Package evaluation to test JudiLing on Julia 1.11.8 (29b3528cce*) started at 2026-01-20T17:09:11.765 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Activating project at `~/.julia/environments/v1.11` Set-up completed after 8.28s ################################################################################ # Installation # Installing JudiLing... Resolving package versions... Installed Conda ──── v1.10.3 Installed JudiLing ─ v1.0.0 Updating `~/.julia/environments/v1.11/Project.toml` [b43a184b] + JudiLing v1.0.0 Updating `~/.julia/environments/v1.11/Manifest.toml` [621f4979] + AbstractFFTs v1.5.0 [7d9f7c33] + Accessors v0.1.43 [79e6a3ab] + Adapt v4.4.0 [66dad0bd] + AliasTables v1.1.3 [dce04be8] + ArgCheck v2.5.0 [a9b6321e] + Atomix v1.1.2 ⌅ [15f4f7f2] + AutoHashEquals v0.2.0 [fbb218c0] + BSON v0.3.9 [198e06fe] + BangBang v0.4.6 [9718e550] + Baselet v0.1.1 ⌅ [6e4b80f9] + BenchmarkTools v1.5.0 [d1d4a3ce] + BitFlags v0.1.9 [336ed68f] + CSV v0.10.15 [082447d4] + ChainRules v1.72.6 [d360d2e6] + ChainRulesCore v1.26.0 [944b1d66] + CodecZlib v0.7.8 [bbf7d656] + CommonSubexpressions v0.3.1 [34da2185] + Compat v4.18.1 [a33af91c] + CompositionsBase v0.1.2 [f0e56b4a] + ConcurrentUtilities v2.5.0 [8f4d0f93] + Conda v1.10.3 [187b0558] + ConstructionBase v1.6.0 [6add18c4] + ContextVariablesX v0.1.3 [a8cc5b0e] + Crayons v4.1.1 [9a962f9c] + DataAPI v1.16.0 [124859b0] + DataDeps v0.7.13 [a93c6f00] + DataFrames v1.8.1 [2e981812] + DataLoaders v0.1.3 ⌅ [864edb3b] + DataStructures v0.18.22 [e2d170a0] + DataValueInterfaces v1.0.0 [244e2a9f] + DefineSingletons v0.1.2 [8bb1440f] + DelimitedFiles v1.9.1 [163ba53b] + DiffResults v1.1.0 [b552c78f] + DiffRules v1.15.1 [b4f34e82] + Distances v0.10.12 [31c24e10] + Distributions v0.25.123 ⌅ [ffbed154] + DocStringExtensions v0.8.6 [c5bfea45] + Embeddings v0.4.6 [f151be2c] + EnzymeCore v0.8.18 [460bff9d] + ExceptionUnwrapping v0.1.11 [cc61a311] + FLoops v0.2.2 [b9860ae5] + FLoopsBase v0.1.1 [48062228] + FilePathsBase v0.9.24 [1a297f60] + FillArrays v1.16.0 [587475ba] + Flux v0.16.7 [f6369f11] + ForwardDiff v1.3.1 [d9f16b24] + Functors v0.5.2 [46192b85] + GPUArraysCore v0.2.0 [92fee26a] + GZip v0.6.2 ⌅ [91feb7a0] + GoogleDrive v0.1.3 [cd3eb016] + HTTP v1.10.19 [076d061b] + HashArrayMappedTries v0.2.0 [34004b35] + HypergeometricFunctions v0.3.28 [7869d1d1] + IRTools v0.4.15 [22cec73e] + InitialValues v0.3.1 [842dd82b] + InlineStrings v1.4.5 [3587e190] + InverseFunctions v0.1.17 [41ab1584] + InvertedIndices v1.3.1 [92d709cd] + IrrationalConstants v0.2.6 [82899510] + IteratorInterfaceExtensions v1.0.0 [692b3bcd] + JLLWrappers v1.7.1 ⌅ [682c06a0] + JSON v0.21.4 [b43a184b] + JudiLing v1.0.0 [b14d175d] + JuliaVariables v0.2.4 [63c18a36] + KernelAbstractions v0.9.39 [b964fa9f] + LaTeXStrings v1.4.0 ⌅ [7f8f8fb0] + LearnBase v0.3.0 [2ab3a3ac] + LogExpFunctions v0.3.29 [e6f89c97] + LoggingExtras v1.2.0 [c2834f40] + MLCore v1.0.0 [7e8f7934] + MLDataDevices v1.17.1 ⌃ [9920b226] + MLDataPattern v0.5.4 [66a33bbf] + MLLabelUtils v0.5.7 [d8e11817] + MLStyle v0.4.17 [f1d291b0] + MLUtils v0.4.8 [1914dd2f] + MacroTools v0.5.16 [dbb5928d] + MappedArrays v0.4.3 [739be429] + MbedTLS v1.1.9 [128add7d] + MicroCollections v0.2.0 [e1d29d7a] + Missings v1.2.0 [872c559c] + NNlib v0.9.33 [77ba4419] + NaNMath v1.1.3 [71a1bf82] + NameResolution v0.1.5 [0b1bfda6] + OneHotArrays v0.2.10 [4d8831e6] + OpenSSL v1.6.1 [3bd65402] + Optimisers v0.4.7 [bac558e1] + OrderedCollections v1.8.1 ⌃ [90014a1f] + PDMats v0.11.35 [d96e819e] + Parameters v0.12.3 [69de0a69] + Parsers v2.8.3 [2dfb63ee] + PooledArrays v1.4.3 ⌅ [aea7be01] + PrecompileTools v1.2.1 [21216c6a] + Preferences v1.5.1 [8162dcfd] + PrettyPrint v0.2.0 [08abe8d2] + PrettyTables v3.1.2 [33c8b6b6] + ProgressLogging v0.1.6 [92933f4c] + ProgressMeter v1.11.0 [43287f4e] + PtrArrays v1.3.0 [1fd47b50] + QuadGK v2.11.2 [c1ae055f] + RealDot v0.1.0 [3cdcf5f2] + RecipesBase v1.3.4 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.1 [79098fc4] + Rmath v0.9.0 [431bcebd] + SciMLPublic v1.0.1 [7e506255] + ScopedValues v1.5.0 [6c6a2e73] + Scratch v1.3.0 [91c51154] + SentinelArrays v1.4.9 [efcf1570] + Setfield v1.1.2 [605ecd9f] + ShowCases v0.1.0 [777ac1f9] + SimpleBufferStream v1.2.0 [699a6c99] + SimpleTraits v0.9.5 [a2af1166] + SortingAlgorithms v1.2.2 [dc90abb0] + SparseInverseSubset v0.1.2 [276daf66] + SpecialFunctions v2.6.1 [171d559e] + SplittablesBase v0.1.15 [90137ffa] + StaticArrays v1.9.16 [1e83bf80] + StaticArraysCore v1.4.4 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.8.0 ⌅ [2913bbd2] + StatsBase v0.33.21 [4c63d2b9] + StatsFuns v1.5.2 [892a3eda] + StringManipulation v0.4.2 [09ab397b] + StructArrays v0.7.2 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.1 ⌅ [b189fb0b] + ThreadPools v1.2.1 [3bb67fe8] + TranscodingStreams v0.11.3 [28d57a85] + Transducers v0.4.85 [5c2747f8] + URIs v1.6.1 [3a884ed6] + UnPack v1.0.2 [013be700] + UnsafeAtomics v0.3.0 [81def892] + VersionParsing v1.3.0 [ea10d353] + WeakRefStrings v1.4.2 [76eceee3] + WorkerUtilities v1.6.1 [e88e6eb3] + Zygote v0.7.10 [700de1a5] + ZygoteRules v0.2.7 [458c3c95] + OpenSSL_jll v3.5.4+0 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [f50d1b31] + Rmath_jll v0.5.1+0 [0dad84c5] + ArgTools v1.1.2 [56f22d72] + Artifacts v1.11.0 [2a0f44e3] + Base64 v1.11.0 [ade2ca70] + Dates v1.11.0 [8ba89e20] + Distributed v1.11.0 [f43a241f] + Downloads v1.6.0 [7b1f6079] + FileWatching v1.11.0 [9fa8497b] + Future v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [b27032c2] + LibCURL v0.6.4 [76f85450] + LibGit2 v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.11.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [ca575930] + NetworkOptions v1.2.0 [de0858da] + Printf v1.11.0 [9abbd945] + Profile v1.11.0 [3fa0cd96] + REPL v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v0.7.0 [9e88b42a] + Serialization v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.11.0 [f489334b] + StyledStrings v1.11.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [8dfed614] + Test v1.11.0 [cf7118a7] + UUIDs v1.11.0 [4ec0a83e] + Unicode v1.11.0 [e66e0078] + CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] + LibCURL_jll v8.6.0+0 [e37daf67] + LibGit2_jll v1.7.2+0 [29816b5a] + LibSSH2_jll v1.11.0+1 [c8ffd9c3] + MbedTLS_jll v2.28.6+0 [14a3606d] + MozillaCACerts_jll v2023.12.12 [4536629a] + OpenBLAS_jll v0.3.27+1 [05823500] + OpenLibm_jll v0.8.5+0 [bea87d4a] + SuiteSparse_jll v7.7.0+0 [83775a58] + Zlib_jll v1.2.13+1 [8e850b90] + libblastrampoline_jll v5.11.0+0 [8e850ede] + nghttp2_jll v1.59.0+0 [3f19e933] + p7zip_jll v17.4.0+2 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` Building Conda → `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/8f06b0cfa4c514c7b9546756dbae91fcfbc92dc9/build.log` Installation completed after 15.41s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompiling project... 80768.2 ms ✓ Zygote 3187.4 ms ✓ NNlib → NNlibSpecialFunctionsExt 2963.7 ms ✓ NNlib → NNlibForwardDiffExt 20288.8 ms ✓ MLUtils 5718.3 ms ✓ Distributions → DistributionsChainRulesCoreExt 232617.1 ms ✓ Zygote → ZygoteDistancesExt 76266.5 ms ✓ MLDataDevices → ZygoteExt 35554.1 ms ✓ MLDataDevices → MLUtilsExt 153535.4 ms ✓ Flux 157442.8 ms ✓ JudiLing 10 dependencies successfully precompiled in 821 seconds. 232 already precompiled. Precompilation completed after 887.95s ################################################################################ # Testing # Testing JudiLing ┌ Warning: Could not use exact versions of packages in manifest, re-resolving └ @ Pkg.Operations /opt/julia/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:1924 Status `/tmp/jl_eIhNxQ/Project.toml` [336ed68f] CSV v0.10.15 [8f4d0f93] Conda v1.10.3 [a93c6f00] DataFrames v1.8.1 [2e981812] DataLoaders v0.1.3 [587475ba] Flux v0.16.7 [b43a184b] JudiLing v1.0.0 [438e738f] PyCall v1.96.4 [1bc83da4] SafeTestsets v0.1.0 [37e2e46d] LinearAlgebra v1.11.0 [2f01184e] SparseArrays v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_eIhNxQ/Manifest.toml` [621f4979] AbstractFFTs v1.5.0 [7d9f7c33] Accessors v0.1.43 [79e6a3ab] Adapt v4.4.0 [66dad0bd] AliasTables v1.1.3 [dce04be8] ArgCheck v2.5.0 [a9b6321e] Atomix v1.1.2 ⌅ [15f4f7f2] AutoHashEquals v0.2.0 [fbb218c0] BSON v0.3.9 [198e06fe] BangBang v0.4.6 [9718e550] Baselet v0.1.1 ⌅ [6e4b80f9] BenchmarkTools v1.5.0 [d1d4a3ce] BitFlags v0.1.9 [336ed68f] CSV v0.10.15 [082447d4] ChainRules v1.72.6 [d360d2e6] ChainRulesCore v1.26.0 [944b1d66] CodecZlib v0.7.8 [bbf7d656] CommonSubexpressions v0.3.1 [34da2185] Compat v4.18.1 [a33af91c] CompositionsBase v0.1.2 [f0e56b4a] ConcurrentUtilities v2.5.0 [8f4d0f93] Conda v1.10.3 [187b0558] ConstructionBase v1.6.0 [6add18c4] ContextVariablesX v0.1.3 [a8cc5b0e] Crayons v4.1.1 [9a962f9c] DataAPI v1.16.0 [124859b0] DataDeps v0.7.13 [a93c6f00] DataFrames v1.8.1 [2e981812] DataLoaders v0.1.3 ⌅ [864edb3b] DataStructures v0.18.22 [e2d170a0] DataValueInterfaces v1.0.0 [244e2a9f] DefineSingletons v0.1.2 [8bb1440f] DelimitedFiles v1.9.1 [163ba53b] DiffResults v1.1.0 [b552c78f] DiffRules v1.15.1 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.123 ⌅ [ffbed154] DocStringExtensions v0.8.6 [c5bfea45] Embeddings v0.4.6 [f151be2c] EnzymeCore v0.8.18 [460bff9d] ExceptionUnwrapping v0.1.11 [cc61a311] FLoops v0.2.2 [b9860ae5] FLoopsBase v0.1.1 [48062228] FilePathsBase v0.9.24 [1a297f60] FillArrays v1.16.0 [587475ba] Flux v0.16.7 [f6369f11] ForwardDiff v1.3.1 [d9f16b24] Functors v0.5.2 [46192b85] GPUArraysCore v0.2.0 [92fee26a] GZip v0.6.2 ⌅ [91feb7a0] GoogleDrive v0.1.3 [cd3eb016] HTTP v1.10.19 [076d061b] HashArrayMappedTries v0.2.0 [34004b35] HypergeometricFunctions v0.3.28 [7869d1d1] IRTools v0.4.15 [22cec73e] InitialValues v0.3.1 [842dd82b] InlineStrings v1.4.5 [3587e190] InverseFunctions v0.1.17 [41ab1584] InvertedIndices v1.3.1 [92d709cd] IrrationalConstants v0.2.6 [82899510] IteratorInterfaceExtensions v1.0.0 [692b3bcd] JLLWrappers v1.7.1 ⌅ [682c06a0] JSON v0.21.4 [b43a184b] JudiLing v1.0.0 [b14d175d] JuliaVariables v0.2.4 [63c18a36] KernelAbstractions v0.9.39 [b964fa9f] LaTeXStrings v1.4.0 ⌅ [7f8f8fb0] LearnBase v0.3.0 [2ab3a3ac] LogExpFunctions v0.3.29 [e6f89c97] LoggingExtras v1.2.0 [c2834f40] MLCore v1.0.0 [7e8f7934] MLDataDevices v1.17.1 ⌃ [9920b226] MLDataPattern v0.5.4 [66a33bbf] MLLabelUtils v0.5.7 [d8e11817] MLStyle v0.4.17 [f1d291b0] MLUtils v0.4.8 [1914dd2f] MacroTools v0.5.16 [dbb5928d] MappedArrays v0.4.3 [739be429] MbedTLS v1.1.9 [128add7d] MicroCollections v0.2.0 [e1d29d7a] Missings v1.2.0 [872c559c] NNlib v0.9.33 [77ba4419] NaNMath v1.1.3 [71a1bf82] NameResolution v0.1.5 [0b1bfda6] OneHotArrays v0.2.10 [4d8831e6] OpenSSL v1.6.1 [3bd65402] Optimisers v0.4.7 [bac558e1] OrderedCollections v1.8.1 ⌃ [90014a1f] PDMats v0.11.35 [d96e819e] Parameters v0.12.3 [69de0a69] Parsers v2.8.3 [2dfb63ee] PooledArrays v1.4.3 ⌅ [aea7be01] PrecompileTools v1.2.1 [21216c6a] Preferences v1.5.1 [8162dcfd] PrettyPrint v0.2.0 [08abe8d2] PrettyTables v3.1.2 [33c8b6b6] ProgressLogging v0.1.6 [92933f4c] ProgressMeter v1.11.0 [43287f4e] PtrArrays v1.3.0 [438e738f] PyCall v1.96.4 [1fd47b50] QuadGK v2.11.2 [c1ae055f] RealDot v0.1.0 [3cdcf5f2] RecipesBase v1.3.4 [189a3867] Reexport v1.2.2 [ae029012] Requires v1.3.1 [79098fc4] Rmath v0.9.0 [1bc83da4] SafeTestsets v0.1.0 [431bcebd] SciMLPublic v1.0.1 [7e506255] ScopedValues v1.5.0 [6c6a2e73] Scratch v1.3.0 [91c51154] SentinelArrays v1.4.9 [efcf1570] Setfield v1.1.2 [605ecd9f] ShowCases v0.1.0 [777ac1f9] SimpleBufferStream v1.2.0 [699a6c99] SimpleTraits v0.9.5 [a2af1166] SortingAlgorithms v1.2.2 [dc90abb0] SparseInverseSubset v0.1.2 [276daf66] SpecialFunctions v2.6.1 [171d559e] SplittablesBase v0.1.15 [90137ffa] StaticArrays v1.9.16 [1e83bf80] StaticArraysCore v1.4.4 [10745b16] Statistics v1.11.1 [82ae8749] StatsAPI v1.8.0 ⌅ [2913bbd2] StatsBase v0.33.21 [4c63d2b9] StatsFuns v1.5.2 [892a3eda] StringManipulation v0.4.2 [09ab397b] StructArrays v0.7.2 [3783bdb8] TableTraits v1.0.1 [bd369af6] Tables v1.12.1 ⌅ [b189fb0b] ThreadPools v1.2.1 [3bb67fe8] TranscodingStreams v0.11.3 [28d57a85] Transducers v0.4.85 [5c2747f8] URIs v1.6.1 [3a884ed6] UnPack v1.0.2 [013be700] UnsafeAtomics v0.3.0 [81def892] VersionParsing v1.3.0 [ea10d353] WeakRefStrings v1.4.2 [76eceee3] WorkerUtilities v1.6.1 [e88e6eb3] Zygote v0.7.10 [700de1a5] ZygoteRules v0.2.7 [458c3c95] OpenSSL_jll v3.5.4+0 [efe28fd5] OpenSpecFun_jll v0.5.6+0 [f50d1b31] Rmath_jll v0.5.1+0 [0dad84c5] ArgTools v1.1.2 [56f22d72] Artifacts v1.11.0 [2a0f44e3] Base64 v1.11.0 [ade2ca70] Dates v1.11.0 [8ba89e20] Distributed v1.11.0 [f43a241f] Downloads v1.6.0 [7b1f6079] FileWatching v1.11.0 [9fa8497b] Future v1.11.0 [b77e0a4c] InteractiveUtils v1.11.0 [b27032c2] LibCURL v0.6.4 [76f85450] LibGit2 v1.11.0 [8f399da3] Libdl v1.11.0 [37e2e46d] LinearAlgebra v1.11.0 [56ddb016] Logging v1.11.0 [d6f4376e] Markdown v1.11.0 [a63ad114] Mmap v1.11.0 [ca575930] NetworkOptions v1.2.0 [de0858da] Printf v1.11.0 [9abbd945] Profile v1.11.0 [3fa0cd96] REPL v1.11.0 [9a3f8284] Random v1.11.0 [ea8e919c] SHA v0.7.0 [9e88b42a] Serialization v1.11.0 [6462fe0b] Sockets v1.11.0 [2f01184e] SparseArrays v1.11.0 [f489334b] StyledStrings v1.11.0 [4607b0f0] SuiteSparse [fa267f1f] TOML v1.0.3 [8dfed614] Test v1.11.0 [cf7118a7] UUIDs v1.11.0 [4ec0a83e] Unicode v1.11.0 [e66e0078] CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] LibCURL_jll v8.6.0+0 [e37daf67] LibGit2_jll v1.7.2+0 [29816b5a] LibSSH2_jll v1.11.0+1 [c8ffd9c3] MbedTLS_jll v2.28.6+0 [14a3606d] MozillaCACerts_jll v2023.12.12 [4536629a] OpenBLAS_jll v0.3.27+1 [05823500] OpenLibm_jll v0.8.5+0 [bea87d4a] SuiteSparse_jll v7.7.0+0 [83775a58] Zlib_jll v1.2.13+1 [8e850b90] libblastrampoline_jll v5.11.0+0 [8e850ede] nghttp2_jll v1.59.0+0 [3f19e933] p7zip_jll v17.4.0+2 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. Testing Running tests... Precompiling JudiLing... 2127.4 ms ✓ ThreadPools 32058.1 ms ✓ Transducers → TransducersDataFramesExt 2806.9 ms ✓ BenchmarkTools 6757.1 ms ✓ BangBang → BangBangDataFramesExt 2243.4 ms ✓ ForwardDiff → ForwardDiffStaticArraysExt 65181.4 ms ✓ Zygote 1865.7 ms ✓ KernelAbstractions → EnzymeExt 4155.2 ms ✓ GoogleDrive 2399.1 ms ✓ NNlib → NNlibEnzymeCoreExt 2895.6 ms ✓ NNlib → NNlibSpecialFunctionsExt 2912.2 ms ✓ NNlib → NNlibForwardDiffExt 3308.2 ms ✓ OneHotArrays 21008.9 ms ✓ MLUtils 4525.2 ms ✓ DataLoaders 6899.0 ms ✓ Zygote → ZygoteDistancesExt 4789.7 ms ✓ MLDataDevices → ZygoteExt 30030.2 ms ✓ Embeddings 2372.6 ms ✓ MLDataDevices → OneHotArraysExt 73568.6 ms ✓ MLDataDevices → MLUtilsExt 71842.6 ms ✓ Flux 181172.7 ms ✓ JudiLing 21 dependencies successfully precompiled in 534 seconds. 219 already precompiled. [ Info: Installing pyndl dependencies... [ Info: Running `conda install -q -y numpy` in root environment Channels: - conda-forge Platform: linux-64 Collecting package metadata (repodata.json): ...working... done Solving environment: ...working... done # All requested packages already installed. [ Info: Running `conda install -q -y cython` in root environment Channels: - conda-forge Platform: linux-64 Collecting package metadata (repodata.json): ...working... done Solving environment: ...working... done ## Package Plan ## environment location: /home/pkgeval/.julia/conda/3/x86_64 added / updated specs: - cython The following packages will be downloaded: package | build ---------------------------|----------------- cython-3.2.4 | py312h68e6be4_0 3.6 MB conda-forge ------------------------------------------------------------ Total: 3.6 MB The following NEW packages will be INSTALLED: cython conda-forge/linux-64::cython-3.2.4-py312h68e6be4_0 Preparing transaction: ...working... done Verifying transaction: ...working... done Executing transaction: ...working... done [ Info: Running `conda install -q -y pandas` in root environment Channels: - conda-forge Platform: linux-64 Collecting package metadata (repodata.json): ...working... done Solving environment: ...working... done ## Package Plan ## environment location: /home/pkgeval/.julia/conda/3/x86_64 added / updated specs: - pandas The following packages will be downloaded: package | build ---------------------------|----------------- pandas-2.3.3 | py312hf79963d_1 14.4 MB conda-forge python-dateutil-2.9.0.post0| pyhe01879c_2 228 KB conda-forge python-tzdata-2025.3 | pyhd8ed1ab_0 140 KB conda-forge pytz-2025.2 | pyhd8ed1ab_0 185 KB conda-forge six-1.17.0 | pyhe01879c_1 18 KB conda-forge ------------------------------------------------------------ Total: 15.0 MB The following NEW packages will be INSTALLED: pandas conda-forge/linux-64::pandas-2.3.3-py312hf79963d_1 python-dateutil conda-forge/noarch::python-dateutil-2.9.0.post0-pyhe01879c_2 python-tzdata conda-forge/noarch::python-tzdata-2025.3-pyhd8ed1ab_0 pytz conda-forge/noarch::pytz-2025.2-pyhd8ed1ab_0 six conda-forge/noarch::six-1.17.0-pyhe01879c_1 Preparing transaction: ...working... done Verifying transaction: ...working... done Executing transaction: ...working... done [ Info: Running `conda install -q -y xarray` in root environment Channels: - conda-forge Platform: linux-64 Collecting package metadata (repodata.json): ...working... done Solving environment: ...working... done ## Package Plan ## environment location: /home/pkgeval/.julia/conda/3/x86_64 added / updated specs: - xarray The following packages will be downloaded: package | build ---------------------------|----------------- xarray-2025.12.0 | pyhcf101f3_0 971 KB conda-forge ------------------------------------------------------------ Total: 971 KB The following NEW packages will be INSTALLED: xarray conda-forge/noarch::xarray-2025.12.0-pyhcf101f3_0 Preparing transaction: ...working... done Verifying transaction: ...working... done Executing transaction: ...working... done [ Info: Running `conda install -q -y netcdf4` in root environment Channels: - conda-forge Platform: linux-64 Collecting package metadata (repodata.json): ...working... done Solving environment: ...working... done ## Package Plan ## environment location: /home/pkgeval/.julia/conda/3/x86_64 added / updated specs: - netcdf4 The following packages will be downloaded: package | build ---------------------------|----------------- attr-2.5.2 | h39aace5_0 66 KB conda-forge blosc-1.21.6 | he440d0b_1 47 KB conda-forge cftime-1.6.5 | py312h4f23490_1 417 KB conda-forge hdf4-4.2.15 | h2a13503_7 739 KB conda-forge hdf5-1.14.6 |nompi_h1b119a7_104 3.5 MB conda-forge libaec-1.1.4 | h3f801dc_0 36 KB conda-forge libjpeg-turbo-3.1.2 | hb03c661_0 619 KB conda-forge libnetcdf-4.9.3 |nompi_h11f7409_103 851 KB conda-forge libzip-1.11.2 | h6991a6a_0 106 KB conda-forge netcdf4-1.7.4 |nompi_py312h25f8dc5_102 1.1 MB conda-forge snappy-1.2.2 | h03e3b7b_1 45 KB conda-forge zlib-1.3.1 | hb9d3cd8_2 90 KB conda-forge ------------------------------------------------------------ Total: 7.6 MB The following NEW packages will be INSTALLED: attr conda-forge/linux-64::attr-2.5.2-h39aace5_0 blosc conda-forge/linux-64::blosc-1.21.6-he440d0b_1 cftime conda-forge/linux-64::cftime-1.6.5-py312h4f23490_1 hdf4 conda-forge/linux-64::hdf4-4.2.15-h2a13503_7 hdf5 conda-forge/linux-64::hdf5-1.14.6-nompi_h1b119a7_104 libaec conda-forge/linux-64::libaec-1.1.4-h3f801dc_0 libjpeg-turbo conda-forge/linux-64::libjpeg-turbo-3.1.2-hb03c661_0 libnetcdf conda-forge/linux-64::libnetcdf-4.9.3-nompi_h11f7409_103 libzip conda-forge/linux-64::libzip-1.11.2-h6991a6a_0 netcdf4 conda-forge/linux-64::netcdf4-1.7.4-nompi_py312h25f8dc5_102 snappy conda-forge/linux-64::snappy-1.2.2-h03e3b7b_1 zlib conda-forge/linux-64::zlib-1.3.1-hb9d3cd8_2 Preparing transaction: ...working... done Verifying transaction: ...working... done Executing transaction: ...working... done [ Info: Installing pyndl... Collecting pyndl Downloading pyndl-1.2.4.tar.gz (38 kB) Installing build dependencies: started Installing build dependencies: finished with status 'done' Getting requirements to build wheel: started Getting requirements to build wheel: finished with status 'done' Preparing metadata (pyproject.toml): started Preparing metadata (pyproject.toml): finished with status 'done' Requirement already satisfied: Cython>=3.0.0 in /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/site-packages (from pyndl) (3.2.4) Requirement already satisfied: netCDF4>=1.6.0 in /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/site-packages (from pyndl) (1.7.4) Requirement already satisfied: numpy>=1.24.1 in /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/site-packages (from pyndl) (2.4.1) Requirement already satisfied: pandas>=1.4.3 in /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/site-packages (from pyndl) (2.3.3) Collecting scipy>=1.13.0 (from pyndl) Downloading scipy-1.17.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (62 kB) Requirement already satisfied: setuptools>=69.2.0 in /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/site-packages (from pyndl) (80.9.0) Collecting toml>=0.10.2 (from pyndl) Downloading toml-0.10.2-py2.py3-none-any.whl.metadata (7.1 kB) Requirement already satisfied: xarray>=2022.6.0 in /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/site-packages (from pyndl) (2025.12.0) Requirement already satisfied: cftime in /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/site-packages (from netCDF4>=1.6.0->pyndl) (1.6.5) Requirement already satisfied: certifi in /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/site-packages (from netCDF4>=1.6.0->pyndl) (2026.1.4) Requirement already satisfied: python-dateutil>=2.8.2 in /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/site-packages (from pandas>=1.4.3->pyndl) (2.9.0.post0) Requirement already satisfied: pytz>=2020.1 in /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/site-packages (from pandas>=1.4.3->pyndl) (2025.2) Requirement already satisfied: tzdata>=2022.7 in /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/site-packages (from pandas>=1.4.3->pyndl) (2025.3) Requirement already satisfied: six>=1.5 in /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas>=1.4.3->pyndl) (1.17.0) Requirement already satisfied: packaging>=24.1 in /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/site-packages (from xarray>=2022.6.0->pyndl) (25.0) Downloading scipy-1.17.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (35.0 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 35.0/35.0 MB 85.2 MB/s 0:00:00 Downloading toml-0.10.2-py2.py3-none-any.whl (16 kB) Building wheels for collected packages: pyndl Building wheel for pyndl (pyproject.toml): started Building wheel for pyndl (pyproject.toml): finished with status 'done' Created wheel for pyndl: filename=pyndl-1.2.4-cp312-cp312-manylinux_2_36_x86_64.whl size=419052 sha256=73ce21247e699de3d80a8a0ee8935447e3bd094bdabb6bed20c2095a355f9e81 Stored in directory: /home/pkgeval/.cache/pip/wheels/54/2e/5a/2e97aea70bfbbb0826ae8044c842fffdbd945459577bd5862d Successfully built pyndl Installing collected packages: toml, scipy, pyndl Successfully installed pyndl-1.2.4 scipy-1.17.0 toml-0.10.2 /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=476) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=476) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=476) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=476) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=476) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=476) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=476) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=476) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=476) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=476) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=476) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=476) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=476) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=476) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=476) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=476) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() making adjacency matrix... Test Summary: | Pass Total Time pyndl tests | 13 13 14m40.8s Test Summary: | Pass Total Time input tests | 27 27 18.4s Test Summary: | Pass Total Time cholesky tests | 10 10 16.8s Test Summary: | Pass Total Time frequency tests | 3 3 9.7s 6×7 DataFrame Row │ Data #vo voc oco coo oo# oca │ String15 Int64 Int64 Int64 Int64 Int64 Int64 ─────┼──────────────────────────────────────────────────── 1 │ vocoo 1 1 1 1 1 0 2 │ vocaas 1 1 0 0 0 1 3 │ vocat 1 1 0 0 0 1 4 │ vocaamus 1 1 0 0 0 1 5 │ vocaatis 1 1 0 0 0 1 6 │ vocant 1 1 0 0 0 1 6×7 DataFrame Row │ Data S1 S2 S3 S4 S5 S6 │ String15 Float64 Float64 Float64 Float64 Float64 Float64 ─────┼──────────────────────────────────────────────────────────────────────── 1 │ vocoo 15.2465 -20.8614 15.5945 -9.9225 0.401251 4.89594 2 │ vocaas 20.8716 -21.4455 19.1184 -8.76425 -5.05156 10.7338 3 │ vocat 22.2822 -29.0044 9.79204 -5.54939 -1.81678 4.56271 4 │ vocaamus 16.8365 -17.9527 8.56939 -8.22765 9.02656 -0.0304184 5 │ vocaatis 19.8971 -19.9789 10.4833 -6.74136 1.33763 3.6688 6 │ vocant 19.9549 -26.7096 7.54822 -4.31259 5.78011 -3.90409 6×7 DataFrame Row │ Data #vo voc oco coo oo# oca │ String Float64 Float64 Float64 Float64 Float64 Float64 ─────┼─────────────────────────────────────────────────────────────────────────────────────── 1 │ S1 -0.00141756 -0.00141756 -0.0236324 -0.0132617 -0.0108323 0.0222148 2 │ S2 -0.00273367 -0.00273367 0.00451568 0.000452137 0.00437199 -0.00724935 3 │ S3 -0.00153557 -0.00153557 -0.0191963 -0.00511315 -0.00986019 0.0176608 4 │ S4 -0.0013362 -0.0013362 0.00183685 0.00286875 0.00883363 -0.00317305 5 │ S5 -0.00294256 -0.00294256 -0.0174473 -0.00299004 0.0797393 0.0145048 6 │ S6 0.00229429 0.00229429 0.0123282 -0.00299352 0.0275983 -0.0100339 6×7 DataFrame Row │ Data #vo voc oco coo oo# oca │ String15 Float64 Float64 Float64 Float64 Float64 Float64 ─────┼───────────────────────────────────────────────────────────────────────────── 1 │ vocoo 0.988964 0.988964 0.862193 0.866111 0.869592 0.126771 2 │ vocaas 1.00243 1.00243 -0.0444658 -0.0342458 -0.0382966 1.0469 3 │ vocat 1.00112 1.00112 0.00442709 -0.0183283 0.0107253 0.996696 4 │ vocaamus 0.998892 0.998892 0.00706063 0.016144 0.0557306 0.991831 5 │ vocaatis 1.01126 1.01126 0.0808647 0.0567661 -0.0188942 0.930395 6 │ vocant 0.995767 0.995767 0.00154401 -0.0189361 -0.0530909 0.994223 6×7 DataFrame Row │ Data S1 S2 S3 S4 S5 S6 │ String Float64 Float64 Float64 Float64 Float64 Float64 ─────┼──────────────────────────────────────────────────────────────────────── 1 │ #vo 5.72851 -7.76108 3.5086 -2.02855 -0.436928 1.58815 2 │ voc 5.72851 -7.76108 3.5086 -2.02855 -0.436928 1.58815 3 │ oco 1.42971 -3.03506 1.62397 -1.31801 -0.234167 0.88979 4 │ coo 0.820988 0.0194814 1.38457 -0.821628 -0.0331436 0.074187 5 │ oo# 1.52235 -2.32403 5.54109 -3.70934 1.54308 0.75419 6 │ oca 4.29879 -4.72601 1.88464 -0.710533 -0.202761 0.698355 6×7 DataFrame Row │ Data S1 S2 S3 S4 S5 S6 │ String15 Float64 Float64 Float64 Float64 Float64 Float64 ─────┼────────────────────────────────────────────────────────────────────── 1 │ vocoo 15.2301 -20.8618 15.5668 -9.90607 0.401914 4.89446 2 │ vocaas 21.1856 -21.497 18.7371 -8.27066 -6.04744 10.5006 3 │ vocat 22.2289 -28.9307 9.7931 -5.54923 -1.80306 4.54148 4 │ vocaamus 16.8829 -18.2816 7.88645 -8.25267 8.51863 -1.95841 5 │ vocaatis 19.7887 -19.1196 10.4815 -6.73917 1.86 3.51972 6 │ vocant 20.6622 -26.5988 5.84837 -5.02826 4.97536 -3.94898 6×7 DataFrame Row │ Data #vo voc oco coo oo# oca │ String Int64 Int64 Int64 Int64 Int64 Int64 ─────┼────────────────────────────────────────────────── 1 │ #vo 0 1 0 0 0 0 2 │ voc 0 0 1 0 0 1 3 │ oco 0 0 0 1 0 0 4 │ coo 0 0 0 0 1 0 5 │ oo# 0 0 0 0 0 0 6 │ oca 0 0 0 0 0 0 6×7 DataFrame Row │ Data vocoo vocaas vocat vocaamus vocaatis vocant │ String15 Float64 Float64 Float64 Float64 Float64 Float64 ─────┼────────────────────────────────────────────────────────────────────── 1 │ vocoo 0.942076 0.383369 0.41285 0.371298 0.404084 0.365981 2 │ vocaas 0.322011 0.951319 0.523442 0.633156 0.57495 0.487577 3 │ vocat 0.381475 0.565054 0.964657 0.484204 0.463868 0.538595 4 │ vocaamus 0.292859 0.525276 0.449704 0.975416 0.494743 0.377306 5 │ vocaatis 0.325014 0.574024 0.476682 0.480271 0.947895 0.405188 6 │ vocant 0.330853 0.509106 0.5232 0.442983 0.400525 0.987605 6×7 DataFrame Row │ Data vocoo vocaas vocat vocaamus vocaatis vocant │ String15 Int64 Int64 Int64 Int64 Int64 Int64 ─────┼──────────────────────────────────────────────────────────── 1 │ vocoo 1 0 0 0 0 0 2 │ vocaas 0 1 0 0 0 0 3 │ vocat 0 0 1 0 0 0 4 │ vocaamus 0 0 0 1 0 0 5 │ vocaatis 0 0 0 0 1 0 6 │ vocant 0 0 0 0 0 1 Test Summary: | Total Time display tests | 0 53.6s ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:556 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:556 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:556 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:556 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:556 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:556 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:263 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:726 ┌ Warning: eval_SC_loose: You set k=1. Note that if there are duplicate vectors in the S/C matrix, it is not guaranteed that eval_SC_loose with k=1 gives the same result as eval_SC. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:728 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:726 ┌ Warning: eval_SC_loose: You set k=1. Note that if there are duplicate vectors in the S/C matrix, it is not guaranteed that eval_SC_loose with k=1 gives the same result as eval_SC. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:728 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:726 ┌ Warning: eval_SC_loose: You set k=1. Note that if there are duplicate vectors in the S/C matrix, it is not guaranteed that eval_SC_loose with k=1 gives the same result as eval_SC. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:728 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:726 ┌ Warning: eval_SC_loose: You set k=1. Note that if there are duplicate vectors in the S/C matrix, it is not guaranteed that eval_SC_loose with k=1 gives the same result as eval_SC. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:728 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:726 ┌ Warning: eval_SC_loose: You set k=1. Note that if there are duplicate vectors in the S/C matrix, it is not guaranteed that eval_SC_loose with k=1 gives the same result as eval_SC. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:728 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:726 ┌ Warning: eval_SC_loose: You set k=1. Note that if there are duplicate vectors in the S/C matrix, it is not guaranteed that eval_SC_loose with k=1 gives the same result as eval_SC. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:728 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:726 ┌ Warning: eval_SC_loose: You set k=1. Note that if there are duplicate vectors in the S/C matrix, it is not guaranteed that eval_SC_loose with k=1 gives the same result as eval_SC. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:728 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:726 ┌ Warning: eval_SC_loose: You set k=1. Note that if there are duplicate vectors in the S/C matrix, it is not guaranteed that eval_SC_loose with k=1 gives the same result as eval_SC. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:728 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:726 ┌ Warning: eval_SC_loose: You set k=1. Note that if there are duplicate vectors in the S/C matrix, it is not guaranteed that eval_SC_loose with k=1 gives the same result as eval_SC. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:728 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:726 ┌ Warning: accuracy_comprehension: This dataset contains homophones/homographs. Note that some of the results on the correctness of comprehended base/inflections may be misleading. See documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:88 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:263 Test Summary: | Pass Total Time eval tests | 150 150 1m02.9s Test Summary: | Total Time find_path tests | 0 6.7s Test Summary: | Pass Total Time make_adjacency_matrix tests | 7 7 0.8s true Test Summary: | Pass Total Time make_cue_matrix tests | 21 21 7.4s true Test Summary: | Pass Total Time make_semantic_matrix tests | 72 72 7.8s Test Summary: | Total Time make_yt_matrix tests | 0 0.0s Test Summary: | Pass Total Time output_matrix tests | 10 10 16.1s Test Summary: | Total Time preprocess tests | 0 1.1s ┌ Warning: test_combo: test_combo is deprecated. While it will remain in the package it is no longer actively maintained. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/test_combo.jl:132 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/eval.jl:263 ┌ Warning: test_combo: test_combo is deprecated. While it will remain in the package it is no longer actively maintained. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/test_combo.jl:132 ┌ Warning: test_combo: test_combo is deprecated. While it will remain in the package it is no longer actively maintained. └ @ JudiLing ~/.julia/packages/JudiLing/vjHJF/src/test_combo.jl:132 Test Summary: | Total Time test_combo tests | 0 22.4s Test Summary: | Pass Total Time wh tests | 5 5 5.3s Precompiling Flux... 65965.5 ms ✓ Zygote 4892.8 ms ✓ MLDataDevices → ZygoteExt 105335.3 ms ✓ Flux 3 dependencies successfully precompiled in 179 seconds. 155 already precompiled. Precompiling StatsFunsChainRulesCoreExt... 4056.8 ms ✓ StatsFuns → StatsFunsChainRulesCoreExt 1 dependency successfully precompiled in 4 seconds. 30 already precompiled. Precompiling DistributionsChainRulesCoreExt... 5141.8 ms ✓ Distributions → DistributionsChainRulesCoreExt 1 dependency successfully precompiled in 8 seconds. 56 already precompiled. Precompiling StatsFunsInverseFunctionsExt... 2297.6 ms ✓ StatsFuns → StatsFunsInverseFunctionsExt 1 dependency successfully precompiled in 3 seconds. 27 already precompiled. Precompiling ZygoteDistancesExt... 7146.2 ms ✓ Zygote → ZygoteDistancesExt 1 dependency successfully precompiled in 8 seconds. 74 already precompiled. Setting up model... ┌ Warning: No functional GPU backend found! Defaulting to CPU. │ │ 1. If no GPU is available, nothing needs to be done. Set `MLDATADEVICES_SILENCE_WARN_NO_GPU=1` to silence this warning. │ 2. If GPU is available, load the corresponding trigger package. │ a. `CUDA.jl` and `cuDNN.jl` (or just `LuxCUDA.jl`) for NVIDIA CUDA Support. │ b. `AMDGPU.jl` for AMD GPU ROCM Support. │ c. `Metal.jl` for Apple Metal GPU Support. (Experimental) │ d. `oneAPI.jl` for Intel oneAPI GPU Support. (Experimental) │ e. `OpenCL.jl` for OpenCL support. (Experimental) └ @ MLDataDevices.Internal ~/.julia/packages/MLDataDevices/7bVRF/src/internal.jl:114 model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up data structures... Setting up optimizer... Training... Progress: 2%|▉ | ETA: 1:34:40 Training loss: 136.2665052702724   Progress: 64%|██████████████████████████▎ | ETA: 0:01:07 Training loss: 11.349522587968652   Progress: 84%|██████████████████████████████████▌ | ETA: 0:00:23 Training loss: 6.3262308384091765   Progress: 89%|████████████████████████████████████▌ | ETA: 0:00:15 Training loss: 5.682227675346057   Progress: 94%|██████████████████████████████████████▌ | ETA: 0:00:08 Training loss: 5.108680811532662   Progress: 99%|████████████████████████████████████████▋| ETA: 0:00:01 Training loss: 4.5827585391573   Progress: 100%|█████████████████████████████████████████| Time: 0:01:59 Training loss: 4.482754494799172 Setting up model... model = Chain(Dense(200 => 1000, relu), Dense(1000 => 33)) Setting up data structures... Setting up optimizer... Training... ┌ Warning: Layer with Float32 parameters got Float64 input. │ The input will be converted, but any earlier layers may be very slow. │ layer = Dense(200 => 1000, relu) # 201_000 parameters │ summary(x) = "200×3 Matrix{Float64}" └ @ Flux ~/.julia/packages/Flux/WMUyh/src/layers/stateless.jl:60 Progress: 2%|▉ | ETA: 0:23:04 Training loss: 7.5811825   Progress: 5%|██ | ETA: 0:08:59 Training loss: 10.196899   Progress: 9%|███▊ | ETA: 0:04:48 Training loss: 7.591371   Progress: 14%|█████▊ | ETA: 0:02:56 Training loss: 0.65303767   Progress: 19%|███████▊ | ETA: 0:02:02 Training loss: 1.79281   Progress: 24%|█████████▉ | ETA: 0:01:31 Training loss: 0.88282037   Progress: 29%|███████████▉ | ETA: 0:01:11 Training loss: 0.19883391   Progress: 34%|██████████████ | ETA: 0:00:56 Training loss: 0.47882608   Progress: 39%|████████████████ | ETA: 0:00:46 Training loss: 0.04718313   Progress: 44%|██████████████████ | ETA: 0:00:37 Training loss: 0.13567324   Progress: 49%|████████████████████▏ | ETA: 0:00:31 Training loss: 0.061197184   Progress: 54%|██████████████████████▏ | ETA: 0:00:25 Training loss: 0.014187865   Progress: 59%|████████████████████████▎ | ETA: 0:00:21 Training loss: 0.0360049   Progress: 64%|██████████████████████████▎ | ETA: 0:00:17 Training loss: 0.008427495   Progress: 69%|████████████████████████████▎ | ETA: 0:00:13 Training loss: 0.0048226444   Progress: 74%|██████████████████████████████▍ | ETA: 0:00:11 Training loss: 0.0077873045   Progress: 79%|████████████████████████████████▍ | ETA: 0:00:08 Training loss: 0.001863128   Progress: 84%|██████████████████████████████████▌ | ETA: 0:00:06 Training loss: 0.00085880555   Progress: 89%|████████████████████████████████████▌ | ETA: 0:00:04 Training loss: 0.0015391329   Progress: 94%|██████████████████████████████████████▌ | ETA: 0:00:02 Training loss: 0.00068362546   Progress: 98%|████████████████████████████████████████▏| ETA: 0:00:01 Training loss: 0.00022815836   Progress: 100%|█████████████████████████████████████████| Time: 0:00:30 Training loss: 0.00021136894 Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up data structures... Setting up optimizer... Training... Progress: 2%|▉ | ETA: 0:02:30 Training loss: 136.2665052702724 Validation loss: 115.66047845446742 Validation accuracy: 0.0       Progress: 5%|██ | ETA: 0:01:01 Training loss: 133.48384257801834 Validation loss: 113.76005568180845 Validation accuracy: 0.0       Progress: 8%|███▎ | ETA: 0:00:38 Training loss: 130.2244481256631 Validation loss: 111.67203162481937 Validation accuracy: 0.0       Progress: 13%|█████▍ | ETA: 0:00:23 Training loss: 122.9682799017793 Validation loss: 107.5220194869742 Validation accuracy: 0.0       Progress: 18%|███████▍ | ETA: 0:00:16 Training loss: 112.87174311991774 Validation loss: 102.34176588599477 Validation accuracy: 0.0       Progress: 23%|█████████▍ | ETA: 0:00:12 Training loss: 99.77496584310455 Validation loss: 96.15977154356932 Validation accuracy: 0.0       Progress: 28%|███████████▌ | ETA: 0:00:10 Training loss: 84.1947632063882 Validation loss: 89.37636856004738 Validation accuracy: 0.0       Progress: 33%|█████████████▌ | ETA: 0:00:08 Training loss: 67.3383769995764 Validation loss: 82.7465966601977 Validation accuracy: 0.0       Progress: 38%|███████████████▋ | ETA: 0:00:07 Training loss: 50.93859374630417 Validation loss: 77.17948277751736 Validation accuracy: 0.0       Progress: 43%|█████████████████▋ | ETA: 0:00:06 Training loss: 36.77646524993327 Validation loss: 73.35334209798748 Validation accuracy: 0.0       Progress: 48%|███████████████████▋ | ETA: 0:00:05 Training loss: 26.004763411832396 Validation loss: 71.37241363306589 Validation accuracy: 0.0       Progress: 52%|█████████████████████▍ | ETA: 0:00:04 Training loss: 20.003568121576137 Validation loss: 70.66919529345951 Validation accuracy: 0.0       Progress: 56%|███████████████████████ | ETA: 0:00:04 Training loss: 15.96152919257431 Validation loss: 70.22935654668183 Validation accuracy: 0.0       Progress: 60%|████████████████████████▋ | ETA: 0:00:03 Training loss: 13.26558732911634 Validation loss: 69.72084599937997 Validation accuracy: 0.0       Progress: 64%|██████████████████████████▎ | ETA: 0:00:03 Training loss: 11.349522587968652 Validation loss: 69.06982070436703 Validation accuracy: 0.0       Progress: 68%|███████████████████████████▉ | ETA: 0:00:02 Training loss: 9.867303707987764 Validation loss: 68.35172756912476 Validation accuracy: 0.0       Progress: 73%|█████████████████████████████▉ | ETA: 0:00:02 Training loss: 8.405613313483148 Validation loss: 67.54859583015724 Validation accuracy: 0.0       Progress: 78%|████████████████████████████████ | ETA: 0:00:01 Training loss: 7.297831295560228 Validation loss: 67.03303518037602 Validation accuracy: 0.0       Progress: 82%|█████████████████████████████████▋ | ETA: 0:00:01 Training loss: 6.617918412801383 Validation loss: 66.81097392553144 Validation accuracy: 0.0       Progress: 87%|███████████████████████████████████▋ | ETA: 0:00:01 Training loss: 5.928623288983933 Validation loss: 66.67087933064991 Validation accuracy: 0.0       Progress: 92%|█████████████████████████████████████▊ | ETA: 0:00:00 Training loss: 5.331857688099792 Validation loss: 66.64499378080498 Validation accuracy: 0.0       Progress: 100%|█████████████████████████████████████████| Time: 0:00:05 Training loss: 4.482754494799172 Validation loss: 66.65935106439977 Validation accuracy: 0.0 Setting up model... model = Chain(Dense(200 => 1000, relu), Dense(1000 => 33)) Setting up data structures... Setting up optimizer... Training... Progress: 2%|▉ | ETA: 0:03:19 Training loss: 7.5811825 Validation loss: 23.362167 Validation accuracy: 0.0       Progress: 12%|████▉ | ETA: 0:00:31 Training loss: 2.565964 Validation loss: 12.899682 Validation accuracy: 0.1667       Progress: 17%|███████ | ETA: 0:00:22 Training loss: 2.9981523 Validation loss: 11.494986 Validation accuracy: 0.1667       Progress: 21%|████████▋ | ETA: 0:00:17 Training loss: 0.32272664 Validation loss: 10.852688 Validation accuracy: 0.0       Progress: 29%|███████████▉ | ETA: 0:00:11 Training loss: 0.19883391 Validation loss: 10.369055 Validation accuracy: 0.0       Progress: 37%|███████████████▏ | ETA: 0:00:08 Training loss: 0.124336846 Validation loss: 10.04429 Validation accuracy: 0.0       Progress: 45%|██████████████████▌ | ETA: 0:00:06 Training loss: 0.07518856 Validation loss: 9.988228 Validation accuracy: 0.0       Progress: 53%|█████████████████████▊ | ETA: 0:00:05 Training loss: 0.03508255 Validation loss: 9.8308 Validation accuracy: 0.0       Progress: 62%|█████████████████████████▍ | ETA: 0:00:03 Training loss: 0.0053788233 Validation loss: 9.807602 Validation accuracy: 0.0       Progress: 69%|████████████████████████████▎ | ETA: 0:00:02 Training loss: 0.0048226444 Validation loss: 9.775978 Validation accuracy: 0.0       Progress: 79%|████████████████████████████████▍ | ETA: 0:00:01 Training loss: 0.001863128 Validation loss: 9.741303 Validation accuracy: 0.0       Progress: 88%|████████████████████████████████████▏ | ETA: 0:00:01 Training loss: 0.0017720652 Validation loss: 9.7679405 Validation accuracy: 0.0       Progress: 98%|████████████████████████████████████████▏| ETA: 0:00:00 Training loss: 0.00022815836 Validation loss: 9.759056 Validation accuracy: 0.0       Progress: 100%|█████████████████████████████████████████| Time: 0:00:05 Training loss: 0.00021136894 Validation loss: 9.768948 Validation accuracy: 0.0 Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up data structures... Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up data structures... Setting up optimizer... Training... 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Setting up optimizer... Training... 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Setting up optimizer... Training... 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Setting up optimizer... Training... 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Setting up optimizer... Training... 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Setting up optimizer... Training... 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Setting up optimizer... Training... Progress: 8%|███▎ | ETA: 0:00:01 Training loss: 124.32594551906992 Validation loss: 113.24866127177317 Validation accuracy: 0.0       Progress: 19%|███████▊ | ETA: 0:00:01 Training loss: 103.48790585704384 Validation loss: 103.69620893758277 Validation accuracy: 0.0   Setting up model... model = Chain(Dense(32 => 1000, relu), Dense(1000 => 200)) Setting up data structures... Setting up optimizer... Training... Progress: 8%|███▎ | ETA: 0:00:01 Training loss: 124.26587698159236 Validation loss: 113.0557084250126 Validation accuracy: 0.0   Setting up model... model = Chain(Dense(32 => 1000, relu), Dense(1000 => 200)) Setting up data structures... Setting up optimizer... Training... 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Setting up optimizer... Training... 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Setting up optimizer... Training... 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Training loss: 2.8157015e-13 Validation loss: 8.431296 Validation accuracy: 0.1667       Progress: 100%|█████████████████████████████████████████| Time: 0:00:05 Training loss: 2.7676104e-13 Validation loss: 8.431295 Validation accuracy: 0.1667 Setting up model... model = Chain(Dense(200 => 1000, relu), Dense(1000 => 33)) Setting up data structures... Setting up optimizer... Training... Progress: 5%|██ | ETA: 0:00:02 Training loss: 52.860466 Validation loss: 44.584724 Validation accuracy: 0.0       Progress: 10%|████▏ | ETA: 0:00:02 Training loss: 47.286404 Validation loss: 41.801083 Validation accuracy: 0.0       Progress: 15%|██████▏ | ETA: 0:00:02 Training loss: 42.13684 Validation loss: 39.198555 Validation accuracy: 0.0       Progress: 20%|████████▎ | ETA: 0:00:02 Training loss: 37.41176 Validation loss: 36.77913 Validation accuracy: 0.0       Progress: 25%|██████████▎ | ETA: 0:00:02 Training loss: 33.093 Validation loss: 34.542213 Validation accuracy: 0.0       Progress: 30%|████████████▎ | ETA: 0:00:02 Training loss: 29.17408 Validation loss: 32.492012 Validation accuracy: 0.0       Progress: 35%|██████████████▍ | ETA: 0:00:02 Training loss: 25.641188 Validation loss: 30.616184 Validation accuracy: 0.0       Progress: 39%|████████████████ | ETA: 0:00:02 Training loss: 23.08317 Validation loss: 29.236883 Validation accuracy: 0.0       Progress: 44%|██████████████████ | ETA: 0:00:01 Training loss: 20.198547 Validation loss: 27.65979 Validation accuracy: 0.0       Progress: 49%|████████████████████▏ | ETA: 0:00:01 Training loss: 17.63769 Validation loss: 26.240067 Validation accuracy: 0.0       Progress: 54%|██████████████████████▏ | ETA: 0:00:01 Training loss: 15.368069 Validation loss: 24.96125 Validation accuracy: 0.0       Progress: 58%|███████████████████████▊ | ETA: 0:00:01 Training loss: 13.744491 Validation loss: 24.030487 Validation accuracy: 0.0       Progress: 63%|█████████████████████████▉ | ETA: 0:00:01 Training loss: 11.939568 Validation loss: 22.975124 Validation accuracy: 0.0       Progress: 67%|███████████████████████████▌ | ETA: 0:00:01 Training loss: 10.662196 Validation loss: 22.210947 Validation accuracy: 0.0       Progress: 72%|█████████████████████████████▌ | ETA: 0:00:01 Training loss: 9.248027 Validation loss: 21.347275 Validation accuracy: 0.0       Progress: 77%|███████████████████████████████▋ | ETA: 0:00:01 Training loss: 8.016506 Validation loss: 20.575783 Validation accuracy: 0.0       Progress: 82%|█████████████████████████████████▋ | ETA: 0:00:00 Training loss: 6.9473243 Validation loss: 19.888271 Validation accuracy: 0.0       Progress: 87%|███████████████████████████████████▋ | ETA: 0:00:00 Training loss: 6.021545 Validation loss: 19.274435 Validation accuracy: 0.0       Progress: 92%|█████████████████████████████████████▊ | ETA: 0:00:00 Training loss: 5.2250338 Validation loss: 18.724663 Validation accuracy: 0.0       Progress: 96%|███████████████████████████████████████▍ | ETA: 0:00:00 Training loss: 4.6686864 Validation loss: 18.327805 Validation accuracy: 0.0       Progress: 100%|█████████████████████████████████████████| Time: 0:00:02 Training loss: 4.177827 Validation loss: 17.968048 Validation accuracy: 0.0 Setting up model... model = Chain(Dense(200 => 1000, relu), Dense(1000 => 33)) Setting up data structures... Setting up optimizer... Training... Progress: 6%|██▌ | ETA: 0:00:02 Training loss: 0.19461608 Validation loss: 249.20517 Validation accuracy: 0.0       Progress: 16%|██████▌ | ETA: 0:00:01 Training loss: 0.32895526 Validation loss: 1251.8013 Validation accuracy: 0.0       Progress: 26%|██████████▋ | ETA: 0:00:01 Training loss: 0.40056175 Validation loss: 1983.2764 Validation accuracy: 0.0       Progress: 36%|██████████████▊ | ETA: 0:00:01 Training loss: 0.4314163 Validation loss: 2344.0608 Validation accuracy: 0.0       Progress: 46%|██████████████████▉ | ETA: 0:00:01 Training loss: 0.44331276 Validation loss: 2498.1265 Validation accuracy: 0.0       Progress: 56%|███████████████████████ | ETA: 0:00:00 Training loss: 0.44717836 Validation loss: 2559.7144 Validation accuracy: 0.0       Progress: 66%|███████████████████████████ | ETA: 0:00:00 Training loss: 0.44776955 Validation loss: 2583.5254 Validation accuracy: 0.0       Progress: 76%|███████████████████████████████▏ | ETA: 0:00:00 Training loss: 0.44702828 Validation loss: 2592.5786 Validation accuracy: 0.0       Progress: 86%|███████████████████████████████████▎ | ETA: 0:00:00 Training loss: 0.44572076 Validation loss: 2595.9968 Validation accuracy: 0.0       Progress: 96%|███████████████████████████████████████▍ | ETA: 0:00:00 Training loss: 0.4441453 Validation loss: 2597.3071 Validation accuracy: 0.0       Progress: 100%|█████████████████████████████████████████| Time: 0:00:01 Training loss: 0.44346893 Validation loss: 2597.5679 Validation accuracy: 0.0 Setting up model... model = Chain(Dense(200 => 200, relu), Dense(200 => 33)) Setting up data structures... Setting up optimizer... Training... Progress: 14%|█████▊ | ETA: 0:00:01 Training loss: 4.814867 Validation loss: 27.095016 Validation accuracy: 0.1667       Progress: 37%|███████████████▏ | ETA: 0:00:00 Training loss: 0.38192987 Validation loss: 23.514156 Validation accuracy: 0.1667       Progress: 65%|██████████████████████████▋ | ETA: 0:00:00 Training loss: 0.025932139 Validation loss: 23.15291 Validation accuracy: 0.1667       Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 Training loss: 0.0005761981 Validation loss: 23.222952 Validation accuracy: 0.1667 ┌ Warning: `Flux.params(m...)` is deprecated. Use `Flux.trainable(model)` for parameter collection, │ and the explicit `gradient(m -> loss(m, x, y), model)` for gradient computation. └ @ Flux ~/.julia/packages/Flux/WMUyh/src/deprecations.jl:93 Setting up model... model = Chain(Dense(200 => 500), Dense(500 => 500), Dense(500 => 33)) Setting up data structures... Setting up optimizer... Training... Progress: 2%|▉ | ETA: 0:03:18 Training loss: 299.4347 Validation loss: 91.30579 Validation accuracy: 0.1667       Progress: 4%|█▋ | ETA: 0:01:40 Training loss: 32.32986 Validation loss: 69.95957 Validation accuracy: 0.3333       Progress: 11%|████▌ | ETA: 0:00:35 Training loss: 20.59612 Validation loss: 52.317017 Validation accuracy: 0.3333       Progress: 16%|██████▌ | ETA: 0:00:23 Training loss: 1.6595192 Validation loss: 42.598343 Validation accuracy: 0.3333       Progress: 23%|█████████▍ | ETA: 0:00:15 Training loss: 3.7717888 Validation loss: 42.465538 Validation accuracy: 0.3333       Progress: 31%|████████████▊ | ETA: 0:00:10 Training loss: 2.887811 Validation loss: 41.80809 Validation accuracy: 0.3333       Progress: 38%|███████████████▋ | ETA: 0:00:08 Training loss: 0.3334595 Validation loss: 41.96548 Validation accuracy: 0.3333       Progress: 45%|██████████████████▌ | ETA: 0:00:06 Training loss: 0.46892768 Validation loss: 41.536415 Validation accuracy: 0.3333       Progress: 52%|█████████████████████▍ | ETA: 0:00:05 Training loss: 0.20027825 Validation loss: 41.46425 Validation accuracy: 0.3333       Progress: 59%|████████████████████████▎ | ETA: 0:00:04 Training loss: 0.060498055 Validation loss: 41.195854 Validation accuracy: 0.3333       Progress: 66%|███████████████████████████ | ETA: 0:00:03 Training loss: 0.065153345 Validation loss: 41.362827 Validation accuracy: 0.3333       Progress: 74%|██████████████████████████████▍ | ETA: 0:00:02 Training loss: 0.005717308 Validation loss: 41.16881 Validation accuracy: 0.3333       Progress: 81%|█████████████████████████████████▎ | ETA: 0:00:01 Training loss: 0.010780984 Validation loss: 41.270126 Validation accuracy: 0.3333       Progress: 88%|████████████████████████████████████▏ | ETA: 0:00:01 Training loss: 0.004624473 Validation loss: 41.269184 Validation accuracy: 0.3333       Progress: 95%|███████████████████████████████████████ | ETA: 0:00:00 Training loss: 0.00095488457 Validation loss: 41.26852 Validation accuracy: 0.3333       Progress: 100%|█████████████████████████████████████████| Time: 0:00:06 Training loss: 0.0018686217 Validation loss: 41.273216 Validation accuracy: 0.3333 Setting up model... model = Chain(Dense(200 => 1000, relu), Dense(1000 => 33), σ) Setting up data structures... Setting up optimizer... Training... Progress: 2%|▉ | ETA: 0:46:24 Training loss: 0.39982933 Validation loss: 1.9066198 Validation accuracy: 0.0       Progress: 12%|████▉ | ETA: 0:06:58 Training loss: 0.0063018384 Validation loss: 3.7745562 Validation accuracy: 0.0       Progress: 22%|█████████ | ETA: 0:03:22 Training loss: 0.0003009053 Validation loss: 3.9533677 Validation accuracy: 0.0       Progress: 32%|█████████████▏ | ETA: 0:02:01 Training loss: 4.0143692e-5 Validation loss: 3.9900155 Validation accuracy: 0.0       Progress: 42%|█████████████████▎ | ETA: 0:01:19 Training loss: 2.0935218e-5 Validation loss: 4.000949 Validation accuracy: 0.0       Progress: 52%|█████████████████████▍ | ETA: 0:00:53 Training loss: 1.4310478e-5 Validation loss: 4.010237 Validation accuracy: 0.0       Progress: 62%|█████████████████████████▍ | ETA: 0:00:35 Training loss: 1.0096172e-5 Validation loss: 4.016531 Validation accuracy: 0.0       Progress: 72%|█████████████████████████████▌ | ETA: 0:00:22 Training loss: 7.680028e-6 Validation loss: 4.022082 Validation accuracy: 0.0       Progress: 82%|█████████████████████████████████▋ | ETA: 0:00:13 Training loss: 6.2769373e-6 Validation loss: 4.02531 Validation accuracy: 0.0       Progress: 92%|█████████████████████████████████████▊ | ETA: 0:00:05 Training loss: 5.4044094e-6 Validation loss: 4.027918 Validation accuracy: 0.0       Progress: 100%|█████████████████████████████████████████| Time: 0:00:57 Training loss: 4.923299e-6 Validation loss: 4.0296454 Validation accuracy: 0.0 Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up data structures... Setting up optimizer... Training... Progress: 3%|█▎ | ETA: 0:00:04 Training loss: 135.33196816308538   Progress: 8%|███▎ | ETA: 0:00:03 Training loss: 130.13941916840923   Progress: 13%|█████▍ | ETA: 0:00:02 Training loss: 122.78921984408221   Progress: 18%|███████▍ | ETA: 0:00:02 Training loss: 112.60239292227845   Progress: 23%|█████████▍ | ETA: 0:00:02 Training loss: 99.43726102222382   Progress: 28%|███████████▌ | ETA: 0:00:02 Training loss: 83.72956161694948   Progress: 33%|█████████████▌ | ETA: 0:00:02 Training loss: 66.69962191989026   Progress: 38%|███████████████▋ | ETA: 0:00:02 Training loss: 50.095712301721555   Progress: 43%|█████████████████▋ | ETA: 0:00:01 Training loss: 35.774255217729554   Progress: 48%|███████████████████▋ | ETA: 0:00:01 Training loss: 25.008041992360983   Progress: 53%|█████████████████████▊ | ETA: 0:00:01 Training loss: 18.01171160590189   Progress: 58%|███████████████████████▊ | ETA: 0:00:01 Training loss: 13.896588559213788   Progress: 63%|█████████████████████████▉ | ETA: 0:00:01 Training loss: 11.340939385423454   Progress: 68%|███████████████████████████▉ | ETA: 0:00:01 Training loss: 9.488460841826337   Progress: 73%|█████████████████████████████▉ | ETA: 0:00:01 Training loss: 8.055084350962472   Progress: 78%|████████████████████████████████ | ETA: 0:00:01 Training loss: 6.977868440421696   Progress: 83%|██████████████████████████████████ | ETA: 0:00:00 Training loss: 6.173659580727615   Progress: 88%|████████████████████████████████████▏ | ETA: 0:00:00 Training loss: 5.522939059141304   Progress: 93%|██████████████████████████████████████▏ | ETA: 0:00:00 Training loss: 4.944699663371199   Progress: 97%|███████████████████████████████████████▊ | ETA: 0:00:00 Training loss: 4.515641002083727   Progress: 100%|█████████████████████████████████████████| Time: 0:00:02 Training loss: 4.211690263599279 Setting up model... model = Chain(Dense(200 => 1000, relu), Dense(1000 => 33)) Setting up data structures... Setting up optimizer... Training... Progress: 5%|██ | ETA: 0:00:02 Training loss: 7.911422   Progress: 10%|████▏ | ETA: 0:00:02 Training loss: 7.334908   Progress: 15%|██████▏ | ETA: 0:00:02 Training loss: 2.432165   Progress: 20%|████████▎ | ETA: 0:00:02 Training loss: 0.29070842   Progress: 25%|██████████▎ | ETA: 0:00:02 Training loss: 1.2864163   Progress: 30%|████████████▎ | ETA: 0:00:02 Training loss: 0.15564735   Progress: 35%|██████████████▍ | ETA: 0:00:02 Training loss: 0.26175955   Progress: 40%|████████████████▍ | ETA: 0:00:01 Training loss: 0.20543401   Progress: 45%|██████████████████▌ | ETA: 0:00:01 Training loss: 0.019336345   Progress: 50%|████████████████████▌ | ETA: 0:00:01 Training loss: 0.08581792   Progress: 55%|██████████████████████▌ | ETA: 0:00:01 Training loss: 0.028387548   Progress: 60%|████████████████████████▋ | ETA: 0:00:01 Training loss: 0.007130475   Progress: 64%|██████████████████████████▎ | ETA: 0:00:01 Training loss: 0.020535626   Progress: 69%|████████████████████████████▎ | ETA: 0:00:01 Training loss: 0.0024874364   Progress: 74%|██████████████████████████████▍ | ETA: 0:00:01 Training loss: 0.003814346   Progress: 79%|████████████████████████████████▍ | ETA: 0:00:01 Training loss: 0.004671381   Progress: 84%|██████████████████████████████████▌ | ETA: 0:00:00 Training loss: 0.0011338764   Progress: 89%|████████████████████████████████████▌ | ETA: 0:00:00 Training loss: 0.0003716756   Progress: 94%|██████████████████████████████████████▌ | ETA: 0:00:00 Training loss: 0.0008212495   Progress: 99%|████████████████████████████████████████▋| ETA: 0:00:00 Training loss: 0.000521733   Progress: 100%|█████████████████████████████████████████| Time: 0:00:02 Training loss: 0.00052282447 Making fac C ========== Timestep 1 Calculating Yt... Calculating Mt... Auto mode: Sparsity: 0.0101010101010101 Returning a sparse matrix format Calculating Ythat... Sparsity: 0.030303030303030304 Finding paths... ========== Timestep 2 average 1.0 of paths currently Calculating Yt... Calculating Mt... Auto mode: Sparsity: 0.0101010101010101 Returning a sparse matrix format Calculating Ythat... Sparsity: 0.030303030303030304 Finding paths... ========== Timestep 3 average 1.0 of paths currently Calculating Yt... Calculating Mt... Auto mode: Sparsity: 0.0202020202020202 Returning a sparse matrix format Calculating Ythat... Sparsity: 0.06060606060606061 Finding paths... ========== Timestep 4 average 1.0 of paths currently Calculating Yt... Calculating Mt... Auto mode: Sparsity: 0.030303030303030304 Returning a sparse matrix format Calculating Ythat... Sparsity: 0.09090909090909091 Finding paths... ========== Timestep 5 average 1.0 of paths currently Calculating Yt... Calculating Mt... Auto mode: Sparsity: 0.030303030303030304 Returning a sparse matrix format Calculating Ythat... Sparsity: 0.09090909090909091 Finding paths... ========== Timestep 6 average 0.3333333333333333 of paths currently Calculating Yt... Calculating Mt... Auto mode: Sparsity: 0.0101010101010101 Returning a sparse matrix format Calculating Ythat... Sparsity: 0.030303030303030304 Finding paths... ========== Timestep 7 average 0.0 of paths currently Calculating Yt... Calculating Mt... Auto mode: Sparsity: 0.0 Returning a sparse matrix format Calculating Ythat... Sparsity: 0.0 Finding paths... Evaluating paths... average 1.0 of paths to evaluate Progress: 67%|███████████████████████████▍ | ETA: 0:00:01 Progress: 100%|█████████████████████████████████████████| Time: 0:00:01 Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up optimizer... Setting up data for evaluation... Setting up data loader... Training... Progress: 67%|███████████████████████████▍ | ETA: 0:00:05 Step loss: 131.14099893438816 Overall loss: 135.85572493843063 Overall accuracy: 0.3333       Progress: 100%|█████████████████████████████████████████| Time: 0:00:10 Step loss: 146.49485746489026 Overall loss: 135.1610744416433 Overall accuracy: 0.3333 Done! Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up optimizer... Setting up data for evaluation... Setting up data loader... Training... Progress: 67%|███████████████████████████▍ | ETA: 0:00:00 Step loss: 130.16812457299167 Overall loss: 134.46400128629588 Overall accuracy: 0.3333       Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 Step loss: 144.39176453339 Overall loss: 132.97095160544072 Overall accuracy: 0.6667 Done! Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up optimizer... Setting up data for evaluation... Setting up data loader... Training... Progress: 33%|█████████████▋ | ETA: 0:00:00 Step loss: 145.65858509349266 Overall loss: 134.1443622338372 Overall accuracy: 0.3333       Progress: 75%|██████████████████████████████▊ | ETA: 0:00:00 Step loss: 140.70036382302956 Overall loss: 130.01272937505908 Overall accuracy: 0.6667       Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 Step loss: 136.7197594604057 Overall loss: 126.98830851273253 Overall accuracy: 0.6667 Done! Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up optimizer... Setting up data for evaluation... Setting up data loader... Training... Progress: 44%|██████████████████▎ | ETA: 0:00:00 Step loss: 129.92146674246942 Overall loss: 134.244940628968 Overall accuracy: 1.0       Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 Step loss: 125.3289995899268 Overall loss: 130.24475423530154 Overall accuracy: 0.3333 Done! Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up optimizer... Setting up data for evaluation... Setting up data loader... Training... Progress: 80%|████████████████████████████████▊ | ETA: 0:00:00 Step loss: 127.25452932074396 Overall loss: 131.33763075062595 Overall accuracy: 0.6667   Done! Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up optimizer... Setting up data for evaluation... Setting up data loader... Training... Progress: 67%|███████████████████████████▍ | ETA: 0:00:00 Step loss: 136.2665052695647 Overall loss: 135.36709303725198 Overall accuracy: 1.0       Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 Step loss: 135.36709303725198 Overall loss: 134.44507860114422 Overall accuracy: 1.0 Done! Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up optimizer... Setting up data for evaluation... Setting up data loader... Training... Progress: 33%|█████████████▋ | ETA: 0:00:00 Step loss: 146.49485746489026 Overall loss: 135.1610744416433 Overall accuracy: 0.3333       Progress: 78%|███████████████████████████████▉ | ETA: 0:00:00 Step loss: 128.13642244677604 Overall loss: 132.1802342837042 Overall accuracy: 0.3333       Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 Step loss: 142.00239555793692 Overall loss: 130.4477443735128 Overall accuracy: 0.6667 Done! Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up optimizer... Setting up data for evaluation... Setting up data loader... Training... Progress: 44%|██████████████████▎ | ETA: 0:00:00 Step loss: 145.65858509349266 Overall loss: 134.1443622338372 Overall accuracy: 0.3333       Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 Step loss: 140.70036382302956 Overall loss: 130.01272937505908 Overall accuracy: 0.6667 Done! WARNING: Method definition compute_target_corr(Any, Any, Any, Any, Any, Any, Any) in module ##deep learning tests#260 at /home/pkgeval/.julia/packages/JudiLing/vjHJF/test/deep_learning_tests.jl:619 overwritten at /home/pkgeval/.julia/packages/JudiLing/vjHJF/test/deep_learning_tests.jl:666. Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up optimizer... Setting up data for evaluation... Setting up data loader... Training... Progress: 40%|████████████████▍ | ETA: 0:00:00 Step loss: 139.05467735440448 Overall loss: 138.01479947270724 Overall accuracy: 1.0   Done! WARNING: Method definition compute_target_corr(Any, Any, Any, Any, Any, Any, Any) in module ##deep learning tests#260 at /home/pkgeval/.julia/packages/JudiLing/vjHJF/test/deep_learning_tests.jl:666 overwritten at /home/pkgeval/.julia/packages/JudiLing/vjHJF/test/deep_learning_tests.jl:691. Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up optimizer... Setting up data for evaluation... Setting up data loader... Training... Progress: 33%|█████████████▋ | ETA: 0:00:00 Step loss: 147.07400355839548 Overall loss: 135.57267106773455 Overall accuracy: 0.6667       Progress: 78%|███████████████████████████████▉ | ETA: 0:00:00 Step loss: 128.24459519503844 Overall loss: 132.74668750892215 Overall accuracy: 0.6667       Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 Step loss: 142.87674765216178 Overall loss: 131.10236280057742 Overall accuracy: 0.6667 Done! Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up optimizer... Setting up data for evaluation... Setting up data loader... Training... Done! Test Summary: | Pass Total Time deep learning tests | 96 96 9m08.0s Testing JudiLing tests passed Testing completed after 1651.71s PkgEval succeeded after 2613.99s