# Fluid: Resource-aware Hyperparameter Tuning Engine

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* Successive Halving 
  * Workers underutilized, only one job to run 

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* More efficient, but there're also some problems 

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* Goal: Utilized & Useful work? 
* Resource-aware hyperparameter tuning 
  * Previous work: Hypersched (resource management), but more specialized on the algorithm itself 

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* Intra-GPU sharing (pack jobs in single GPU)
  * Current schedule: FIFO queue to manage to jobs 

### Design and Algorithms 

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* Intra: packing several jobs on single GPU 

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* Very helpful when people read the paper (very helpful in communicating the ideas) 
* Overhead of packing? 

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* Packing overhead of doing the placement 
  * Limit the number of packing trails 

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* Model fits on the single worker 

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* Makespan of all the trials 

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* Improvement more prominent when applying for asynchronous version 
* Intuition: 
  * If the runtime is very scaled 
* Parameters they are tuning
  * Learning rate, dropout rate 
  * Number of layers 
  * Batch size 
* Failures 
  * Know reasonable ranges 

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* Multiple hyperparameter jobs 
  * Multiple trial groups 
  * But extra parallelism there? 
  * Different things in the same trial group 
* Variability 
  * Some 
  * More? 
* Space sharing & Parallelism 
  * Automatically parallelism, don't need anything from hyperparameter 
  * Queue problem, but in tight space 


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