> ML_LITERATURE // PASZKE-2019-PYTORCH-IMPERATIVE-STYLE-HIGH-PERFORMANCE-DEEP-LEARNING-LIBRARY_v1.0
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, Soumith Chintala · Advances in Neural Information Processing Systems (NeurIPS) (2019)
systems2019industry-standardartifactsAvailable
Principal Contribution
Imperative dynamic graph construction (tape-based autograd) deeply integrated with native Python idioms and high-performance C++ backend.
Operational Relevance
Directly guides deployment choices and architecture selection for task-multiclass-classification, task-text-generation.
Assumptions
- Standard empirical regularity and statistical stability hold across evaluation domains
Limitations
- Performance characteristics depend on domain distribution and compute allocation parameters
Connected Algorithms, Architectures & Tools
Related Algorithms:
Related Architectures:
Implementing Libraries: