.%20However%2C%20the%20time-consuming%20nature%20of%20deep%20learning%20training%20makes%20HP%20optimization%20(HPO)%20a%20costly%20endeavor%2C%20slowing%20down%20the%20development%20of%20efficient%20HPO%20tools.%20While%20zero-cost%20benchmarks%2C%20which%20provide%20performance%20and%20runtime%20without%20actual%20training%2C%20offer%20a%20solution%20for%20non-parallel%20setups%2C%20they%20fall%20short%20in%20parallel%20setups%20as%20each%20worker%20must%20communicate%20its%20queried%20runtime%20to%20return%20its%20evaluation%20in%20the%20exact%20order.%20This%20work%20addresses%20this%20challenge%20by%20introducing%20a%20user-friendly%20Python%20package%20that%20facilitates%20efficient%20parallel%20HPO%20with%20zero-cost%20benchmarks.%20Our%20approach%20calculates%20the%20exact%20return%20order%20based%20on%20the%20information%20stored%20in%20file%20system%2C%20eliminating%20the%20need%20for%20long%20waiting%20times%20and%20enabling%20much%20faster%20HPO%20evaluations.%20We%20first%20verify%20the%20correctness%20of%20our%20approach%20through%20extensive%20testing%20and%20the%20experiments%20with%206%20popular%20HPO%20libraries%20show%20its%20applicability%20to%20diverse%20libraries%20and%20its%20ability%20to%20achieve%20over%201000x%20speedup%20compared%20to%20a%20traditional%20approach.%20Our%20package%20can%20be%20installed%20via%20pip%20install%20mfhpo-simulator.)
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