Snapchat/GiGL
Aug 2025 – Aug 2025
1 Month active
Languages Used
Python
Technical Skills
BigQueryCloud ComputingCloud StorageConfiguration ManagementData EngineeringData Modeling
Neil Shah
PROFILE
Neil Shah
Worked on modernizing the Knowledge Graph Embedding Experimental Module in the Snapchat/GiGL repository, focusing on improving scalability and reproducibility for KGE experiments. The project involved standardizing storage path constants and developing shared utilities to support distributed checkpointing and graph dataset handling. Integrated PyTorch TorchRec for enhanced machine learning workflows and introduced Hydra-based configuration parsing to streamline experimental setup and management. Leveraged Python and cloud technologies such as BigQuery and GCS to ensure robust data engineering practices. The work established a more maintainable and extensible foundation for future KGE experimentation, emphasizing modularity and efficient configuration management throughout the module.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
2,133
Activity Months1
Your Network
8 peopleWork History
August 2025
3 Commits • 1 Features
Aug 1, 2025In August 2025, Snapchat/GiGL delivered a modernization of the Knowledge Graph Embedding (KGE) Experimental Module. The work standardizes storage path constants, adds shared utilities for distributed checkpointing, graph dataset handling, and PyTorch TorchRec integration, and introduces Hydra-based experimental config parsing to simplify reproducible KGE experiments. These changes lay the groundwork for scalable, repeatable KGE experiments within the project.
3 Commits • 1 Features
Aug 1, 2025In August 2025, Snapchat/GiGL delivered a modernization of the Knowledge Graph Embedding (KGE) Experimental Module. The work standardizes storage path constants, adds shared utilities for distributed checkpointing, graph dataset handling, and PyTorch TorchRec integration, and introduces Hydra-based experimental config parsing to simplify reproducible KGE experiments. These changes lay the groundwork for scalable, repeatable KGE experiments within the project.
August 2025
Activity
Loading activity data...
Quality Metrics
Correctness86.6%
Maintainability86.6%
Architecture86.6%
Performance73.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
BigQueryCloud ComputingCloud StorageConfiguration ManagementData EngineeringData ModelingDistributed SystemsGCSMachine LearningPyTorchPythonSystem Design
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline