> For the complete documentation index, see [llms.txt](https://sliu583.gitbook.io/blog/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://sliu583.gitbook.io/blog/specific-work/shivarams-group/group-papers/fluid-resource-aware-hyperparameter-tuning-engine.md).

# Fluid: Resource-aware Hyperparameter Tuning Engine

![](/files/-MZsLdilqvzEu-pJHjfJ)

![](/files/-MZsLj-hsiz8S3S0ACvx)

![](/files/-MZsLlyDl7ZygPjcQ-R2)

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

![](/files/-MZsMCVAuMmWpntdBsPG)

* More efficient, but there're also some problems 

![](/files/-MZsMOgvx4UN4Fhujweo)

![](/files/-MZsMZSMbm42jQLMnt76)

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

![](/files/-MZsNHk2H1HUnapDFSQK)

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

### Design and Algorithms 

![](/files/-MZsNyD7d1-OoFVq4qCa)

* Intra: packing several jobs on single GPU 

![](/files/-MZsOK6nR9yBv7VlXRKE)

![](/files/-MZsOzRwLDey2u7ZBkat)

![](/files/-MZsPLLC0usKwHa2bk5m)

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

![](/files/-MZsPiBoqoxTFdPrtLW0)

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

![](/files/-MZsPxs4Wz1IQFO-c61H)

![](/files/-MZsREk6jbPfkpdew3tK)

* Model fits on the single worker 

![](/files/-MZsRjkJfCOwlKwSvObA)

* Makespan of all the trials 

![](/files/-MZsRoFE3Wco18ldEkQ6)

* 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 

![](/files/-MZsTJ5pXbzo7uUn1ah4)

* 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 


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://sliu583.gitbook.io/blog/specific-work/shivarams-group/group-papers/fluid-resource-aware-hyperparameter-tuning-engine.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.