DACBench is an open-source benchmarking suite for dynamic algorithm configuration, supporting hyperparameter optimization and reproducible experiments. It is designed for machine learning researchers and practitioners to evaluate and compare DAC methods.
DACBench sits in PulseGate's Data science & ML workbench category. It focuses on providing standardized benchmarks for evaluating dynamic algorithm configuration methods in machine learning. It is built as an open-source project for machine learning researchers and practitioners. DACBench is open source under the Apache-2.0 license. It runs on the command line.
It is developed by automl, and the product first shipped in 2020. Development happens publicly on GitHub with 38 stars and 50 commits in the last 90 days. Key capabilities include benchmarking suite, dynamic algorithm configuration, and HPO support.
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