Summary of Fast Benchmarking Of Asynchronous Multi-fidelity Optimization on Zero-cost Benchmarks, by Shuhei Watanabe and Neeratyoy Mallik and Edward Bergman and Frank Hutter
Fast Benchmarking of Asynchronous Multi-Fidelity Optimization on Zero-Cost Benchmarks
by Shuhei Watanabe, Neeratyoy Mallik, Edward Bergman, Frank Hutter
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Python package, called “mfhpo-simulator,” enables efficient parallel hyperparameter optimization (HPO) with zero-cost benchmarks, which is crucial for deep learning development. The package addresses the challenge of long waiting times in parallel setups by calculating the exact return order based on file system information. This approach significantly speeds up HPO evaluations, achieving over 1000x speedup compared to traditional methods. The package’s applicability is demonstrated through experiments with six popular HPO libraries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a tool that makes it easier and faster to test different combinations of settings for machine learning models. Right now, finding the best combination takes a lot of time because each setting needs to be tested one by one. The new tool uses special files on a computer’s hard drive to keep track of which settings are being tried, allowing many tests to happen at the same time without waiting for one test to finish before starting the next. This makes the process much faster and more efficient. |
Keywords
* Artificial intelligence * Deep learning * Hyperparameter * Machine learning * Optimization
