NVIDIA/recsys-examples
Apr 2025 – Apr 2025
1 Month active
Languages Used
PythonShell
Technical Skills
Deep LearningDistributed SystemsMachine LearningPyTorchRecommendation Systems
j
PROFILE
J
J. Qian developed a dynamic embedding tables example script with distributed training for the NVIDIA/recsys-examples repository, focusing on scalable experimentation infrastructure for recommender systems. Leveraging Python and PyTorch, J. Qian integrated torchrec and the dynamicemb library to demonstrate dynamic embedding configurations, utilizing components such as DynamicEmbeddingShardingPlanner and DynamicEmbeddingCollectionSharder. The implementation included generating sparse features and orchestrating end-to-end distributed training with forward and backward passes, providing a reusable reference for teams adopting dynamic embeddings. This work enhanced platform readiness for large-scale recommendation models, reflecting depth in distributed systems and machine learning engineering within a production-oriented context.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
280
Activity Months1
Your Network
24 peopleSame Organization
@antfin.com11
Work History
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025: Key deliverable was a Dynamic Embedding Tables Example Script with Distributed Training for NVIDIA/recsys-examples. The script demonstrates dynamic embedding tables using torchrec and the dynamicemb library, including a distributed training setup, embedding configurations, and the use of DynamicEmbeddingShardingPlanner and DynamicEmbeddingCollectionSharder. The implementation also generates sparse features and runs forward/backward passes to illustrate the end-to-end workflow. There were no customer-facing bug fixes this month; the focus was on building scalable experimentation infrastructure and a reusable reference implementation. This work enhances platform readiness for large-scale recommender models and accelerates onboarding for teams adopting dynamic embeddings.
1 Commits • 1 Features
Apr 1, 2025April 2025: Key deliverable was a Dynamic Embedding Tables Example Script with Distributed Training for NVIDIA/recsys-examples. The script demonstrates dynamic embedding tables using torchrec and the dynamicemb library, including a distributed training setup, embedding configurations, and the use of DynamicEmbeddingShardingPlanner and DynamicEmbeddingCollectionSharder. The implementation also generates sparse features and runs forward/backward passes to illustrate the end-to-end workflow. There were no customer-facing bug fixes this month; the focus was on building scalable experimentation infrastructure and a reusable reference implementation. This work enhances platform readiness for large-scale recommender models and accelerates onboarding for teams adopting dynamic embeddings.
April 2025
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonShell
Technical Skills
Deep LearningDistributed SystemsMachine LearningPyTorchRecommendation Systems
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