NVIDIA/physicsnemo
Feb 2025 – Mar 2025
2 Months active
Languages Used
C++Python
Technical Skills
AutogradCUDACode ManagementCode OptimizationDeep LearningDistributed Systems
Corey Adams
PROFILE
Corey Adams
Corey Anderson contributed to NVIDIA/physicsnemo by developing profiling and domain-parallelism features that enhance performance visibility and scalable data distribution for large AI workloads. He integrated PyTorch profiler, line_profiler, and NVIDIA Nsight, leveraging Flash Attention and torch.compile to optimize GPU computing. Corey introduced ShardTensor, a domain-parallel primitive extending PyTorch DTensor for uneven device sharding, and implemented custom collectives for key tensor operations. He also improved release management by ensuring clean artifact handling and strengthened distributed utilities with version-aware compatibility checks and robust state management. His work, primarily in Python and C++, improved reliability, maintainability, and onboarding for distributed deep learning workflows.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
5Total
Bugs
1
Commits
5
Features
3
Lines of code
11,588
Activity Months2
Your Network
1376 people
Work History
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for NVIDIA/physicsnemo: Delivered reliability and compatibility enhancements for distributed utilities and profiling. Key outcomes include version-aware utilities for PyTorch distributed features, a profiler stability fix addressing circular import issues, and test updates to reflect version gating. These changes reduce runtime errors across varied PyTorch versions, improve maintainability, and simplify onboarding for users integrating distributed tooling. Impact highlights: - Reduced profiling runtime errors by stabilizing state management and avoiding circular imports. - Enabled safe usage of newer PyTorch features (e.g., DTensor) only when supported, preserving backward compatibility. - Updated tests to skip appropriately based on PyTorch version to ensure accurate validation. Commits touched: - 1e0057e29aa09d79ff735dc6ffced74c3762304b (PyTorch version compatibility utilities for distributed utilities) - 930ddd7d820979f0eb4a9f4ee552919425d51111 (Profiler stability and circular import bug fix)
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for NVIDIA/physicsnemo: Delivered reliability and compatibility enhancements for distributed utilities and profiling. Key outcomes include version-aware utilities for PyTorch distributed features, a profiler stability fix addressing circular import issues, and test updates to reflect version gating. These changes reduce runtime errors across varied PyTorch versions, improve maintainability, and simplify onboarding for users integrating distributed tooling. Impact highlights: - Reduced profiling runtime errors by stabilizing state management and avoiding circular imports. - Enabled safe usage of newer PyTorch features (e.g., DTensor) only when supported, preserving backward compatibility. - Updated tests to skip appropriately based on PyTorch version to ensure accurate validation. Commits touched: - 1e0057e29aa09d79ff735dc6ffced74c3762304b (PyTorch version compatibility utilities for distributed utilities) - 930ddd7d820979f0eb4a9f4ee552919425d51111 (Profiler stability and circular import bug fix)
March 2025
February 2025
3 Commits • 2 Features
Feb 1, 2025February 2025 — NVIDIA/physicsnemo: Delivered profiling and domain-parallelism capabilities with a focus on performance visibility, scalable data distribution, and release hygiene. Key commits include 416a7ddcb9ec99927f2042de597451588a4ea99b (Profiling (#787)) and 39234e33d1847e8a4ba50ce5da8fd09562da2b41 (had note about release artifacts), and e0e97398acd06c3fea3acee518ab14e911b5d10d (Enable Domain Parallelism with ShardTensor (#784)). These efforts enable users to identify bottlenecks with PyTorch profiler, line_profiler, and Nsight, leverage Flash Attention and torch.compile for speedups, distribute large inputs with the new ShardTensor domain-parallel primitive, and ensure clean release artifacts for safer adoption.
February 2025
3 Commits • 2 Features
Feb 1, 2025February 2025 — NVIDIA/physicsnemo: Delivered profiling and domain-parallelism capabilities with a focus on performance visibility, scalable data distribution, and release hygiene. Key commits include 416a7ddcb9ec99927f2042de597451588a4ea99b (Profiling (#787)) and 39234e33d1847e8a4ba50ce5da8fd09562da2b41 (had note about release artifacts), and e0e97398acd06c3fea3acee518ab14e911b5d10d (Enable Domain Parallelism with ShardTensor (#784)). These efforts enable users to identify bottlenecks with PyTorch profiler, line_profiler, and Nsight, leverage Flash Attention and torch.compile for speedups, distribute large inputs with the new ShardTensor domain-parallel primitive, and ensure clean release artifacts for safer adoption.
Activity
Loading activity data...
Quality Metrics
Correctness90.0%
Maintainability86.0%
Architecture90.0%
Performance86.0%
AI Usage36.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
AutogradCUDACode ManagementCode OptimizationCode RefactoringDeep LearningDistributed SystemsFlash AttentionGPU ComputingNVIDIA NsightParallel ComputingPerformance ProfilingPyTorchPythonPython Development
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline