Add optimize_for_deployment for AutoGluon_hq
#544
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PGijsbers
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* Update AutoGluon `max_memory` from 0.1 to 0.4 in persist_models (#543) * Add `optimize_for_deployment` for AutoGluon_hq (#544) * Reduce training time by 10% if a high_quality preset is used (#546) * Reduce training time by 10% if a high_quality preset is used High quality presets perform a post-fit step which takes 10~15% of total time (by Nick's estimate). To ensure comparisons stay reasonably fair we pre-emptively tell AutoGluon to use less time, so that all frameworks' models are based on "max_total_time" amount of effort. * Allow preset to be str or list and still reduce if hq or gq * Add identical markers to identify fit/inferencetime/predict stages (#548) * Add start_time, stop_time and log_time to failure.csv (#547) This helps more quickly identify at what stage the failure took place. E.g., if it's just a few minutes in, it is probably setup failure (such as connectivity issues). * Docker/permissions (#550) * Remove ownership changing and starting as user for docker images Since the USER is overwritten by `-u` for non-Windows platforms, which creates issues when the account running the docker image is not the same as the one that created it. * Dont run docker as root since images no longer have associated user * Ignore some additional files not needed to run the benchmark * Create root dir if it does not exist This is required, because otherwise in docker mode a non-existent directory is mounted, which is by default locked to `root` permissions. This in turn makes the benchmark app unable to create the subdirectories when the image is run as user. * Further remove user info from docker build and add run_as option The run_as option is then configurable so that it can be enabled for people who run into issues. Unfortunately, I observed different behavior from two systems with the same OS and docker versions installed. So for now I give up on one unified solution. * Update GAMA for v23.0.0 (#551) --------- Co-authored-by: Nick Erickson <innixma@gmail.com>
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optimize_for_deploymentfor AutoGluon_hqThis disables some of the post-fit logic in favor of a more optimized fit process that reduces disk usage by >10x and reduces overall fit time without any compromise to result quality or inference speed.
Not necessarily critical for the upcoming benchmark run, but it will lead to slightly faster training times due to only the necessary models being refit, rather than all models being refit, making it less likely that AutoGluon goes significantly above the time limit.