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Improve Pareto frontier plot for AutoAC #148678
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/148678
Note: Links to docs will display an error until the docs builds have been completed. ⏳ No Failures, 1 PendingAs of commit 79f0696 with merge base 5fb0f45 ( UNSTABLE - The following job is marked as unstable, possibly due to flakiness on trunk:
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This was added in #126320. It's a very nice feature, which can be used to predict memory usage for different budget values. However, it had some limitations, notably in terms of resolution (it only sampled 21 points across the whole range thus missed many threshold values) and in distributed settings. Here I fix those by using recursive binary searches to identify all thresholds (up to a resolution of 1e-3, which can be made configurable) and output them in SVG (to be able to discern different points), plus I add the rank to the filename and store it in a user-define directory. ghstack-source-id: 4deba4a Pull Request resolved: #148678
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@pytorchbot merge |
Merge failedReason: Approvers from one of the following sets are needed:
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@pytorchbot merge |
Merge startedYour change will be merged once all checks pass (ETA 0-4 Hours). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |


Stack from ghstack (oldest at bottom):
This was added in #126320. It's a very nice feature, which can be used to predict memory usage for different budget values.
However, it had some limitations, notably in terms of resolution (it only sampled 21 points across the whole range thus missed many threshold values) and in distributed settings.
Here I fix those by using recursive binary searches to identify all thresholds (up to a resolution of 1e-3, which can be made configurable) and output them in SVG (to be able to discern different points), plus I add the rank to the filename and store it in a user-define directory.
cc @zou3519 @Chillee @samdow @kshitij12345