notebooks: add FAST_MODE and active overfit-mode controls to catalog benchmark notebook#50
Draft
charlesmartin14 wants to merge 1 commit intomainfrom
Draft
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Motivation
Description
FAST_MODEand related runtime tuning variables (RANDOM_SAMPLE_SIZE,MAX_OVERFIT_MODELS,MAX_OVERFIT_PAIRS_PER_RUN,INCLUDE_CONTROL_CASE,CHECKPOINT_SAVE_EVERY) to reduce work in quick runs and print warnings when caps are active.ACTIVE_OVERFIT_MODES(selected strong overfit modes in fast mode) and makeMAX_OVERFIT_CASESconditional onFAST_MODE, then useACTIVE_OVERFIT_MODESthroughout training and plotting logic instead of slicingOVERFIT_MODESdirectly.FAST_MODEby changingtraining_scheduleto use smallernum_boost_roundvalues and settingearly_stopping_roundsto lower values, and expose WeightWatcher parameters (WW_T_POINTS,WW_NFOLDS,WW_RANDOMIZE) for faster analysis.MAX_OVERFIT_PAIRS_PER_RUN), more sparse checkpoint writes (CHECKPOINT_SAVE_EVERY), update aggregation/checkpointing to save intermediate aggregated CSVs, and add a FAST_MODE validation warning about weak overfit signals.Testing
FAST_MODEon a reduced sample (caps active) producing console logs of dataset/overfit progress and saving checkpoint files such ascheckpoint_results.csvandcheckpoint_results_good_plus_overfit.csv, and the run completed without runtime exceptions.CHECKPOINT_AGGREGATED_CSVandresults_per_dataset.csvin the experiment checkpoint directory during the interactive run.Codex Task