CompatHelper: bump compat for Turing to 0.34 for package EpiAware, (keep existing compat) #449

github-actions · 2024-09-01T21:51:40Z

This pull request changes the compat entry for the Turing package from 0.32, 0.33 to 0.32, 0.33, 0.34 for package EpiAware.
This keeps the compat entries for earlier versions.

Note: I have not tested your package with this new compat entry.
It is your responsibility to make sure that your package tests pass before you merge this pull request.

…eep existing compat)

seabbs · 2024-09-01T22:08:36Z

So it looks like compathelper is broken and the suggested fix is to close and reopen these PRs (JuliaRegistries/CompatHelper.jl#500)

codecov-commenter · 2024-09-01T22:56:51Z

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 89.91%. Comparing base (9096d9c) to head (a967258).

Additional details and impacted files

@@           Coverage Diff           @@
##             main     #449   +/-   ##
=======================================
  Coverage   89.91%   89.91%           
=======================================
  Files          57       57           
  Lines         734      734           
=======================================
  Hits          660      660           
  Misses         74       74

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

github-actions · 2024-09-01T23:00:37Z

Benchmark result

Judge result

Benchmark Report for /home/runner/work/Rt-without-renewal/Rt-without-renewal

Job Properties

Time of benchmarks:
- Target: 1 Sep 2024 - 22:36
- Baseline: 1 Sep 2024 - 23:00
Package commits:
- Target: 702e8d
- Baseline: 9096d9
Julia commits:
- Target: 6f3fdf
- Baseline: 6f3fdf
Julia command flags:
- Target: None
- Baseline: None
Environment variables:
- Target: None
- Baseline: None

Results

A ratio greater than 1.0 denotes a possible regression (marked with ❌), while a ratio less
than 1.0 denotes a possible improvement (marked with ✅). Only significant results - results
that indicate possible regressions or improvements - are shown below (thus, an empty table means that all
benchmark results remained invariant between builds).

ID	time ratio	memory ratio
`["EpiAwareUtils", "censored_pmf"]`	0.93 (5%) ✅	1.00 (1%)
`["EpiInfModels", "DirectInfections", "evaluation", "linked"]`	0.93 (5%) ✅	1.00 (1%)
`["EpiInfModels", "DirectInfections", "evaluation", "standard"]`	1.08 (5%) ❌	1.00 (1%)
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	0.93 (5%) ✅	1.00 (1%)
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	0.93 (5%) ✅	1.00 (1%)
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	1.07 (5%) ❌	1.00 (1%)
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.87 (5%) ✅	1.00 (1%)
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.88 (5%) ✅	1.00 (1%)
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.88 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	0.94 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.90 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	0.92 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.90 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.05 (5%) ❌	1.00 (1%)
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.08 (5%) ❌	1.00 (1%)
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.10 (5%) ❌	1.00 (1%)
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.95 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.06 (5%) ❌	1.00 (1%)
`["EpiLatentModels", "DiffLatentModel", "evaluation", "linked"]`	0.90 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "DiffLatentModel", "evaluation", "standard"]`	0.89 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	1.06 (5%) ❌	1.00 (1%)
`["EpiLatentModels", "HierarchicalNormal", "evaluation", "linked"]`	1.13 (5%) ❌	1.00 (1%)
`["EpiLatentModels", "HierarchicalNormal", "evaluation", "standard"]`	1.07 (5%) ❌	1.00 (1%)
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	0.86 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	0.93 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.06 (5%) ❌	1.00 (1%)
`["EpiLatentModels", "Intercept", "evaluation", "linked"]`	1.08 (5%) ❌	1.00 (1%)
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	0.89 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	0.89 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	1.09 (5%) ❌	1.00 (1%)
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	0.95 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "PrefixLatentModel", "evaluation", "standard"]`	0.92 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.21 (5%) ❌	1.00 (1%)
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	0.92 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	1.08 (5%) ❌	1.00 (1%)
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	0.95 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.91 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	0.91 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	0.92 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.92 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "RecordExpectedLatent", "evaluation", "linked"]`	1.14 (5%) ❌	1.00 (1%)
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.07 (5%) ❌	1.00 (1%)
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	1.08 (5%) ❌	1.00 (1%)
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.07 (5%) ❌	1.00 (1%)
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.89 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "TransformLatentModel", "evaluation", "linked"]`	0.94 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	0.92 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	0.95 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	0.92 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.95 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.94 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "broadcast_weekly", "evaluation", "linked"]`	1.05 (5%) ❌	1.00 (1%)
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	1.06 (5%) ❌	1.00 (1%)
`["EpiObsModels", "Ascertainment", "evaluation", "linked"]`	0.93 (5%) ✅	1.00 (1%)
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.07 (5%) ❌	1.00 (1%)
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.93 (5%) ✅	1.00 (1%)
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.90 (5%) ✅	1.00 (1%)
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	0.94 (5%) ✅	1.00 (1%)
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.92 (5%) ✅	1.00 (1%)
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.92 (5%) ✅	1.00 (1%)
`["EpiObsModels", "NegativeBinomialError", "evaluation", "standard"]`	0.92 (5%) ✅	1.00 (1%)
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	0.94 (5%) ✅	1.00 (1%)
`["EpiObsModels", "PoissonError", "evaluation", "linked"]`	0.93 (5%) ✅	1.00 (1%)
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	0.95 (5%) ✅	1.00 (1%)
`["EpiObsModels", "PrefixObservationModel", "evaluation", "standard"]`	1.07 (5%) ❌	1.00 (1%)
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	0.93 (5%) ✅	1.00 (1%)
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	0.92 (5%) ✅	1.00 (1%)
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	1.08 (5%) ❌	1.00 (1%)
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.92 (5%) ✅	1.00 (1%)
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.91 (5%) ✅	1.00 (1%)
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	0.94 (5%) ✅	1.00 (1%)
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.88 (5%) ✅	1.00 (1%)
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.89 (5%) ✅	1.00 (1%)
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	0.94 (5%) ✅	1.00 (1%)
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	0.94 (5%) ✅	1.00 (1%)
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.90 (5%) ✅	1.00 (1%)
`["EpiObsModels", "observation_error", "no missing obs", "evaluation", "linked"]`	0.94 (5%) ✅	1.00 (1%)
`["EpiObsModels", "observation_error", "no missing obs", "evaluation", "standard"]`	0.93 (5%) ✅	1.00 (1%)
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	0.91 (5%) ✅	1.00 (1%)
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.91 (5%) ✅	1.00 (1%)
`["EpiObsModels", "observation_error", "partially missing obs", "evaluation", "linked"]`	0.90 (5%) ✅	1.00 (1%)
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	0.92 (5%) ✅	1.00 (1%)
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.06 (5%) ❌	1.00 (1%)

Benchmark Group List

Here's a list of all the benchmark groups executed by this job:

["EpiAwareUtils"]
["EpiInfModels", "DirectInfections", "evaluation"]
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiInfModels", "ExpGrowthRate", "evaluation"]
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "AR", "evaluation"]
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "BroadcastLatentModel", "evaluation"]
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "CombineLatentModels", "evaluation"]
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "ConcatLatentModels", "evaluation"]
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "DiffLatentModel", "evaluation"]
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "HierarchicalNormal", "evaluation"]
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "Intercept", "evaluation"]
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "PrefixLatentModel", "evaluation"]
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "RandomWalk", "evaluation"]
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "RecordExpectedLatent", "evaluation"]
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "TransformLatentModel", "evaluation"]
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "broadcast_dayofweek", "evaluation"]
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "broadcast_weekly", "evaluation"]
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "Ascertainment", "evaluation"]
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "LatentDelay", "evaluation"]
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "NegativeBinomialError", "evaluation"]
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "PoissonError", "evaluation"]
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "PrefixObservationModel", "evaluation"]
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "StackObservationModels", "evaluation"]
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "ascertainment_dayofweek", "evaluation"]
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "observation_error", "missing obs", "evaluation"]
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "observation_error", "no missing obs", "evaluation"]
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "observation_error", "partially missing obs", "evaluation"]
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]

Julia versioninfo

Target

Julia Version 1.10.5
Commit 6f3fdf7b362 (2024-08-27 14:19 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.4 LTS
  uname: Linux 6.5.0-1025-azure #26~22.04.1-Ubuntu SMP Thu Jul 11 22:33:04 UTC 2024 x86_64 x86_64
  CPU: AMD EPYC 7763 64-Core Processor: 
              speed         user         nice          sys         idle          irq
       #1  2665 MHz       5472 s          0 s        505 s      11646 s          0 s
       #2  2976 MHz       4911 s          0 s        525 s      12197 s          0 s
       #3  3263 MHz       4528 s          0 s        537 s      12572 s          0 s
       #4  3266 MHz       5270 s          0 s        515 s      11843 s          0 s
  Memory: 15.606491088867188 GB (13477.66796875 MB free)
  Uptime: 1767.66 sec
  Load Avg:  1.0  1.01  1.0
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Baseline

Julia Version 1.10.5
Commit 6f3fdf7b362 (2024-08-27 14:19 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.4 LTS
  uname: Linux 6.5.0-1025-azure #26~22.04.1-Ubuntu SMP Thu Jul 11 22:33:04 UTC 2024 x86_64 x86_64
  CPU: AMD EPYC 7763 64-Core Processor: 
              speed         user         nice          sys         idle          irq
       #1  2445 MHz       8176 s          0 s        835 s      22882 s          0 s
       #2  3176 MHz       6877 s          0 s        787 s      24237 s          0 s
       #3  3249 MHz       8592 s          0 s        923 s      22397 s          0 s
       #4  3243 MHz       9938 s          0 s        931 s      21036 s          0 s
  Memory: 15.606491088867188 GB (13115.01171875 MB free)
  Uptime: 3197.33 sec
  Load Avg:  1.01  1.02  1.0
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Target result

Benchmark Report for /home/runner/work/Rt-without-renewal/Rt-without-renewal

Job Properties

Time of benchmark: 1 Sep 2024 - 22:36
Package commit: 702e8d
Julia commit: 6f3fdf
Julia command flags: None
Environment variables: None

Results

Below is a table of this job's results, obtained by running the benchmarks.
The values listed in the ID column have the structure [parent_group, child_group, ..., key], and can be used to
index into the BaseBenchmarks suite to retrieve the corresponding benchmarks.
The percentages accompanying time and memory values in the below table are noise tolerances. The "true"
time/memory value for a given benchmark is expected to fall within this percentage of the reported value.
An empty cell means that the value was zero.

ID	time	memory	allocations
`["EpiAwareUtils", "censored_pmf"]`	994.800 ns (5%)	352 bytes (1%)	4
`["EpiInfModels", "DirectInfections", "evaluation", "linked"]`	284.747 ns (5%)	432 bytes (1%)	7
`["EpiInfModels", "DirectInfections", "evaluation", "standard"]`	311.418 ns (5%)	432 bytes (1%)	7
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	405.025 ns (5%)	784 bytes (1%)	13
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	404.271 ns (5%)	784 bytes (1%)	13
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	9.528 μs (5%)	5.62 KiB (1%)	115
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	9.558 μs (5%)	5.62 KiB (1%)	115
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	511.109 ns (5%)	272 bytes (1%)	6
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	509.144 ns (5%)	272 bytes (1%)	6
`["EpiInfModels", "ExpGrowthRate", "evaluation", "linked"]`	194.945 ns (5%)	256 bytes (1%)	4
`["EpiInfModels", "ExpGrowthRate", "evaluation", "standard"]`	194.345 ns (5%)	256 bytes (1%)	4
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	291.154 ns (5%)	512 bytes (1%)	9
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	291.498 ns (5%)	512 bytes (1%)	9
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	9.277 μs (5%)	5.64 KiB (1%)	114
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	9.441 μs (5%)	5.64 KiB (1%)	114
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	573.739 ns (5%)	272 bytes (1%)	6
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	502.213 ns (5%)	272 bytes (1%)	6
`["EpiLatentModels", "AR", "evaluation", "linked"]`	1.998 μs (5%)	3.84 KiB (1%)	45
`["EpiLatentModels", "AR", "evaluation", "standard"]`	1.606 μs (5%)	2.80 KiB (1%)	38
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	3.327 μs (5%)	11.69 KiB (1%)	55
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	2.904 μs (5%)	10.12 KiB (1%)	46
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	100.638 μs (5%)	55.31 KiB (1%)	1113
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	67.235 μs (5%)	40.64 KiB (1%)	818
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	15.980 μs (5%)	8.44 KiB (1%)	225
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	14.517 μs (5%)	7.31 KiB (1%)	207
`["EpiLatentModels", "BroadcastLatentModel", "evaluation", "linked"]`	1.458 μs (5%)	3.05 KiB (1%)	34
`["EpiLatentModels", "BroadcastLatentModel", "evaluation", "standard"]`	1.299 μs (5%)	2.17 KiB (1%)	30
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.936 μs (5%)	5.16 KiB (1%)	41
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.673 μs (5%)	4.28 KiB (1%)	37
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	44.243 μs (5%)	24.41 KiB (1%)	447
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	26.760 μs (5%)	16.86 KiB (1%)	333
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	2.792 μs (5%)	1.00 KiB (1%)	27
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	2.756 μs (5%)	1.00 KiB (1%)	27
`["EpiLatentModels", "CombineLatentModels", "evaluation", "linked"]`	60.983 μs (5%)	52.27 KiB (1%)	580
`["EpiLatentModels", "CombineLatentModels", "evaluation", "standard"]`	57.157 μs (5%)	37.69 KiB (1%)	536
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	120.024 μs (5%)	119.19 KiB (1%)	1184
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	121.006 μs (5%)	89.31 KiB (1%)	1092
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	178.603 μs (5%)	107.81 KiB (1%)	1710
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	136.504 μs (5%)	79.61 KiB (1%)	1378
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	16.381 μs (5%)	8.58 KiB (1%)	226
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	16.561 μs (5%)	7.45 KiB (1%)	208
`["EpiLatentModels", "ConcatLatentModels", "evaluation", "linked"]`	12.062 μs (5%)	30.39 KiB (1%)	214
`["EpiLatentModels", "ConcatLatentModels", "evaluation", "standard"]`	9.147 μs (5%)	21.95 KiB (1%)	184
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	14.017 μs (5%)	34.09 KiB (1%)	224
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	9.458 μs (5%)	25.66 KiB (1%)	194
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	74.930 μs (5%)	56.38 KiB (1%)	719
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	54.802 μs (5%)	42.72 KiB (1%)	580
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	4.829 μs (5%)	2.19 KiB (1%)	52
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	4.946 μs (5%)	2.19 KiB (1%)	52
`["EpiLatentModels", "DiffLatentModel", "evaluation", "linked"]`	1.873 μs (5%)	4.17 KiB (1%)	37
`["EpiLatentModels", "DiffLatentModel", "evaluation", "standard"]`	1.342 μs (5%)	2.48 KiB (1%)	31
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	3.018 μs (5%)	12.62 KiB (1%)	45
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	2.614 μs (5%)	10.94 KiB (1%)	39
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	59.993 μs (5%)	38.81 KiB (1%)	748
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	41.748 μs (5%)	31.91 KiB (1%)	633
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	7.043 μs (5%)	2.22 KiB (1%)	51
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	6.498 μs (5%)	2.22 KiB (1%)	51
`["EpiLatentModels", "HierarchicalNormal", "evaluation", "linked"]`	439.513 ns (5%)	1.00 KiB (1%)	8
`["EpiLatentModels", "HierarchicalNormal", "evaluation", "standard"]`	354.024 ns (5%)	864 bytes (1%)	7
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.503 μs (5%)	5.28 KiB (1%)	14
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	820.353 ns (5%)	5.12 KiB (1%)	13
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	39.083 μs (5%)	19.83 KiB (1%)	376
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	26.058 μs (5%)	14.45 KiB (1%)	266
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.231 μs (5%)	656 bytes (1%)	11
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.062 μs (5%)	656 bytes (1%)	11
`["EpiLatentModels", "Intercept", "evaluation", "linked"]`	252.339 ns (5%)	336 bytes (1%)	5
`["EpiLatentModels", "Intercept", "evaluation", "standard"]`	235.301 ns (5%)	336 bytes (1%)	5
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	321.336 ns (5%)	640 bytes (1%)	10
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	319.862 ns (5%)	640 bytes (1%)	10
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	4.307 μs (5%)	3.53 KiB (1%)	76
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	3.985 μs (5%)	3.53 KiB (1%)	76
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	448.925 ns (5%)	240 bytes (1%)	3
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	456.377 ns (5%)	240 bytes (1%)	3
`["EpiLatentModels", "PrefixLatentModel", "evaluation", "linked"]`	1.846 μs (5%)	3.47 KiB (1%)	30
`["EpiLatentModels", "PrefixLatentModel", "evaluation", "standard"]`	1.579 μs (5%)	3.00 KiB (1%)	27
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	3.039 μs (5%)	7.75 KiB (1%)	36
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	2.324 μs (5%)	7.28 KiB (1%)	33
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	45.174 μs (5%)	22.16 KiB (1%)	397
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	27.271 μs (5%)	16.47 KiB (1%)	285
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.151 μs (5%)	656 bytes (1%)	11
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.060 μs (5%)	656 bytes (1%)	11
`["EpiLatentModels", "RandomWalk", "evaluation", "linked"]`	886.410 ns (5%)	1.86 KiB (1%)	18
`["EpiLatentModels", "RandomWalk", "evaluation", "standard"]`	723.658 ns (5%)	1.42 KiB (1%)	16
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.649 μs (5%)	8.73 KiB (1%)	25
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.657 μs (5%)	8.30 KiB (1%)	23
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	43.421 μs (5%)	26.19 KiB (1%)	487
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	30.156 μs (5%)	20.53 KiB (1%)	376
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	3.731 μs (5%)	1.31 KiB (1%)	27
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	3.171 μs (5%)	1.31 KiB (1%)	27
`["EpiLatentModels", "RecordExpectedLatent", "evaluation", "linked"]`	600.011 ns (5%)	1.19 KiB (1%)	12
`["EpiLatentModels", "RecordExpectedLatent", "evaluation", "standard"]`	485.138 ns (5%)	896 bytes (1%)	10
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	749.000 ns (5%)	1.72 KiB (1%)	18
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	682.386 ns (5%)	1.41 KiB (1%)	16
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	42.019 μs (5%)	19.08 KiB (1%)	380
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	26.830 μs (5%)	13.55 KiB (1%)	269
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.164 μs (5%)	400 bytes (1%)	11
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	891.435 ns (5%)	400 bytes (1%)	11
`["EpiLatentModels", "TransformLatentModel", "evaluation", "linked"]`	287.466 ns (5%)	384 bytes (1%)	6
`["EpiLatentModels", "TransformLatentModel", "evaluation", "standard"]`	287.296 ns (5%)	384 bytes (1%)	6
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	370.642 ns (5%)	704 bytes (1%)	11
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	372.517 ns (5%)	704 bytes (1%)	11
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	4.324 μs (5%)	3.84 KiB (1%)	81
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	4.265 μs (5%)	3.84 KiB (1%)	81
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	481.791 ns (5%)	192 bytes (1%)	3
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	523.545 ns (5%)	192 bytes (1%)	3
`["EpiLatentModels", "broadcast_dayofweek", "evaluation", "linked"]`	2.123 μs (5%)	4.16 KiB (1%)	44
`["EpiLatentModels", "broadcast_dayofweek", "evaluation", "standard"]`	1.538 μs (5%)	2.84 KiB (1%)	38
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	2.762 μs (5%)	10.00 KiB (1%)	51
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	2.439 μs (5%)	8.69 KiB (1%)	45
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	58.049 μs (5%)	35.58 KiB (1%)	689
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	41.087 μs (5%)	29.05 KiB (1%)	574
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.160 μs (5%)	1.22 KiB (1%)	27
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	4.846 μs (5%)	1.22 KiB (1%)	27
`["EpiLatentModels", "broadcast_weekly", "evaluation", "linked"]`	2.158 μs (5%)	4.52 KiB (1%)	47
`["EpiLatentModels", "broadcast_weekly", "evaluation", "standard"]`	1.682 μs (5%)	2.62 KiB (1%)	37
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	2.918 μs (5%)	7.69 KiB (1%)	57
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	2.289 μs (5%)	5.53 KiB (1%)	45
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	84.397 μs (5%)	41.95 KiB (1%)	771
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	47.289 μs (5%)	28.44 KiB (1%)	513
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.006 μs (5%)	1.81 KiB (1%)	49
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	4.640 μs (5%)	1.69 KiB (1%)	47
`["EpiObsModels", "Ascertainment", "evaluation", "linked"]`	3.084 μs (5%)	3.42 KiB (1%)	49
`["EpiObsModels", "Ascertainment", "evaluation", "standard"]`	3.282 μs (5%)	3.42 KiB (1%)	49
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	4.211 μs (5%)	3.77 KiB (1%)	56
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	4.111 μs (5%)	3.77 KiB (1%)	56
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	50.484 μs (5%)	39.05 KiB (1%)	905
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	35.005 μs (5%)	33.83 KiB (1%)	796
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.532 μs (5%)	96 bytes (1%)	2
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.196 μs (5%)	96 bytes (1%)	2
`["EpiObsModels", "LatentDelay", "evaluation", "linked"]`	16.571 μs (5%)	22.14 KiB (1%)	206
`["EpiObsModels", "LatentDelay", "evaluation", "standard"]`	16.470 μs (5%)	22.14 KiB (1%)	206
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	22.281 μs (5%)	22.36 KiB (1%)	211
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	19.787 μs (5%)	22.36 KiB (1%)	211
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	281.926 μs (5%)	293.61 KiB (1%)	6804
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	265.856 μs (5%)	288.39 KiB (1%)	6695
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	48.581 μs (5%)	96 bytes (1%)	2
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	47.309 μs (5%)	96 bytes (1%)	2
`["EpiObsModels", "NegativeBinomialError", "evaluation", "linked"]`	1.157 μs (5%)	336 bytes (1%)	5
`["EpiObsModels", "NegativeBinomialError", "evaluation", "standard"]`	1.050 μs (5%)	336 bytes (1%)	5
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.678 μs (5%)	560 bytes (1%)	10
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.700 μs (5%)	560 bytes (1%)	10
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	44.604 μs (5%)	36.33 KiB (1%)	899
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	32.120 μs (5%)	31.11 KiB (1%)	790
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.794 μs (5%)	96 bytes (1%)	2
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.604 μs (5%)	96 bytes (1%)	2
`["EpiObsModels", "PoissonError", "evaluation", "linked"]`	1.432 μs (5%)	1.80 KiB (1%)	22
`["EpiObsModels", "PoissonError", "evaluation", "standard"]`	1.218 μs (5%)	1.38 KiB (1%)	18
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	3.205 μs (5%)	7.75 KiB (1%)	31
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	2.701 μs (5%)	4.52 KiB (1%)	25
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	127.057 μs (5%)	91.00 KiB (1%)	1913
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	23.233 μs (5%)	29.25 KiB (1%)	712
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	6.743 μs (5%)	176 bytes (1%)	2
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	3.954 μs (5%)	176 bytes (1%)	2
`["EpiObsModels", "PrefixObservationModel", "evaluation", "linked"]`	1.674 μs (5%)	1.44 KiB (1%)	26
`["EpiObsModels", "PrefixObservationModel", "evaluation", "standard"]`	1.623 μs (5%)	1.44 KiB (1%)	26
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.711 μs (5%)	1.66 KiB (1%)	31
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.652 μs (5%)	1.66 KiB (1%)	31
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	22.813 μs (5%)	12.91 KiB (1%)	283
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	8.890 μs (5%)	7.69 KiB (1%)	174
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.319 μs (5%)	96 bytes (1%)	2
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.034 μs (5%)	96 bytes (1%)	2
`["EpiObsModels", "StackObservationModels", "evaluation", "linked"]`	6.642 μs (5%)	5.48 KiB (1%)	93
`["EpiObsModels", "StackObservationModels", "evaluation", "standard"]`	7.023 μs (5%)	5.48 KiB (1%)	93
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	7.950 μs (5%)	5.83 KiB (1%)	100
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	7.845 μs (5%)	5.83 KiB (1%)	100
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	60.373 μs (5%)	49.23 KiB (1%)	1020
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	40.075 μs (5%)	44.02 KiB (1%)	911
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.844 μs (5%)	96 bytes (1%)	2
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.636 μs (5%)	96 bytes (1%)	2
`["EpiObsModels", "ascertainment_dayofweek", "evaluation", "linked"]`	4.430 μs (5%)	8.88 KiB (1%)	75
`["EpiObsModels", "ascertainment_dayofweek", "evaluation", "standard"]`	4.130 μs (5%)	7.62 KiB (1%)	67
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	5.594 μs (5%)	15.88 KiB (1%)	83
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	5.031 μs (5%)	14.62 KiB (1%)	75
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	80.711 μs (5%)	60.41 KiB (1%)	1139
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	62.286 μs (5%)	53.94 KiB (1%)	1022
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.261 μs (5%)	544 bytes (1%)	11
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.128 μs (5%)	544 bytes (1%)	11
`["EpiObsModels", "observation_error", "missing obs", "evaluation", "linked"]`	1.520 μs (5%)	2.97 KiB (1%)	31
`["EpiObsModels", "observation_error", "missing obs", "evaluation", "standard"]`	1.023 μs (5%)	1.41 KiB (1%)	21
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	2.006 μs (5%)	4.03 KiB (1%)	38
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.611 μs (5%)	2.47 KiB (1%)	28
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	27.411 μs (5%)	24.73 KiB (1%)	490
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	12.473 μs (5%)	17.28 KiB (1%)	352
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	2.138 μs (5%)	144 bytes (1%)	2
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	2.067 μs (5%)	144 bytes (1%)	2
`["EpiObsModels", "observation_error", "no missing obs", "evaluation", "linked"]`	420.080 ns (5%)	288 bytes (1%)	5
`["EpiObsModels", "observation_error", "no missing obs", "evaluation", "standard"]`	384.220 ns (5%)	288 bytes (1%)	5
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	538.268 ns (5%)	512 bytes (1%)	10
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	491.653 ns (5%)	512 bytes (1%)	10
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	24.286 μs (5%)	18.72 KiB (1%)	414
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	10.329 μs (5%)	12.83 KiB (1%)	286
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.823 μs (5%)	96 bytes (1%)	2
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.686 μs (5%)	96 bytes (1%)	2
`["EpiObsModels", "observation_error", "partially missing obs", "evaluation", "linked"]`	1.652 μs (5%)	2.05 KiB (1%)	27
`["EpiObsModels", "observation_error", "partially missing obs", "evaluation", "standard"]`	1.646 μs (5%)	1.73 KiB (1%)	25
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.660 μs (5%)	2.22 KiB (1%)	26
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.491 μs (5%)	1.91 KiB (1%)	24
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	36.077 μs (5%)	23.89 KiB (1%)	499
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	22.852 μs (5%)	17.69 KiB (1%)	369
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	2.143 μs (5%)	112 bytes (1%)	2
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	2.073 μs (5%)	112 bytes (1%)	2

Benchmark Group List

Here's a list of all the benchmark groups executed by this job:

["EpiAwareUtils"]
["EpiInfModels", "DirectInfections", "evaluation"]
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiInfModels", "ExpGrowthRate", "evaluation"]
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "AR", "evaluation"]
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "BroadcastLatentModel", "evaluation"]
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "CombineLatentModels", "evaluation"]
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "ConcatLatentModels", "evaluation"]
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "DiffLatentModel", "evaluation"]
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "HierarchicalNormal", "evaluation"]
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "Intercept", "evaluation"]
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "PrefixLatentModel", "evaluation"]
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "RandomWalk", "evaluation"]
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "RecordExpectedLatent", "evaluation"]
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "TransformLatentModel", "evaluation"]
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "broadcast_dayofweek", "evaluation"]
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "broadcast_weekly", "evaluation"]
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "Ascertainment", "evaluation"]
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "LatentDelay", "evaluation"]
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "NegativeBinomialError", "evaluation"]
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "PoissonError", "evaluation"]
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "PrefixObservationModel", "evaluation"]
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "StackObservationModels", "evaluation"]
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "ascertainment_dayofweek", "evaluation"]
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "observation_error", "missing obs", "evaluation"]
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "observation_error", "no missing obs", "evaluation"]
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "observation_error", "partially missing obs", "evaluation"]
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]

Julia versioninfo

Julia Version 1.10.5
Commit 6f3fdf7b362 (2024-08-27 14:19 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.4 LTS
  uname: Linux 6.5.0-1025-azure #26~22.04.1-Ubuntu SMP Thu Jul 11 22:33:04 UTC 2024 x86_64 x86_64
  CPU: AMD EPYC 7763 64-Core Processor: 
              speed         user         nice          sys         idle          irq
       #1  2665 MHz       5472 s          0 s        505 s      11646 s          0 s
       #2  2976 MHz       4911 s          0 s        525 s      12197 s          0 s
       #3  3263 MHz       4528 s          0 s        537 s      12572 s          0 s
       #4  3266 MHz       5270 s          0 s        515 s      11843 s          0 s
  Memory: 15.606491088867188 GB (13477.66796875 MB free)
  Uptime: 1767.66 sec
  Load Avg:  1.0  1.01  1.0
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Baseline result

Benchmark Report for /home/runner/work/Rt-without-renewal/Rt-without-renewal

Job Properties

Time of benchmark: 1 Sep 2024 - 23:0
Package commit: 9096d9
Julia commit: 6f3fdf
Julia command flags: None
Environment variables: None

Results

Below is a table of this job's results, obtained by running the benchmarks.
The values listed in the ID column have the structure [parent_group, child_group, ..., key], and can be used to
index into the BaseBenchmarks suite to retrieve the corresponding benchmarks.
The percentages accompanying time and memory values in the below table are noise tolerances. The "true"
time/memory value for a given benchmark is expected to fall within this percentage of the reported value.
An empty cell means that the value was zero.

ID	time	memory	allocations
`["EpiAwareUtils", "censored_pmf"]`	1.070 μs (5%)	352 bytes (1%)	4
`["EpiInfModels", "DirectInfections", "evaluation", "linked"]`	305.000 ns (5%)	432 bytes (1%)	7
`["EpiInfModels", "DirectInfections", "evaluation", "standard"]`	288.447 ns (5%)	432 bytes (1%)	7
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	434.460 ns (5%)	784 bytes (1%)	13
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	436.894 ns (5%)	784 bytes (1%)	13
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	9.367 μs (5%)	5.62 KiB (1%)	115
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	8.946 μs (5%)	5.62 KiB (1%)	115
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	585.429 ns (5%)	272 bytes (1%)	6
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	580.171 ns (5%)	272 bytes (1%)	6
`["EpiInfModels", "ExpGrowthRate", "evaluation", "linked"]`	198.185 ns (5%)	256 bytes (1%)	4
`["EpiInfModels", "ExpGrowthRate", "evaluation", "standard"]`	202.698 ns (5%)	256 bytes (1%)	4
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	283.759 ns (5%)	512 bytes (1%)	9
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	284.930 ns (5%)	512 bytes (1%)	9
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	9.477 μs (5%)	5.64 KiB (1%)	114
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	9.507 μs (5%)	5.64 KiB (1%)	114
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	565.870 ns (5%)	272 bytes (1%)	6
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	572.868 ns (5%)	272 bytes (1%)	6
`["EpiLatentModels", "AR", "evaluation", "linked"]`	2.090 μs (5%)	3.84 KiB (1%)	45
`["EpiLatentModels", "AR", "evaluation", "standard"]`	1.663 μs (5%)	2.80 KiB (1%)	38
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	3.446 μs (5%)	11.69 KiB (1%)	55
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	2.902 μs (5%)	10.12 KiB (1%)	46
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	106.999 μs (5%)	55.31 KiB (1%)	1113
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	69.760 μs (5%)	40.64 KiB (1%)	818
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	17.763 μs (5%)	8.44 KiB (1%)	225
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	14.928 μs (5%)	7.31 KiB (1%)	207
`["EpiLatentModels", "BroadcastLatentModel", "evaluation", "linked"]`	1.443 μs (5%)	3.05 KiB (1%)	34
`["EpiLatentModels", "BroadcastLatentModel", "evaluation", "standard"]`	1.254 μs (5%)	2.17 KiB (1%)	30
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.996 μs (5%)	5.16 KiB (1%)	41
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.719 μs (5%)	4.28 KiB (1%)	37
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	46.166 μs (5%)	24.41 KiB (1%)	447
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	28.954 μs (5%)	16.86 KiB (1%)	333
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	3.086 μs (5%)	1.00 KiB (1%)	27
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	2.613 μs (5%)	1.00 KiB (1%)	27
`["EpiLatentModels", "CombineLatentModels", "evaluation", "linked"]`	61.626 μs (5%)	52.27 KiB (1%)	580
`["EpiLatentModels", "CombineLatentModels", "evaluation", "standard"]`	57.337 μs (5%)	37.69 KiB (1%)	536
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	117.990 μs (5%)	119.19 KiB (1%)	1184
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	112.239 μs (5%)	89.31 KiB (1%)	1092
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	180.367 μs (5%)	107.81 KiB (1%)	1710
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	137.927 μs (5%)	79.61 KiB (1%)	1378
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	16.891 μs (5%)	8.58 KiB (1%)	226
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	16.530 μs (5%)	7.45 KiB (1%)	208
`["EpiLatentModels", "ConcatLatentModels", "evaluation", "linked"]`	11.862 μs (5%)	30.39 KiB (1%)	214
`["EpiLatentModels", "ConcatLatentModels", "evaluation", "standard"]`	9.267 μs (5%)	21.95 KiB (1%)	184
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	12.714 μs (5%)	34.09 KiB (1%)	224
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	9.557 μs (5%)	25.66 KiB (1%)	194
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	74.158 μs (5%)	56.38 KiB (1%)	719
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	53.500 μs (5%)	42.72 KiB (1%)	580
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.091 μs (5%)	2.19 KiB (1%)	52
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	4.680 μs (5%)	2.19 KiB (1%)	52
`["EpiLatentModels", "DiffLatentModel", "evaluation", "linked"]`	2.072 μs (5%)	4.17 KiB (1%)	37
`["EpiLatentModels", "DiffLatentModel", "evaluation", "standard"]`	1.505 μs (5%)	2.48 KiB (1%)	31
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	2.979 μs (5%)	12.62 KiB (1%)	45
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	2.499 μs (5%)	10.94 KiB (1%)	39
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	59.752 μs (5%)	38.81 KiB (1%)	748
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	39.274 μs (5%)	31.91 KiB (1%)	633
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	6.717 μs (5%)	2.22 KiB (1%)	51
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	6.590 μs (5%)	2.22 KiB (1%)	51
`["EpiLatentModels", "HierarchicalNormal", "evaluation", "linked"]`	389.380 ns (5%)	1.00 KiB (1%)	8
`["EpiLatentModels", "HierarchicalNormal", "evaluation", "standard"]`	331.048 ns (5%)	864 bytes (1%)	7
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.501 μs (5%)	5.28 KiB (1%)	14
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	958.444 ns (5%)	5.12 KiB (1%)	13
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	42.129 μs (5%)	19.83 KiB (1%)	376
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	27.361 μs (5%)	14.45 KiB (1%)	266
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.164 μs (5%)	656 bytes (1%)	11
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.106 μs (5%)	656 bytes (1%)	11
`["EpiLatentModels", "Intercept", "evaluation", "linked"]`	232.825 ns (5%)	336 bytes (1%)	5
`["EpiLatentModels", "Intercept", "evaluation", "standard"]`	235.557 ns (5%)	336 bytes (1%)	5
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	360.382 ns (5%)	640 bytes (1%)	10
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	360.772 ns (5%)	640 bytes (1%)	10
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	3.954 μs (5%)	3.53 KiB (1%)	76
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	4.196 μs (5%)	3.53 KiB (1%)	76
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	450.226 ns (5%)	240 bytes (1%)	3
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	460.706 ns (5%)	240 bytes (1%)	3
`["EpiLatentModels", "PrefixLatentModel", "evaluation", "linked"]`	1.867 μs (5%)	3.47 KiB (1%)	30
`["EpiLatentModels", "PrefixLatentModel", "evaluation", "standard"]`	1.707 μs (5%)	3.00 KiB (1%)	27
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	2.521 μs (5%)	7.75 KiB (1%)	36
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	2.540 μs (5%)	7.28 KiB (1%)	33
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	41.758 μs (5%)	22.16 KiB (1%)	397
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	28.784 μs (5%)	16.47 KiB (1%)	285
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.268 μs (5%)	656 bytes (1%)	11
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.089 μs (5%)	656 bytes (1%)	11
`["EpiLatentModels", "RandomWalk", "evaluation", "linked"]`	878.707 ns (5%)	1.86 KiB (1%)	18
`["EpiLatentModels", "RandomWalk", "evaluation", "standard"]`	707.787 ns (5%)	1.42 KiB (1%)	16
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.810 μs (5%)	8.73 KiB (1%)	25
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.651 μs (5%)	8.30 KiB (1%)	23
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	47.249 μs (5%)	26.19 KiB (1%)	487
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	29.386 μs (5%)	20.53 KiB (1%)	376
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	3.713 μs (5%)	1.31 KiB (1%)	27
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	3.446 μs (5%)	1.31 KiB (1%)	27
`["EpiLatentModels", "RecordExpectedLatent", "evaluation", "linked"]`	528.592 ns (5%)	1.19 KiB (1%)	12
`["EpiLatentModels", "RecordExpectedLatent", "evaluation", "standard"]`	470.776 ns (5%)	896 bytes (1%)	10
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	787.775 ns (5%)	1.72 KiB (1%)	18
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	635.200 ns (5%)	1.41 KiB (1%)	16
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	39.043 μs (5%)	19.08 KiB (1%)	380
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	27.150 μs (5%)	13.55 KiB (1%)	269
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.083 μs (5%)	400 bytes (1%)	11
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	998.158 ns (5%)	400 bytes (1%)	11
`["EpiLatentModels", "TransformLatentModel", "evaluation", "linked"]`	306.579 ns (5%)	384 bytes (1%)	6
`["EpiLatentModels", "TransformLatentModel", "evaluation", "standard"]`	284.912 ns (5%)	384 bytes (1%)	6
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	378.220 ns (5%)	704 bytes (1%)	11
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	403.905 ns (5%)	704 bytes (1%)	11
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	4.566 μs (5%)	3.84 KiB (1%)	81
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	4.642 μs (5%)	3.84 KiB (1%)	81
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	509.449 ns (5%)	192 bytes (1%)	3
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	557.586 ns (5%)	192 bytes (1%)	3
`["EpiLatentModels", "broadcast_dayofweek", "evaluation", "linked"]`	2.090 μs (5%)	4.16 KiB (1%)	44
`["EpiLatentModels", "broadcast_dayofweek", "evaluation", "standard"]`	1.561 μs (5%)	2.84 KiB (1%)	38
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	2.770 μs (5%)	10.00 KiB (1%)	51
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	2.353 μs (5%)	8.69 KiB (1%)	45
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	60.512 μs (5%)	35.58 KiB (1%)	689
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	40.546 μs (5%)	29.05 KiB (1%)	574
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.077 μs (5%)	1.22 KiB (1%)	27
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	4.630 μs (5%)	1.22 KiB (1%)	27
`["EpiLatentModels", "broadcast_weekly", "evaluation", "linked"]`	2.054 μs (5%)	4.52 KiB (1%)	47
`["EpiLatentModels", "broadcast_weekly", "evaluation", "standard"]`	1.629 μs (5%)	2.62 KiB (1%)	37
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	2.908 μs (5%)	7.69 KiB (1%)	57
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	2.216 μs (5%)	5.53 KiB (1%)	45
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	85.510 μs (5%)	41.95 KiB (1%)	771
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	44.534 μs (5%)	28.44 KiB (1%)	513
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.218 μs (5%)	1.81 KiB (1%)	49
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	4.484 μs (5%)	1.69 KiB (1%)	47
`["EpiObsModels", "Ascertainment", "evaluation", "linked"]`	3.325 μs (5%)	3.42 KiB (1%)	49
`["EpiObsModels", "Ascertainment", "evaluation", "standard"]`	3.287 μs (5%)	3.42 KiB (1%)	49
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	4.202 μs (5%)	3.77 KiB (1%)	56
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	3.831 μs (5%)	3.77 KiB (1%)	56
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	50.034 μs (5%)	39.05 KiB (1%)	905
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	34.073 μs (5%)	33.83 KiB (1%)	796
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.929 μs (5%)	96 bytes (1%)	2
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.762 μs (5%)	96 bytes (1%)	2
`["EpiObsModels", "LatentDelay", "evaluation", "linked"]`	16.311 μs (5%)	22.14 KiB (1%)	206
`["EpiObsModels", "LatentDelay", "evaluation", "standard"]`	16.340 μs (5%)	22.14 KiB (1%)	206
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	22.021 μs (5%)	22.36 KiB (1%)	211
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	21.160 μs (5%)	22.36 KiB (1%)	211
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	279.983 μs (5%)	293.61 KiB (1%)	6804
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	257.511 μs (5%)	288.39 KiB (1%)	6695
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	53.089 μs (5%)	96 bytes (1%)	2
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	51.356 μs (5%)	96 bytes (1%)	2
`["EpiObsModels", "NegativeBinomialError", "evaluation", "linked"]`	1.162 μs (5%)	336 bytes (1%)	5
`["EpiObsModels", "NegativeBinomialError", "evaluation", "standard"]`	1.138 μs (5%)	336 bytes (1%)	5
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.794 μs (5%)	560 bytes (1%)	10
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.713 μs (5%)	560 bytes (1%)	10
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	45.686 μs (5%)	36.33 KiB (1%)	899
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	30.818 μs (5%)	31.11 KiB (1%)	790
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.873 μs (5%)	96 bytes (1%)	2
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.717 μs (5%)	96 bytes (1%)	2
`["EpiObsModels", "PoissonError", "evaluation", "linked"]`	1.540 μs (5%)	1.80 KiB (1%)	22
`["EpiObsModels", "PoissonError", "evaluation", "standard"]`	1.213 μs (5%)	1.38 KiB (1%)	18
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	3.296 μs (5%)	7.75 KiB (1%)	31
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	2.651 μs (5%)	4.52 KiB (1%)	25
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	129.582 μs (5%)	91.00 KiB (1%)	1913
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	24.485 μs (5%)	29.25 KiB (1%)	712
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	6.646 μs (5%)	176 bytes (1%)	2
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	3.914 μs (5%)	176 bytes (1%)	2
`["EpiObsModels", "PrefixObservationModel", "evaluation", "linked"]`	1.687 μs (5%)	1.44 KiB (1%)	26
`["EpiObsModels", "PrefixObservationModel", "evaluation", "standard"]`	1.513 μs (5%)	1.44 KiB (1%)	26
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.834 μs (5%)	1.66 KiB (1%)	31
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.790 μs (5%)	1.66 KiB (1%)	31
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	21.100 μs (5%)	12.91 KiB (1%)	283
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	8.690 μs (5%)	7.69 KiB (1%)	174
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.368 μs (5%)	96 bytes (1%)	2
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.124 μs (5%)	96 bytes (1%)	2
`["EpiObsModels", "StackObservationModels", "evaluation", "linked"]`	6.547 μs (5%)	5.48 KiB (1%)	93
`["EpiObsModels", "StackObservationModels", "evaluation", "standard"]`	7.061 μs (5%)	5.48 KiB (1%)	93
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	7.975 μs (5%)	5.83 KiB (1%)	100
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	7.865 μs (5%)	5.83 KiB (1%)	100
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	59.070 μs (5%)	49.23 KiB (1%)	1020
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	41.217 μs (5%)	44.02 KiB (1%)	911
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.901 μs (5%)	96 bytes (1%)	2
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`["EpiObsModels", "ascertainment_dayofweek", "evaluation", "linked"]`	4.464 μs (5%)	8.88 KiB (1%)	75
`["EpiObsModels", "ascertainment_dayofweek", "evaluation", "standard"]`	4.191 μs (5%)	7.62 KiB (1%)	67
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	5.739 μs (5%)	15.88 KiB (1%)	83
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	5.358 μs (5%)	14.62 KiB (1%)	75
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	82.053 μs (5%)	60.41 KiB (1%)	1139
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	63.358 μs (5%)	53.94 KiB (1%)	1022
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`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.737 μs (5%)	544 bytes (1%)	11
`["EpiObsModels", "observation_error", "missing obs", "evaluation", "linked"]`	1.532 μs (5%)	2.97 KiB (1%)	31
`["EpiObsModels", "observation_error", "missing obs", "evaluation", "standard"]`	1.015 μs (5%)	1.41 KiB (1%)	21
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	2.131 μs (5%)	4.03 KiB (1%)	38
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.626 μs (5%)	2.47 KiB (1%)	28
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	27.541 μs (5%)	24.73 KiB (1%)	490
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	13.235 μs (5%)	17.28 KiB (1%)	352
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	2.372 μs (5%)	144 bytes (1%)	2
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	2.104 μs (5%)	144 bytes (1%)	2
`["EpiObsModels", "observation_error", "no missing obs", "evaluation", "linked"]`	446.407 ns (5%)	288 bytes (1%)	5
`["EpiObsModels", "observation_error", "no missing obs", "evaluation", "standard"]`	414.191 ns (5%)	288 bytes (1%)	5
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	544.866 ns (5%)	512 bytes (1%)	10
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	537.566 ns (5%)	512 bytes (1%)	10
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	23.624 μs (5%)	18.72 KiB (1%)	414
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	10.570 μs (5%)	12.83 KiB (1%)	286
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	2.009 μs (5%)	96 bytes (1%)	2
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.707 μs (5%)	96 bytes (1%)	2
`["EpiObsModels", "observation_error", "partially missing obs", "evaluation", "linked"]`	1.829 μs (5%)	2.05 KiB (1%)	27
`["EpiObsModels", "observation_error", "partially missing obs", "evaluation", "standard"]`	1.672 μs (5%)	1.73 KiB (1%)	25
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.676 μs (5%)	2.22 KiB (1%)	26
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.493 μs (5%)	1.91 KiB (1%)	24
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	39.023 μs (5%)	23.89 KiB (1%)	499
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	22.762 μs (5%)	17.69 KiB (1%)	369
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	2.194 μs (5%)	112 bytes (1%)	2
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.955 μs (5%)	112 bytes (1%)	2

Benchmark Group List

Here's a list of all the benchmark groups executed by this job:

["EpiAwareUtils"]
["EpiInfModels", "DirectInfections", "evaluation"]
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiInfModels", "ExpGrowthRate", "evaluation"]
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "AR", "evaluation"]
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "BroadcastLatentModel", "evaluation"]
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "CombineLatentModels", "evaluation"]
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "ConcatLatentModels", "evaluation"]
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "DiffLatentModel", "evaluation"]
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "HierarchicalNormal", "evaluation"]
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "Intercept", "evaluation"]
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "PrefixLatentModel", "evaluation"]
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "RandomWalk", "evaluation"]
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "RecordExpectedLatent", "evaluation"]
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "TransformLatentModel", "evaluation"]
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "broadcast_dayofweek", "evaluation"]
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "broadcast_weekly", "evaluation"]
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "Ascertainment", "evaluation"]
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "LatentDelay", "evaluation"]
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "NegativeBinomialError", "evaluation"]
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "PoissonError", "evaluation"]
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "PrefixObservationModel", "evaluation"]
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "StackObservationModels", "evaluation"]
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "ascertainment_dayofweek", "evaluation"]
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "observation_error", "missing obs", "evaluation"]
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "observation_error", "no missing obs", "evaluation"]
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "observation_error", "partially missing obs", "evaluation"]
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]

Julia versioninfo

Julia Version 1.10.5
Commit 6f3fdf7b362 (2024-08-27 14:19 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.4 LTS
  uname: Linux 6.5.0-1025-azure #26~22.04.1-Ubuntu SMP Thu Jul 11 22:33:04 UTC 2024 x86_64 x86_64
  CPU: AMD EPYC 7763 64-Core Processor: 
              speed         user         nice          sys         idle          irq
       #1  2445 MHz       8176 s          0 s        835 s      22882 s          0 s
       #2  3176 MHz       6877 s          0 s        787 s      24237 s          0 s
       #3  3249 MHz       8592 s          0 s        923 s      22397 s          0 s
       #4  3243 MHz       9938 s          0 s        931 s      21036 s          0 s
  Memory: 15.606491088867188 GB (13115.01171875 MB free)
  Uptime: 3197.33 sec
  Load Avg:  1.01  1.02  1.0
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Runtime information

Runtime Info
BLAS #threads	2
`BLAS.vendor()`	`lbt`
`Sys.CPU_THREADS`	4

lscpu output:

Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      48 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             4
On-line CPU(s) list:                0-3
Vendor ID:                          AuthenticAMD
Model name:                         AMD EPYC 7763 64-Core Processor
CPU family:                         25
Model:                              1
Thread(s) per core:                 2
Core(s) per socket:                 2
Socket(s):                          1
Stepping:                           1
BogoMIPS:                           4890.86
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext invpcid_single vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr rdpru arat npt nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload umip vaes vpclmulqdq rdpid fsrm
Virtualization:                     AMD-V
Hypervisor vendor:                  Microsoft
Virtualization type:                full
L1d cache:                          64 KiB (2 instances)
L1i cache:                          64 KiB (2 instances)
L2 cache:                           1 MiB (2 instances)
L3 cache:                           32 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-3
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Vulnerable: Safe RET, no microcode
Vulnerability Spec store bypass:    Vulnerable
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Cpu Property	Value
Brand	AMD EPYC 7763 64-Core Processor
Vendor	:AMD
Architecture	:Unknown
Model	Family: 0xaf, Model: 0x01, Stepping: 0x01, Type: 0x00
Cores	16 physical cores, 16 logical cores (on executing CPU)
	No Hyperthreading hardware capability detected
Clock Frequencies	Not supported by CPU
Data Cache	Level 1:3 : (32, 512, 32768) kbytes
	64 byte cache line size
Address Size	48 bits virtual, 48 bits physical
SIMD	256 bit = 32 byte max. SIMD vector size
Time Stamp Counter	TSC is accessible via `rdtsc`
	TSC runs at constant rate (invariant from clock frequency)
Perf. Monitoring	Performance Monitoring Counters (PMC) are not supported
Hypervisor	Yes, Microsoft

CompatHelper: bump compat for Turing to 0.34 for package EpiAware, (k…

a967258

…eep existing compat)

github-actions bot force-pushed the compathelper/new_version/2024-09-01-21-51-38-787-03134721270 branch from a0195fb to a967258 Compare September 1, 2024 21:51

seabbs closed this Sep 1, 2024

seabbs reopened this Sep 1, 2024

seabbs enabled auto-merge September 1, 2024 22:08

seabbs approved these changes Sep 1, 2024

View reviewed changes

seabbs added this pull request to the merge queue Sep 1, 2024

Merged via the queue into main with commit e660145 Sep 2, 2024
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seabbs deleted the compathelper/new_version/2024-09-01-21-51-38-787-03134721270 branch September 2, 2024 00:02

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CompatHelper: bump compat for Turing to 0.34 for package EpiAware, (keep existing compat) #449

CompatHelper: bump compat for Turing to 0.34 for package EpiAware, (keep existing compat) #449

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Judge result

Benchmark Report for /home/runner/work/Rt-without-renewal/Rt-without-renewal

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Benchmark Report for /home/runner/work/Rt-without-renewal/Rt-without-renewal

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Julia versioninfo

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Benchmark Report for /home/runner/work/Rt-without-renewal/Rt-without-renewal

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CompatHelper: bump compat for Turing to 0.34 for package EpiAware, (keep existing compat) #449

CompatHelper: bump compat for Turing to 0.34 for package EpiAware, (keep existing compat) #449

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github-actions bot commented Sep 1, 2024

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Benchmark Report for /home/runner/work/Rt-without-renewal/Rt-without-renewal

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Benchmark Report for /home/runner/work/Rt-without-renewal/Rt-without-renewal

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Benchmark Report for /home/runner/work/Rt-without-renewal/Rt-without-renewal

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