Snapchat/GiGL
Apr 2025 – Oct 2025
7 Months active
Languages Used
C++PythonMakefileYAMLJinjaJupyter NotebookSQLMarkdown
Technical Skills
Data EngineeringData PartitioningDistributed SystemsGraph Neural NetworksMachine LearningPyTorch
mkolodner-sc
PROFILE
Mkolodner-sc
Over seven months, Michael Kolodner engineered scalable distributed data processing and machine learning pipelines for the Snapchat/GiGL repository. He developed modular data loaders, partitioners, and exporters to support heterogeneous and homogeneous graph workloads, leveraging Python, PyTorch, and BigQuery. His work included implementing memory-efficient partitioning strategies, asynchronous data loading, and robust end-to-end training and inference workflows. Michael refactored core components for reliability, introduced feature flags for configurable exports, and expanded test coverage to ensure production readiness. By integrating cloud-native tools and optimizing data handling, he improved reproducibility, data integrity, and scalability across distributed training and evaluation environments in GiGL.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
52Total
Bugs
5
Commits
52
Features
24
Lines of code
22,001
Activity Months7
Your Network
23 people
Work History
October 2025
3 Commits • 3 Features
Oct 1, 2025October 2025 (Snapchat/GiGL): Delivered scalable data processing and end-to-end export capabilities, driven by a new feature flag for embedding population, sharded read enhancements for BigQuery data processing, and a refactored exporter suite to enable predictions export to GCS and loading into BigQuery. The work improves data quality, reduces processing payloads, and strengthens the end-to-end data pipeline with expanded test coverage.
3 Commits • 3 Features
Oct 1, 2025October 2025 (Snapchat/GiGL): Delivered scalable data processing and end-to-end export capabilities, driven by a new feature flag for embedding population, sharded read enhancements for BigQuery data processing, and a refactored exporter suite to enable predictions export to GCS and loading into BigQuery. The work improves data quality, reduces processing payloads, and strengthens the end-to-end data pipeline with expanded test coverage.
October 2025
September 2025
7 Commits • 2 Features
Sep 1, 2025In September 2025 (Month: 2025-09), Snapchat/GiGL focused on stabilizing distributed data processing, improving data handling, and updating testing coverage. Key delivered work included: 1) Fix for training failures caused by sorting feature keys in preprocessed metadata, restoring correct functionality for distributed training (DDP) with a dedicated commit. 2) Distributed dataset loading improvements: TFRecordDataLoader now returns labels separately; DistDataset migrated to its own file; dataset build simplified; partitioning handling unified, enabling more scalable data processing across workers. 3) Bug fix for link prediction examples: ensure model saving occurs on the primary process and timing is measured accurately, improving reproducibility of results. 4) GLT upgrade: bumped GraphLearn for PyTorch (GLT) and added unit tests for distributed neighbor loaders around isolated nodes in both heterogeneous and homogeneous graphs, improving test coverage and reliability. Overall impact: more reliable distributed training, clearer data pipelines, improved evaluation rigor, and better alignment with production workflows. Technologies/skills demonstrated: PyTorch DDP, TFRecordDataLoader, DistDataset architecture, dataset partitioning strategies, GLT integration, unit testing, and container/docker updates.
September 2025
7 Commits • 2 Features
Sep 1, 2025In September 2025 (Month: 2025-09), Snapchat/GiGL focused on stabilizing distributed data processing, improving data handling, and updating testing coverage. Key delivered work included: 1) Fix for training failures caused by sorting feature keys in preprocessed metadata, restoring correct functionality for distributed training (DDP) with a dedicated commit. 2) Distributed dataset loading improvements: TFRecordDataLoader now returns labels separately; DistDataset migrated to its own file; dataset build simplified; partitioning handling unified, enabling more scalable data processing across workers. 3) Bug fix for link prediction examples: ensure model saving occurs on the primary process and timing is measured accurately, improving reproducibility of results. 4) GLT upgrade: bumped GraphLearn for PyTorch (GLT) and added unit tests for distributed neighbor loaders around isolated nodes in both heterogeneous and homogeneous graphs, improving test coverage and reliability. Overall impact: more reliable distributed training, clearer data pipelines, improved evaluation rigor, and better alignment with production workflows. Technologies/skills demonstrated: PyTorch DDP, TFRecordDataLoader, DistDataset architecture, dataset partitioning strategies, GLT integration, unit testing, and container/docker updates.
August 2025
5 Commits • 1 Features
Aug 1, 2025August 2025 performance summary for Snapchat/GiGL: Implemented scalable node-based data partitioning with HashedNodeSplitter, expanded loading to support node-labels, and introduced node-label separation with features; added Node Classification support in Dataset and Dataloaders; clarified TFRecord integer feature handling to prevent precision loss; introduced early-fail on invalid Node IDs to improve data quality. These changes collectively improve distribution reliability, data integrity, and developer productivity in distributed training pipelines.
5 Commits • 1 Features
Aug 1, 2025August 2025 performance summary for Snapchat/GiGL: Implemented scalable node-based data partitioning with HashedNodeSplitter, expanded loading to support node-labels, and introduced node-label separation with features; added Node Classification support in Dataset and Dataloaders; clarified TFRecord integer feature handling to prevent precision loss; introduced early-fail on invalid Node IDs to improve data quality. These changes collectively improve distribution reliability, data integrity, and developer productivity in distributed training pipelines.
August 2025
July 2025
18 Commits • 6 Features
Jul 1, 2025July 2025 GiGL monthly summary: Delivered end-to-end distributed training for homogeneous graphs in the E2E framework, including refactored inference and new training/testing modules to enable scalable pipelines. Extended E2E to heterogeneous graphs with updated configuration and data loading/model initialization. Implemented cross-graph link prediction with hid_dim/out_dim parameterization across homogeneous and heterogeneous graphs. Improved distributed data loading, sampling, and instrumentation with enhanced fanout handling, logging, and robustness. Strengthened reliability with HGT edge-type ordering validation and unit tests to prevent indexing errors.
July 2025
18 Commits • 6 Features
Jul 1, 2025July 2025 GiGL monthly summary: Delivered end-to-end distributed training for homogeneous graphs in the E2E framework, including refactored inference and new training/testing modules to enable scalable pipelines. Extended E2E to heterogeneous graphs with updated configuration and data loading/model initialization. Implemented cross-graph link prediction with hid_dim/out_dim parameterization across homogeneous and heterogeneous graphs. Improved distributed data loading, sampling, and instrumentation with enhanced fanout handling, logging, and robustness. Strengthened reliability with HGT edge-type ordering validation and unit tests to prevent indexing errors.
June 2025
9 Commits • 7 Features
Jun 1, 2025June 2025 performance summary for Snapchat/GiGL: Delivered major data-loading, dataset-building, and modularity improvements across GiGL, enabling more scalable experiments and robust data handling. Implemented ABLP DataLoader enhancements with DistABLPLoader, extended sampling for heterogeneous graphs, and corrected batch handling; added configurable label-to-edge conversion; optimized distributed partitioning with edge-feature awareness; introduced InfiniteIterator for cyclic data iteration; and advanced modular retrieval loss and link prediction components to improve experimentation flexibility.
9 Commits • 7 Features
Jun 1, 2025June 2025 performance summary for Snapchat/GiGL: Delivered major data-loading, dataset-building, and modularity improvements across GiGL, enabling more scalable experiments and robust data handling. Implemented ABLP DataLoader enhancements with DistABLPLoader, extended sampling for heterogeneous graphs, and corrected batch handling; added configurable label-to-edge conversion; optimized distributed partitioning with edge-feature awareness; introduced InfiniteIterator for cyclic data iteration; and advanced modular retrieval loss and link prediction components to improve experimentation flexibility.
June 2025
May 2025
8 Commits • 3 Features
May 1, 2025May 2025 monthly summary for Snapchat/GiGL focusing on delivering production-ready end-to-end graph inference and performance improvements, stabilizing CI, and expanding test coverage. This period delivered four main threads: end-to-end GLT-enabled GraphLearn inference, data partitioning and asynchronous loading optimizations, targeted bug work to stabilize CI, and improved reliability through concurrency-focused tests. The work advances production readiness for heterogeneous graph workloads, improves data throughput and memory efficiency, and strengthens CI stability and test coverage across distributed loaders.
May 2025
8 Commits • 3 Features
May 1, 2025May 2025 monthly summary for Snapchat/GiGL focusing on delivering production-ready end-to-end graph inference and performance improvements, stabilizing CI, and expanding test coverage. This period delivered four main threads: end-to-end GLT-enabled GraphLearn inference, data partitioning and asynchronous loading optimizations, targeted bug work to stabilize CI, and improved reliability through concurrency-focused tests. The work advances production readiness for heterogeneous graph workloads, improves data throughput and memory efficiency, and strengthens CI stability and test coverage across distributed loaders.
April 2025
2 Commits • 2 Features
Apr 1, 2025April 2025 — Snapchat/GiGL: Delivered two high-impact features to strengthen distributed training/inference scalability and reproducibility. Implemented a memory-conscious range-based partitioner for distributed link prediction and extended the dataset factory with URI-based loading. No major bugs reported this period; changes provide clear API surfaces and traceable commits to support larger-scale experiments.
2 Commits • 2 Features
Apr 1, 2025April 2025 — Snapchat/GiGL: Delivered two high-impact features to strengthen distributed training/inference scalability and reproducibility. Implemented a memory-conscious range-based partitioner for distributed link prediction and extended the dataset factory with URI-based loading. No major bugs reported this period; changes provide clear API surfaces and traceable commits to support larger-scale experiments.
April 2025
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Quality Metrics
Correctness89.8%
Maintainability85.6%
Architecture86.8%
Performance77.0%
AI Usage20.4%
Skills & Technologies
Programming Languages
C++JinjaJupyter NotebookMakefileMarkdownPythonSQLShellTypeScriptYAML
Technical Skills
Algorithm OptimizationApache AvroApache BeamAsynchronous ProgrammingBackend DevelopmentBigQueryCI/CDCloud ComputingCode OrganizationCode RefactoringConfiguration ManagementData EngineeringData LoadingData PartitioningData Preprocessing
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