training data loading from ImageFolder to MyGPUdataset, enable dynamic node count, parameterize dataset attributes, and streamline preprocessing by removing pre_transform. Major bugs fixed: - README Documentation Table Fix: Correct mismatched data distribution tables by adding the missing L10 column for IID and Non-IID tables to accurately reflect data distribution. Overall impact and accomplishments: - Improved data analysis fidelity for non-IID scenarios and more robust experimental setups due to per-node statistics. - Enhanced training scalability and performance with GPU-accelerated dataset loading and a flexible, dynamic configuration for node counts and dataset attributes. - Documentation accuracy improved, reducing confusion around data distribution across IID/Non-IID scenarios; refactors further prepared the codebase for future experiments. Technologies and skills demonstrated: - GPU-accelerated data loading and dataset architecture (MyGPUdataset) - Dynamic configuration and parameterization of dataset attributes - Data preprocessing optimization and refactoring (removing pre_transform, robust label handling) - Documentation hygiene and cross-checking data distribution tables
5 Commits • 2 Features
Mar 1, 2025Monthly summary for 2025-03 (jo2lxq/wafl): Key features delivered: - Non-IID Data Preprocessing Statistics: Compute and save per-node mean and standard deviation during non-IID filter creation to support analysis and model training. - GPU-Accelerated Dataset Loading and Dynamic Configuration: Migrate training data loading from ImageFolder to MyGPUdataset, enable dynamic node count, parameterize dataset attributes, and streamline preprocessing by removing pre_transform. Major bugs fixed: - README Documentation Table Fix: Correct mismatched data distribution tables by adding the missing L10 column for IID and Non-IID tables to accurately reflect data distribution. Overall impact and accomplishments: - Improved data analysis fidelity for non-IID scenarios and more robust experimental setups due to per-node statistics. - Enhanced training scalability and performance with GPU-accelerated dataset loading and a flexible, dynamic configuration for node counts and dataset attributes. - Documentation accuracy improved, reducing confusion around data distribution across IID/Non-IID scenarios; refactors further prepared the codebase for future experiments. Technologies and skills demonstrated: - GPU-accelerated data loading and dataset architecture (MyGPUdataset) - Dynamic configuration and parameterization of dataset attributes - Data preprocessing optimization and refactoring (removing pre_transform, robust label handling) - Documentation hygiene and cross-checking data distribution tables
March 2025
Activity
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Quality Metrics
Correctness84.0%
Maintainability84.0%
Architecture84.0%
Performance72.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPythonShell
Technical Skills
Data LoadingData PreprocessingDataset ManagementDeep LearningDocumentationFederated LearningGPU ComputingMachine LearningPyTorchRefactoring
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