Issue 465: Add an infection generating model for ODE problems #510

SamuelBrand1 · 2024-10-17T16:19:32Z

This PR closes #465 .

Contributions

This PR contributes the following:

An InfectionODEProcess subtype of AbstractTuringEpiModel which defines the behaviour of a class of ODE based epi model.
An ODEParams type for passing to the method of generate_latent_infs that dispatches the ODE solver.
The generate_latent_infs method.

ODE problem details

The ODE problem specialises to

The initial condition u0 must be a vector or matrix.
The parameters passed to the ODE solver must be a vector or matrix.
The generated latent infections must be definable as a map on the sol object that is returned from an ODE solve call.

Minor contributions and things to note

Added a benchmark for InfectionODEProcess based generation.
A version change changed the precise output of a docstring causing the doctest to fail, I caught this.
I think sciml style might have changed because JuliaFormatter made a large number of small whitespace changes to files outside the scope.
I have tested updating the Chatzilena et al showcase with the new constructors and it works just as well with more elegant code. I've interpreted that as another issue but happy to commit to this PR if it adds.

This seems to hit a lot more files, maybe a change in sciml style?

github-actions · 2024-10-17T16:19:44Z

Try this Pull Request!

Open Julia and type:

import Pkg
Pkg.activate(temp=true)
Pkg.add(url="https://github.com/CDCgov/Rt-without-renewal", rev="sciml-infection-model", subdir="EpiAware")
using EpiAware

codecov-commenter · 2024-10-17T17:17:25Z

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 90.51%. Comparing base (a5ff3b4) to head (85a01e1).

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #510      +/-   ##
==========================================
+ Coverage   89.64%   90.51%   +0.86%     
==========================================
  Files          53       56       +3     
  Lines         753      822      +69     
==========================================
+ Hits          675      744      +69     
  Misses         78       78

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

SamuelBrand1 · 2024-10-18T08:37:00Z

Updating JuliaFormatter fixed the local setting of the code format back into alignment with CI

EpiAware/src/EpiAwareUtils/prefix_submodel.jl

EpiAware/src/EpiInfModels/EpiInfModels.jl

EpiAware/src/EpiInfModels/InfectionODEProcess.jl

seabbs

This looks really nice. Mostly renaming along with some questions where I think the SciML approach is clashing with our current approach elsewhere.

My particular concern is the pattern you envisage for integrating this with a current (or future generate_epiaware call).

seabbs · 2024-11-11T16:26:51Z

I think I've nearly fully addressed this concern. Where we might be missing something is not having a NullLatentModel type which just generates nothing values. I wanted to leave the option open for the ODE models to be conditional on a latent process outcome, e.g. as per a Renewal model, but that is not currently used. A nothing returning process would let us use the current infra for model specification.

Good idea

EpiAware/src/EpiAwareUtils/EpiAwareUtils.jl

EpiAware/src/EpiInfModels/ODEProcess.jl

seabbs

Nice. Close. One more round of tightening and some push back about the use of the latent model.

EpiAware/src/EpiInfModels/ODEProcess.jl

EpiAware/src/EpiLatentModels/odemodels/SEIRParams.jl

EpiAware/src/EpiInfModels/ODEProcess.jl

seabbs · 2024-11-14T13:55:22Z

EpiAware/src/EpiLatentModels/odemodels/SEIRParams.jl

+  - `p`: A vector containing the parameters `[β, α, γ]` where `β` is the infectiousness rate,
+  `α` is the incubation rate, and `γ` is the recovery rate.
+"""
+@model function EpiAwareBase.generate_latent(params::SEIRParams, Z_t)


The implicit promise of this being a latent model is that it will work with the library of latent model functionality. Does this implementation do so? If no do we need to rethink the idea of making this a latent model

I'm not sure off the top of my head... I think this an example of something we should think about generally #525

and refactor example

github-actions · 2024-11-15T15:15:39Z

Benchmark result

Judge result

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

Job Properties

Time of benchmarks:
- Target: 15 Nov 2024 - 14:44
- Baseline: 15 Nov 2024 - 15:15
Package commits:
- Target: 8c4369
- Baseline: a5ff3b
Julia commits:
- Target: 8f5b7c
- Baseline: 8f5b7c
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
`["EpiInfModels", "DirectInfections", "evaluation", "linked"]`	0.89 (5%) ✅	1.00 (1%)
`["EpiInfModels", "DirectInfections", "evaluation", "standard"]`	0.84 (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"]`	0.90 (5%) ✅	1.00 (1%)
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.90 (5%) ✅	1.00 (1%)
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.88 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.94 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.95 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "CombineLatentModels", "evaluation", "linked"]`	0.95 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "CombineLatentModels", "evaluation", "standard"]`	0.95 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.94 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.89 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.92 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.86 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.89 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.06 (5%) ❌	1.00 (1%)
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.85 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.85 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.06 (5%) ❌	1.00 (1%)
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.87 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.88 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.93 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.95 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.86 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.89 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.90 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.92 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.91 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.92 (5%) ✅	1.00 (1%)
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.95 (5%) ✅	1.00 (1%)
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	0.94 (5%) ✅	1.00 (1%)
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.82 (5%) ✅	1.00 (1%)
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.83 (5%) ✅	1.00 (1%)
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	0.94 (5%) ✅	1.00 (1%)
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	0.94 (5%) ✅	1.00 (1%)
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.74 (5%) ✅	1.00 (1%)
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.83 (5%) ✅	1.00 (1%)
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	0.94 (5%) ✅	1.00 (1%)
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.80 (5%) ✅	1.00 (1%)
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.83 (5%) ✅	1.00 (1%)
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.77 (5%) ✅	1.00 (1%)
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.82 (5%) ✅	1.00 (1%)
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	0.94 (5%) ✅	1.00 (1%)
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.84 (5%) ✅	1.00 (1%)
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.86 (5%) ✅	1.00 (1%)
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	0.95 (5%) ✅	1.00 (1%)
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	0.94 (5%) ✅	1.00 (1%)
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.83 (5%) ✅	1.00 (1%)
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.84 (5%) ✅	1.00 (1%)
`["EpiObsModels", "StackObservationModels", "evaluation", "linked"]`	0.89 (5%) ✅	1.00 (1%)
`["EpiObsModels", "StackObservationModels", "evaluation", "standard"]`	0.90 (5%) ✅	1.00 (1%)
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	0.93 (5%) ✅	1.00 (1%)
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	0.91 (5%) ✅	1.00 (1%)
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	0.94 (5%) ✅	1.00 (1%)
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	0.93 (5%) ✅	1.00 (1%)
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.84 (5%) ✅	1.00 (1%)
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.84 (5%) ✅	1.00 (1%)
`["EpiObsModels", "TransformObservationModel", "evaluation", "linked"]`	0.94 (5%) ✅	1.00 (1%)
`["EpiObsModels", "TransformObservationModel", "evaluation", "standard"]`	0.94 (5%) ✅	1.00 (1%)
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	0.94 (5%) ✅	1.00 (1%)
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.83 (5%) ✅	1.00 (1%)
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.83 (5%) ✅	1.00 (1%)
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.84 (5%) ✅	1.00 (1%)
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.83 (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", "RecordExpectedObs", "evaluation"]
["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "RecordExpectedObs", "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", "TransformObservationModel", "evaluation"]
["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "TransformObservationModel", "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 1.11.1
Commit 8f5b7ca12ad (2024-10-16 10:53 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.5 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     0 MHz       8568 s          0 s        683 s      19551 s          0 s
       #2     0 MHz       8673 s          0 s        677 s      19459 s          0 s
       #3     0 MHz      10676 s          0 s        775 s      17357 s          0 s
       #4     0 MHz      11443 s          0 s        798 s      16561 s          0 s
  Memory: 15.606491088867188 GB (13007.87890625 MB free)
  Uptime: 2887.72 sec
  Load Avg:  1.0  1.02  1.14
  WORD_SIZE: 64
  LLVM: libLLVM-16.0.6 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Baseline

Julia Version 1.11.1
Commit 8f5b7ca12ad (2024-10-16 10:53 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.5 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     0 MHz      13469 s          0 s       1069 s      32586 s          0 s
       #2     0 MHz      11974 s          0 s       1042 s      34111 s          0 s
       #3     0 MHz      16272 s          0 s       1197 s      29667 s          0 s
       #4     0 MHz      16207 s          0 s       1171 s      29749 s          0 s
  Memory: 15.606491088867188 GB (12884.15234375 MB free)
  Uptime: 4723.29 sec
  Load Avg:  1.0  1.02  1.06
  WORD_SIZE: 64
  LLVM: libLLVM-16.0.6 (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: 15 Nov 2024 - 14:44
Package commit: 8c4369
Julia commit: 8f5b7c
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"]`	2.103 μs (5%)	416 bytes (1%)	10
`["EpiInfModels", "DirectInfections", "evaluation", "linked"]`	292.611 ns (5%)	464 bytes (1%)	10
`["EpiInfModels", "DirectInfections", "evaluation", "standard"]`	274.050 ns (5%)	464 bytes (1%)	10
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	424.970 ns (5%)	816 bytes (1%)	18
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	417.970 ns (5%)	816 bytes (1%)	18
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	10.199 μs (5%)	5.55 KiB (1%)	125
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	10.199 μs (5%)	5.55 KiB (1%)	125
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	496.861 ns (5%)	256 bytes (1%)	7
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	510.342 ns (5%)	256 bytes (1%)	7
`["EpiInfModels", "ExpGrowthRate", "evaluation", "linked"]`	185.835 ns (5%)	288 bytes (1%)	8
`["EpiInfModels", "ExpGrowthRate", "evaluation", "standard"]`	176.418 ns (5%)	288 bytes (1%)	8
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	275.007 ns (5%)	544 bytes (1%)	15
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	264.643 ns (5%)	544 bytes (1%)	15
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	10.099 μs (5%)	5.53 KiB (1%)	124
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	10.009 μs (5%)	5.53 KiB (1%)	124
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	504.420 ns (5%)	256 bytes (1%)	7
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	495.881 ns (5%)	256 bytes (1%)	7
`["EpiLatentModels", "AR", "evaluation", "linked"]`	4.825 μs (5%)	4.19 KiB (1%)	91
`["EpiLatentModels", "AR", "evaluation", "standard"]`	4.474 μs (5%)	3.20 KiB (1%)	84
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	6.224 μs (5%)	12.20 KiB (1%)	108
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	5.641 μs (5%)	10.64 KiB (1%)	97
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	124.403 μs (5%)	54.25 KiB (1%)	1214
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	83.647 μs (5%)	39.95 KiB (1%)	884
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	18.294 μs (5%)	7.81 KiB (1%)	224
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	16.901 μs (5%)	6.69 KiB (1%)	188
`["EpiLatentModels", "BroadcastLatentModel", "evaluation", "linked"]`	962.208 ns (5%)	2.75 KiB (1%)	34
`["EpiLatentModels", "BroadcastLatentModel", "evaluation", "standard"]`	718.912 ns (5%)	1.88 KiB (1%)	30
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.301 μs (5%)	4.83 KiB (1%)	45
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.058 μs (5%)	3.95 KiB (1%)	41
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	47.038 μs (5%)	23.58 KiB (1%)	458
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	29.956 μs (5%)	16.50 KiB (1%)	350
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	3.034 μs (5%)	992 bytes (1%)	29
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	2.819 μs (5%)	992 bytes (1%)	29
`["EpiLatentModels", "CombineLatentModels", "evaluation", "linked"]`	21.830 μs (5%)	45.67 KiB (1%)	383
`["EpiLatentModels", "CombineLatentModels", "evaluation", "standard"]`	18.795 μs (5%)	31.16 KiB (1%)	339
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	48.691 μs (5%)	105.72 KiB (1%)	805
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	41.538 μs (5%)	75.84 KiB (1%)	709
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	157.595 μs (5%)	99.81 KiB (1%)	1574
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	112.120 μs (5%)	71.98 KiB (1%)	1207
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	18.775 μs (5%)	7.92 KiB (1%)	225
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	17.422 μs (5%)	6.80 KiB (1%)	189
`["EpiLatentModels", "ConcatLatentModels", "evaluation", "linked"]`	43.531 μs (5%)	37.27 KiB (1%)	496
`["EpiLatentModels", "ConcatLatentModels", "evaluation", "standard"]`	41.317 μs (5%)	28.83 KiB (1%)	466
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	44.373 μs (5%)	40.86 KiB (1%)	511
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	42.430 μs (5%)	32.42 KiB (1%)	481
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	118.453 μs (5%)	62.64 KiB (1%)	1012
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	97.804 μs (5%)	49.45 KiB (1%)	879
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.407 μs (5%)	2.06 KiB (1%)	51
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.215 μs (5%)	2.06 KiB (1%)	51
`["EpiLatentModels", "DiffLatentModel", "evaluation", "linked"]`	7.915 μs (5%)	5.55 KiB (1%)	102
`["EpiLatentModels", "DiffLatentModel", "evaluation", "standard"]`	7.506 μs (5%)	3.86 KiB (1%)	96
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	8.876 μs (5%)	13.92 KiB (1%)	115
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	8.696 μs (5%)	12.23 KiB (1%)	109
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	84.278 μs (5%)	39.58 KiB (1%)	824
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	66.815 μs (5%)	33.14 KiB (1%)	715
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	6.861 μs (5%)	2.12 KiB (1%)	50
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	6.828 μs (5%)	2.12 KiB (1%)	50
`["EpiLatentModels", "HierarchicalNormal", "evaluation", "linked"]`	398.277 ns (5%)	1.03 KiB (1%)	14
`["EpiLatentModels", "HierarchicalNormal", "evaluation", "standard"]`	304.417 ns (5%)	896 bytes (1%)	13
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.016 μs (5%)	5.12 KiB (1%)	23
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	923.389 ns (5%)	4.97 KiB (1%)	22
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	43.040 μs (5%)	19.03 KiB (1%)	381
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	27.261 μs (5%)	14.17 KiB (1%)	278
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.095 μs (5%)	608 bytes (1%)	9
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	923.312 ns (5%)	608 bytes (1%)	9
`["EpiLatentModels", "Intercept", "evaluation", "linked"]`	219.034 ns (5%)	400 bytes (1%)	10
`["EpiLatentModels", "Intercept", "evaluation", "standard"]`	208.136 ns (5%)	400 bytes (1%)	10
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	330.749 ns (5%)	704 bytes (1%)	17
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	315.226 ns (5%)	704 bytes (1%)	17
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	5.402 μs (5%)	3.62 KiB (1%)	87
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	5.375 μs (5%)	3.62 KiB (1%)	87
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	398.960 ns (5%)	208 bytes (1%)	4
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	400.507 ns (5%)	208 bytes (1%)	4
`["EpiLatentModels", "PrefixLatentModel", "evaluation", "linked"]`	1.837 μs (5%)	3.56 KiB (1%)	40
`["EpiLatentModels", "PrefixLatentModel", "evaluation", "standard"]`	1.653 μs (5%)	3.09 KiB (1%)	37
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	2.760 μs (5%)	7.66 KiB (1%)	49
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	2.494 μs (5%)	7.19 KiB (1%)	46
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	46.427 μs (5%)	21.42 KiB (1%)	406
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	29.976 μs (5%)	16.25 KiB (1%)	301
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.085 μs (5%)	608 bytes (1%)	9
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	914.079 ns (5%)	608 bytes (1%)	9
`["EpiLatentModels", "RandomWalk", "evaluation", "linked"]`	592.534 ns (5%)	1.72 KiB (1%)	21
`["EpiLatentModels", "RandomWalk", "evaluation", "standard"]`	431.060 ns (5%)	1.28 KiB (1%)	19
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.504 μs (5%)	8.11 KiB (1%)	32
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.317 μs (5%)	7.67 KiB (1%)	30
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	52.519 μs (5%)	25.47 KiB (1%)	501
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	35.607 μs (5%)	20.28 KiB (1%)	396
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	3.617 μs (5%)	1.28 KiB (1%)	29
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	3.442 μs (5%)	1.28 KiB (1%)	29
`["EpiLatentModels", "RecordExpectedLatent", "evaluation", "linked"]`	553.449 ns (5%)	1.25 KiB (1%)	20
`["EpiLatentModels", "RecordExpectedLatent", "evaluation", "standard"]`	415.156 ns (5%)	960 bytes (1%)	18
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	700.340 ns (5%)	1.78 KiB (1%)	29
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	555.807 ns (5%)	1.47 KiB (1%)	27
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	43.422 μs (5%)	18.31 KiB (1%)	387
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	27.141 μs (5%)	13.30 KiB (1%)	283
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.019 μs (5%)	352 bytes (1%)	9
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	839.486 ns (5%)	352 bytes (1%)	9
`["EpiLatentModels", "TransformLatentModel", "evaluation", "linked"]`	266.567 ns (5%)	448 bytes (1%)	12
`["EpiLatentModels", "TransformLatentModel", "evaluation", "standard"]`	256.220 ns (5%)	448 bytes (1%)	12
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	350.425 ns (5%)	768 bytes (1%)	19
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	340.413 ns (5%)	768 bytes (1%)	19
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	5.751 μs (5%)	3.93 KiB (1%)	95
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	5.711 μs (5%)	3.93 KiB (1%)	95
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	499.392 ns (5%)	160 bytes (1%)	4
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	499.907 ns (5%)	160 bytes (1%)	4
`["EpiLatentModels", "broadcast_dayofweek", "evaluation", "linked"]`	1.254 μs (5%)	3.73 KiB (1%)	43
`["EpiLatentModels", "broadcast_dayofweek", "evaluation", "standard"]`	932.821 ns (5%)	2.42 KiB (1%)	37
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	2.213 μs (5%)	9.80 KiB (1%)	54
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.847 μs (5%)	8.48 KiB (1%)	48
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	69.040 μs (5%)	34.64 KiB (1%)	699
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	50.755 μs (5%)	28.58 KiB (1%)	590
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	4.972 μs (5%)	1.19 KiB (1%)	29
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	4.861 μs (5%)	1.19 KiB (1%)	29
`["EpiLatentModels", "broadcast_weekly", "evaluation", "linked"]`	8.306 μs (5%)	5.78 KiB (1%)	113
`["EpiLatentModels", "broadcast_weekly", "evaluation", "standard"]`	7.760 μs (5%)	3.95 KiB (1%)	103
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	8.856 μs (5%)	8.98 KiB (1%)	130
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	8.219 μs (5%)	6.83 KiB (1%)	116
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	101.912 μs (5%)	42.11 KiB (1%)	852
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	61.796 μs (5%)	29.47 KiB (1%)	599
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.530 μs (5%)	1.69 KiB (1%)	48
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	4.918 μs (5%)	1.56 KiB (1%)	44
`["EpiObsModels", "Ascertainment", "evaluation", "linked"]`	3.330 μs (5%)	3.58 KiB (1%)	63
`["EpiObsModels", "Ascertainment", "evaluation", "standard"]`	3.254 μs (5%)	3.58 KiB (1%)	63
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	4.133 μs (5%)	3.92 KiB (1%)	72
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	4.096 μs (5%)	3.92 KiB (1%)	72
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	76.884 μs (5%)	38.77 KiB (1%)	918
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	61.325 μs (5%)	34.02 KiB (1%)	815
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.624 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.549 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "LatentDelay", "evaluation", "linked"]`	14.407 μs (5%)	22.08 KiB (1%)	412
`["EpiObsModels", "LatentDelay", "evaluation", "standard"]`	14.407 μs (5%)	22.08 KiB (1%)	412
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	18.074 μs (5%)	22.30 KiB (1%)	419
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	18.004 μs (5%)	22.30 KiB (1%)	419
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	527.059 μs (5%)	293.05 KiB (1%)	7011
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	515.689 μs (5%)	288.30 KiB (1%)	6908
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	50.976 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	50.655 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "NegativeBinomialError", "evaluation", "linked"]`	1.125 μs (5%)	400 bytes (1%)	10
`["EpiObsModels", "NegativeBinomialError", "evaluation", "standard"]`	1.086 μs (5%)	400 bytes (1%)	10
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.591 μs (5%)	624 bytes (1%)	17
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.573 μs (5%)	624 bytes (1%)	17
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	72.867 μs (5%)	35.95 KiB (1%)	903
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	58.199 μs (5%)	31.20 KiB (1%)	800
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.574 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.549 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "PoissonError", "evaluation", "linked"]`	1.365 μs (5%)	1.83 KiB (1%)	30
`["EpiObsModels", "PoissonError", "evaluation", "standard"]`	1.027 μs (5%)	1.47 KiB (1%)	26
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	3.171 μs (5%)	7.78 KiB (1%)	43
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	2.468 μs (5%)	4.70 KiB (1%)	35
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	165.340 μs (5%)	86.44 KiB (1%)	1870
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	46.187 μs (5%)	29.38 KiB (1%)	727
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	6.087 μs (5%)	176 bytes (1%)	3
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	4.125 μs (5%)	176 bytes (1%)	3
`["EpiObsModels", "PrefixObservationModel", "evaluation", "linked"]`	1.663 μs (5%)	1.56 KiB (1%)	34
`["EpiObsModels", "PrefixObservationModel", "evaluation", "standard"]`	1.590 μs (5%)	1.56 KiB (1%)	34
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.839 μs (5%)	1.78 KiB (1%)	41
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.748 μs (5%)	1.78 KiB (1%)	41
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	27.120 μs (5%)	12.59 KiB (1%)	290
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	12.874 μs (5%)	7.84 KiB (1%)	187
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.120 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	966.278 ns (5%)	96 bytes (1%)	3
`["EpiObsModels", "RecordExpectedObs", "evaluation", "linked"]`	781.183 ns (5%)	480 bytes (1%)	14
`["EpiObsModels", "RecordExpectedObs", "evaluation", "standard"]`	733.524 ns (5%)	480 bytes (1%)	14
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.071 μs (5%)	704 bytes (1%)	21
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.023 μs (5%)	704 bytes (1%)	21
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	45.866 μs (5%)	22.98 KiB (1%)	537
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	32.160 μs (5%)	18.23 KiB (1%)	434
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	3.157 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	2.997 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "StackObservationModels", "evaluation", "linked"]`	6.761 μs (5%)	5.81 KiB (1%)	117
`["EpiObsModels", "StackObservationModels", "evaluation", "standard"]`	6.705 μs (5%)	5.81 KiB (1%)	117
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	7.589 μs (5%)	6.16 KiB (1%)	126
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	7.474 μs (5%)	6.16 KiB (1%)	126
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	89.148 μs (5%)	49.09 KiB (1%)	1044
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	71.404 μs (5%)	44.34 KiB (1%)	941
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	6.306 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	6.096 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "TransformObservationModel", "evaluation", "linked"]`	1.431 μs (5%)	672 bytes (1%)	16
`["EpiObsModels", "TransformObservationModel", "evaluation", "standard"]`	1.383 μs (5%)	672 bytes (1%)	16
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.935 μs (5%)	896 bytes (1%)	23
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.847 μs (5%)	896 bytes (1%)	23
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	72.777 μs (5%)	35.59 KiB (1%)	869
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	58.129 μs (5%)	30.84 KiB (1%)	766
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.753 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.584 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "ascertainment_dayofweek", "evaluation", "linked"]`	4.075 μs (5%)	9.09 KiB (1%)	100
`["EpiObsModels", "ascertainment_dayofweek", "evaluation", "standard"]`	3.654 μs (5%)	7.84 KiB (1%)	92
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	6.252 μs (5%)	16.22 KiB (1%)	111
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	5.721 μs (5%)	14.97 KiB (1%)	103
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	105.207 μs (5%)	59.83 KiB (1%)	1164
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	86.993 μs (5%)	53.88 KiB (1%)	1054
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.367 μs (5%)	496 bytes (1%)	9
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.208 μs (5%)	496 bytes (1%)	9
`["EpiObsModels", "observation_error", "missing obs", "evaluation", "linked"]`	1.394 μs (5%)	3.05 KiB (1%)	38
`["EpiObsModels", "observation_error", "missing obs", "evaluation", "standard"]`	891.857 ns (5%)	1.48 KiB (1%)	28
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.792 μs (5%)	4.11 KiB (1%)	48
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.220 μs (5%)	2.55 KiB (1%)	38
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	39.984 μs (5%)	25.78 KiB (1%)	533
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	23.344 μs (5%)	18.80 KiB (1%)	401
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	2.193 μs (5%)	144 bytes (1%)	3
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.957 μs (5%)	144 bytes (1%)	3
`["EpiObsModels", "observation_error", "no missing obs", "evaluation", "linked"]`	424.010 ns (5%)	352 bytes (1%)	10
`["EpiObsModels", "observation_error", "no missing obs", "evaluation", "standard"]`	366.413 ns (5%)	352 bytes (1%)	10
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	556.016 ns (5%)	576 bytes (1%)	17
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	491.799 ns (5%)	576 bytes (1%)	17
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	33.062 μs (5%)	19.64 KiB (1%)	446
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	17.744 μs (5%)	14.22 KiB (1%)	324
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.842 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.627 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "observation_error", "partially missing obs", "evaluation", "linked"]`	28.684 μs (5%)	3.02 KiB (1%)	64
`["EpiObsModels", "observation_error", "partially missing obs", "evaluation", "standard"]`	28.494 μs (5%)	2.70 KiB (1%)	62
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	19.566 μs (5%)	2.73 KiB (1%)	51
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	19.346 μs (5%)	2.42 KiB (1%)	49
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	62.056 μs (5%)	26.11 KiB (1%)	555
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	42.079 μs (5%)	20.38 KiB (1%)	431
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	2.170 μs (5%)	112 bytes (1%)	3
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.969 μs (5%)	112 bytes (1%)	3

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", "RecordExpectedObs", "evaluation"]
["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "RecordExpectedObs", "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", "TransformObservationModel", "evaluation"]
["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "TransformObservationModel", "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 1.11.1
Commit 8f5b7ca12ad (2024-10-16 10:53 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.5 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     0 MHz       8568 s          0 s        683 s      19551 s          0 s
       #2     0 MHz       8673 s          0 s        677 s      19459 s          0 s
       #3     0 MHz      10676 s          0 s        775 s      17357 s          0 s
       #4     0 MHz      11443 s          0 s        798 s      16561 s          0 s
  Memory: 15.606491088867188 GB (13007.87890625 MB free)
  Uptime: 2887.72 sec
  Load Avg:  1.0  1.02  1.14
  WORD_SIZE: 64
  LLVM: libLLVM-16.0.6 (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: 15 Nov 2024 - 15:15
Package commit: a5ff3b
Julia commit: 8f5b7c
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"]`	2.098 μs (5%)	416 bytes (1%)	10
`["EpiInfModels", "DirectInfections", "evaluation", "linked"]`	330.017 ns (5%)	464 bytes (1%)	10
`["EpiInfModels", "DirectInfections", "evaluation", "standard"]`	327.052 ns (5%)	464 bytes (1%)	10
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	422.553 ns (5%)	816 bytes (1%)	18
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	406.065 ns (5%)	816 bytes (1%)	18
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	10.440 μs (5%)	5.55 KiB (1%)	125
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	10.279 μs (5%)	5.55 KiB (1%)	125
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	572.792 ns (5%)	256 bytes (1%)	7
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	569.570 ns (5%)	256 bytes (1%)	7
`["EpiInfModels", "ExpGrowthRate", "evaluation", "linked"]`	184.745 ns (5%)	288 bytes (1%)	8
`["EpiInfModels", "ExpGrowthRate", "evaluation", "standard"]`	176.805 ns (5%)	288 bytes (1%)	8
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	271.890 ns (5%)	544 bytes (1%)	15
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	260.781 ns (5%)	544 bytes (1%)	15
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	10.169 μs (5%)	5.53 KiB (1%)	124
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	10.209 μs (5%)	5.53 KiB (1%)	124
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	558.933 ns (5%)	256 bytes (1%)	7
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	563.015 ns (5%)	256 bytes (1%)	7
`["EpiLatentModels", "AR", "evaluation", "linked"]`	4.755 μs (5%)	4.19 KiB (1%)	91
`["EpiLatentModels", "AR", "evaluation", "standard"]`	4.397 μs (5%)	3.20 KiB (1%)	84
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	6.176 μs (5%)	12.20 KiB (1%)	108
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	5.744 μs (5%)	10.64 KiB (1%)	97
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	127.690 μs (5%)	54.25 KiB (1%)	1214
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	84.529 μs (5%)	39.95 KiB (1%)	884
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	18.955 μs (5%)	7.81 KiB (1%)	224
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	17.693 μs (5%)	6.69 KiB (1%)	188
`["EpiLatentModels", "BroadcastLatentModel", "evaluation", "linked"]`	916.889 ns (5%)	2.75 KiB (1%)	34
`["EpiLatentModels", "BroadcastLatentModel", "evaluation", "standard"]`	689.148 ns (5%)	1.88 KiB (1%)	30
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.278 μs (5%)	4.83 KiB (1%)	45
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.053 μs (5%)	3.95 KiB (1%)	41
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	49.493 μs (5%)	23.58 KiB (1%)	458
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	30.717 μs (5%)	16.50 KiB (1%)	350
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	3.239 μs (5%)	992 bytes (1%)	29
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	2.980 μs (5%)	992 bytes (1%)	29
`["EpiLatentModels", "CombineLatentModels", "evaluation", "linked"]`	23.073 μs (5%)	45.67 KiB (1%)	383
`["EpiLatentModels", "CombineLatentModels", "evaluation", "standard"]`	19.807 μs (5%)	31.16 KiB (1%)	339
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	49.302 μs (5%)	105.72 KiB (1%)	805
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	42.160 μs (5%)	75.84 KiB (1%)	709
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	157.596 μs (5%)	99.81 KiB (1%)	1574
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	112.180 μs (5%)	71.98 KiB (1%)	1207
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	19.547 μs (5%)	7.92 KiB (1%)	225
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	18.254 μs (5%)	6.80 KiB (1%)	189
`["EpiLatentModels", "ConcatLatentModels", "evaluation", "linked"]`	44.734 μs (5%)	37.27 KiB (1%)	496
`["EpiLatentModels", "ConcatLatentModels", "evaluation", "standard"]`	42.881 μs (5%)	28.83 KiB (1%)	466
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	46.527 μs (5%)	40.86 KiB (1%)	511
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	43.822 μs (5%)	32.42 KiB (1%)	481
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	121.578 μs (5%)	62.64 KiB (1%)	1012
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	98.745 μs (5%)	49.45 KiB (1%)	879
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.732 μs (5%)	2.06 KiB (1%)	51
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.475 μs (5%)	2.06 KiB (1%)	51
`["EpiLatentModels", "DiffLatentModel", "evaluation", "linked"]`	7.932 μs (5%)	5.55 KiB (1%)	102
`["EpiLatentModels", "DiffLatentModel", "evaluation", "standard"]`	7.502 μs (5%)	3.86 KiB (1%)	96
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	9.217 μs (5%)	13.92 KiB (1%)	115
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	8.716 μs (5%)	12.23 KiB (1%)	109
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	87.304 μs (5%)	39.58 KiB (1%)	824
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	68.249 μs (5%)	33.14 KiB (1%)	715
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	7.728 μs (5%)	2.12 KiB (1%)	50
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	7.436 μs (5%)	2.12 KiB (1%)	50
`["EpiLatentModels", "HierarchicalNormal", "evaluation", "linked"]`	399.966 ns (5%)	1.03 KiB (1%)	14
`["EpiLatentModels", "HierarchicalNormal", "evaluation", "standard"]`	305.518 ns (5%)	896 bytes (1%)	13
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.023 μs (5%)	5.12 KiB (1%)	23
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	880.471 ns (5%)	4.97 KiB (1%)	22
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	44.273 μs (5%)	19.03 KiB (1%)	381
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	27.642 μs (5%)	14.17 KiB (1%)	278
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.268 μs (5%)	608 bytes (1%)	9
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.037 μs (5%)	608 bytes (1%)	9
`["EpiLatentModels", "Intercept", "evaluation", "linked"]`	216.982 ns (5%)	400 bytes (1%)	10
`["EpiLatentModels", "Intercept", "evaluation", "standard"]`	205.118 ns (5%)	400 bytes (1%)	10
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	316.896 ns (5%)	704 bytes (1%)	17
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	297.610 ns (5%)	704 bytes (1%)	17
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	5.454 μs (5%)	3.62 KiB (1%)	87
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	5.450 μs (5%)	3.62 KiB (1%)	87
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	468.430 ns (5%)	208 bytes (1%)	4
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	473.540 ns (5%)	208 bytes (1%)	4
`["EpiLatentModels", "PrefixLatentModel", "evaluation", "linked"]`	1.818 μs (5%)	3.56 KiB (1%)	40
`["EpiLatentModels", "PrefixLatentModel", "evaluation", "standard"]`	1.625 μs (5%)	3.09 KiB (1%)	37
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	2.602 μs (5%)	7.66 KiB (1%)	49
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	2.388 μs (5%)	7.19 KiB (1%)	46
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	48.561 μs (5%)	21.42 KiB (1%)	406
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	31.129 μs (5%)	16.25 KiB (1%)	301
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.248 μs (5%)	608 bytes (1%)	9
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.035 μs (5%)	608 bytes (1%)	9
`["EpiLatentModels", "RandomWalk", "evaluation", "linked"]`	592.844 ns (5%)	1.72 KiB (1%)	21
`["EpiLatentModels", "RandomWalk", "evaluation", "standard"]`	445.586 ns (5%)	1.28 KiB (1%)	19
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.514 μs (5%)	8.11 KiB (1%)	32
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.315 μs (5%)	7.67 KiB (1%)	30
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	53.911 μs (5%)	25.47 KiB (1%)	501
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	36.208 μs (5%)	20.28 KiB (1%)	396
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	3.900 μs (5%)	1.28 KiB (1%)	29
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	3.638 μs (5%)	1.28 KiB (1%)	29
`["EpiLatentModels", "RecordExpectedLatent", "evaluation", "linked"]`	553.903 ns (5%)	1.25 KiB (1%)	20
`["EpiLatentModels", "RecordExpectedLatent", "evaluation", "standard"]`	406.894 ns (5%)	960 bytes (1%)	18
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	697.469 ns (5%)	1.78 KiB (1%)	29
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	550.551 ns (5%)	1.47 KiB (1%)	27
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	44.324 μs (5%)	18.31 KiB (1%)	387
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	27.862 μs (5%)	13.30 KiB (1%)	283
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.179 μs (5%)	352 bytes (1%)	9
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	940.597 ns (5%)	352 bytes (1%)	9
`["EpiLatentModels", "TransformLatentModel", "evaluation", "linked"]`	261.709 ns (5%)	448 bytes (1%)	12
`["EpiLatentModels", "TransformLatentModel", "evaluation", "standard"]`	252.970 ns (5%)	448 bytes (1%)	12
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	347.700 ns (5%)	768 bytes (1%)	19
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	338.919 ns (5%)	768 bytes (1%)	19
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	5.776 μs (5%)	3.93 KiB (1%)	95
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	5.714 μs (5%)	3.93 KiB (1%)	95
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	553.361 ns (5%)	160 bytes (1%)	4
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	544.667 ns (5%)	160 bytes (1%)	4
`["EpiLatentModels", "broadcast_dayofweek", "evaluation", "linked"]`	1.222 μs (5%)	3.73 KiB (1%)	43
`["EpiLatentModels", "broadcast_dayofweek", "evaluation", "standard"]`	906.226 ns (5%)	2.42 KiB (1%)	37
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	2.189 μs (5%)	9.80 KiB (1%)	54
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.793 μs (5%)	8.48 KiB (1%)	48
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	69.681 μs (5%)	34.64 KiB (1%)	699
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	52.198 μs (5%)	28.58 KiB (1%)	590
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.487 μs (5%)	1.19 KiB (1%)	29
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.273 μs (5%)	1.19 KiB (1%)	29
`["EpiLatentModels", "broadcast_weekly", "evaluation", "linked"]`	8.152 μs (5%)	5.78 KiB (1%)	113
`["EpiLatentModels", "broadcast_weekly", "evaluation", "standard"]`	7.589 μs (5%)	3.95 KiB (1%)	103
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	8.827 μs (5%)	8.98 KiB (1%)	130
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	8.209 μs (5%)	6.83 KiB (1%)	116
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	102.162 μs (5%)	42.11 KiB (1%)	852
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	63.880 μs (5%)	29.47 KiB (1%)	599
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.844 μs (5%)	1.69 KiB (1%)	48
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.151 μs (5%)	1.56 KiB (1%)	44
`["EpiObsModels", "Ascertainment", "evaluation", "linked"]`	3.414 μs (5%)	3.58 KiB (1%)	63
`["EpiObsModels", "Ascertainment", "evaluation", "standard"]`	3.357 μs (5%)	3.58 KiB (1%)	63
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	3.957 μs (5%)	3.92 KiB (1%)	72
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	3.964 μs (5%)	3.92 KiB (1%)	72
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	80.641 μs (5%)	38.77 KiB (1%)	918
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	65.133 μs (5%)	34.02 KiB (1%)	815
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	6.859 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	6.723 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "LatentDelay", "evaluation", "linked"]`	14.077 μs (5%)	22.08 KiB (1%)	412
`["EpiObsModels", "LatentDelay", "evaluation", "standard"]`	13.996 μs (5%)	22.08 KiB (1%)	412
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	18.134 μs (5%)	22.30 KiB (1%)	419
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	17.873 μs (5%)	22.30 KiB (1%)	419
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	562.696 μs (5%)	293.05 KiB (1%)	7011
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	546.276 μs (5%)	288.30 KiB (1%)	6908
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	68.800 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	61.195 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "NegativeBinomialError", "evaluation", "linked"]`	1.126 μs (5%)	400 bytes (1%)	10
`["EpiObsModels", "NegativeBinomialError", "evaluation", "standard"]`	1.082 μs (5%)	400 bytes (1%)	10
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.687 μs (5%)	624 bytes (1%)	17
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.553 μs (5%)	624 bytes (1%)	17
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	73.868 μs (5%)	35.95 KiB (1%)	903
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	59.171 μs (5%)	31.20 KiB (1%)	800
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	6.988 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	6.692 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "PoissonError", "evaluation", "linked"]`	1.381 μs (5%)	1.83 KiB (1%)	30
`["EpiObsModels", "PoissonError", "evaluation", "standard"]`	1.042 μs (5%)	1.47 KiB (1%)	26
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	3.199 μs (5%)	7.78 KiB (1%)	43
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	2.524 μs (5%)	4.70 KiB (1%)	35
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	164.699 μs (5%)	86.44 KiB (1%)	1870
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	47.098 μs (5%)	29.38 KiB (1%)	727
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	7.871 μs (5%)	176 bytes (1%)	3
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.057 μs (5%)	176 bytes (1%)	3
`["EpiObsModels", "PrefixObservationModel", "evaluation", "linked"]`	1.726 μs (5%)	1.56 KiB (1%)	34
`["EpiObsModels", "PrefixObservationModel", "evaluation", "standard"]`	1.659 μs (5%)	1.56 KiB (1%)	34
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.921 μs (5%)	1.78 KiB (1%)	41
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.855 μs (5%)	1.78 KiB (1%)	41
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	27.632 μs (5%)	12.59 KiB (1%)	290
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	13.495 μs (5%)	7.84 KiB (1%)	187
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.339 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.128 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "RecordExpectedObs", "evaluation", "linked"]`	783.824 ns (5%)	480 bytes (1%)	14
`["EpiObsModels", "RecordExpectedObs", "evaluation", "standard"]`	739.064 ns (5%)	480 bytes (1%)	14
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.063 μs (5%)	704 bytes (1%)	21
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`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	48.430 μs (5%)	22.98 KiB (1%)	537
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	34.194 μs (5%)	18.23 KiB (1%)	434
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	3.822 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	3.580 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "StackObservationModels", "evaluation", "linked"]`	7.557 μs (5%)	5.81 KiB (1%)	117
`["EpiObsModels", "StackObservationModels", "evaluation", "standard"]`	7.439 μs (5%)	5.81 KiB (1%)	117
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	8.198 μs (5%)	6.16 KiB (1%)	126
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	8.200 μs (5%)	6.16 KiB (1%)	126
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	94.969 μs (5%)	49.09 KiB (1%)	1044
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	76.754 μs (5%)	44.34 KiB (1%)	941
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	7.478 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	7.264 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "TransformObservationModel", "evaluation", "linked"]`	1.525 μs (5%)	672 bytes (1%)	16
`["EpiObsModels", "TransformObservationModel", "evaluation", "standard"]`	1.474 μs (5%)	672 bytes (1%)	16
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	2.009 μs (5%)	896 bytes (1%)	23
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.934 μs (5%)	896 bytes (1%)	23
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	75.632 μs (5%)	35.59 KiB (1%)	869
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	61.716 μs (5%)	30.84 KiB (1%)	766
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	6.936 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	6.754 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "ascertainment_dayofweek", "evaluation", "linked"]`	4.101 μs (5%)	9.09 KiB (1%)	100
`["EpiObsModels", "ascertainment_dayofweek", "evaluation", "standard"]`	3.622 μs (5%)	7.84 KiB (1%)	92
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	6.170 μs (5%)	16.22 KiB (1%)	111
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	5.696 μs (5%)	14.97 KiB (1%)	103
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	108.013 μs (5%)	59.83 KiB (1%)	1164
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	90.581 μs (5%)	53.88 KiB (1%)	1054
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	6.394 μs (5%)	496 bytes (1%)	9
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	6.258 μs (5%)	496 bytes (1%)	9
`["EpiObsModels", "observation_error", "missing obs", "evaluation", "linked"]`	1.416 μs (5%)	3.05 KiB (1%)	38
`["EpiObsModels", "observation_error", "missing obs", "evaluation", "standard"]`	904.878 ns (5%)	1.48 KiB (1%)	28
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.857 μs (5%)	4.11 KiB (1%)	48
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.267 μs (5%)	2.55 KiB (1%)	38
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	40.416 μs (5%)	25.78 KiB (1%)	533
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	23.735 μs (5%)	18.80 KiB (1%)	401
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	2.209 μs (5%)	144 bytes (1%)	3
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.982 μs (5%)	144 bytes (1%)	3
`["EpiObsModels", "observation_error", "no missing obs", "evaluation", "linked"]`	434.131 ns (5%)	352 bytes (1%)	10
`["EpiObsModels", "observation_error", "no missing obs", "evaluation", "standard"]`	372.048 ns (5%)	352 bytes (1%)	10
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	549.316 ns (5%)	576 bytes (1%)	17
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	490.097 ns (5%)	576 bytes (1%)	17
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	32.581 μs (5%)	19.64 KiB (1%)	446
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	17.903 μs (5%)	14.22 KiB (1%)	324
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.843 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.604 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "observation_error", "partially missing obs", "evaluation", "linked"]`	29.155 μs (5%)	3.02 KiB (1%)	64
`["EpiObsModels", "observation_error", "partially missing obs", "evaluation", "standard"]`	28.974 μs (5%)	2.70 KiB (1%)	62
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	19.326 μs (5%)	2.73 KiB (1%)	51
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	19.186 μs (5%)	2.42 KiB (1%)	49
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	63.209 μs (5%)	26.11 KiB (1%)	555
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	42.480 μs (5%)	20.38 KiB (1%)	431
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	2.234 μs (5%)	112 bytes (1%)	3
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.978 μs (5%)	112 bytes (1%)	3

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", "RecordExpectedObs", "evaluation"]
["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "RecordExpectedObs", "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", "TransformObservationModel", "evaluation"]
["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "TransformObservationModel", "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 1.11.1
Commit 8f5b7ca12ad (2024-10-16 10:53 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.5 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     0 MHz      13469 s          0 s       1069 s      32586 s          0 s
       #2     0 MHz      11974 s          0 s       1042 s      34111 s          0 s
       #3     0 MHz      16272 s          0 s       1197 s      29667 s          0 s
       #4     0 MHz      16207 s          0 s       1171 s      29749 s          0 s
  Memory: 15.606491088867188 GB (12884.15234375 MB free)
  Uptime: 4723.29 sec
  Load Avg:  1.0  1.02  1.06
  WORD_SIZE: 64
  LLVM: libLLVM-16.0.6 (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:                           4890.85
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

seabbs

I think this looks good for now but I find the use of latent model here a bit jarring and indicative of a wider design flaw. I see the issue to discuss so let's do that there.

Less fussily though this is great functionality to have

* Patch: Switch to fork of benchmarkCI (#520) * patch to fork of benchmarkCI * put fork version of BenchmarkCI in [sources] * swap order * add EpiAware [source] * fix path * rm benchmarkCI from project * Patch fix: add `Manifest.toml` to benchmarking (#524) * trigger * Update benchmark.yaml * Update benchmark.yaml * commit benchmark Manifest * try alternate approach * Update benchmark.yaml * Update EpiMethod.jl * Update benchmark.yaml * change baseline to origin/main * remove trigger * rm other trigger * Issue 465: Add an infection generating model for ODE problems (#510) * CompatHelper: bump compat for Turing to 0.35 for package EpiAware, (drop existing compat) (#516) * CompatHelper: bump compat for Turing to 0.35 for package EpiAware, (drop existing compat) * Update Project.toml * fix Project.toml --------- Co-authored-by: CompatHelper Julia <[email protected]> Co-authored-by: Sam Abbott <[email protected]> Co-authored-by: Samuel Brand <[email protected]> Co-authored-by: Samuel Brand <[email protected]> * CompatHelper: bump compat for DynamicPPL to 0.30 for package EpiAware, (drop existing compat) (#528) Co-authored-by: CompatHelper Julia <[email protected]> * rename IDD -> IID * rename test file * Issue 529: Create null Latent model (#530) * Null Latent model * Null Latent model * fix doctest * fix generate_epiaware unit tests New usage of RW * fix turing method test underlying std of step size changed name * fix broadcast test Underlying std param changed name * fix HN unit test Default std prior had changed * fix AR unit tests --------- Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: CompatHelper Julia <[email protected]> Co-authored-by: Sam Abbott <[email protected]>

* draft MA method * draft MA methd * use IDD for epsilon in all models * add MA benchmark * Add docs and tests for IDD * make episilon_t a arg of the latent model constructors * improve MA correctness * fully import EpiAwareUtils * add a test for IDD * add tests for MA.jl * add doc tests and unit tests + start on helper fn * more updatres to AR appraoc * chase down partial arg changes * clean up AR * clean up and add arma and arima helpers * Contributions towards Arma/Arima models (#531) * Patch: Switch to fork of benchmarkCI (#520) * patch to fork of benchmarkCI * put fork version of BenchmarkCI in [sources] * swap order * add EpiAware [source] * fix path * rm benchmarkCI from project * Patch fix: add `Manifest.toml` to benchmarking (#524) * trigger * Update benchmark.yaml * Update benchmark.yaml * commit benchmark Manifest * try alternate approach * Update benchmark.yaml * Update EpiMethod.jl * Update benchmark.yaml * change baseline to origin/main * remove trigger * rm other trigger * Issue 465: Add an infection generating model for ODE problems (#510) * CompatHelper: bump compat for Turing to 0.35 for package EpiAware, (drop existing compat) (#516) * CompatHelper: bump compat for Turing to 0.35 for package EpiAware, (drop existing compat) * Update Project.toml * fix Project.toml --------- Co-authored-by: CompatHelper Julia <[email protected]> Co-authored-by: Sam Abbott <[email protected]> Co-authored-by: Samuel Brand <[email protected]> Co-authored-by: Samuel Brand <[email protected]> * CompatHelper: bump compat for DynamicPPL to 0.30 for package EpiAware, (drop existing compat) (#528) Co-authored-by: CompatHelper Julia <[email protected]> * rename IDD -> IID * rename test file * Issue 529: Create null Latent model (#530) * Null Latent model * Null Latent model * fix doctest * fix generate_epiaware unit tests New usage of RW * fix turing method test underlying std of step size changed name * fix broadcast test Underlying std param changed name * fix HN unit test Default std prior had changed * fix AR unit tests --------- Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: CompatHelper Julia <[email protected]> Co-authored-by: Sam Abbott <[email protected]> * revert define_ namming * clean out repeated utils from merge * fix MA tests * fix RW tests - feel made about RandomWalk vs AR naming * fix remaining unit tests that aren't doctests * update latent recovery test * try and fix doctests automatically * update all doctests to output nothing - this is awful * add doctests for arima and arma * fix doctest * clean up deps * update replication studies * add interface tests for combination functions and add benchmarks * add some basic theoretical properties tests * name change IDD -> IID benchmarks * moving all the constructors because this PR is too contained * catch missing using * update iid benchmark: * update extraction * remove old param namme from case study * get the dot * get the dot * fix initial guess point for MAP opt * Update index.jl * add a compile time branch for HN * add a compile time branch for HN * update test * add a new constructor to get old default behaviour * update docs * update docs - using the structs for priors is very brittle * reorder prior plots --------- Co-authored-by: Samuel Brand <[email protected]> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: CompatHelper Julia <[email protected]> Co-authored-by: Samuel Brand <[email protected]>

SamuelBrand1 added 8 commits October 17, 2024 17:03

catch docstring now failing because output changed

196aa20

add OrdinaryDiffEq deps

6acdb44

Add InfectionODEProcess

df2bed6

add unit tests

01e11e7

add benchmark

3d9d7eb

reformat

08ea0cf

This seems to hit a lot more files, maybe a change in sciml style?

add line

d88279e

Update InfectionODEProcess.jl

98dea81

SamuelBrand1 requested a review from seabbs October 17, 2024 16:19

reformat

c5f47ff

SamuelBrand1 mentioned this pull request Oct 18, 2024

BenchmarkCI is incompatible with DiffEqBase #511

Closed