핵심 개념
Introducing a Python package for efficient parallel HPO with zero-cost benchmarks, achieving over 1000x speedup compared to traditional approaches.
초록
Deep learning success relies on hyperparameter optimization (HPO).
Zero-cost benchmarks offer a solution for non-parallel setups.
Challenges in maintaining return order in parallel setups addressed.
Introduction of a user-friendly Python package for efficient parallel HPO.
Extensive testing and experiments show significant speedup compared to traditional approaches.
Applicability to diverse HPO libraries demonstrated.
Reduction in CO2 production highlighted.
Limitations include assumptions about worker behavior and OS compatibility.
통계
"Our package can be installed via pip install mfhpo-simulator."
"Our wrapper successfully replicates the results obtained by the naïve simulation."
"Our package significantly reduces the CO2 production that experiments using zero-cost benchmarks would have caused."
인용구
"Our approach calculates the exact return order based on the information stored in the file system."
"Our package can be installed via pip install mfhpo-simulator."
"Our wrapper successfully replicates the results obtained by the naïve simulation."