An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Describe the issue:
Currently, user can only impose gpu resource constraint using useActiveGpu, maxTrialNumberPerGpu, trialGpuNumber, and gpuIndices in local mode. However, modern gpu have very large memory and a lot of workstations have multiple computers. It is necessary and would be beneficial to allow factional gpu resources allocation, i.e. 10 tasks with 5 of them only using the first gpu and the second half only using the second gpu; this feature is similar to ray tune fractional resources.
Describe the issue: Currently, user can only impose gpu resource constraint using
useActiveGpu,maxTrialNumberPerGpu,trialGpuNumber, andgpuIndicesin local mode. However, modern gpu have very large memory and a lot of workstations have multiple computers. It is necessary and would be beneficial to allow factional gpu resources allocation, i.e. 10 tasks with 5 of them only using the first gpu and the second half only using the second gpu; this feature is similar to ray tune fractional resources.Environment:
Configuration:
Log message: Doesn't matter here.
How to reproduce it?: