pytorch/vision
Feb 2025 – Feb 2025
1 Month active
Languages Used
C++CUDA
Technical Skills
CUDA programmingComputer VisionPerformance OptimizationPyTorch
Alexandre Ghelfi, PhD
PROFILE
Alexandre Ghelfi, Phd
Worked on performance and scalability features for PyTorch’s vision and reinforcement learning repositories, focusing on CUDA programming, computer vision, and multiprocessing. Developed a CUDA kernel for Non-Maximum Suppression in pytorch/vision, enabling index gathering directly on the GPU to eliminate CPU-GPU data transfers and reduce inference latency for large-scale vision workloads. In pytorch/rl, implemented per-worker frames_per_batch control in multi-data collectors using Python and C++, allowing more granular scheduling and improved resource utilization in distributed reinforcement learning pipelines. Both features were delivered with supporting documentation and tests, emphasizing production-readiness and scalability for real-time and distributed machine learning applications.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
236
Activity Months2
Your Network
80 peopleWork History
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025: Implemented per-worker frames_per_batch control in multi-data collectors for PyTorch RL, enabling per-worker frame counts to improve resource utilization and data throughput. This feature reduces bottlenecks in distributed data collection and lays groundwork for scalable RL training.
1 Commits • 1 Features
Jun 1, 2025June 2025: Implemented per-worker frames_per_batch control in multi-data collectors for PyTorch RL, enabling per-worker frame counts to improve resource utilization and data throughput. This feature reduces bottlenecks in distributed data collection and lays groundwork for scalable RL training.
June 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for pytorch/vision: Delivered a performance-focused NMS optimization by keeping index gathering on the CUDA device. Introduced a new CUDA kernel, gather_keep_from_mask, to process the mask directly on the GPU, eliminating CPU-GPU data transfers and significantly boosting throughput for large numbers of boxes. This improves end-to-end inference latency and scalability for real-time vision workloads in production. Commit e239710ccd5020a743e6e3e24702f801f32b82e0 with message 'Speed-up NMS by keeping index gathering on cuda device (#8766)'.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for pytorch/vision: Delivered a performance-focused NMS optimization by keeping index gathering on the CUDA device. Introduced a new CUDA kernel, gather_keep_from_mask, to process the mask directly on the GPU, eliminating CPU-GPU data transfers and significantly boosting throughput for large numbers of boxes. This improves end-to-end inference latency and scalability for real-time vision workloads in production. Commit e239710ccd5020a743e6e3e24702f801f32b82e0 with message 'Speed-up NMS by keeping index gathering on cuda device (#8766)'.
Activity
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Quality Metrics
Correctness95.0%
Maintainability80.0%
Architecture95.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CUDAPython
Technical Skills
CUDA programmingComputer VisionData CollectionMultiprocessingPerformance OptimizationPyTorchReinforcement LearningTesting
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
pytorch/rl
Jun 2025 – Jun 2025
1 Month active
Languages Used
Python
Technical Skills
Data CollectionMultiprocessingReinforcement LearningTesting