EnzymeAD / EnzymeAD/ReactantServer.jl

XLA Autotune utility

Open
#60 1 comment 0 reactions 0 assignees View on GitHub
enhancement
Dominant language
Julia
Stars
5
Forks
2
Avg merge
15m
Merged PRs (30d)
10

Description

Right now any autotuning that happens interferes with the memory high water mark. It would also be better if we could just do the autotuning ahead of time for every model once and get it out of the way, using a memory configuration that is least likely to go OoM.

Contributor guide

Open the contributing guide

Research direction

Start by locating the existing XLA autotuning path and the handling of the memory high-water mark. Determine how autotuning currently affects memory and how models are identified for one-time tuning. Done means autotuning can be performed ahead of serving with a memory configuration that minimizes out-of-memory risk and no longer interferes with the high-water mark.

Written by the indexing model from the issue text.

Assessment

Tech stack
julia
Domain
backend, performance
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Active
Clarity
Needs clarification
Newbie friendliness
35/100

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