Package evaluation of GeometricMachineLearning on Julia 1.10.9 (96dc2d8c45*) started at 2025-06-06T22:49:25.839 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 5.11s ################################################################################ # Installation # Installing GeometricMachineLearning... Resolving package versions... Updating `~/.julia/environments/v1.10/Project.toml` [194d25b2] + GeometricMachineLearning v0.4.4 Updating `~/.julia/environments/v1.10/Manifest.toml` [47edcb42] + ADTypes v1.14.0 [621f4979] + AbstractFFTs v1.5.0 [60874f82] + AbstractNeuralNetworks v0.6.2 [1520ce14] + AbstractTrees v0.4.5 [7d9f7c33] + Accessors v0.1.42 [79e6a3ab] + Adapt v4.3.0 [66dad0bd] + AliasTables v1.1.3 [4fba245c] + ArrayInterface v7.19.0 [4c555306] + ArrayLayouts v1.11.1 [a9b6321e] + Atomix v1.1.1 [aae01518] + BandedMatrices v1.9.4 ⌅ [e2ed5e7c] + Bijections v0.1.10 [fa961155] + CEnum v0.5.0 [082447d4] + ChainRules v1.72.4 [d360d2e6] + ChainRulesCore v1.25.1 [cdddcdb0] + ChainRulesTestUtils v1.13.0 [861a8166] + Combinatorics v1.0.3 [38540f10] + CommonSolve v0.2.4 [bbf7d656] + CommonSubexpressions v0.3.1 [f70d9fcc] + CommonWorldInvalidations v1.0.0 [34da2185] + Compat v4.16.0 [b152e2b5] + CompositeTypes v0.1.4 [a33af91c] + CompositionsBase v0.1.2 [187b0558] + ConstructionBase v1.5.8 [a8cc5b0e] + Crayons v4.1.1 [9a962f9c] + DataAPI v1.16.0 [864edb3b] + DataStructures v0.18.22 [e2d170a0] + DataValueInterfaces v1.0.0 [163ba53b] + DiffResults v1.1.0 [b552c78f] + DiffRules v1.15.1 [b4f34e82] + Distances v0.10.12 [31c24e10] + Distributions v0.25.120 [ffbed154] + DocStringExtensions v0.9.4 [5b8099bc] + DomainSets v0.7.15 [7c1d4256] + DynamicPolynomials v0.6.2 [4e289a0a] + EnumX v1.0.5 [e2ba6199] + ExprTools v0.1.10 [55351af7] + ExproniconLite v0.10.14 [1a297f60] + FillArrays v1.13.0 [26cc04aa] + FiniteDifferences v0.12.32 [1fa38f19] + Format v1.3.7 [f6369f11] + ForwardDiff v1.0.1 [069b7b12] + FunctionWrappers v1.1.3 [77dc65aa] + FunctionWrappersWrappers v0.1.3 [0c68f7d7] + GPUArrays v11.2.2 [46192b85] + GPUArraysCore v0.2.0 [9a0b12b7] + GeometricBase v0.10.11 [c85262ba] + GeometricEquations v0.18.2 [194d25b2] + GeometricMachineLearning v0.4.4 [7843afe4] + GeometricSolutions v0.4.2 [f67ccb44] + HDF5 v0.17.2 [076d061b] + HashArrayMappedTries v0.2.0 [34004b35] + HypergeometricFunctions v0.3.28 [7869d1d1] + IRTools v0.4.14 [18e54dd8] + IntegerMathUtils v0.1.2 [8197267c] + IntervalSets v0.7.11 [3587e190] + InverseFunctions v0.1.17 [92d709cd] + IrrationalConstants v0.2.4 [82899510] + IteratorInterfaceExtensions v1.0.0 [692b3bcd] + JLLWrappers v1.7.0 [682c06a0] + JSON v0.21.4 [ae98c720] + Jieko v0.2.1 [63c18a36] + KernelAbstractions v0.9.34 [929cbde3] + LLVM v9.4.0 [b964fa9f] + LaTeXStrings v1.4.0 [23fbe1c1] + Latexify v0.16.8 ⌅ [5078a376] + LazyArrays v2.3.2 [2ab3a3ac] + LogExpFunctions v0.3.29 [3da0fdf6] + MPIPreferences v0.1.11 [1914dd2f] + MacroTools v0.5.16 [e1d29d7a] + Missings v1.2.0 [2e0e35c7] + Moshi v0.3.5 [102ac46a] + MultivariatePolynomials v0.5.9 [d8a4904e] + MutableArithmetics v1.6.4 [872c559c] + NNlib v0.9.30 [77ba4419] + NaNMath v1.1.3 [6fe1bfb0] + OffsetArrays v1.17.0 [bac558e1] + OrderedCollections v1.8.1 [90014a1f] + PDMats v0.11.35 [d96e819e] + Parameters v0.12.3 [69de0a69] + Parsers v2.8.3 ⌅ [aea7be01] + PrecompileTools v1.2.1 [21216c6a] + Preferences v1.4.3 [08abe8d2] + PrettyTables v2.4.0 [27ebfcd6] + Primes v0.5.7 [92933f4c] + ProgressMeter v1.10.4 [43287f4e] + PtrArrays v1.3.0 [1fd47b50] + QuadGK v2.11.2 [c1ae055f] + RealDot v0.1.0 [3cdcf5f2] + RecipesBase v1.3.4 [731186ca] + RecursiveArrayTools v3.33.0 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.1 [708f8203] + Richardson v1.4.2 [79098fc4] + Rmath v0.8.0 [7e49a35a] + RuntimeGeneratedFunctions v0.5.15 [1bc83da4] + SafeTestsets v0.1.0 [0bca4576] + SciMLBase v2.97.0 [c0aeaf25] + SciMLOperators v1.3.1 [53ae85a6] + SciMLStructures v1.7.0 [7e506255] + ScopedValues v1.3.0 [efcf1570] + Setfield v1.1.2 [a2af1166] + SortingAlgorithms v1.2.1 [dc90abb0] + SparseInverseSubset v0.1.2 [276daf66] + SpecialFunctions v2.5.1 [90137ffa] + StaticArrays v1.9.13 [1e83bf80] + StaticArraysCore v1.4.3 [82ae8749] + StatsAPI v1.7.1 [2913bbd2] + StatsBase v0.34.5 [4c63d2b9] + StatsFuns v1.5.0 [892a3eda] + StringManipulation v0.4.1 [09ab397b] + StructArrays v0.7.1 [fd094767] + Suppressor v0.2.8 [2efcf032] + SymbolicIndexingInterface v0.3.40 [19f23fe9] + SymbolicLimits v0.2.2 [aed23131] + SymbolicNeuralNetworks v0.3.3 [d1185830] + SymbolicUtils v3.29.0 [0c5d862f] + Symbolics v6.40.0 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.1 [ed4db957] + TaskLocalValues v0.1.2 [8ea1fca8] + TermInterface v2.0.0 [a759f4b9] + TimerOutputs v0.5.29 [3a884ed6] + UnPack v1.0.2 [a7c27f48] + Unityper v0.1.6 [013be700] + UnsafeAtomics v0.3.0 [770da0de] + UpdateJulia v0.4.4 [897b6980] + WeakValueDicts v0.1.0 ⌅ [e88e6eb3] + Zygote v0.6.77 [700de1a5] + ZygoteRules v0.2.7 [0234f1f7] + HDF5_jll v1.14.6+0 [e33a78d0] + Hwloc_jll v2.12.1+0 [dad2f222] + LLVMExtra_jll v0.0.36+0 [7cb0a576] + MPICH_jll v4.3.0+1 [f1f71cc9] + MPItrampoline_jll v5.5.3+0 [9237b28f] + MicrosoftMPI_jll v10.1.4+3 [fe0851c0] + OpenMPI_jll v5.0.7+2 [458c3c95] + OpenSSL_jll v3.5.0+0 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [f50d1b31] + Rmath_jll v0.5.1+0 [477f73a3] + libaec_jll v1.1.3+0 [0dad84c5] + ArgTools v1.1.1 [56f22d72] + Artifacts [2a0f44e3] + Base64 [ade2ca70] + Dates [8ba89e20] + Distributed [f43a241f] + Downloads v1.6.0 [7b1f6079] + FileWatching [9fa8497b] + Future [b77e0a4c] + InteractiveUtils [4af54fe1] + LazyArtifacts [b27032c2] + LibCURL v0.6.4 [76f85450] + LibGit2 [8f399da3] + Libdl [37e2e46d] + LinearAlgebra [56ddb016] + Logging [d6f4376e] + Markdown [a63ad114] + Mmap [ca575930] + NetworkOptions v1.2.0 [44cfe95a] + Pkg v1.10.0 [de0858da] + Printf [3fa0cd96] + REPL [9a3f8284] + Random [ea8e919c] + SHA v0.7.0 [9e88b42a] + Serialization [6462fe0b] + Sockets [2f01184e] + SparseArrays v1.10.0 [10745b16] + Statistics v1.10.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [a4e569a6] + Tar v1.10.0 [8dfed614] + Test [cf7118a7] + UUIDs [4ec0a83e] + Unicode [e66e0078] + CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] + LibCURL_jll v8.4.0+0 [e37daf67] + LibGit2_jll v1.6.4+0 [29816b5a] + LibSSH2_jll v1.11.0+1 [c8ffd9c3] + MbedTLS_jll v2.28.2+1 [14a3606d] + MozillaCACerts_jll v2023.1.10 [4536629a] + OpenBLAS_jll v0.3.23+4 [05823500] + OpenLibm_jll v0.8.5+0 [bea87d4a] + SuiteSparse_jll v7.2.1+1 [83775a58] + Zlib_jll v1.2.13+1 [8e850b90] + libblastrampoline_jll v5.11.0+0 [8e850ede] + nghttp2_jll v1.52.0+1 [3f19e933] + p7zip_jll v17.4.0+2 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m` Installation completed after 14.79s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 526.14s ################################################################################ # Testing # Testing GeometricMachineLearning Status `/tmp/jl_igCOz6/Project.toml` [60874f82] AbstractNeuralNetworks v0.6.2 [aae01518] BandedMatrices v1.9.4 [082447d4] ChainRules v1.72.4 [d360d2e6] ChainRulesCore v1.25.1 [cdddcdb0] ChainRulesTestUtils v1.13.0 [b4f34e82] Distances v0.10.12 [e30172f5] Documenter v1.11.4 [26cc04aa] FiniteDifferences v0.12.32 [f6369f11] ForwardDiff v1.0.1 [9a0b12b7] GeometricBase v0.10.11 [c85262ba] GeometricEquations v0.18.2 [dcce2d33] GeometricIntegrators v0.14.8 [194d25b2] GeometricMachineLearning v0.4.4 [18cb22b4] GeometricProblems v0.6.8 [7843afe4] GeometricSolutions v0.4.2 [f67ccb44] HDF5 v0.17.2 [63c18a36] KernelAbstractions v0.9.34 ⌅ [5078a376] LazyArrays v2.3.2 [872c559c] NNlib v0.9.30 [92933f4c] ProgressMeter v1.10.4 [1bc83da4] SafeTestsets v0.1.0 [2913bbd2] StatsBase v0.34.5 [aed23131] SymbolicNeuralNetworks v0.3.3 [0c5d862f] Symbolics v6.40.0 [a759f4b9] TimerOutputs v0.5.29 [770da0de] UpdateJulia v0.4.4 ⌅ [e88e6eb3] Zygote v0.6.77 [700de1a5] ZygoteRules v0.2.7 [b77e0a4c] InteractiveUtils [37e2e46d] LinearAlgebra [de0858da] Printf [9a3f8284] Random [2f01184e] SparseArrays v1.10.0 [8dfed614] Test Status `/tmp/jl_igCOz6/Manifest.toml` [47edcb42] ADTypes v1.14.0 [a4c015fc] ANSIColoredPrinters v0.0.1 [621f4979] AbstractFFTs v1.5.0 [60874f82] AbstractNeuralNetworks v0.6.2 [1520ce14] AbstractTrees v0.4.5 [7d9f7c33] Accessors v0.1.42 [79e6a3ab] Adapt v4.3.0 [66dad0bd] AliasTables v1.1.3 [4fba245c] ArrayInterface v7.19.0 [4c555306] ArrayLayouts v1.11.1 [a9b6321e] Atomix v1.1.1 [aae01518] BandedMatrices v1.9.4 [0e736298] Bessels v0.2.8 ⌅ [e2ed5e7c] Bijections v0.1.10 [8e7c35d0] BlockArrays v1.6.3 [fa961155] CEnum v0.5.0 [082447d4] ChainRules v1.72.4 [d360d2e6] ChainRulesCore v1.25.1 [cdddcdb0] ChainRulesTestUtils v1.13.0 [944b1d66] CodecZlib v0.7.8 [861a8166] Combinatorics v1.0.3 [38540f10] CommonSolve v0.2.4 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SharedArrays [6462fe0b] Sockets [2f01184e] SparseArrays v1.10.0 [10745b16] Statistics v1.10.0 [4607b0f0] SuiteSparse [fa267f1f] TOML v1.0.3 [a4e569a6] Tar v1.10.0 [8dfed614] Test [cf7118a7] UUIDs [4ec0a83e] Unicode [e66e0078] CompilerSupportLibraries_jll v1.1.1+0 [781609d7] GMP_jll v6.2.1+6 [deac9b47] LibCURL_jll v8.4.0+0 [e37daf67] LibGit2_jll v1.6.4+0 [29816b5a] LibSSH2_jll v1.11.0+1 [3a97d323] MPFR_jll v4.2.0+1 [c8ffd9c3] MbedTLS_jll v2.28.2+1 [14a3606d] MozillaCACerts_jll v2023.1.10 [4536629a] OpenBLAS_jll v0.3.23+4 [05823500] OpenLibm_jll v0.8.5+0 [efcefdf7] PCRE2_jll v10.42.0+1 [bea87d4a] SuiteSparse_jll v7.2.1+1 [83775a58] Zlib_jll v1.2.13+1 [8e850b90] libblastrampoline_jll v5.11.0+0 [8e850ede] nghttp2_jll v1.52.0+1 [3f19e933] p7zip_jll v17.4.0+2 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. Testing Running tests... [ Info: You have provided a matrix as input. The axes will be interpreted as (i) system dimension and (ii) number of parameters. [ Info: You have provided a matrix as input. The axes will be interpreted as (i) system dimension and (ii) number of parameters. [ Info: You have provided a matrix as input. The axes will be interpreted as (i) system dimension and (ii) number of parameters. [ Info: You have provided a matrix as input. The axes will be interpreted as (i) system dimension and (ii) number of parameters. Test Summary: | Pass Total Time PSD tests | 10 10 1m25.8s [ Info: You have provided a matrix as input. The axes will be interpreted as (i) system dimension and (ii) number of parameters. Progress: 2%|▉ | ETA: 0:39:11 TrainingLoss: 1.2816999731153007   Progress: 100%|█████████████████████████████████████████| Time: 0:00:50 TrainingLoss: 0.31035712056221537 [ Info: You have provided a matrix as input. The axes will be interpreted as (i) system dimension and (ii) number of parameters. Progress: 40%|████████████████▍ | ETA: 0:00:00 TrainingLoss: 1.1071970180453177   Progress: 90%|████████████████████████████████████▉ | ETA: 0:00:00 TrainingLoss: 0.8519162586722139   Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 TrainingLoss: 0.8058460747077654 [ Info: You have provided a matrix as input. The axes will be interpreted as (i) system dimension and (ii) number of parameters. Progress: 2%|▉ | ETA: 0:05:39 TrainingLoss: 1.0094979949224023   Progress: 4%|█▋ | ETA: 0:02:50 TrainingLoss: 0.8986277185924152   Progress: 6%|██▌ | ETA: 0:01:53 TrainingLoss: 0.7965540888284857   Progress: 8%|███▎ | ETA: 0:01:24 TrainingLoss: 0.7129156254685747   Progress: 10%|████▏ | ETA: 0:01:07 TrainingLoss: 0.6326650269518537   Progress: 12%|████▉ | ETA: 0:00:56 TrainingLoss: 0.5600899975077268   Progress: 14%|█████▊ | ETA: 0:00:48 TrainingLoss: 0.49943751436039874   Progress: 16%|██████▌ | ETA: 0:00:41 TrainingLoss: 0.4542985772316662   Progress: 18%|███████▍ | ETA: 0:00:37 TrainingLoss: 0.4251523026881435   Progress: 20%|████████▎ | ETA: 0:00:33 TrainingLoss: 0.40397582128350357   Progress: 22%|█████████ | ETA: 0:00:29 TrainingLoss: 0.3884964272322549   Progress: 24%|█████████▉ | ETA: 0:00:27 TrainingLoss: 0.37578972820310896   Progress: 26%|██████████▋ | ETA: 0:00:24 TrainingLoss: 0.36597860069056115   Progress: 28%|███████████▌ | ETA: 0:00:22 TrainingLoss: 0.3573804130276463   Progress: 30%|████████████▎ | ETA: 0:00:21 TrainingLoss: 0.35180107427171536   Progress: 32%|█████████████▏ | ETA: 0:00:19 TrainingLoss: 0.3481892883914172   Progress: 34%|██████████████ | ETA: 0:00:18 TrainingLoss: 0.3459790676665156   Progress: 36%|██████████████▊ | ETA: 0:00:16 TrainingLoss: 0.3442595775922099   Progress: 38%|███████████████▋ | ETA: 0:00:15 TrainingLoss: 0.34339877247335027   Progress: 40%|████████████████▍ | ETA: 0:00:14 TrainingLoss: 0.34266316531212804   Progress: 42%|█████████████████▎ | ETA: 0:00:13 TrainingLoss: 0.34240722776858246   Progress: 44%|██████████████████ | ETA: 0:00:12 TrainingLoss: 0.3408348279159204   Progress: 46%|██████████████████▉ | ETA: 0:00:12 TrainingLoss: 0.34061430159945916   Progress: 48%|███████████████████▋ | ETA: 0:00:11 TrainingLoss: 0.34041168812769973   Progress: 50%|████████████████████▌ | ETA: 0:00:10 TrainingLoss: 0.33973495417470706   Progress: 52%|█████████████████████▍ | ETA: 0:00:09 TrainingLoss: 0.3402719819818939   Progress: 54%|██████████████████████▏ | ETA: 0:00:09 TrainingLoss: 0.3398760846443786   Progress: 56%|███████████████████████ | ETA: 0:00:08 TrainingLoss: 0.33963422718903524   Progress: 58%|███████████████████████▊ | ETA: 0:00:08 TrainingLoss: 0.33915875744722757   Progress: 60%|████████████████████████▋ | ETA: 0:00:07 TrainingLoss: 0.33896786654162814   Progress: 62%|█████████████████████████▍ | ETA: 0:00:07 TrainingLoss: 0.33846438363665726   Progress: 64%|██████████████████████████▎ | ETA: 0:00:06 TrainingLoss: 0.3384970388572496   Progress: 66%|███████████████████████████ | ETA: 0:00:06 TrainingLoss: 0.3380762052415591   Progress: 68%|███████████████████████████▉ | ETA: 0:00:05 TrainingLoss: 0.33821580993654676   Progress: 70%|████████████████████████████▊ | ETA: 0:00:05 TrainingLoss: 0.3374638057487168   Progress: 72%|█████████████████████████████▌ | ETA: 0:00:05 TrainingLoss: 0.3374182453467938   Progress: 74%|██████████████████████████████▍ | ETA: 0:00:04 TrainingLoss: 0.33738078159815277   Progress: 76%|███████████████████████████████▏ | ETA: 0:00:04 TrainingLoss: 0.3370356311515773   Progress: 78%|████████████████████████████████ | ETA: 0:00:03 TrainingLoss: 0.3370847276255259   Progress: 80%|████████████████████████████████▊ | ETA: 0:00:03 TrainingLoss: 0.3367727008186475   Progress: 82%|█████████████████████████████████▋ | ETA: 0:00:03 TrainingLoss: 0.3369324112207386   Progress: 84%|██████████████████████████████████▌ | ETA: 0:00:02 TrainingLoss: 0.33613661132190986   Progress: 86%|███████████████████████████████████▎ | ETA: 0:00:02 TrainingLoss: 0.33603229162793175   Progress: 88%|████████████████████████████████████▏ | ETA: 0:00:02 TrainingLoss: 0.3360245381722643   Progress: 90%|████████████████████████████████████▉ | ETA: 0:00:01 TrainingLoss: 0.33596250478355916   Progress: 93%|██████████████████████████████████████▏ | ETA: 0:00:01 TrainingLoss: 0.33629685314535596   Progress: 96%|███████████████████████████████████████▍ | ETA: 0:00:01 TrainingLoss: 0.3359027055079226   Progress: 99%|████████████████████████████████████████▋| ETA: 0:00:00 TrainingLoss: 0.3354418167541534   Progress: 100%|█████████████████████████████████████████| Time: 0:00:13 TrainingLoss: 0.33582699936369526 [ Info: You have provided a matrix as input. The axes will be interpreted as (i) system dimension and (ii) number of parameters. Progress: 20%|████████▎ | ETA: 0:00:01 TrainingLoss: 0.7552064597587445   Progress: 40%|████████████████▍ | ETA: 0:00:00 TrainingLoss: 0.6321219441286908   Progress: 60%|████████████████████████▋ | ETA: 0:00:00 TrainingLoss: 0.5761795434383256   Progress: 80%|████████████████████████████████▊ | ETA: 0:00:00 TrainingLoss: 0.5365361124443165   Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 TrainingLoss: 0.5040494985390713 Test Summary: | Pass Total Time SymplecticAutoencoder tests | 10 10 1m50.6s [ Info: You have provided a matrix as input. The axes will be interpreted as (i) system dimension and (ii) number of parameters. Progress: 2%|▉ | ETA: 0:19:48 TrainingLoss: 1.1157266194850208   Progress: 3%|█▎ | ETA: 0:13:08 TrainingLoss: 0.9879865193180905   Progress: 4%|█▋ | ETA: 0:09:47 TrainingLoss: 0.8766600845007227   Progress: 5%|██ | ETA: 0:07:47 TrainingLoss: 0.7758148675259757   Progress: 6%|██▌ | ETA: 0:06:27 TrainingLoss: 0.6786124227601487   Progress: 7%|██▉ | ETA: 0:05:29 TrainingLoss: 0.5947934107715798   Progress: 8%|███▎ | ETA: 0:04:46 TrainingLoss: 0.5329246213562933   Progress: 9%|███▊ | ETA: 0:04:13 TrainingLoss: 0.4977662846187029   Progress: 10%|████▏ | ETA: 0:03:46 TrainingLoss: 0.4846445145082101   Progress: 11%|████▌ | ETA: 0:03:24 TrainingLoss: 0.47322532147076846   Progress: 12%|████▉ | ETA: 0:03:06 TrainingLoss: 0.4603491947881985   Progress: 13%|█████▍ | ETA: 0:02:50 TrainingLoss: 0.4535042495977925   Progress: 14%|█████▊ | ETA: 0:02:37 TrainingLoss: 0.4449807490293292   Progress: 15%|██████▏ | ETA: 0:02:25 TrainingLoss: 0.43814405166875253   Progress: 17%|███████ | ETA: 0:02:06 TrainingLoss: 0.42669939052938266   Progress: 18%|███████▍ | ETA: 0:01:58 TrainingLoss: 0.4219415118634869   Progress: 20%|████████▎ | ETA: 0:01:45 TrainingLoss: 0.4113910016707439   Progress: 22%|█████████ | ETA: 0:01:33 TrainingLoss: 0.40530476346995964   Progress: 24%|█████████▉ | ETA: 0:01:24 TrainingLoss: 0.40052554424082504   Progress: 26%|██████████▋ | ETA: 0:01:16 TrainingLoss: 0.3946592219287331   Progress: 28%|███████████▌ | ETA: 0:01:09 TrainingLoss: 0.39156359150338604   Progress: 30%|████████████▎ | ETA: 0:01:03 TrainingLoss: 0.3904736272730894   Progress: 32%|█████████████▏ | ETA: 0:00:58 TrainingLoss: 0.38765502340635805   Progress: 34%|██████████████ | ETA: 0:00:53 TrainingLoss: 0.38734934113555863   Progress: 35%|██████████████▍ | ETA: 0:00:51 TrainingLoss: 0.38816947033752436   Progress: 37%|███████████████▏ | ETA: 0:00:47 TrainingLoss: 0.3848457362549464   Progress: 39%|████████████████ | ETA: 0:00:44 TrainingLoss: 0.38464790564269763   Progress: 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0:00:18 TrainingLoss: 0.3779909483403422   Progress: 64%|██████████████████████████▎ | ETA: 0:00:17 TrainingLoss: 0.37798464331756565   Progress: 66%|███████████████████████████ | ETA: 0:00:16 TrainingLoss: 0.37617426230921736   Progress: 67%|███████████████████████████▌ | ETA: 0:00:15 TrainingLoss: 0.37683580196310296   Progress: 69%|████████████████████████████▎ | ETA: 0:00:14 TrainingLoss: 0.37773103657187274   Progress: 71%|█████████████████████████████▏ | ETA: 0:00:13 TrainingLoss: 0.3770756407897993   Progress: 73%|█████████████████████████████▉ | ETA: 0:00:12 TrainingLoss: 0.3743360912621616   Progress: 75%|██████████████████████████████▊ | ETA: 0:00:10 TrainingLoss: 0.374389967332482   Progress: 76%|███████████████████████████████▏ | ETA: 0:00:10 TrainingLoss: 0.37577577658859224   Progress: 78%|████████████████████████████████ | ETA: 0:00:09 TrainingLoss: 0.37550297574799835   Progress: 80%|████████████████████████████████▊ | ETA: 0:00:08 TrainingLoss: 0.37436541025901693   Progress: 82%|█████████████████████████████████▋ | ETA: 0:00:07 TrainingLoss: 0.3742237906819671   Progress: 83%|██████████████████████████████████ | ETA: 0:00:07 TrainingLoss: 0.3747935898480209   Progress: 85%|██████████████████████████████████▉ | ETA: 0:00:06 TrainingLoss: 0.37439941903739105   Progress: 87%|███████████████████████████████████▋ | ETA: 0:00:05 TrainingLoss: 0.37499037357330023   Progress: 89%|████████████████████████████████████▌ | ETA: 0:00:04 TrainingLoss: 0.37353780012764315   Progress: 91%|█████████████████████████████████████▎ | ETA: 0:00:03 TrainingLoss: 0.3733440905187511   Progress: 93%|██████████████████████████████████████▏ | ETA: 0:00:03 TrainingLoss: 0.3731590509466052   Progress: 94%|██████████████████████████████████████▌ | ETA: 0:00:02 TrainingLoss: 0.3731130018617924   Progress: 96%|███████████████████████████████████████▍ | ETA: 0:00:01 TrainingLoss: 0.3713184727373683   Progress: 98%|████████████████████████████████████████▏| ETA: 0:00:01 TrainingLoss: 0.3729839042217974   Progress: 100%|█████████████████████████████████████████| Time: 0:00:33 TrainingLoss: 0.37296662494358357 [ Info: You have provided a matrix as input. The axes will be interpreted as (i) system dimension and (ii) number of parameters. Progress: 1%|▍ | ETA: 0:18:14 TrainingLoss: 2.750779313334391   Progress: 2%|█ | ETA: 0:07:16 TrainingLoss: 1.7244961391589244   Progress: 4%|█▉ | ETA: 0:04:00 TrainingLoss: 1.0500050409984854   Progress: 6%|██▋ | ETA: 0:02:45 TrainingLoss: 0.8334760195977555   Progress: 8%|███▌ | ETA: 0:02:05 TrainingLoss: 0.7255516882659067   Progress: 10%|████▎ | ETA: 0:01:40 TrainingLoss: 0.6736763140708204   Progress: 12%|████▉ | ETA: 0:01:27 TrainingLoss: 0.6564751048372739   Progress: 14%|█████▊ | ETA: 0:01:13 TrainingLoss: 0.6420452108826793   Progress: 16%|██████▌ | ETA: 0:01:03 TrainingLoss: 0.6364680930818899   Progress: 18%|███████▍ | ETA: 0:00:56 TrainingLoss: 0.630903150082227   Progress: 20%|████████ | ETA: 0:00:51 TrainingLoss: 0.6183574382739719   Progress: 22%|████████▉ | ETA: 0:00:45 TrainingLoss: 0.6126283606135831   Progress: 24%|█████████▋ | ETA: 0:00:41 TrainingLoss: 0.6032242129193641   Progress: 26%|██████████▌ | ETA: 0:00:37 TrainingLoss: 0.5870062957779494   Progress: 28%|███████████▎ | ETA: 0:00:34 TrainingLoss: 0.5787254082646266   Progress: 30%|████████████▏ | ETA: 0:00:31 TrainingLoss: 0.55837632455872   Progress: 32%|████████████▉ | ETA: 0:00:28 TrainingLoss: 0.546286740926047   Progress: 33%|█████████████▌ | ETA: 0:00:27 TrainingLoss: 0.529475158712307   Progress: 35%|██████████████▍ | ETA: 0:00:25 TrainingLoss: 0.5139803032195264   Progress: 37%|███████████████▏ | ETA: 0:00:23 TrainingLoss: 0.49257941148157897   Progress: 39%|████████████████ | ETA: 0:00:21 TrainingLoss: 0.4761799437778959   Progress: 41%|████████████████▊ | ETA: 0:00:20 TrainingLoss: 0.4609217912198453   Progress: 43%|█████████████████▋ | ETA: 0:00:18 TrainingLoss: 0.44442805177215977   Progress: 44%|██████████████████▎ | ETA: 0:00:17 TrainingLoss: 0.43765551529798197   Progress: 46%|███████████████████▏ | ETA: 0:00:16 TrainingLoss: 0.42718733754866745   Progress: 48%|███████████████████▋ | ETA: 0:00:15 TrainingLoss: 0.42403182906416964   Progress: 50%|████████████████████▎ | ETA: 0:00:15 TrainingLoss: 0.4181531152767832   Progress: 52%|█████████████████████▏ | ETA: 0:00:14 TrainingLoss: 0.4148812156180481   Progress: 53%|█████████████████████▊ | ETA: 0:00:13 TrainingLoss: 0.41342513596829306   Progress: 55%|██████████████████████▌ | ETA: 0:00:12 TrainingLoss: 0.41114815934614013   Progress: 57%|███████████████████████▍ | ETA: 0:00:11 TrainingLoss: 0.41423329466571307   Progress: 59%|████████████████████████▎ | ETA: 0:00:10 TrainingLoss: 0.4086525641456846   Progress: 61%|█████████████████████████ | ETA: 0:00:10 TrainingLoss: 0.40770096894406443   Progress: 63%|█████████████████████████▉ | ETA: 0:00:09 TrainingLoss: 0.4062054148262031   Progress: 65%|██████████████████████████▋ | ETA: 0:00:08 TrainingLoss: 0.40390690537560947   Progress: 67%|███████████████████████████▌ | ETA: 0:00:08 TrainingLoss: 0.40751703899626324   Progress: 68%|████████████████████████████▏ | ETA: 0:00:07 TrainingLoss: 0.40407708548820986   Progress: 70%|████████████████████████████▉ | ETA: 0:00:07 TrainingLoss: 0.40321757576105194   Progress: 72%|█████████████████████████████▊ | ETA: 0:00:06 TrainingLoss: 0.40390098499976895   Progress: 74%|██████████████████████████████▌ | ETA: 0:00:05 TrainingLoss: 0.40055983198150025   Progress: 76%|███████████████████████████████▍ | ETA: 0:00:05 TrainingLoss: 0.40104050889593745   Progress: 78%|████████████████████████████████▏ | ETA: 0:00:04 TrainingLoss: 0.40029896521164626   Progress: 80%|█████████████████████████████████ | ETA: 0:00:04 TrainingLoss: 0.3995366842860509   Progress: 82%|█████████████████████████████████▉ | ETA: 0:00:03 TrainingLoss: 0.3999860530788921   Progress: 84%|██████████████████████████████████▋ | ETA: 0:00:03 TrainingLoss: 0.39713632782241076   Progress: 86%|███████████████████████████████████▌ | ETA: 0:00:03 TrainingLoss: 0.3952878103813627   Progress: 88%|████████████████████████████████████▎ | ETA: 0:00:02 TrainingLoss: 0.39776250755718706   Progress: 90%|█████████████████████████████████████▏ | ETA: 0:00:02 TrainingLoss: 0.39890409958334305   Progress: 92%|█████████████████████████████████████▉ | ETA: 0:00:01 TrainingLoss: 0.3984976868238461   Progress: 94%|██████████████████████████████████████▌ | ETA: 0:00:01 TrainingLoss: 0.39540582433666693   Progress: 96%|███████████████████████████████████████▍ | ETA: 0:00:01 TrainingLoss: 0.3962280741684434   Progress: 98%|████████████████████████████████████████▏| ETA: 0:00:00 TrainingLoss: 0.39475428922213934   Progress: 100%|█████████████████████████████████████████| Time: 0:00:17 TrainingLoss: 0.39590203009706953 Test Summary: | Pass Total Time Check if autoencoder error is lower than PSD error | 2 2 1m07.5s [ Info: You have provided a NamedTuple with keys q and p; the data are tensors. This is interpreted as *symplectic data*. ┌ Warning: Solver took 1000 iterations. └ @ SimpleSolvers ~/.julia/packages/SimpleSolvers/jTeAA/src/nonlinear/nonlinear_solver_status.jl:140 ┌ Warning: Solver took 1000 iterations. └ @ SimpleSolvers ~/.julia/packages/SimpleSolvers/jTeAA/src/nonlinear/nonlinear_solver_status.jl:140 ┌ Warning: Solver took 1000 iterations. └ @ SimpleSolvers ~/.julia/packages/SimpleSolvers/jTeAA/src/nonlinear/nonlinear_solver_status.jl:140 [ Info: You have provided a NamedTuple with keys q and p; the data are tensors. This is interpreted as *symplectic data*. [ Info: You have provided a NamedTuple with keys q and p; the data are tensors. This is interpreted as *symplectic data*. [ Info: You have provided a NamedTuple with keys q and p; the data are tensors. This is interpreted as *symplectic data*. [ Info: You have provided a NamedTuple with keys q and p; the data are tensors. This is interpreted as *symplectic data*. Test Summary: | Pass Total Time Check reduced model | 10 10 9m05.1s Test Summary: | Pass Total Time Check parameterlength | 9 9 0.6s Test Summary: | Pass Total Time Arrays #1 | 1801 1801 39.5s Test Summary: | Pass Total Time Map to skew | 1 1 0.6s Test Summary: | Pass Total Time Sampling of arrays | 8 8 1.0s Test Summary: | Pass Total Time Addition tests for custom arrays | 12 12 12.5s Test Summary: | Pass Total Time Scalar multiplication tests for custom arrays | 12 12 0.8s Test Summary: | Pass Total Time Matrix multiplication tests for custom arrays | 4 4 11.0s Test Summary: | Pass Total Time Test constructors for custom arrays | 5 5 2.8s Test Summary: | Pass Total Time Symplectic Potential (array tests) | 40 40 5.2s Test Summary: | Pass Total Time Test StiefelLieAlgHorMatrix constructors and lifts | 24 24 27.1s Test Summary: | Pass Total Time Test GrassmannLieAlgHorMatrix constructors and lifts | 24 24 29.0s Test Summary: | Pass Total Time Test triangular matrices | 13 13 14.8s Test Summary: | Pass Total Time Manifolds (Stiefel): | 74 74 31.8s Test Summary: | Pass Total Time Manifolds (Grassmann): | 270 270 2.5s Test Summary: | Pass Total Time Gradient Layer | 144 144 1m36.1s Test Summary: | Pass Total Time Test symplecticity of upscaling layer | 3 3 10.1s [ Info: You have provided an input and an output. Test Summary: | Pass Total Time Hamiltonian Neural Network | 2 2 25.0s Test Summary: | Pass Total Time Manifold Neural Network Layers | 432 432 1m20.3s Test Summary: | Pass Total Time Custom tensor matrix multiplication | 11 11 11.0s Test Summary: | Pass Total Time Custom inverse for 2x2, 3x3, 4x4, 5x5 matrices | 110 110 39.3s ┌ Warning: inference tests have been disabled └ @ ChainRulesTestUtils ~/.julia/packages/ChainRulesTestUtils/Ko1Wr/src/global_config.jl:13 Test Summary: | Pass Total Time Custom AD rules for kernels | 1690 1690 1m13.0s Test Summary: | Pass Total Time ResNet | 128 128 19.0s Test Summary: | Pass Total Time Test setup of MultiHeadAttention layer Stiefel weights | 96 96 4.9s Test Summary: | Pass Total Time Test geodesic and Cayley retr for the MultiHeadAttention layer w/ St weights | 96 96 2.6s Test Summary: | Pass Total Time Test the correct setup of the various optimizer caches for MultiHeadAttention | 288 288 5.8s Test Summary: | Pass Total Time Check if the transformer can be applied to a tensor. | 4 4 19.7s Test Summary: | Pass Total Time Check if the gradient/pullback of MultiHeadAttention changes type in St case | 4 4 44.9s Test Summary: | Pass Total Time Check if the optimization_step! changes the parameters of the transformer | 2 2 47.2s Test Summary: | Pass Total Time Attention layer #1 | 48 48 1m03.4s Test Summary: | Pass Total Time Classification layer | 4 4 8.0s Test Summary: | Pass Total Time Optimizer #1 | 135 135 1.1s Test Summary: | Pass Total Time Optimizer #2 | 196 196 17.1s Test Summary: | Pass Total Time Optimizer #3 | 220 220 35.3s Test Summary: | Pass Total Time Optimizer #4 | 16 16 9.5s Test Summary: | Pass Total Time Optimizer #5 | 16 16 6.9s ┌ Info: You have provided a tensor with three axes as input. They will be interpreted as └ (i) system dimension, (ii) number of time steps and (iii) number of params. 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Total Time BFGS Optimizer tests | 2 2 20.4s Test Summary: | Pass Total Time Data | 42 42 8.1s Test Summary: | Pass Total Time Batch | 11 11 10.4s Test Summary: | Pass Total Time Matching | 7 7 5.7s [ Info: You have provided a NamedTuple with keys q and p; the data are matrices. This is interpreted as *symplectic data*. [ Info: You have provided a NamedTuple with keys q and p; the data are tensors. This is interpreted as *symplectic data*. Test Summary: | Time Test data loader for q and p data | None 11.0s [ Info: You provided a tensor and a vector as input. This will be treated as a classification problem (MNIST). Tensor axes: (i) & (ii) image axes and (iii) parameter dimension. Test Summary: | Pass Total Time Test mnist_utils. | 3139 3139 14.8s ┌ Info: You have provided a tensor with three axes as input. They will be interpreted as └ (i) system dimension, (ii) number of time steps and (iii) number of params. Test Summary: | Pass Total Time Test the data loader in combination with optimization_step! | 1 1 32.4s WARNING: Method definition (::Type{AbstractNeuralNetworks.Chain{LT} where LT<:Tuple})(AbstractNeuralNetworks.Chain{LT} where LT<:Tuple, AbstractNeuralNetworks.AbstractExplicitLayer{N, M} where M where N) in module AbstractNeuralNetworks at /home/pkgeval/.julia/packages/AbstractNeuralNetworks/eqJgA/src/chain.jl:69 overwritten in module ##Optimizer functor with data loader for Adam #2491 at /home/pkgeval/.julia/packages/GeometricMachineLearning/KOTrh/test/data_loader/optimizer_functor_with_adam.jl:8. [ Info: You provided a tensor and a vector as input. This will be treated as a classification problem (MNIST). Tensor axes: (i) & (ii) image axes and (iii) parameter dimension. Test Summary: | Pass Total Time Optimizer functor with data loader for Adam | 1 1 24.0s [ Info: You have provided a NamedTuple with keys q and p; the data are tensors. This is interpreted as *symplectic data*. Test Summary: | Pass Total Time Test data loader for a tensor (q and p data) | 76 76 1.1s ┌ Info: You have provided a tensor with three axes as input. They will be interpreted as └ (i) system dimension, (ii) number of time steps and (iii) number of params. Progress: 20%|████████▎ | ETA: 0:00:13 TrainingLoss: 0.6021645144976507   Progress: 90%|████████████████████████████████████▉ | ETA: 0:00:00 TrainingLoss: 0.032699878731008276   Progress: 100%|█████████████████████████████████████████| Time: 0:00:03 TrainingLoss: 0.021366144527344248 Test Summary: | Pass Total Time Test NetworkLoss + Optimizer | 1 1 4.8s Test Summary: | Pass Total Time Test parallel inverses | 110 110 0.7s Test Summary: | Pass Total Time Test parallel Cayley | 60 60 9.5s Test Summary: | Pass Total Time Test volume-preserving feedforward neural network | 8 8 57.7s Test Summary: | Pass Total Time SympNet integrator | 2 2 2.4s Test Summary: | Pass Total Time Regular transformer integrator | 2 2 5.9s [ Info: You have provided a matrix as input. The axes will be interpreted as (i) system dimension and (ii) number of parameters. ┌ Info: You have provided a tensor with three axes as input. They will be interpreted as └ (i) system dimension, (ii) number of time steps and (iii) number of params. Test Summary: | Pass Total Time Batch functor(s) | 8 8 3.3s Test Summary: | Pass Total Time Volume-Preserving Transformer (skew-symmetric tests) | 40 40 1.9s Test Summary: | Pass Total Time Volume-Preserving Transformer (cayley-transform tests) | 40 40 4.1s Test Summary: | Pass Total Time Linear Symplectic Attention | 4 4 1.5s Test Summary: | Pass Total Time Linear Symplectic Transformer | 4 4 9.1s [ Info: You have provided an input and an output. 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