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Issue 340: Add a prefix to benchmarks #414

Merged
merged 1 commit into from
Aug 5, 2024
Merged

Issue 340: Add a prefix to benchmarks #414

merged 1 commit into from
Aug 5, 2024

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seabbs
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@seabbs seabbs commented Jul 31, 2024

This PR partially addresses #340 by adding a prefix to any warnings produced by make_turing_suite based on the input model. This should help us locate potential issues more easily.

@damonbayer if you have time to review this that would be great. We are looking at the output of the benchmark CI to see if it has worked.

You can run the prefix_warnings wrapper using this example/

# Define a struct instead of a function for g
struct G
    name::String
    threshold::Float64
end

# Define a function that uses the struct G
function process(g::G, x)
    if x < g.threshold
        @warn "Input is below threshold"
    end
    return x^2
end

# Define a function f that uses process with G
function f()
    g = G("MyProcessor", 0.0)
    result = process(g, -5)
    println("Result from process: $result")
    return result
end

# The prefix_warnings function remains the same
function prefix_warnings(f, g)
    original_stderr = stderr
    (read_pipe, write_pipe) = redirect_stderr()
    result = nothing
    try
        result = f()
    finally
        redirect_stderr(original_stderr)
        close(write_pipe)
        output = String(read(read_pipe))
        if !isempty(output)
            println(stderr, "\nWarnings from $(g):")
            println(stderr, output)
        end
    end
    return result
end

# Run the example
g = G("MyProcessor", 0.0)
result = prefix_warnings(f, g)
println("Final result: $result")

In principle yes the code in benchmarks should also have tests but I think we can leave for now as I am hoping that the Turing devs put something better than this in upstream.

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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="partial-issue340", subdir="EpiAware")
using EpiAware

@codecov-commenter
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Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 93.57%. Comparing base (2e30981) to head (fc41656).
Report is 1 commits behind head on main.

Additional details and impacted files
@@           Coverage Diff           @@
##             main     #414   +/-   ##
=======================================
  Coverage   93.57%   93.57%           
=======================================
  Files          54       54           
  Lines         560      560           
=======================================
  Hits          524      524           
  Misses         36       36           

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

@seabbs
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seabbs commented Jul 31, 2024

Annoyingly it looks like we might not get any transcient gradient warnings on this go which makes checking this really is working a bit of a pain!

@seabbs seabbs enabled auto-merge July 31, 2024 22:53
@damonbayer
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Annoyingly it looks like we might not get any transcient gradient warnings on this go which makes checking this really is working a bit of a pain!

Doesn't seem wise to approve without a demonstration of it working in CI. Can you force it to warn and then fix it so we see a demonstration?

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Benchmark result

Judge result

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

Job Properties

  • Time of benchmarks:
    • Target: 31 Jul 2024 - 22:57
    • Baseline: 31 Jul 2024 - 23:21
  • Package commits:
    • Target: ec4497
    • Baseline: 418379
  • Julia commits:
    • Target: 48d4fd
    • Baseline: 48d4fd
  • 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", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 0.95 (5%) ✅ 1.00 (1%)
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 0.93 (5%) ✅ 1.00 (1%)
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 1.07 (5%) ❌ 1.00 (1%)
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 0.93 (5%) ✅ 1.00 (1%)
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 0.92 (5%) ✅ 1.00 (1%)
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 1.07 (5%) ❌ 1.00 (1%)
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 1.07 (5%) ❌ 1.00 (1%)
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 1.05 (5%) ❌ 1.00 (1%)
["EpiLatentModels", "ConcatLatentModels", "evaluation", "linked"] 1.06 (5%) ❌ 1.00 (1%)
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 0.88 (5%) ✅ 1.00 (1%)
["EpiLatentModels", "DiffLatentModel", "evaluation", "linked"] 1.09 (5%) ❌ 1.00 (1%)
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 0.94 (5%) ✅ 1.00 (1%)
["EpiLatentModels", "HierarchicalNormal", "evaluation", "linked"] 1.09 (5%) ❌ 1.00 (1%)
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 0.68 (5%) ✅ 1.00 (1%)
["EpiLatentModels", "Intercept", "evaluation", "linked"] 1.21 (5%) ❌ 1.00 (1%)
["EpiLatentModels", "Intercept", "evaluation", "standard"] 1.22 (5%) ❌ 1.00 (1%)
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 0.81 (5%) ✅ 1.00 (1%)
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 1.06 (5%) ❌ 1.00 (1%)
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 1.06 (5%) ❌ 1.00 (1%)
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 0.95 (5%) ✅ 1.00 (1%)
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 0.92 (5%) ✅ 1.00 (1%)
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 0.88 (5%) ✅ 1.00 (1%)
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 1.08 (5%) ❌ 1.00 (1%)
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 1.11 (5%) ❌ 1.00 (1%)
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 0.85 (5%) ✅ 1.00 (1%)
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 1.99 (5%) ❌ 1.00 (1%)
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 2.32 (5%) ❌ 1.00 (1%)
["EpiObsModels", "observation_error", "missing obs", "evaluation", "linked"] 1.07 (5%) ❌ 1.00 (1%)
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 0.95 (5%) ✅ 1.00 (1%)
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 1.10 (5%) ❌ 1.00 (1%)

Benchmark Group List

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

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

Julia versioninfo

Target

Julia Version 1.10.4
Commit 48d4fd48430 (2024-06-04 10:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.4 LTS
  uname: Linux 6.5.0-1024-azure #25~22.04.1-Ubuntu SMP Mon Jun 17 18:38:57 UTC 2024 x86_64 x86_64
  CPU: AMD EPYC 7763 64-Core Processor: 
              speed         user         nice          sys         idle          irq
       #1  3242 MHz       5358 s          0 s        511 s      12378 s          0 s
       #2  3225 MHz       5738 s          0 s        527 s      11977 s          0 s
       #3  2445 MHz       6065 s          0 s        634 s      11560 s          0 s
       #4  2445 MHz       6199 s          0 s        612 s      11439 s          0 s
  Memory: 15.606491088867188 GB (13264.34765625 MB free)
  Uptime: 1829.71 sec
  Load Avg:  1.02  1.05  1.07
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Baseline

Julia Version 1.10.4
Commit 48d4fd48430 (2024-06-04 10:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.4 LTS
  uname: Linux 6.5.0-1024-azure #25~22.04.1-Ubuntu SMP Mon Jun 17 18:38:57 UTC 2024 x86_64 x86_64
  CPU: AMD EPYC 7763 64-Core Processor: 
              speed         user         nice          sys         idle          irq
       #1  2987 MHz       8091 s          0 s        833 s      23568 s          0 s
       #2  2445 MHz      10073 s          0 s        959 s      21459 s          0 s
       #3  3241 MHz       9546 s          0 s        945 s      22017 s          0 s
       #4  3298 MHz       9002 s          0 s        980 s      22516 s          0 s
  Memory: 15.606491088867188 GB (13186.0390625 MB free)
  Uptime: 3256.96 sec
  Load Avg:  1.04  1.02  1.0
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Target result

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

Job Properties

  • Time of benchmark: 31 Jul 2024 - 22:57
  • Package commit: ec4497
  • Julia commit: 48d4fd
  • 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 GC time memory allocations
["EpiAwareUtils", "censored_pmf"] 1.081 μs (5%) 352 bytes (1%) 4
["EpiInfModels", "DirectInfections", "evaluation", "linked"] 308.708 ns (5%) 432 bytes (1%) 7
["EpiInfModels", "DirectInfections", "evaluation", "standard"] 309.879 ns (5%) 432 bytes (1%) 7
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 450.439 ns (5%) 784 bytes (1%) 13
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 454.182 ns (5%) 784 bytes (1%) 13
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 9.578 μs (5%) 5.62 KiB (1%) 115
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 9.397 μs (5%) 5.62 KiB (1%) 115
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 543.622 ns (5%) 272 bytes (1%) 6
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 548.118 ns (5%) 272 bytes (1%) 6
["EpiInfModels", "ExpGrowthRate", "evaluation", "linked"] 216.272 ns (5%) 256 bytes (1%) 4
["EpiInfModels", "ExpGrowthRate", "evaluation", "standard"] 212.959 ns (5%) 256 bytes (1%) 4
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 308.734 ns (5%) 512 bytes (1%) 9
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 307.081 ns (5%) 512 bytes (1%) 9
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 9.467 μs (5%) 5.64 KiB (1%) 114
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 9.408 μs (5%) 5.64 KiB (1%) 114
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 543.397 ns (5%) 272 bytes (1%) 6
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 542.656 ns (5%) 272 bytes (1%) 6
["EpiLatentModels", "AR", "evaluation", "linked"] 2.003 μs (5%) 3.84 KiB (1%) 45
["EpiLatentModels", "AR", "evaluation", "standard"] 1.610 μs (5%) 2.80 KiB (1%) 38
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 3.478 μs (5%) 11.69 KiB (1%) 55
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 2.938 μs (5%) 10.12 KiB (1%) 46
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 108.673 μs (5%) 55.31 KiB (1%) 1113
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 69.500 μs (5%) 40.64 KiB (1%) 818
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 17.022 μs (5%) 8.44 KiB (1%) 225
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 15.449 μs (5%) 7.31 KiB (1%) 207
["EpiLatentModels", "BroadcastLatentModel", "evaluation", "linked"] 1.527 μs (5%) 3.05 KiB (1%) 34
["EpiLatentModels", "BroadcastLatentModel", "evaluation", "standard"] 1.306 μs (5%) 2.17 KiB (1%) 30
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 2.062 μs (5%) 5.16 KiB (1%) 41
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 1.720 μs (5%) 4.28 KiB (1%) 37
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 46.237 μs (5%) 24.41 KiB (1%) 447
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 28.753 μs (5%) 16.86 KiB (1%) 333
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 2.989 μs (5%) 1.00 KiB (1%) 27
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 2.724 μs (5%) 1.00 KiB (1%) 27
["EpiLatentModels", "CombineLatentModels", "evaluation", "linked"] 61.836 μs (5%) 52.27 KiB (1%) 580
["EpiLatentModels", "CombineLatentModels", "evaluation", "standard"] 57.888 μs (5%) 37.69 KiB (1%) 536
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 128.281 μs (5%) 119.19 KiB (1%) 1184
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 121.177 μs (5%) 89.31 KiB (1%) 1092
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 195.667 μs (5%) 107.81 KiB (1%) 1710
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 147.126 μs (5%) 79.61 KiB (1%) 1378
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 17.452 μs (5%) 8.58 KiB (1%) 226
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 15.970 μs (5%) 7.45 KiB (1%) 208
["EpiLatentModels", "ConcatLatentModels", "evaluation", "linked"] 12.073 μs (5%) 30.39 KiB (1%) 214
["EpiLatentModels", "ConcatLatentModels", "evaluation", "standard"] 9.107 μs (5%) 21.95 KiB (1%) 184
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 12.062 μs (5%) 34.09 KiB (1%) 224
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 10.319 μs (5%) 25.66 KiB (1%) 194
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 76.152 μs (5%) 56.38 KiB (1%) 719
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 52.177 μs (5%) 42.72 KiB (1%) 580
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 5.206 μs (5%) 2.19 KiB (1%) 52
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 4.971 μs (5%) 2.19 KiB (1%) 52
["EpiLatentModels", "DiffLatentModel", "evaluation", "linked"] 2.000 μs (5%) 4.17 KiB (1%) 37
["EpiLatentModels", "DiffLatentModel", "evaluation", "standard"] 1.355 μs (5%) 2.48 KiB (1%) 31
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 3.097 μs (5%) 12.62 KiB (1%) 45
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 2.607 μs (5%) 10.94 KiB (1%) 39
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 61.405 μs (5%) 38.81 KiB (1%) 748
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 41.808 μs (5%) 31.91 KiB (1%) 633
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 6.640 μs (5%) 2.22 KiB (1%) 51
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 6.412 μs (5%) 2.22 KiB (1%) 51
["EpiLatentModels", "HierarchicalNormal", "evaluation", "linked"] 431.206 ns (5%) 1.00 KiB (1%) 8
["EpiLatentModels", "HierarchicalNormal", "evaluation", "standard"] 370.257 ns (5%) 864 bytes (1%) 7
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 950.700 ns (5%) 5.28 KiB (1%) 14
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 857.600 ns (5%) 5.12 KiB (1%) 13
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 43.091 μs (5%) 19.83 KiB (1%) 376
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 27.061 μs (5%) 14.45 KiB (1%) 266
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 1.310 μs (5%) 656 bytes (1%) 11
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 1.089 μs (5%) 656 bytes (1%) 11
["EpiLatentModels", "Intercept", "evaluation", "linked"] 276.492 ns (5%) 336 bytes (1%) 5
["EpiLatentModels", "Intercept", "evaluation", "standard"] 277.930 ns (5%) 336 bytes (1%) 5
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 359.199 ns (5%) 640 bytes (1%) 10
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 360.346 ns (5%) 640 bytes (1%) 10
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 4.267 μs (5%) 3.53 KiB (1%) 76
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 4.317 μs (5%) 3.53 KiB (1%) 76
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 460.102 ns (5%) 240 bytes (1%) 3
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 460.508 ns (5%) 240 bytes (1%) 3
["EpiLatentModels", "PrefixLatentModel", "evaluation", "linked"] 1.876 μs (5%) 3.47 KiB (1%) 30
["EpiLatentModels", "PrefixLatentModel", "evaluation", "standard"] 1.723 μs (5%) 3.00 KiB (1%) 27
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 2.495 μs (5%) 7.75 KiB (1%) 36
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 2.360 μs (5%) 7.28 KiB (1%) 33
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 46.286 μs (5%) 22.16 KiB (1%) 397
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 29.545 μs (5%) 16.47 KiB (1%) 285
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 1.312 μs (5%) 656 bytes (1%) 11
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 1.084 μs (5%) 656 bytes (1%) 11
["EpiLatentModels", "RandomWalk", "evaluation", "linked"] 895.895 ns (5%) 1.86 KiB (1%) 18
["EpiLatentModels", "RandomWalk", "evaluation", "standard"] 790.698 ns (5%) 1.42 KiB (1%) 16
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 1.809 μs (5%) 8.73 KiB (1%) 25
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 1.686 μs (5%) 8.30 KiB (1%) 23
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 46.497 μs (5%) 26.19 KiB (1%) 487
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 29.866 μs (5%) 20.53 KiB (1%) 376
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 3.632 μs (5%) 1.31 KiB (1%) 27
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 3.368 μs (5%) 1.31 KiB (1%) 27
["EpiLatentModels", "RecordExpectedLatent", "evaluation", "linked"] 579.464 ns (5%) 1.19 KiB (1%) 12
["EpiLatentModels", "RecordExpectedLatent", "evaluation", "standard"] 484.236 ns (5%) 896 bytes (1%) 10
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 783.282 ns (5%) 1.72 KiB (1%) 18
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 663.303 ns (5%) 1.41 KiB (1%) 16
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 43.371 μs (5%) 19.08 KiB (1%) 380
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 27.191 μs (5%) 13.55 KiB (1%) 269
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 1.224 μs (5%) 400 bytes (1%) 11
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 1.005 μs (5%) 400 bytes (1%) 11
["EpiLatentModels", "TransformLatentModel", "evaluation", "linked"] 309.324 ns (5%) 384 bytes (1%) 6
["EpiLatentModels", "TransformLatentModel", "evaluation", "standard"] 307.386 ns (5%) 384 bytes (1%) 6
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 409.470 ns (5%) 704 bytes (1%) 11
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 401.655 ns (5%) 704 bytes (1%) 11
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 4.597 μs (5%) 3.84 KiB (1%) 81
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 4.604 μs (5%) 3.84 KiB (1%) 81
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 552.202 ns (5%) 192 bytes (1%) 3
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 551.457 ns (5%) 192 bytes (1%) 3
["EpiLatentModels", "broadcast_dayofweek", "evaluation", "linked"] 2.203 μs (5%) 4.16 KiB (1%) 44
["EpiLatentModels", "broadcast_dayofweek", "evaluation", "standard"] 1.684 μs (5%) 2.84 KiB (1%) 38
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 2.776 μs (5%) 10.00 KiB (1%) 51
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 2.480 μs (5%) 8.69 KiB (1%) 45
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 59.091 μs (5%) 35.58 KiB (1%) 689
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 40.556 μs (5%) 29.05 KiB (1%) 574
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 5.168 μs (5%) 1.22 KiB (1%) 27
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 4.971 μs (5%) 1.22 KiB (1%) 27
["EpiLatentModels", "broadcast_weekly", "evaluation", "linked"] 2.218 μs (5%) 4.52 KiB (1%) 47
["EpiLatentModels", "broadcast_weekly", "evaluation", "standard"] 1.673 μs (5%) 2.62 KiB (1%) 37
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 2.893 μs (5%) 7.69 KiB (1%) 57
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 2.284 μs (5%) 5.53 KiB (1%) 45
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 85.250 μs (5%) 41.95 KiB (1%) 771
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 47.138 μs (5%) 28.44 KiB (1%) 513
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 5.288 μs (5%) 1.81 KiB (1%) 49
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 4.642 μs (5%) 1.69 KiB (1%) 47
["EpiObsModels", "Ascertainment", "evaluation", "linked"] 3.497 μs (5%) 3.42 KiB (1%) 49
["EpiObsModels", "Ascertainment", "evaluation", "standard"] 3.453 μs (5%) 3.42 KiB (1%) 49
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 4.328 μs (5%) 3.77 KiB (1%) 56
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 4.226 μs (5%) 3.77 KiB (1%) 56
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 50.474 μs (5%) 41.55 KiB (1%) 965
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 35.105 μs (5%) 36.33 KiB (1%) 856
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 5.734 μs (5%) 96 bytes (1%) 2
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 5.604 μs (5%) 96 bytes (1%) 2
["EpiObsModels", "LatentDelay", "evaluation", "linked"] 18.724 μs (5%) 22.14 KiB (1%) 206
["EpiObsModels", "LatentDelay", "evaluation", "standard"] 18.675 μs (5%) 22.14 KiB (1%) 206
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 23.494 μs (5%) 22.36 KiB (1%) 211
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 22.993 μs (5%) 22.36 KiB (1%) 211
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 301.624 μs (5%) 317.86 KiB (1%) 7386
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 277.619 μs (5%) 312.64 KiB (1%) 7277
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 51.707 μs (5%) 96 bytes (1%) 2
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 51.596 μs (5%) 96 bytes (1%) 2
["EpiObsModels", "NegativeBinomialError", "evaluation", "linked"] 1.221 μs (5%) 336 bytes (1%) 5
["EpiObsModels", "NegativeBinomialError", "evaluation", "standard"] 1.202 μs (5%) 336 bytes (1%) 5
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 1.871 μs (5%) 560 bytes (1%) 10
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 1.745 μs (5%) 560 bytes (1%) 10
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 45.996 μs (5%) 38.83 KiB (1%) 959
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 30.877 μs (5%) 33.61 KiB (1%) 850
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 5.754 μs (5%) 96 bytes (1%) 2
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 5.701 μs (5%) 96 bytes (1%) 2
["EpiObsModels", "PoissonError", "evaluation", "linked"] 1.567 μs (5%) 1.80 KiB (1%) 22
["EpiObsModels", "PoissonError", "evaluation", "standard"] 1.233 μs (5%) 1.38 KiB (1%) 18
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 3.224 μs (5%) 7.75 KiB (1%) 31
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 2.618 μs (5%) 4.52 KiB (1%) 25
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 139.621 μs (5%) 91.00 KiB (1%) 1913
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 25.908 μs (5%) 29.25 KiB (1%) 712
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 6.222 μs (5%) 176 bytes (1%) 2
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 4.221 μs (5%) 176 bytes (1%) 2
["EpiObsModels", "PrefixObservationModel", "evaluation", "linked"] 1.713 μs (5%) 1.44 KiB (1%) 26
["EpiObsModels", "PrefixObservationModel", "evaluation", "standard"] 1.675 μs (5%) 1.44 KiB (1%) 26
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 1.917 μs (5%) 1.66 KiB (1%) 31
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 1.849 μs (5%) 1.66 KiB (1%) 31
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 22.723 μs (5%) 13.16 KiB (1%) 289
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 8.716 μs (5%) 7.94 KiB (1%) 180
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 1.335 μs (5%) 96 bytes (1%) 2
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 1.072 μs (5%) 96 bytes (1%) 2
["EpiObsModels", "StackObservationModels", "evaluation", "linked"] 7.138 μs (5%) 5.48 KiB (1%) 93
["EpiObsModels", "StackObservationModels", "evaluation", "standard"] 7.096 μs (5%) 5.48 KiB (1%) 93
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 8.078 μs (5%) 5.83 KiB (1%) 100
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 7.968 μs (5%) 5.83 KiB (1%) 100
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 59.511 μs (5%) 51.73 KiB (1%) 1080
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 41.869 μs (5%) 46.52 KiB (1%) 971
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 6.312 μs (5%) 96 bytes (1%) 2
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 6.073 μs (5%) 96 bytes (1%) 2
["EpiObsModels", "ascertainment_dayofweek", "evaluation", "linked"] 4.445 μs (5%) 8.88 KiB (1%) 75
["EpiObsModels", "ascertainment_dayofweek", "evaluation", "standard"] 4.256 μs (5%) 7.62 KiB (1%) 67
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 5.656 μs (5%) 15.88 KiB (1%) 83
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 5.338 μs (5%) 14.62 KiB (1%) 75
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 83.556 μs (5%) 60.41 KiB (1%) 1139
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 64.961 μs (5%) 53.94 KiB (1%) 1022
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 5.746 μs (5%) 544 bytes (1%) 11
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 5.519 μs (5%) 544 bytes (1%) 11
["EpiObsModels", "observation_error", "missing obs", "evaluation", "linked"] 1.666 μs (5%) 2.97 KiB (1%) 31
["EpiObsModels", "observation_error", "missing obs", "evaluation", "standard"] 1.045 μs (5%) 1.41 KiB (1%) 21
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 2.258 μs (5%) 4.03 KiB (1%) 38
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 1.658 μs (5%) 2.47 KiB (1%) 28
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 29.696 μs (5%) 24.73 KiB (1%) 490
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 13.666 μs (5%) 17.28 KiB (1%) 352
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 2.312 μs (5%) 144 bytes (1%) 2
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 2.004 μs (5%) 144 bytes (1%) 2
["EpiObsModels", "observation_error", "no missing obs", "evaluation", "linked"] 442.959 ns (5%) 288 bytes (1%) 5
["EpiObsModels", "observation_error", "no missing obs", "evaluation", "standard"] 415.975 ns (5%) 288 bytes (1%) 5
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 589.451 ns (5%) 512 bytes (1%) 10
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 543.005 ns (5%) 512 bytes (1%) 10
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 23.975 μs (5%) 18.72 KiB (1%) 414
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 10.520 μs (5%) 12.83 KiB (1%) 286
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 1.993 μs (5%) 96 bytes (1%) 2
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 1.714 μs (5%) 96 bytes (1%) 2
["EpiObsModels", "observation_error", "partially missing obs", "evaluation", "linked"] 1.849 μs (5%) 2.05 KiB (1%) 27
["EpiObsModels", "observation_error", "partially missing obs", "evaluation", "standard"] 1.668 μs (5%) 1.73 KiB (1%) 25
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 1.725 μs (5%) 2.22 KiB (1%) 26
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 1.536 μs (5%) 1.91 KiB (1%) 24
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 40.055 μs (5%) 23.89 KiB (1%) 499
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 23.333 μs (5%) 17.69 KiB (1%) 369
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 2.319 μs (5%) 112 bytes (1%) 2
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 2.028 μs (5%) 112 bytes (1%) 2

Benchmark Group List

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

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

Julia versioninfo

Julia Version 1.10.4
Commit 48d4fd48430 (2024-06-04 10:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.4 LTS
  uname: Linux 6.5.0-1024-azure #25~22.04.1-Ubuntu SMP Mon Jun 17 18:38:57 UTC 2024 x86_64 x86_64
  CPU: AMD EPYC 7763 64-Core Processor: 
              speed         user         nice          sys         idle          irq
       #1  3242 MHz       5358 s          0 s        511 s      12378 s          0 s
       #2  3225 MHz       5738 s          0 s        527 s      11977 s          0 s
       #3  2445 MHz       6065 s          0 s        634 s      11560 s          0 s
       #4  2445 MHz       6199 s          0 s        612 s      11439 s          0 s
  Memory: 15.606491088867188 GB (13264.34765625 MB free)
  Uptime: 1829.71 sec
  Load Avg:  1.02  1.05  1.07
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Baseline result

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

Job Properties

  • Time of benchmark: 31 Jul 2024 - 23:21
  • Package commit: 418379
  • Julia commit: 48d4fd
  • 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 GC time memory allocations
["EpiAwareUtils", "censored_pmf"] 1.086 μs (5%) 352 bytes (1%) 4
["EpiInfModels", "DirectInfections", "evaluation", "linked"] 302.558 ns (5%) 432 bytes (1%) 7
["EpiInfModels", "DirectInfections", "evaluation", "standard"] 308.500 ns (5%) 432 bytes (1%) 7
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 467.759 ns (5%) 784 bytes (1%) 13
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 435.437 ns (5%) 784 bytes (1%) 13
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 9.438 μs (5%) 5.62 KiB (1%) 115
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 9.458 μs (5%) 5.62 KiB (1%) 115
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 572.601 ns (5%) 272 bytes (1%) 6
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 586.878 ns (5%) 272 bytes (1%) 6
["EpiInfModels", "ExpGrowthRate", "evaluation", "linked"] 220.506 ns (5%) 256 bytes (1%) 4
["EpiInfModels", "ExpGrowthRate", "evaluation", "standard"] 220.049 ns (5%) 256 bytes (1%) 4
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 307.583 ns (5%) 512 bytes (1%) 9
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 285.813 ns (5%) 512 bytes (1%) 9
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 9.327 μs (5%) 5.64 KiB (1%) 114
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 9.378 μs (5%) 5.64 KiB (1%) 114
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 585.685 ns (5%) 272 bytes (1%) 6
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 589.994 ns (5%) 272 bytes (1%) 6
["EpiLatentModels", "AR", "evaluation", "linked"] 2.069 μs (5%) 3.84 KiB (1%) 45
["EpiLatentModels", "AR", "evaluation", "standard"] 1.639 μs (5%) 2.80 KiB (1%) 38
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 3.512 μs (5%) 11.69 KiB (1%) 55
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 2.983 μs (5%) 10.12 KiB (1%) 46
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 108.593 μs (5%) 55.31 KiB (1%) 1113
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 69.870 μs (5%) 40.64 KiB (1%) 818
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 15.840 μs (5%) 8.44 KiB (1%) 225
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 14.417 μs (5%) 7.31 KiB (1%) 207
["EpiLatentModels", "BroadcastLatentModel", "evaluation", "linked"] 1.549 μs (5%) 3.05 KiB (1%) 34
["EpiLatentModels", "BroadcastLatentModel", "evaluation", "standard"] 1.301 μs (5%) 2.17 KiB (1%) 30
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 2.106 μs (5%) 5.16 KiB (1%) 41
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 1.713 μs (5%) 4.28 KiB (1%) 37
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 46.857 μs (5%) 24.41 KiB (1%) 447
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 28.373 μs (5%) 16.86 KiB (1%) 333
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 3.099 μs (5%) 1.00 KiB (1%) 27
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 2.801 μs (5%) 1.00 KiB (1%) 27
["EpiLatentModels", "CombineLatentModels", "evaluation", "linked"] 63.229 μs (5%) 52.27 KiB (1%) 580
["EpiLatentModels", "CombineLatentModels", "evaluation", "standard"] 57.878 μs (5%) 37.69 KiB (1%) 536
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 129.352 μs (5%) 119.19 KiB (1%) 1184
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 122.249 μs (5%) 89.31 KiB (1%) 1092
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 196.318 μs (5%) 107.81 KiB (1%) 1710
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 139.681 μs (5%) 79.61 KiB (1%) 1378
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 16.762 μs (5%) 8.58 KiB (1%) 226
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 16.050 μs (5%) 7.45 KiB (1%) 208
["EpiLatentModels", "ConcatLatentModels", "evaluation", "linked"] 11.342 μs (5%) 30.39 KiB (1%) 214
["EpiLatentModels", "ConcatLatentModels", "evaluation", "standard"] 8.846 μs (5%) 21.95 KiB (1%) 184
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 13.745 μs (5%) 34.09 KiB (1%) 224
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 10.269 μs (5%) 25.66 KiB (1%) 194
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 76.223 μs (5%) 56.38 KiB (1%) 719
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 54.072 μs (5%) 42.72 KiB (1%) 580
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 5.315 μs (5%) 2.19 KiB (1%) 52
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 5.155 μs (5%) 2.19 KiB (1%) 52
["EpiLatentModels", "DiffLatentModel", "evaluation", "linked"] 1.839 μs (5%) 4.17 KiB (1%) 37
["EpiLatentModels", "DiffLatentModel", "evaluation", "standard"] 1.365 μs (5%) 2.48 KiB (1%) 31
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 3.084 μs (5%) 12.62 KiB (1%) 45
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 2.695 μs (5%) 10.94 KiB (1%) 39
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 62.227 μs (5%) 38.81 KiB (1%) 748
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 42.780 μs (5%) 31.91 KiB (1%) 633
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 6.781 μs (5%) 2.22 KiB (1%) 51
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 6.803 μs (5%) 2.22 KiB (1%) 51
["EpiLatentModels", "HierarchicalNormal", "evaluation", "linked"] 394.156 ns (5%) 1.00 KiB (1%) 8
["EpiLatentModels", "HierarchicalNormal", "evaluation", "standard"] 365.971 ns (5%) 864 bytes (1%) 7
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 1.394 μs (5%) 5.28 KiB (1%) 14
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 877.667 ns (5%) 5.12 KiB (1%) 13
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 41.998 μs (5%) 19.83 KiB (1%) 376
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 26.630 μs (5%) 14.45 KiB (1%) 266
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 1.252 μs (5%) 656 bytes (1%) 11
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 1.063 μs (5%) 656 bytes (1%) 11
["EpiLatentModels", "Intercept", "evaluation", "linked"] 228.728 ns (5%) 336 bytes (1%) 5
["EpiLatentModels", "Intercept", "evaluation", "standard"] 228.096 ns (5%) 336 bytes (1%) 5
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 348.338 ns (5%) 640 bytes (1%) 10
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 347.523 ns (5%) 640 bytes (1%) 10
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 4.312 μs (5%) 3.53 KiB (1%) 76
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 4.259 μs (5%) 3.53 KiB (1%) 76
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 461.781 ns (5%) 240 bytes (1%) 3
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 471.184 ns (5%) 240 bytes (1%) 3
["EpiLatentModels", "PrefixLatentModel", "evaluation", "linked"] 1.885 μs (5%) 3.47 KiB (1%) 30
["EpiLatentModels", "PrefixLatentModel", "evaluation", "standard"] 1.726 μs (5%) 3.00 KiB (1%) 27
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 3.068 μs (5%) 7.75 KiB (1%) 36
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 2.320 μs (5%) 7.28 KiB (1%) 33
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 46.557 μs (5%) 22.16 KiB (1%) 397
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 29.705 μs (5%) 16.47 KiB (1%) 285
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 1.239 μs (5%) 656 bytes (1%) 11
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 1.061 μs (5%) 656 bytes (1%) 11
["EpiLatentModels", "RandomWalk", "evaluation", "linked"] 905.184 ns (5%) 1.86 KiB (1%) 18
["EpiLatentModels", "RandomWalk", "evaluation", "standard"] 782.619 ns (5%) 1.42 KiB (1%) 16
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 1.849 μs (5%) 8.73 KiB (1%) 25
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 1.661 μs (5%) 8.30 KiB (1%) 23
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 47.539 μs (5%) 26.19 KiB (1%) 487
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 29.987 μs (5%) 20.53 KiB (1%) 376
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 3.762 μs (5%) 1.31 KiB (1%) 27
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 3.527 μs (5%) 1.31 KiB (1%) 27
["EpiLatentModels", "RecordExpectedLatent", "evaluation", "linked"] 578.000 ns (5%) 1.19 KiB (1%) 12
["EpiLatentModels", "RecordExpectedLatent", "evaluation", "standard"] 484.793 ns (5%) 896 bytes (1%) 10
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 809.964 ns (5%) 1.72 KiB (1%) 18
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 674.741 ns (5%) 1.41 KiB (1%) 16
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 42.569 μs (5%) 19.08 KiB (1%) 380
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 26.931 μs (5%) 13.55 KiB (1%) 269
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 1.151 μs (5%) 400 bytes (1%) 11
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 959.609 ns (5%) 400 bytes (1%) 11
["EpiLatentModels", "TransformLatentModel", "evaluation", "linked"] 308.313 ns (5%) 384 bytes (1%) 6
["EpiLatentModels", "TransformLatentModel", "evaluation", "standard"] 309.263 ns (5%) 384 bytes (1%) 6
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 432.819 ns (5%) 704 bytes (1%) 11
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 436.040 ns (5%) 704 bytes (1%) 11
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 4.606 μs (5%) 3.84 KiB (1%) 81
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 4.642 μs (5%) 3.84 KiB (1%) 81
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 532.587 ns (5%) 192 bytes (1%) 3
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 542.233 ns (5%) 192 bytes (1%) 3
["EpiLatentModels", "broadcast_dayofweek", "evaluation", "linked"] 2.161 μs (5%) 4.16 KiB (1%) 44
["EpiLatentModels", "broadcast_dayofweek", "evaluation", "standard"] 1.711 μs (5%) 2.84 KiB (1%) 38
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 2.876 μs (5%) 10.00 KiB (1%) 51
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 2.447 μs (5%) 8.69 KiB (1%) 45
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 59.391 μs (5%) 35.58 KiB (1%) 689
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 40.104 μs (5%) 29.05 KiB (1%) 574
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 5.372 μs (5%) 1.22 KiB (1%) 27
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 4.935 μs (5%) 1.22 KiB (1%) 27
["EpiLatentModels", "broadcast_weekly", "evaluation", "linked"] 2.203 μs (5%) 4.52 KiB (1%) 47
["EpiLatentModels", "broadcast_weekly", "evaluation", "standard"] 1.693 μs (5%) 2.62 KiB (1%) 37
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 2.885 μs (5%) 7.69 KiB (1%) 57
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 2.606 μs (5%) 5.53 KiB (1%) 45
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 85.661 μs (5%) 41.95 KiB (1%) 771
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 48.180 μs (5%) 28.44 KiB (1%) 513
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 5.390 μs (5%) 1.81 KiB (1%) 49
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 4.640 μs (5%) 1.69 KiB (1%) 47
["EpiObsModels", "Ascertainment", "evaluation", "linked"] 3.455 μs (5%) 3.42 KiB (1%) 49
["EpiObsModels", "Ascertainment", "evaluation", "standard"] 3.428 μs (5%) 3.42 KiB (1%) 49
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 4.344 μs (5%) 3.77 KiB (1%) 56
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 4.207 μs (5%) 3.77 KiB (1%) 56
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 50.174 μs (5%) 41.55 KiB (1%) 965
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 34.024 μs (5%) 36.33 KiB (1%) 856
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 5.861 μs (5%) 96 bytes (1%) 2
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 5.567 μs (5%) 96 bytes (1%) 2
["EpiObsModels", "LatentDelay", "evaluation", "linked"] 18.434 μs (5%) 22.14 KiB (1%) 206
["EpiObsModels", "LatentDelay", "evaluation", "standard"] 18.354 μs (5%) 22.14 KiB (1%) 206
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 23.775 μs (5%) 22.36 KiB (1%) 211
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 22.682 μs (5%) 22.36 KiB (1%) 211
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 278.942 μs (5%) 317.86 KiB (1%) 7386
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 278.220 μs (5%) 312.64 KiB (1%) 7277
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 50.955 μs (5%) 96 bytes (1%) 2
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 51.045 μs (5%) 96 bytes (1%) 2
["EpiObsModels", "NegativeBinomialError", "evaluation", "linked"] 1.211 μs (5%) 336 bytes (1%) 5
["EpiObsModels", "NegativeBinomialError", "evaluation", "standard"] 1.197 μs (5%) 336 bytes (1%) 5
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 1.869 μs (5%) 560 bytes (1%) 10
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 1.730 μs (5%) 560 bytes (1%) 10
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 46.096 μs (5%) 38.83 KiB (1%) 959
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 31.408 μs (5%) 33.61 KiB (1%) 850
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 5.784 μs (5%) 96 bytes (1%) 2
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 5.125 μs (5%) 96 bytes (1%) 2
["EpiObsModels", "PoissonError", "evaluation", "linked"] 1.546 μs (5%) 1.80 KiB (1%) 22
["EpiObsModels", "PoissonError", "evaluation", "standard"] 1.221 μs (5%) 1.38 KiB (1%) 18
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 3.354 μs (5%) 7.75 KiB (1%) 31
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 2.621 μs (5%) 4.52 KiB (1%) 25
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 140.133 μs (5%) 91.00 KiB (1%) 1913
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 25.828 μs (5%) 29.25 KiB (1%) 712
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 7.330 μs (5%) 176 bytes (1%) 2
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 4.209 μs (5%) 176 bytes (1%) 2
["EpiObsModels", "PrefixObservationModel", "evaluation", "linked"] 1.726 μs (5%) 1.44 KiB (1%) 26
["EpiObsModels", "PrefixObservationModel", "evaluation", "standard"] 1.698 μs (5%) 1.44 KiB (1%) 26
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 1.893 μs (5%) 1.66 KiB (1%) 31
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 1.835 μs (5%) 1.66 KiB (1%) 31
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 22.933 μs (5%) 13.16 KiB (1%) 289
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 8.473 μs (5%) 7.94 KiB (1%) 180
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 668.942 ns (5%) 96 bytes (1%) 2
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 462.619 ns (5%) 96 bytes (1%) 2
["EpiObsModels", "StackObservationModels", "evaluation", "linked"] 7.251 μs (5%) 5.48 KiB (1%) 93
["EpiObsModels", "StackObservationModels", "evaluation", "standard"] 7.223 μs (5%) 5.48 KiB (1%) 93
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 8.172 μs (5%) 5.83 KiB (1%) 100
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 8.326 μs (5%) 5.83 KiB (1%) 100
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 59.451 μs (5%) 51.73 KiB (1%) 1080
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 41.899 μs (5%) 46.52 KiB (1%) 971
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 6.372 μs (5%) 96 bytes (1%) 2
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 6.123 μs (5%) 96 bytes (1%) 2
["EpiObsModels", "ascertainment_dayofweek", "evaluation", "linked"] 4.421 μs (5%) 8.88 KiB (1%) 75
["EpiObsModels", "ascertainment_dayofweek", "evaluation", "standard"] 4.186 μs (5%) 7.62 KiB (1%) 67
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 5.625 μs (5%) 15.88 KiB (1%) 83
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 5.240 μs (5%) 14.62 KiB (1%) 75
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 82.986 μs (5%) 60.41 KiB (1%) 1139
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 65.082 μs (5%) 53.94 KiB (1%) 1022
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 5.749 μs (5%) 544 bytes (1%) 11
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 5.475 μs (5%) 544 bytes (1%) 11
["EpiObsModels", "observation_error", "missing obs", "evaluation", "linked"] 1.550 μs (5%) 2.97 KiB (1%) 31
["EpiObsModels", "observation_error", "missing obs", "evaluation", "standard"] 1.039 μs (5%) 1.41 KiB (1%) 21
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 2.239 μs (5%) 4.03 KiB (1%) 38
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 1.687 μs (5%) 2.47 KiB (1%) 28
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 29.475 μs (5%) 24.73 KiB (1%) 490
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 13.726 μs (5%) 17.28 KiB (1%) 352
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 2.327 μs (5%) 144 bytes (1%) 2
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 2.114 μs (5%) 144 bytes (1%) 2
["EpiObsModels", "observation_error", "no missing obs", "evaluation", "linked"] 442.399 ns (5%) 288 bytes (1%) 5
["EpiObsModels", "observation_error", "no missing obs", "evaluation", "standard"] 411.225 ns (5%) 288 bytes (1%) 5
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 583.896 ns (5%) 512 bytes (1%) 10
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 538.163 ns (5%) 512 bytes (1%) 10
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 24.416 μs (5%) 18.72 KiB (1%) 414
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 10.730 μs (5%) 12.83 KiB (1%) 286
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 1.978 μs (5%) 96 bytes (1%) 2
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 1.660 μs (5%) 96 bytes (1%) 2
["EpiObsModels", "observation_error", "partially missing obs", "evaluation", "linked"] 1.845 μs (5%) 2.05 KiB (1%) 27
["EpiObsModels", "observation_error", "partially missing obs", "evaluation", "standard"] 1.721 μs (5%) 1.73 KiB (1%) 25
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"] 1.668 μs (5%) 2.22 KiB (1%) 26
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"] 1.539 μs (5%) 1.91 KiB (1%) 24
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"] 39.684 μs (5%) 23.89 KiB (1%) 499
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"] 23.204 μs (5%) 17.69 KiB (1%) 369
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"] 2.116 μs (5%) 112 bytes (1%) 2
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"] 2.025 μs (5%) 112 bytes (1%) 2

Benchmark Group List

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

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

Julia versioninfo

Julia Version 1.10.4
Commit 48d4fd48430 (2024-06-04 10:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.4 LTS
  uname: Linux 6.5.0-1024-azure #25~22.04.1-Ubuntu SMP Mon Jun 17 18:38:57 UTC 2024 x86_64 x86_64
  CPU: AMD EPYC 7763 64-Core Processor: 
              speed         user         nice          sys         idle          irq
       #1  2987 MHz       8091 s          0 s        833 s      23568 s          0 s
       #2  2445 MHz      10073 s          0 s        959 s      21459 s          0 s
       #3  3241 MHz       9546 s          0 s        945 s      22017 s          0 s
       #4  3298 MHz       9002 s          0 s        980 s      22516 s          0 s
  Memory: 15.606491088867188 GB (13186.0390625 MB free)
  Uptime: 3256.96 sec
  Load Avg:  1.04  1.02  1.0
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Runtime information

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

lscpu output:

Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      48 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             4
On-line CPU(s) list:                0-3
Vendor ID:                          AuthenticAMD
Model name:                         AMD EPYC 7763 64-Core Processor
CPU family:                         25
Model:                              1
Thread(s) per core:                 2
Core(s) per socket:                 2
Socket(s):                          1
Stepping:                           1
BogoMIPS:                           4890.86
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext invpcid_single vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr rdpru arat npt nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload umip vaes vpclmulqdq rdpid fsrm
Virtualization:                     AMD-V
Hypervisor vendor:                  Microsoft
Virtualization type:                full
L1d cache:                          64 KiB (2 instances)
L1i cache:                          64 KiB (2 instances)
L2 cache:                           1 MiB (2 instances)
L3 cache:                           32 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-3
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Vulnerable: Safe RET, no microcode
Vulnerability Spec store bypass:    Vulnerable
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected
Cpu Property Value
Brand AMD EPYC 7763 64-Core Processor
Vendor :AMD
Architecture :Unknown
Model Family: 0xaf, Model: 0x01, Stepping: 0x01, Type: 0x00
Cores 16 physical cores, 16 logical cores (on executing CPU)
No Hyperthreading hardware capability detected
Clock Frequencies Not supported by CPU
Data Cache Level 1:3 : (32, 512, 32768) kbytes
64 byte cache line size
Address Size 48 bits virtual, 48 bits physical
SIMD 256 bit = 32 byte max. SIMD vector size
Time Stamp Counter TSC is accessible via rdtsc
TSC runs at constant rate (invariant from clock frequency)
Perf. Monitoring Performance Monitoring Counters (PMC) are not supported
Hypervisor Yes, Microsoft

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seabbs commented Aug 1, 2024

urggh checking... also it doesn't work :(. I think that this means it is calling the check after the suite has been constructed i.e in benchmarktools which is why wrapping make_turing_suite doesn't work. That seems quite tricky to resolve on our end so maybe will have to push that this is a feature TuringBenchmarking needs.

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seabbs commented Aug 1, 2024

So running this

using BenchmarkTools, TuringBenchmarking, EpiAware, DynamicPPL

locally it catches the depreciation warnings as you might expect but that showed no gradient warnings (the target of interest). Running that suite now to see if this is where they show up (vs when building the suite which was how I thought it was set up to work).

I'm using this as I think this might be where gradient issues live but will also try some of the other submodules (this is worth doing anyway as it helps inform #340)

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seabbs commented Aug 1, 2024

So these are rendered stochastically when you make the suite and they are happening in the obs model (which is good to know for the future). It seems like they aren't actually warnings as expected hence not being caught

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seabbs commented Aug 1, 2024

Hmm weird but they definitely appear to be based on the output of the CI

┌ Warning: There is disagreement in the log-density values!
└ @ TuringBenchmarking ~/.julia/packages/TuringBenchmarking/0wcdT/src/TuringBenchmarking.jl:268
┌────

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seabbs commented Aug 1, 2024

Okay so locally I am seeing these (when they happen):

Warnings from Model{typeof(generate_observations), (:obs_model, :y_t, :Y_t), (), (), Tuple{NegativeBinomialError{HalfNormal{Float64}}, Vector{Float64}, Vector{Float64}}, Tuple{}, DefaultContext}(EpiAware.EpiAwareBase.generate_observations, (obs_model = NegativeBinomialError{HalfNormal{Float64}}(HalfNormal{Float64}=0.01)), y_t = [10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0], Y_t = [10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0]), NamedTuple(), DefaultContext()):
┌ Warning: There is disagreement in the log-density values!
└ @ TuringBenchmarking ~/.julia/packages/TuringBenchmarking/0wcdT/src/TuringBenchmarking.jl:268
┌ Warning: There is disagreement in the gradients!
└ @ TuringBenchmarking ~/.julia/packages/TuringBenchmarking/0wcdT/src/TuringBenchmarking.jl:275
┌ Warning: There is disagreement in the log-density values!
└ @ TuringBenchmarking ~/.julia/packages/TuringBenchmarking/0wcdT/src/TuringBenchmarking.jl:268
┌ Warning: There is disagreement in the gradients!
└ @ TuringBenchmarking ~/.julia/packages/TuringBenchmarking/0wcdT/src/TuringBenchmarking.jl:275

So that is good. I think they aren't showing in the Ci as the thing throwing the warning there is main. (which doesn't have this functionality running). Based on that I think this is safe to merge.

Also this is a good indication that this functionality is helping as we thought this implementation might be a problem but were not quite sure.

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As all potential reviewers are on AL self reviewing. Based on the output in benchmarks I think this is working as expected. Merging is low risk as this is the benchmark suite. There approving.

@seabbs seabbs disabled auto-merge August 5, 2024 09:52
@seabbs seabbs merged commit bc99b2e into main Aug 5, 2024
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@seabbs seabbs deleted the partial-issue340 branch August 5, 2024 09:52
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