Package evaluation of ScikitLearn on Julia 1.12.0-rc1.2 (995ff9db19*) started at 2025-07-14T18:05:22.709 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 9.15s ################################################################################ # Installation # Installing ScikitLearn... Resolving package versions... Installed Conda ── v1.10.2 Installed PyCall ─ v1.96.4 Updating `~/.julia/environments/v1.12/Project.toml` [3646fa90] + ScikitLearn v0.7.0 Updating `~/.julia/environments/v1.12/Manifest.toml` [34da2185] + Compat v4.17.0 [8f4d0f93] + Conda v1.10.2 [a8cc5b0e] + Crayons v4.1.1 [9a962f9c] + DataAPI v1.16.0 [a93c6f00] + DataFrames v1.7.0 [864edb3b] + DataStructures v0.18.22 [e2d170a0] + DataValueInterfaces v1.0.0 [ffbed154] + DocStringExtensions v0.9.5 [842dd82b] + InlineStrings v1.4.4 [41ab1584] + InvertedIndices v1.3.1 [92d709cd] + IrrationalConstants v0.2.4 [c8e1da08] + IterTools v1.10.0 [82899510] + IteratorInterfaceExtensions v1.0.0 [682c06a0] + JSON v0.21.4 [b964fa9f] + LaTeXStrings v1.4.0 [2ab3a3ac] + LogExpFunctions v0.3.29 [1914dd2f] + MacroTools v0.5.16 [e1d29d7a] + Missings v1.2.0 [bac558e1] + OrderedCollections v1.8.1 [d96e819e] + Parameters v0.12.3 [69de0a69] + Parsers v2.8.3 [2dfb63ee] + PooledArrays v1.4.3 [aea7be01] + PrecompileTools v1.3.2 [21216c6a] + Preferences v1.4.3 [08abe8d2] + PrettyTables v2.4.0 [438e738f] + PyCall v1.96.4 [189a3867] + Reexport v1.2.2 [3646fa90] + ScikitLearn v0.7.0 [6e75b9c4] + ScikitLearnBase v0.5.0 [91c51154] + SentinelArrays v1.4.8 [a2af1166] + SortingAlgorithms v1.2.1 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.7.1 ⌅ [2913bbd2] + StatsBase v0.33.21 [892a3eda] + StringManipulation v0.4.1 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.1 [3a884ed6] + UnPack v1.0.2 [81def892] + VersionParsing v1.3.0 [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 [ac6e5ff7] + JuliaSyntaxHighlighting v1.12.0 [b27032c2] + LibCURL v0.6.4 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.12.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [ca575930] + NetworkOptions v1.3.0 [de0858da] + Printf 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 v1.12.0 [f489334b] + StyledStrings v1.11.0 [fa267f1f] + TOML v1.0.3 [cf7118a7] + UUIDs v1.11.0 [4ec0a83e] + Unicode v1.11.0 [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1 [deac9b47] + LibCURL_jll v8.11.1+1 [29816b5a] + LibSSH2_jll v1.11.3+1 [14a3606d] + MozillaCACerts_jll v2025.5.20 [4536629a] + OpenBLAS_jll v0.3.29+0 [458c3c95] + OpenSSL_jll v3.5.1+0 [bea87d4a] + SuiteSparse_jll v7.8.3+2 [83775a58] + Zlib_jll v1.3.1+2 [8e850b90] + libblastrampoline_jll v5.13.1+0 [8e850ede] + nghttp2_jll v1.64.0+1 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m` Building Conda ─→ `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/b19db3927f0db4151cb86d073689f2428e524576/build.log` Building PyCall → `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/9816a3826b0ebf49ab4926e2b18842ad8b5c8f04/build.log` Installation completed after 60.46s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... ┌ Warning: Could not use exact versions of packages in manifest, re-resolving └ @ TestEnv ~/.julia/packages/TestEnv/iS95e/src/julia-1.11/activate_set.jl:75 Precompiling package dependencies... Precompilation completed after 310.37s ################################################################################ # Testing # Testing ScikitLearn Test Could not use exact versions of packages in manifest, re-resolving. Note: if you do not check your manifest file into source control, then you can probably ignore this message. However, if you do check your manifest file into source control, then you probably want to pass the `allow_reresolve = false` kwarg when calling the `Pkg.test` function. Updating `/tmp/jl_vDHKmp/Project.toml` [7806a523] + DecisionTree v0.12.4 [cc18c42c] + GaussianMixtures v0.3.13 [891a1506] + GaussianProcesses v0.12.5 [0db19996] + NBInclude v2.4.0 [d330b81b] + PyPlot v2.11.6 ⌅ [df47a6cb] + RData v0.8.3 [ce6b1742] + RDatasets v0.7.7 [3646fa90] + ScikitLearn v0.7.0 [8dfed614] ~ Test ⇒ v1.11.0 Updating `/tmp/jl_vDHKmp/Manifest.toml` [47edcb42] + ADTypes v1.15.0 [1520ce14] + AbstractTrees v0.4.5 [79e6a3ab] + Adapt v4.3.0 [66dad0bd] + AliasTables v1.1.3 [7d9fca2a] + Arpack v0.5.4 [4fba245c] + ArrayInterface v7.19.0 [336ed68f] + CSV v0.10.15 [324d7699] + CategoricalArrays v0.10.8 [d360d2e6] + ChainRulesCore v1.25.2 [aaaa29a8] + Clustering v0.15.8 [944b1d66] + CodecZlib v0.7.8 [3da002f7] + ColorTypes v0.12.1 [5ae59095] + Colors v0.13.1 [bbf7d656] + CommonSubexpressions v0.3.1 [187b0558] + ConstructionBase v1.6.0 [7806a523] + DecisionTree v0.12.4 [8bb1440f] + DelimitedFiles v1.9.1 [163ba53b] + DiffResults v1.1.0 [b552c78f] + DiffRules v1.15.1 [a0c0ee7d] + DifferentiationInterface v0.7.2 [b4f34e82] + Distances v0.10.12 [31c24e10] + Distributions v0.25.120 [fdbdab4c] + ElasticArrays v1.2.12 [2904ab23] + ElasticPDMats v0.2.3 [4e289a0a] + EnumX v1.0.5 [e2ba6199] + ExprTools v0.1.10 ⌅ [442a2c76] + FastGaussQuadrature v0.4.9 [5789e2e9] + FileIO v1.17.0 [48062228] + FilePathsBase v0.9.24 [1a297f60] + FillArrays v1.13.0 [6a86dc24] + FiniteDiff v2.27.0 [53c48c17] + FixedPointNumbers v0.8.5 ⌅ [f6369f11] + ForwardDiff v0.10.38 [cc18c42c] + GaussianMixtures v0.3.13 [891a1506] + GaussianProcesses v0.12.5 [076d061b] + HashArrayMappedTries v0.2.0 [3587e190] + InverseFunctions v0.1.17 ⌅ [92d709cd] ↓ IrrationalConstants v0.2.4 ⇒ v0.1.1 [033835bb] + JLD2 v0.5.15 [692b3bcd] + JLLWrappers v1.7.0 [d3d80556] + LineSearches v7.4.0 [78c3b35d] + Mocking v0.8.1 [0db19996] + NBInclude v2.4.0 [d41bc354] + NLSolversBase v7.10.0 [77ba4419] + NaNMath v1.1.3 [b8a86587] + NearestNeighbors v0.4.22 [429524aa] + Optim v1.13.2 [90014a1f] + PDMats v0.11.35 [85a6dd25] + PositiveFactorizations v0.2.4 [92933f4c] + ProgressMeter v1.10.4 [43287f4e] + PtrArrays v1.3.0 [d330b81b] + PyPlot v2.11.6 [1fd47b50] + QuadGK v2.11.2 ⌅ [df47a6cb] + RData v0.8.3 [ce6b1742] + RDatasets v0.7.7 [3cdcf5f2] + RecipesBase v1.3.4 [ae029012] + Requires v1.3.1 ⌅ [79098fc4] + Rmath v0.7.1 [3646fa90] + ScikitLearn v0.7.0 [7e506255] + ScopedValues v1.3.0 [6c6a2e73] + Scratch v1.3.0 [efcf1570] + Setfield v1.1.2 [b85f4697] + SoftGlobalScope v1.1.0 [276daf66] + SpecialFunctions v2.5.1 [90137ffa] + StaticArrays v1.9.13 [1e83bf80] + StaticArraysCore v1.4.3 ⌅ [4c63d2b9] + StatsFuns v0.9.18 [dc5dba14] + TZJData v1.5.0+2025b [f269a46b] + TimeZones v1.21.3 [3bb67fe8] + TranscodingStreams v0.11.3 [ea10d353] + WeakRefStrings v1.4.2 [76eceee3] + WorkerUtilities v1.6.1 ⌅ [68821587] + Arpack_jll v3.5.1+1 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 ⌅ [f50d1b31] + Rmath_jll v0.4.3+0 [76f85450] + LibGit2 v1.11.0 [56ddb016] + Logging v1.11.0 [44cfe95a] + Pkg v1.13.0 [3fa0cd96] + REPL v1.11.0 [4607b0f0] + SuiteSparse [a4e569a6] + Tar v1.10.0 [8dfed614] ~ Test ⇒ v1.11.0 [e37daf67] + LibGit2_jll v1.9.0+0 [05823500] + OpenLibm_jll v0.8.5+0 [3f19e933] + p7zip_jll v17.5.0+2 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m` Test Successfully re-resolved Status `/tmp/jl_vDHKmp/Project.toml` [34da2185] Compat v4.17.0 [8f4d0f93] Conda v1.10.2 [a93c6f00] DataFrames v1.7.0 [7806a523] DecisionTree v0.12.4 [cc18c42c] GaussianMixtures v0.3.13 [891a1506] GaussianProcesses v0.12.5 [c8e1da08] IterTools v1.10.0 [1914dd2f] MacroTools v0.5.16 [0db19996] NBInclude v2.4.0 [d96e819e] Parameters v0.12.3 [438e738f] PyCall v1.96.4 [d330b81b] PyPlot v2.11.6 ⌅ [df47a6cb] RData v0.8.3 [ce6b1742] RDatasets v0.7.7 [3646fa90] ScikitLearn v0.7.0 [6e75b9c4] ScikitLearnBase v0.5.0 [10745b16] Statistics v1.11.1 ⌅ [2913bbd2] StatsBase v0.33.21 [81def892] VersionParsing v1.3.0 [8ba89e20] Distributed v1.11.0 [37e2e46d] LinearAlgebra v1.12.0 [de0858da] Printf v1.11.0 [9a3f8284] Random v1.11.0 [2f01184e] SparseArrays v1.12.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_vDHKmp/Manifest.toml` [47edcb42] ADTypes v1.15.0 [1520ce14] AbstractTrees v0.4.5 [79e6a3ab] Adapt v4.3.0 [66dad0bd] AliasTables v1.1.3 [7d9fca2a] Arpack v0.5.4 [4fba245c] ArrayInterface v7.19.0 [336ed68f] CSV v0.10.15 [324d7699] CategoricalArrays v0.10.8 [d360d2e6] ChainRulesCore v1.25.2 [aaaa29a8] Clustering v0.15.8 [944b1d66] CodecZlib v0.7.8 [3da002f7] ColorTypes v0.12.1 [5ae59095] Colors v0.13.1 [bbf7d656] CommonSubexpressions v0.3.1 [34da2185] Compat v4.17.0 [8f4d0f93] Conda v1.10.2 [187b0558] ConstructionBase v1.6.0 [a8cc5b0e] Crayons v4.1.1 [9a962f9c] DataAPI v1.16.0 [a93c6f00] DataFrames v1.7.0 [864edb3b] DataStructures v0.18.22 [e2d170a0] DataValueInterfaces v1.0.0 [7806a523] DecisionTree v0.12.4 [8bb1440f] DelimitedFiles v1.9.1 [163ba53b] DiffResults v1.1.0 [b552c78f] DiffRules v1.15.1 [a0c0ee7d] DifferentiationInterface v0.7.2 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.120 [ffbed154] DocStringExtensions v0.9.5 [fdbdab4c] ElasticArrays v1.2.12 [2904ab23] ElasticPDMats v0.2.3 [4e289a0a] EnumX v1.0.5 [e2ba6199] ExprTools v0.1.10 ⌅ [442a2c76] FastGaussQuadrature v0.4.9 [5789e2e9] FileIO v1.17.0 [48062228] FilePathsBase v0.9.24 [1a297f60] FillArrays v1.13.0 [6a86dc24] FiniteDiff v2.27.0 [53c48c17] FixedPointNumbers v0.8.5 ⌅ [f6369f11] ForwardDiff v0.10.38 [cc18c42c] GaussianMixtures v0.3.13 [891a1506] GaussianProcesses v0.12.5 [076d061b] HashArrayMappedTries v0.2.0 [842dd82b] InlineStrings v1.4.4 [3587e190] InverseFunctions v0.1.17 [41ab1584] InvertedIndices v1.3.1 ⌅ [92d709cd] IrrationalConstants v0.1.1 [c8e1da08] IterTools v1.10.0 [82899510] IteratorInterfaceExtensions v1.0.0 [033835bb] JLD2 v0.5.15 [692b3bcd] JLLWrappers v1.7.0 [682c06a0] JSON v0.21.4 [b964fa9f] LaTeXStrings v1.4.0 [d3d80556] LineSearches v7.4.0 [2ab3a3ac] LogExpFunctions v0.3.29 [1914dd2f] MacroTools v0.5.16 [e1d29d7a] Missings v1.2.0 [78c3b35d] Mocking v0.8.1 [0db19996] NBInclude v2.4.0 [d41bc354] NLSolversBase v7.10.0 [77ba4419] NaNMath v1.1.3 [b8a86587] NearestNeighbors v0.4.22 [429524aa] Optim v1.13.2 [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 [85a6dd25] PositiveFactorizations v0.2.4 [aea7be01] PrecompileTools v1.3.2 [21216c6a] Preferences v1.4.3 [08abe8d2] PrettyTables v2.4.0 [92933f4c] ProgressMeter v1.10.4 [43287f4e] PtrArrays v1.3.0 [438e738f] PyCall v1.96.4 [d330b81b] PyPlot v2.11.6 [1fd47b50] QuadGK v2.11.2 ⌅ [df47a6cb] RData v0.8.3 [ce6b1742] RDatasets v0.7.7 [3cdcf5f2] RecipesBase v1.3.4 [189a3867] Reexport v1.2.2 [ae029012] Requires v1.3.1 ⌅ [79098fc4] Rmath v0.7.1 [3646fa90] ScikitLearn v0.7.0 [6e75b9c4] ScikitLearnBase v0.5.0 [7e506255] ScopedValues v1.3.0 [6c6a2e73] Scratch v1.3.0 [91c51154] SentinelArrays v1.4.8 [efcf1570] Setfield v1.1.2 [b85f4697] SoftGlobalScope v1.1.0 [a2af1166] SortingAlgorithms v1.2.1 [276daf66] SpecialFunctions v2.5.1 [90137ffa] StaticArrays v1.9.13 [1e83bf80] StaticArraysCore v1.4.3 [10745b16] Statistics v1.11.1 [82ae8749] StatsAPI v1.7.1 ⌅ [2913bbd2] StatsBase v0.33.21 ⌅ [4c63d2b9] StatsFuns v0.9.18 [892a3eda] StringManipulation v0.4.1 [dc5dba14] TZJData v1.5.0+2025b [3783bdb8] TableTraits v1.0.1 [bd369af6] Tables v1.12.1 [f269a46b] TimeZones v1.21.3 [3bb67fe8] TranscodingStreams v0.11.3 [3a884ed6] UnPack v1.0.2 [81def892] VersionParsing v1.3.0 [ea10d353] WeakRefStrings v1.4.2 [76eceee3] WorkerUtilities v1.6.1 ⌅ [68821587] Arpack_jll v3.5.1+1 [efe28fd5] OpenSpecFun_jll v0.5.6+0 ⌅ [f50d1b31] Rmath_jll v0.4.3+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 [ac6e5ff7] JuliaSyntaxHighlighting v1.12.0 [b27032c2] LibCURL v0.6.4 [76f85450] LibGit2 v1.11.0 [8f399da3] Libdl v1.11.0 [37e2e46d] LinearAlgebra v1.12.0 [56ddb016] Logging v1.11.0 [d6f4376e] Markdown v1.11.0 [a63ad114] Mmap v1.11.0 [ca575930] NetworkOptions v1.3.0 [44cfe95a] Pkg v1.13.0 [de0858da] Printf 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 v1.12.0 [f489334b] StyledStrings v1.11.0 [4607b0f0] SuiteSparse [fa267f1f] TOML v1.0.3 [a4e569a6] Tar v1.10.0 [8dfed614] Test v1.11.0 [cf7118a7] UUIDs v1.11.0 [4ec0a83e] Unicode v1.11.0 [e66e0078] CompilerSupportLibraries_jll v1.3.0+1 [deac9b47] LibCURL_jll v8.11.1+1 [e37daf67] LibGit2_jll v1.9.0+0 [29816b5a] LibSSH2_jll v1.11.3+1 [14a3606d] MozillaCACerts_jll v2025.5.20 [4536629a] OpenBLAS_jll v0.3.29+0 [05823500] OpenLibm_jll v0.8.5+0 [458c3c95] OpenSSL_jll v3.5.1+0 [bea87d4a] SuiteSparse_jll v7.8.3+2 [83775a58] Zlib_jll v1.3.1+2 [8e850b90] libblastrampoline_jll v5.13.1+0 [8e850ede] nghttp2_jll v1.64.0+1 [3f19e933] p7zip_jll v17.5.0+2 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. Testing Running tests... [ Info: Running `conda install -q -y -c anaconda conda` in root environment Channels: - anaconda - 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: - conda The following packages will be downloaded: package | build ---------------------------|----------------- conda-25.5.1 | py312h06a4308_0 1.2 MB anaconda ------------------------------------------------------------ Total: 1.2 MB The following packages will be UPDATED: conda conda-forge::conda-25.3.0-py312h7900f~ --> anaconda::conda-25.5.1-py312h06a4308_0 Preparing transaction: ...working... done Verifying transaction: ...working... done Executing transaction: ...working... done [ Info: Running `conda install -q -y -c conda-forge 'libstdcxx-ng>=3.4,<13.0'` 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: - libstdcxx-ng[version='>=3.4,<13.0'] The following packages will be downloaded: package | build ---------------------------|----------------- libstdcxx-ng-12.3.0 | hc0a3c3a_7 3.3 MB conda-forge ------------------------------------------------------------ Total: 3.3 MB The following packages will be DOWNGRADED: libstdcxx-ng 15.1.0-h4852527_2 --> 12.3.0-hc0a3c3a_7 Preparing transaction: ...working... done Verifying transaction: ...working... done Executing transaction: ...working... done [ Info: Installing sklearn via the Conda scikit-learn>=1.2,<1.3 package... [ Info: Running `conda config --add channels conda-forge --file /home/pkgeval/.julia/conda/3/x86_64/condarc-julia.yml --force` in root environment /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/site-packages/conda/base/context.py:211: FutureWarning: Adding 'defaults' to channel list implicitly is deprecated and will be removed in 25.9. To remove this warning, please choose a default channel explicitly with conda's regular configuration system, e.g. by adding 'defaults' to the list of channels: conda config --add channels defaults For more information see https://docs.conda.io/projects/conda/en/stable/user-guide/configuration/use-condarc.html deprecated.topic( [ Info: Running `conda install -q -y 'scikit-learn>=1.2,<1.3'` in root environment Channels: - conda-forge - defaults Platform: linux-64 Collecting package metadata (repodata.json): ...working... done Solving environment: ...working... failed LibMambaUnsatisfiableError: Encountered problems while solving: - package scikit-learn-1.2.0-py310h209a8ca_0 requires python >=3.10,<3.11.0a0, but none of the providers can be installed Could not solve for environment specs The following packages are incompatible ├─ pin on python =3.12 * is installable and it requires │ └─ python =3.12 *, which can be installed; └─ scikit-learn >=1.2,<1.3 * is not installable because there are no viable options ├─ scikit-learn [1.2.0|1.2.1|1.2.2] would require │ └─ python >=3.10,<3.11.0a0 *, which conflicts with any installable versions previously reported; ├─ scikit-learn [1.2.0|1.2.1|1.2.2] would require │ └─ python >=3.11,<3.12.0a0 *, which conflicts with any installable versions previously reported; ├─ scikit-learn [1.2.0|1.2.1|1.2.2] would require │ └─ python >=3.8,<3.9.0a0 *, which conflicts with any installable versions previously reported; └─ scikit-learn [1.2.0|1.2.1|1.2.2] would require └─ python >=3.9,<3.10.0a0 *, which conflicts with any installable versions previously reported. Pins seem to be involved in the conflict. Currently pinned specs: - python=3.12 base: Error During Test at /home/pkgeval/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:16 Got exception outside of a @test LoadError: UndefVarError: `CONDA` not defined in `ScikitLearn.Skcore` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] import_sklearn() @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/Skcore.jl:225 [2] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/test_base.jl:272 [3] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [4] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:12 [5] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [6] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:17 [inlined] [7] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [8] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:17 [inlined] [9] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [10] top-level scope @ none:6 [11] eval(m::Module, e::Any) @ Core ./boot.jl:489 [12] exec_options(opts::Base.JLOptions) @ Base ./client.jl:287 [13] _start() @ Base ./client.jl:554 in expression starting at /home/pkgeval/.julia/packages/ScikitLearn/sqLdT/test/test_base.jl:12 caused by: failed process: Process(setenv(`/home/pkgeval/.julia/conda/3/x86_64/bin/conda install -q -y 'scikit-learn>=1.2,<1.3'`,["PYTHONIOENCODING=UTF-8", "LANG=C.UTF-8", "PATH=/usr/local/bin:/usr/local/sbin:/usr/bin:/usr/sbin:/bin:/sbin", "OPENBLAS_MAIN_FREE=1", "CONDARC=/home/pkgeval/.julia/conda/3/x86_64/condarc-julia.yml", "CONDA_PREFIX=/home/pkgeval/.julia/conda/3/x86_64", "JULIA_CPU_THREADS=1", "JULIA_NUM_PRECOMPILE_TASKS=1", "DISPLAY=:1", "JULIA_LOAD_PATH=@:/tmp/jl_vDHKmp", "PKGEVAL=true", "OPENBLAS_NUM_THREADS=1", "CI=true", "JULIA_PKG_PRECOMPILE_AUTO=0", "JULIA_PKGEVAL=true", "JULIA_DEPOT_PATH=/home/pkgeval/.julia:/usr/local/share/julia:", "JULIA_NUM_THREADS=1", "R_HOME=*", "HOME=/home/pkgeval"]), ProcessExited(1)) [1] Stacktrace: [1] pipeline_error @ ./process.jl:598 [inlined] [2] run(::Cmd; wait::Bool) @ Base ./process.jl:513 [3] run @ ./process.jl:510 [inlined] [4] runconda(args::Cmd, env::String) @ Conda ~/.julia/packages/Conda/zReqD/src/Conda.jl:182 [5] add(pkg::String, env::String; channel::String, satisfied_skip_solve::Bool, args::Cmd) @ Conda ~/.julia/packages/Conda/zReqD/src/Conda.jl:343 [6] add (repeats 2 times) @ ~/.julia/packages/Conda/zReqD/src/Conda.jl:326 [inlined] [7] pyimport_conda(modulename::String, condapkg::String, channel::String) @ PyCall ~/.julia/packages/PyCall/1gn3u/src/PyCall.jl:721 [8] import_sklearn() @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/Skcore.jl:219 [9] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/test_base.jl:272 [10] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [11] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:12 [12] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [13] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:17 [inlined] [14] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [15] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:17 [inlined] [16] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [17] top-level scope @ none:6 [18] eval(m::Module, e::Any) @ Core ./boot.jl:489 [19] exec_options(opts::Base.JLOptions) @ Base ./client.jl:287 [20] _start() @ Base ./client.jl:554 caused by: PyError (PyImport_ImportModule The Python package sklearn could not be imported by pyimport. Usually this means that you did not install sklearn in the Python version being used by PyCall. PyCall is currently configured to use the Julia-specific Python distribution installed by the Conda.jl package. To install the sklearn module, you can use `pyimport_conda("sklearn", PKG)`, where PKG is the Anaconda package that contains the module sklearn, or alternatively you can use the Conda package directly (via `using Conda` followed by `Conda.add` etcetera). Alternatively, if you want to use a different Python distribution on your system, such as a system-wide Python (as opposed to the Julia-specific Python), you can re-configure PyCall with that Python. As explained in the PyCall documentation, set ENV["PYTHON"] to the path/name of the python executable you want to use, run Pkg.build("PyCall"), and re-launch Julia. ) ModuleNotFoundError("No module named 'sklearn'") Stacktrace: [1] pyimport(name::String) @ PyCall ~/.julia/packages/PyCall/1gn3u/src/PyCall.jl:558 [2] pyimport_conda(modulename::String, condapkg::String, channel::String) @ PyCall ~/.julia/packages/PyCall/1gn3u/src/PyCall.jl:716 [3] import_sklearn() @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/Skcore.jl:219 [4] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/test_base.jl:272 [5] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [6] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:12 [7] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [8] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:17 [inlined] [9] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [10] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:17 [inlined] [11] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [12] top-level scope @ none:6 [13] eval(m::Module, e::Any) @ Core ./boot.jl:489 [14] exec_options(opts::Base.JLOptions) @ Base ./client.jl:287 [15] _start() @ Base ./client.jl:554 [ Info: Installing sklearn via the Conda scikit-learn>=1.2,<1.3 package... [ Info: Running `conda config --add channels conda-forge --file /home/pkgeval/.julia/conda/3/x86_64/condarc-julia.yml --force` in root environment Warning: 'conda-forge' already in 'channels' list, moving to the top [ Info: Running `conda install -q -y 'scikit-learn>=1.2,<1.3'` in root environment Channels: - conda-forge - defaults Platform: linux-64 Collecting package metadata (repodata.json): ...working... done Solving environment: ...working... failed LibMambaUnsatisfiableError: Encountered problems while solving: - package scikit-learn-1.2.0-py310h209a8ca_0 requires python >=3.10,<3.11.0a0, but none of the providers can be installed Could not solve for environment specs The following packages are incompatible ├─ pin on python =3.12 * is installable and it requires │ └─ python =3.12 *, which can be installed; └─ scikit-learn >=1.2,<1.3 * is not installable because there are no viable options ├─ scikit-learn [1.2.0|1.2.1|1.2.2] would require │ └─ python >=3.10,<3.11.0a0 *, which conflicts with any installable versions previously reported; ├─ scikit-learn [1.2.0|1.2.1|1.2.2] would require │ └─ python >=3.11,<3.12.0a0 *, which conflicts with any installable versions previously reported; ├─ scikit-learn [1.2.0|1.2.1|1.2.2] would require │ └─ python >=3.8,<3.9.0a0 *, which conflicts with any installable versions previously reported; └─ scikit-learn [1.2.0|1.2.1|1.2.2] would require └─ python >=3.9,<3.10.0a0 *, which conflicts with any installable versions previously reported. Pins seem to be involved in the conflict. Currently pinned specs: - python=3.12 pipeline: Error During Test at /home/pkgeval/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:21 Got exception outside of a @test LoadError: UndefVarError: `CONDA` not defined in `ScikitLearn.Skcore` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] import_sklearn() @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/Skcore.jl:225 [2] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/test_pipeline.jl:272 [3] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [4] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:12 [5] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [6] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:22 [inlined] [7] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [8] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:22 [inlined] [9] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [10] top-level scope @ none:6 [11] eval(m::Module, e::Any) @ Core ./boot.jl:489 [12] exec_options(opts::Base.JLOptions) @ Base ./client.jl:287 [13] _start() @ Base ./client.jl:554 in expression starting at /home/pkgeval/.julia/packages/ScikitLearn/sqLdT/test/test_pipeline.jl:17 caused by: failed process: Process(setenv(`/home/pkgeval/.julia/conda/3/x86_64/bin/conda install -q -y 'scikit-learn>=1.2,<1.3'`,["PYTHONIOENCODING=UTF-8", "LANG=C.UTF-8", "PATH=/usr/local/bin:/usr/local/sbin:/usr/bin:/usr/sbin:/bin:/sbin", "OPENBLAS_MAIN_FREE=1", "CONDARC=/home/pkgeval/.julia/conda/3/x86_64/condarc-julia.yml", "CONDA_PREFIX=/home/pkgeval/.julia/conda/3/x86_64", "JULIA_CPU_THREADS=1", "JULIA_NUM_PRECOMPILE_TASKS=1", "DISPLAY=:1", "JULIA_LOAD_PATH=@:/tmp/jl_vDHKmp", "PKGEVAL=true", "OPENBLAS_NUM_THREADS=1", "CI=true", "JULIA_PKG_PRECOMPILE_AUTO=0", "JULIA_PKGEVAL=true", "JULIA_DEPOT_PATH=/home/pkgeval/.julia:/usr/local/share/julia:", "JULIA_NUM_THREADS=1", "R_HOME=*", "HOME=/home/pkgeval"]), ProcessExited(1)) [1] Stacktrace: [1] pipeline_error @ ./process.jl:598 [inlined] [2] run(::Cmd; wait::Bool) @ Base ./process.jl:513 [3] run @ ./process.jl:510 [inlined] [4] runconda(args::Cmd, env::String) @ Conda ~/.julia/packages/Conda/zReqD/src/Conda.jl:182 [5] add(pkg::String, env::String; channel::String, satisfied_skip_solve::Bool, args::Cmd) @ Conda ~/.julia/packages/Conda/zReqD/src/Conda.jl:343 [6] add (repeats 2 times) @ ~/.julia/packages/Conda/zReqD/src/Conda.jl:326 [inlined] [7] pyimport_conda(modulename::String, condapkg::String, channel::String) @ PyCall ~/.julia/packages/PyCall/1gn3u/src/PyCall.jl:721 [8] import_sklearn() @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/Skcore.jl:219 [9] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/test_pipeline.jl:272 [10] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [11] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:12 [12] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [13] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:22 [inlined] [14] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [15] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:22 [inlined] [16] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [17] top-level scope @ none:6 [18] eval(m::Module, e::Any) @ Core ./boot.jl:489 [19] exec_options(opts::Base.JLOptions) @ Base ./client.jl:287 [20] _start() @ Base ./client.jl:554 caused by: PyError (PyImport_ImportModule The Python package sklearn could not be imported by pyimport. Usually this means that you did not install sklearn in the Python version being used by PyCall. PyCall is currently configured to use the Julia-specific Python distribution installed by the Conda.jl package. To install the sklearn module, you can use `pyimport_conda("sklearn", PKG)`, where PKG is the Anaconda package that contains the module sklearn, or alternatively you can use the Conda package directly (via `using Conda` followed by `Conda.add` etcetera). Alternatively, if you want to use a different Python distribution on your system, such as a system-wide Python (as opposed to the Julia-specific Python), you can re-configure PyCall with that Python. As explained in the PyCall documentation, set ENV["PYTHON"] to the path/name of the python executable you want to use, run Pkg.build("PyCall"), and re-launch Julia. ) ModuleNotFoundError("No module named 'sklearn'") Stacktrace: [1] pyimport(name::String) @ PyCall ~/.julia/packages/PyCall/1gn3u/src/PyCall.jl:558 [2] pyimport_conda(modulename::String, condapkg::String, channel::String) @ PyCall ~/.julia/packages/PyCall/1gn3u/src/PyCall.jl:716 [3] import_sklearn() @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/Skcore.jl:219 [4] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/test_pipeline.jl:272 [5] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [6] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:12 [7] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [8] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:22 [inlined] [9] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [10] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:22 [inlined] [11] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [12] top-level scope @ none:6 [13] eval(m::Module, e::Any) @ Core ./boot.jl:489 [14] exec_options(opts::Base.JLOptions) @ Base ./client.jl:287 [15] _start() @ Base ./client.jl:554 ┌ Warning: The least populated class in y has only 2 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=3. └ @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/cross_validation.jl:144 ┌ Warning: The least populated class in y has only 2 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=3. └ @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/cross_validation.jl:144 ┌ Warning: The least populated class in y has only 2 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=3. └ @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/cross_validation.jl:144 ┌ Warning: The least populated class in y has only 2 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=3. └ @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/cross_validation.jl:144 ┌ Warning: The least populated class in y has only 2 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=3. └ @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/cross_validation.jl:144 ┌ Warning: The least populated class in y has only 2 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=3. └ @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/cross_validation.jl:144 ┌ Warning: The least populated class in y has only 2 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=3. └ @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/cross_validation.jl:144 ┌ Warning: The least populated class in y has only 2 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=3. └ @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/cross_validation.jl:144 ┌ Warning: The least populated class in y has only 2 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=3. └ @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/cross_validation.jl:144 ┌ Warning: The least populated class in y has only 2 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=3. └ @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/cross_validation.jl:144 ┌ Warning: The least populated class in y has only 2 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=3. └ @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/cross_validation.jl:144 ┌ Warning: The least populated class in y has only 2 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=3. └ @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/cross_validation.jl:144 ┌ Warning: The least populated class in y has only 2 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=3. └ @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/cross_validation.jl:144 ┌ Warning: The least populated class in y has only 2 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=3. └ @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/cross_validation.jl:144 ┌ Warning: The least populated class in y has only 2 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=3. └ @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/cross_validation.jl:144 ┌ Warning: The least populated class in y has only 2 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=3. └ @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/cross_validation.jl:144 ┌ Warning: The least populated class in y has only 2 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=3. └ @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/cross_validation.jl:144 ┌ Warning: The least populated class in y has only 2 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=3. └ @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/cross_validation.jl:144 ┌ Warning: The least populated class in y has only 2 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=3. └ @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/cross_validation.jl:144 ┌ Warning: The least populated class in y has only 2 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=3. └ @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/cross_validation.jl:144 ┌ Warning: The least populated class in y has only 2 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=3. └ @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/cross_validation.jl:144 Precompiling packages... 2464.5 ms ✓ WeakRefStrings 24064.6 ms ✓ RData 33547.8 ms ✓ CSV 20872.2 ms ✓ RDatasets 4 dependencies successfully precompiled in 82 seconds. 68 already precompiled. Precompiling packages... 4953.8 ms ✓ CategoricalArrays → CategoricalArraysJSONExt 1 dependency successfully precompiled in 5 seconds. 16 already precompiled. [ Info: Installing sklearn via the Conda scikit-learn>=1.2,<1.3 package... [ Info: Running `conda config --add channels conda-forge --file /home/pkgeval/.julia/conda/3/x86_64/condarc-julia.yml --force` in root environment Warning: 'conda-forge' already in 'channels' list, moving to the top [ Info: Running `conda install -q -y 'scikit-learn>=1.2,<1.3'` in root environment Channels: - conda-forge - defaults Platform: linux-64 Collecting package metadata (repodata.json): ...working... done Solving environment: ...working... failed LibMambaUnsatisfiableError: Encountered problems while solving: - package scikit-learn-1.2.0-py310h209a8ca_0 requires python >=3.10,<3.11.0a0, but none of the providers can be installed Could not solve for environment specs The following packages are incompatible ├─ pin on python =3.12 * is installable and it requires │ └─ python =3.12 *, which can be installed; └─ scikit-learn >=1.2,<1.3 * is not installable because there are no viable options ├─ scikit-learn [1.2.0|1.2.1|1.2.2] would require │ └─ python >=3.10,<3.11.0a0 *, which conflicts with any installable versions previously reported; ├─ scikit-learn [1.2.0|1.2.1|1.2.2] would require │ └─ python >=3.11,<3.12.0a0 *, which conflicts with any installable versions previously reported; ├─ scikit-learn [1.2.0|1.2.1|1.2.2] would require │ └─ python >=3.8,<3.9.0a0 *, which conflicts with any installable versions previously reported; └─ scikit-learn [1.2.0|1.2.1|1.2.2] would require └─ python >=3.9,<3.10.0a0 *, which conflicts with any installable versions previously reported. Pins seem to be involved in the conflict. Currently pinned specs: - python=3.12 quickstart: Error During Test at /home/pkgeval/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:34 Got exception outside of a @test LoadError: UndefVarError: `CONDA` not defined in `ScikitLearn.Skcore` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] import_sklearn() @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/Skcore.jl:225 [2] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/test_quickstart.jl:272 [3] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [4] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:12 [5] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [6] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:35 [inlined] [7] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [8] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:35 [inlined] [9] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [10] top-level scope @ none:6 [11] eval(m::Module, e::Any) @ Core ./boot.jl:489 [12] exec_options(opts::Base.JLOptions) @ Base ./client.jl:287 [13] _start() @ Base ./client.jl:554 in expression starting at /home/pkgeval/.julia/packages/ScikitLearn/sqLdT/test/test_quickstart.jl:13 caused by: failed process: Process(setenv(`/home/pkgeval/.julia/conda/3/x86_64/bin/conda install -q -y 'scikit-learn>=1.2,<1.3'`,["PYTHONIOENCODING=UTF-8", "LANG=C.UTF-8", "PATH=/usr/local/bin:/usr/local/sbin:/usr/bin:/usr/sbin:/bin:/sbin", "OPENBLAS_MAIN_FREE=1", "CONDARC=/home/pkgeval/.julia/conda/3/x86_64/condarc-julia.yml", "CONDA_PREFIX=/home/pkgeval/.julia/conda/3/x86_64", "JULIA_CPU_THREADS=1", "JULIA_NUM_PRECOMPILE_TASKS=1", "DISPLAY=:1", "JULIA_LOAD_PATH=@:/tmp/jl_vDHKmp", "PKGEVAL=true", "OPENBLAS_NUM_THREADS=1", "CI=true", "JULIA_PKG_PRECOMPILE_AUTO=0", "JULIA_PKGEVAL=true", "JULIA_DEPOT_PATH=/home/pkgeval/.julia:/usr/local/share/julia:", "JULIA_NUM_THREADS=1", "R_HOME=*", "HOME=/home/pkgeval"]), ProcessExited(1)) [1] Stacktrace: [1] pipeline_error @ ./process.jl:598 [inlined] [2] run(::Cmd; wait::Bool) @ Base ./process.jl:513 [3] run @ ./process.jl:510 [inlined] [4] runconda(args::Cmd, env::String) @ Conda ~/.julia/packages/Conda/zReqD/src/Conda.jl:182 [5] add(pkg::String, env::String; channel::String, satisfied_skip_solve::Bool, args::Cmd) @ Conda ~/.julia/packages/Conda/zReqD/src/Conda.jl:343 [6] add (repeats 2 times) @ ~/.julia/packages/Conda/zReqD/src/Conda.jl:326 [inlined] [7] pyimport_conda(modulename::String, condapkg::String, channel::String) @ PyCall ~/.julia/packages/PyCall/1gn3u/src/PyCall.jl:721 [8] import_sklearn() @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/Skcore.jl:219 [9] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/test_quickstart.jl:272 [10] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [11] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:12 [12] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [13] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:35 [inlined] [14] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [15] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:35 [inlined] [16] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [17] top-level scope @ none:6 [18] eval(m::Module, e::Any) @ Core ./boot.jl:489 [19] exec_options(opts::Base.JLOptions) @ Base ./client.jl:287 [20] _start() @ Base ./client.jl:554 caused by: PyError (PyImport_ImportModule The Python package sklearn could not be imported by pyimport. Usually this means that you did not install sklearn in the Python version being used by PyCall. PyCall is currently configured to use the Julia-specific Python distribution installed by the Conda.jl package. To install the sklearn module, you can use `pyimport_conda("sklearn", PKG)`, where PKG is the Anaconda package that contains the module sklearn, or alternatively you can use the Conda package directly (via `using Conda` followed by `Conda.add` etcetera). Alternatively, if you want to use a different Python distribution on your system, such as a system-wide Python (as opposed to the Julia-specific Python), you can re-configure PyCall with that Python. As explained in the PyCall documentation, set ENV["PYTHON"] to the path/name of the python executable you want to use, run Pkg.build("PyCall"), and re-launch Julia. ) ModuleNotFoundError("No module named 'sklearn'") Stacktrace: [1] pyimport(name::String) @ PyCall ~/.julia/packages/PyCall/1gn3u/src/PyCall.jl:558 [2] pyimport_conda(modulename::String, condapkg::String, channel::String) @ PyCall ~/.julia/packages/PyCall/1gn3u/src/PyCall.jl:716 [3] import_sklearn() @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/Skcore.jl:219 [4] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/test_quickstart.jl:272 [5] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [6] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:12 [7] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [8] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:35 [inlined] [9] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [10] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:35 [inlined] [11] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [12] top-level scope @ none:6 [13] eval(m::Module, e::Any) @ Core ./boot.jl:489 [14] exec_options(opts::Base.JLOptions) @ Base ./client.jl:287 [15] _start() @ Base ./client.jl:554 [ Info: Installing sklearn via the Conda scikit-learn>=1.2,<1.3 package... [ Info: Running `conda config --add channels conda-forge --file /home/pkgeval/.julia/conda/3/x86_64/condarc-julia.yml --force` in root environment Warning: 'conda-forge' already in 'channels' list, moving to the top [ Info: Running `conda install -q -y 'scikit-learn>=1.2,<1.3'` in root environment Channels: - conda-forge - defaults Platform: linux-64 Collecting package metadata (repodata.json): ...working... done Solving environment: ...working... failed LibMambaUnsatisfiableError: Encountered problems while solving: - package scikit-learn-1.2.0-py310h209a8ca_0 requires python >=3.10,<3.11.0a0, but none of the providers can be installed Could not solve for environment specs The following packages are incompatible ├─ pin on python =3.12 * is installable and it requires │ └─ python =3.12 *, which can be installed; └─ scikit-learn >=1.2,<1.3 * is not installable because there are no viable options ├─ scikit-learn [1.2.0|1.2.1|1.2.2] would require │ └─ python >=3.10,<3.11.0a0 *, which conflicts with any installable versions previously reported; ├─ scikit-learn [1.2.0|1.2.1|1.2.2] would require │ └─ python >=3.11,<3.12.0a0 *, which conflicts with any installable versions previously reported; ├─ scikit-learn [1.2.0|1.2.1|1.2.2] would require │ └─ python >=3.8,<3.9.0a0 *, which conflicts with any installable versions previously reported; └─ scikit-learn [1.2.0|1.2.1|1.2.2] would require └─ python >=3.9,<3.10.0a0 *, which conflicts with any installable versions previously reported. Pins seem to be involved in the conflict. Currently pinned specs: - python=3.12 DataFrames: Error During Test at /home/pkgeval/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:37 Got exception outside of a @test LoadError: UndefVarError: `CONDA` not defined in `ScikitLearn.Skcore` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] import_sklearn() @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/Skcore.jl:225 [2] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/test_dataframes.jl:7 [3] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/src/Skcore.jl:272 [inlined] [4] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [5] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:12 [6] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [7] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:38 [inlined] [8] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [9] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:38 [inlined] [10] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [11] top-level scope @ none:6 [12] eval(m::Module, e::Any) @ Core ./boot.jl:489 [13] exec_options(opts::Base.JLOptions) @ Base ./client.jl:287 [14] _start() @ Base ./client.jl:554 in expression starting at /home/pkgeval/.julia/packages/ScikitLearn/sqLdT/test/test_dataframes.jl:3 caused by: failed process: Process(setenv(`/home/pkgeval/.julia/conda/3/x86_64/bin/conda install -q -y 'scikit-learn>=1.2,<1.3'`,["PYTHONIOENCODING=UTF-8", "LANG=C.UTF-8", "PATH=/usr/local/bin:/usr/local/sbin:/usr/bin:/usr/sbin:/bin:/sbin", "OPENBLAS_MAIN_FREE=1", "CONDARC=/home/pkgeval/.julia/conda/3/x86_64/condarc-julia.yml", "CONDA_PREFIX=/home/pkgeval/.julia/conda/3/x86_64", "JULIA_CPU_THREADS=1", "JULIA_NUM_PRECOMPILE_TASKS=1", "DISPLAY=:1", "JULIA_LOAD_PATH=@:/tmp/jl_vDHKmp", "PKGEVAL=true", "OPENBLAS_NUM_THREADS=1", "CI=true", "JULIA_PKG_PRECOMPILE_AUTO=0", "JULIA_PKGEVAL=true", "JULIA_DEPOT_PATH=/home/pkgeval/.julia:/usr/local/share/julia:", "JULIA_NUM_THREADS=1", "R_HOME=*", "HOME=/home/pkgeval"]), ProcessExited(1)) [1] Stacktrace: [1] pipeline_error @ ./process.jl:598 [inlined] [2] run(::Cmd; wait::Bool) @ Base ./process.jl:513 [3] run @ ./process.jl:510 [inlined] [4] runconda(args::Cmd, env::String) @ Conda ~/.julia/packages/Conda/zReqD/src/Conda.jl:182 [5] add(pkg::String, env::String; channel::String, satisfied_skip_solve::Bool, args::Cmd) @ Conda ~/.julia/packages/Conda/zReqD/src/Conda.jl:343 [6] add (repeats 2 times) @ ~/.julia/packages/Conda/zReqD/src/Conda.jl:326 [inlined] [7] pyimport_conda(modulename::String, condapkg::String, channel::String) @ PyCall ~/.julia/packages/PyCall/1gn3u/src/PyCall.jl:721 [8] import_sklearn() @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/Skcore.jl:219 [9] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/test_dataframes.jl:7 [10] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/src/Skcore.jl:272 [inlined] [11] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [12] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:12 [13] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [14] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:38 [inlined] [15] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [16] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:38 [inlined] [17] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [18] top-level scope @ none:6 [19] eval(m::Module, e::Any) @ Core ./boot.jl:489 [20] exec_options(opts::Base.JLOptions) @ Base ./client.jl:287 [21] _start() @ Base ./client.jl:554 caused by: PyError (PyImport_ImportModule The Python package sklearn could not be imported by pyimport. Usually this means that you did not install sklearn in the Python version being used by PyCall. PyCall is currently configured to use the Julia-specific Python distribution installed by the Conda.jl package. To install the sklearn module, you can use `pyimport_conda("sklearn", PKG)`, where PKG is the Anaconda package that contains the module sklearn, or alternatively you can use the Conda package directly (via `using Conda` followed by `Conda.add` etcetera). Alternatively, if you want to use a different Python distribution on your system, such as a system-wide Python (as opposed to the Julia-specific Python), you can re-configure PyCall with that Python. As explained in the PyCall documentation, set ENV["PYTHON"] to the path/name of the python executable you want to use, run Pkg.build("PyCall"), and re-launch Julia. ) ModuleNotFoundError("No module named 'sklearn'") Stacktrace: [1] pyimport(name::String) @ PyCall ~/.julia/packages/PyCall/1gn3u/src/PyCall.jl:558 [2] pyimport_conda(modulename::String, condapkg::String, channel::String) @ PyCall ~/.julia/packages/PyCall/1gn3u/src/PyCall.jl:716 [3] import_sklearn() @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/Skcore.jl:219 [4] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/test_dataframes.jl:7 [5] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/src/Skcore.jl:272 [inlined] [6] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [7] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:12 [8] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [9] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:38 [inlined] [10] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [11] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:38 [inlined] [12] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [13] top-level scope @ none:6 [14] eval(m::Module, e::Any) @ Core ./boot.jl:489 [15] exec_options(opts::Base.JLOptions) @ Base ./client.jl:287 [16] _start() @ Base ./client.jl:554 Testing ../examples/Classifier_Comparison.ipynb Precompiling packages... 2747.5 ms ✓ NBInclude 1 dependency successfully precompiled in 3 seconds. 17 already precompiled. Precompiling packages... 10825.4 ms ✓ PyPlot 1 dependency successfully precompiled in 11 seconds. 39 already precompiled. [ Info: Installing matplotlib via the Conda matplotlib package... [ Info: Running `conda install -q -y matplotlib` in root environment Channels: - conda-forge - defaults 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: - matplotlib The following packages will be downloaded: package | build ---------------------------|----------------- alsa-lib-1.2.14 | hb9d3cd8_0 553 KB conda-forge brotli-1.1.0 | hb9d3cd8_2 19 KB conda-forge brotli-bin-1.1.0 | hb9d3cd8_2 18 KB conda-forge cairo-1.18.4 | h3394656_0 955 KB conda-forge contourpy-1.3.2 | py312h68727a3_0 270 KB conda-forge cycler-0.12.1 | pyhd8ed1ab_1 13 KB conda-forge cyrus-sasl-2.1.28 | hd9c7081_0 205 KB conda-forge dbus-1.16.2 | h3c4dab8_0 428 KB conda-forge double-conversion-3.3.1 | h5888daf_0 68 KB conda-forge font-ttf-dejavu-sans-mono-2.37| hab24e00_0 388 KB conda-forge font-ttf-inconsolata-3.000 | h77eed37_0 94 KB conda-forge font-ttf-source-code-pro-2.038| h77eed37_0 684 KB conda-forge font-ttf-ubuntu-0.83 | h77eed37_3 1.5 MB conda-forge fontconfig-2.15.0 | h7e30c49_1 259 KB conda-forge fonts-conda-ecosystem-1 | 0 4 KB conda-forge fonts-conda-forge-1 | 0 4 KB conda-forge fonttools-4.58.5 | py312h178313f_0 2.7 MB conda-forge freetype-2.13.3 | ha770c72_1 168 KB conda-forge graphite2-1.3.14 | h5888daf_0 96 KB conda-forge harfbuzz-11.2.1 | h3beb420_0 1.7 MB conda-forge kiwisolver-1.4.8 | py312h68727a3_1 70 KB conda-forge lcms2-2.17 | h717163a_0 242 KB conda-forge lerc-4.0.0 | h0aef613_1 258 KB conda-forge libbrotlicommon-1.1.0 | hb9d3cd8_2 67 KB conda-forge libbrotlidec-1.1.0 | hb9d3cd8_2 32 KB conda-forge libbrotlienc-1.1.0 | hb9d3cd8_2 275 KB conda-forge libclang-cpp20.1-20.1.8 |default_hddf928d_0 20.3 MB conda-forge libclang13-20.1.8 |default_ha444ac7_0 11.8 MB conda-forge libcups-2.3.3 | hb8b1518_5 4.3 MB conda-forge libdeflate-1.24 | h86f0d12_0 71 KB conda-forge libdrm-2.4.125 | hb9d3cd8_0 240 KB 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py312hd3ec401_0 7.8 MB conda-forge munkres-1.1.4 | pyhd8ed1ab_1 15 KB conda-forge openjpeg-2.5.3 | h5fbd93e_0 335 KB conda-forge openldap-2.6.10 | he970967_0 762 KB conda-forge pcre2-10.45 | hc749103_0 1.1 MB conda-forge pillow-11.3.0 | py312h80c1187_0 41.0 MB conda-forge pixman-0.46.2 | h29eaf8c_0 393 KB conda-forge pthread-stubs-0.4 | hb9d3cd8_1002 8 KB conda-forge pyparsing-3.2.3 | pyhd8ed1ab_1 94 KB conda-forge pyside6-6.9.0 | py312h91f0f75_0 9.7 MB conda-forge python-dateutil-2.9.0.post0| pyhe01879c_2 228 KB conda-forge qhull-2020.2 | h434a139_5 540 KB conda-forge qt6-main-6.9.0 | h0384650_3 49.6 MB conda-forge six-1.17.0 | pyhd8ed1ab_0 16 KB conda-forge tornado-6.5.1 | py312h66e93f0_0 831 KB conda-forge unicodedata2-16.0.0 | py312h66e93f0_0 395 KB conda-forge wayland-1.24.0 | h3e06ad9_0 323 KB conda-forge xcb-util-0.4.1 | h4f16b4b_2 20 KB conda-forge xcb-util-cursor-0.1.5 | hb9d3cd8_0 20 KB conda-forge xcb-util-image-0.4.0 | hb711507_2 24 KB conda-forge xcb-util-keysyms-0.4.1 | hb711507_0 14 KB conda-forge xcb-util-renderutil-0.3.10 | hb711507_0 17 KB conda-forge xcb-util-wm-0.4.2 | hb711507_0 50 KB conda-forge xkeyboard-config-2.45 | hb9d3cd8_0 383 KB conda-forge xorg-libice-1.1.2 | hb9d3cd8_0 57 KB conda-forge xorg-libsm-1.2.6 | he73a12e_0 27 KB conda-forge xorg-libx11-1.8.12 | h4f16b4b_0 816 KB conda-forge xorg-libxau-1.0.12 | hb9d3cd8_0 14 KB conda-forge xorg-libxcomposite-0.4.6 | hb9d3cd8_2 13 KB conda-forge xorg-libxcursor-1.2.3 | hb9d3cd8_0 32 KB conda-forge xorg-libxdamage-1.1.6 | hb9d3cd8_0 13 KB conda-forge xorg-libxdmcp-1.1.5 | hb9d3cd8_0 19 KB conda-forge xorg-libxext-1.3.6 | hb9d3cd8_0 49 KB conda-forge xorg-libxfixes-6.0.1 | hb9d3cd8_0 19 KB conda-forge xorg-libxi-1.8.2 | hb9d3cd8_0 46 KB conda-forge xorg-libxrandr-1.5.4 | hb9d3cd8_0 29 KB conda-forge xorg-libxrender-0.9.12 | hb9d3cd8_0 32 KB conda-forge xorg-libxtst-1.2.5 | hb9d3cd8_3 32 KB conda-forge xorg-libxxf86vm-1.1.6 | hb9d3cd8_0 17 KB conda-forge ------------------------------------------------------------ Total: 214.5 MB The following NEW packages will be INSTALLED: alsa-lib conda-forge/linux-64::alsa-lib-1.2.14-hb9d3cd8_0 brotli conda-forge/linux-64::brotli-1.1.0-hb9d3cd8_2 brotli-bin conda-forge/linux-64::brotli-bin-1.1.0-hb9d3cd8_2 cairo conda-forge/linux-64::cairo-1.18.4-h3394656_0 contourpy conda-forge/linux-64::contourpy-1.3.2-py312h68727a3_0 cycler conda-forge/noarch::cycler-0.12.1-pyhd8ed1ab_1 cyrus-sasl conda-forge/linux-64::cyrus-sasl-2.1.28-hd9c7081_0 dbus conda-forge/linux-64::dbus-1.16.2-h3c4dab8_0 double-conversion conda-forge/linux-64::double-conversion-3.3.1-h5888daf_0 font-ttf-dejavu-s~ conda-forge/noarch::font-ttf-dejavu-sans-mono-2.37-hab24e00_0 font-ttf-inconsol~ conda-forge/noarch::font-ttf-inconsolata-3.000-h77eed37_0 font-ttf-source-c~ conda-forge/noarch::font-ttf-source-code-pro-2.038-h77eed37_0 font-ttf-ubuntu conda-forge/noarch::font-ttf-ubuntu-0.83-h77eed37_3 fontconfig conda-forge/linux-64::fontconfig-2.15.0-h7e30c49_1 fonts-conda-ecosy~ conda-forge/noarch::fonts-conda-ecosystem-1-0 fonts-conda-forge conda-forge/noarch::fonts-conda-forge-1-0 fonttools conda-forge/linux-64::fonttools-4.58.5-py312h178313f_0 freetype conda-forge/linux-64::freetype-2.13.3-ha770c72_1 graphite2 conda-forge/linux-64::graphite2-1.3.14-h5888daf_0 harfbuzz conda-forge/linux-64::harfbuzz-11.2.1-h3beb420_0 kiwisolver conda-forge/linux-64::kiwisolver-1.4.8-py312h68727a3_1 lcms2 conda-forge/linux-64::lcms2-2.17-h717163a_0 lerc conda-forge/linux-64::lerc-4.0.0-h0aef613_1 libbrotlicommon conda-forge/linux-64::libbrotlicommon-1.1.0-hb9d3cd8_2 libbrotlidec conda-forge/linux-64::libbrotlidec-1.1.0-hb9d3cd8_2 libbrotlienc conda-forge/linux-64::libbrotlienc-1.1.0-hb9d3cd8_2 libclang-cpp20.1 conda-forge/linux-64::libclang-cpp20.1-20.1.8-default_hddf928d_0 libclang13 conda-forge/linux-64::libclang13-20.1.8-default_ha444ac7_0 libcups conda-forge/linux-64::libcups-2.3.3-hb8b1518_5 libdeflate conda-forge/linux-64::libdeflate-1.24-h86f0d12_0 libdrm conda-forge/linux-64::libdrm-2.4.125-hb9d3cd8_0 libegl conda-forge/linux-64::libegl-1.7.0-ha4b6fd6_2 libfreetype conda-forge/linux-64::libfreetype-2.13.3-ha770c72_1 libfreetype6 conda-forge/linux-64::libfreetype6-2.13.3-h48d6fc4_1 libgl conda-forge/linux-64::libgl-1.7.0-ha4b6fd6_2 libglib conda-forge/linux-64::libglib-2.84.2-h3618099_0 libglvnd conda-forge/linux-64::libglvnd-1.7.0-ha4b6fd6_2 libglx conda-forge/linux-64::libglx-1.7.0-ha4b6fd6_2 libjpeg-turbo conda-forge/linux-64::libjpeg-turbo-3.1.0-hb9d3cd8_0 libllvm20 conda-forge/linux-64::libllvm20-20.1.8-hecd9e04_0 libntlm conda-forge/linux-64::libntlm-1.8-hb9d3cd8_0 libopengl conda-forge/linux-64::libopengl-1.7.0-ha4b6fd6_2 libpciaccess conda-forge/linux-64::libpciaccess-0.18-hb9d3cd8_0 libpng conda-forge/linux-64::libpng-1.6.50-h943b412_0 libpq conda-forge/linux-64::libpq-17.5-h27ae623_0 libtiff conda-forge/linux-64::libtiff-4.7.0-hf01ce69_5 libwebp-base conda-forge/linux-64::libwebp-base-1.6.0-hd42ef1d_0 libxcb conda-forge/linux-64::libxcb-1.17.0-h8a09558_0 libxkbcommon conda-forge/linux-64::libxkbcommon-1.10.0-h65c71a3_0 libxslt conda-forge/linux-64::libxslt-1.1.39-h76b75d6_0 matplotlib conda-forge/linux-64::matplotlib-3.10.3-py312h7900ff3_0 matplotlib-base conda-forge/linux-64::matplotlib-base-3.10.3-py312hd3ec401_0 munkres conda-forge/noarch::munkres-1.1.4-pyhd8ed1ab_1 openjpeg conda-forge/linux-64::openjpeg-2.5.3-h5fbd93e_0 openldap conda-forge/linux-64::openldap-2.6.10-he970967_0 pcre2 conda-forge/linux-64::pcre2-10.45-hc749103_0 pillow conda-forge/linux-64::pillow-11.3.0-py312h80c1187_0 pixman conda-forge/linux-64::pixman-0.46.2-h29eaf8c_0 pthread-stubs conda-forge/linux-64::pthread-stubs-0.4-hb9d3cd8_1002 pyparsing conda-forge/noarch::pyparsing-3.2.3-pyhd8ed1ab_1 pyside6 conda-forge/linux-64::pyside6-6.9.0-py312h91f0f75_0 python-dateutil conda-forge/noarch::python-dateutil-2.9.0.post0-pyhe01879c_2 qhull conda-forge/linux-64::qhull-2020.2-h434a139_5 qt6-main conda-forge/linux-64::qt6-main-6.9.0-h0384650_3 six conda-forge/noarch::six-1.17.0-pyhd8ed1ab_0 tornado conda-forge/linux-64::tornado-6.5.1-py312h66e93f0_0 unicodedata2 conda-forge/linux-64::unicodedata2-16.0.0-py312h66e93f0_0 wayland conda-forge/linux-64::wayland-1.24.0-h3e06ad9_0 xcb-util conda-forge/linux-64::xcb-util-0.4.1-h4f16b4b_2 xcb-util-cursor conda-forge/linux-64::xcb-util-cursor-0.1.5-hb9d3cd8_0 xcb-util-image conda-forge/linux-64::xcb-util-image-0.4.0-hb711507_2 xcb-util-keysyms conda-forge/linux-64::xcb-util-keysyms-0.4.1-hb711507_0 xcb-util-renderut~ conda-forge/linux-64::xcb-util-renderutil-0.3.10-hb711507_0 xcb-util-wm conda-forge/linux-64::xcb-util-wm-0.4.2-hb711507_0 xkeyboard-config conda-forge/linux-64::xkeyboard-config-2.45-hb9d3cd8_0 xorg-libice conda-forge/linux-64::xorg-libice-1.1.2-hb9d3cd8_0 xorg-libsm conda-forge/linux-64::xorg-libsm-1.2.6-he73a12e_0 xorg-libx11 conda-forge/linux-64::xorg-libx11-1.8.12-h4f16b4b_0 xorg-libxau conda-forge/linux-64::xorg-libxau-1.0.12-hb9d3cd8_0 xorg-libxcomposite conda-forge/linux-64::xorg-libxcomposite-0.4.6-hb9d3cd8_2 xorg-libxcursor conda-forge/linux-64::xorg-libxcursor-1.2.3-hb9d3cd8_0 xorg-libxdamage conda-forge/linux-64::xorg-libxdamage-1.1.6-hb9d3cd8_0 xorg-libxdmcp conda-forge/linux-64::xorg-libxdmcp-1.1.5-hb9d3cd8_0 xorg-libxext conda-forge/linux-64::xorg-libxext-1.3.6-hb9d3cd8_0 xorg-libxfixes conda-forge/linux-64::xorg-libxfixes-6.0.1-hb9d3cd8_0 xorg-libxi conda-forge/linux-64::xorg-libxi-1.8.2-hb9d3cd8_0 xorg-libxrandr conda-forge/linux-64::xorg-libxrandr-1.5.4-hb9d3cd8_0 xorg-libxrender conda-forge/linux-64::xorg-libxrender-0.9.12-hb9d3cd8_0 xorg-libxtst conda-forge/linux-64::xorg-libxtst-1.2.5-hb9d3cd8_3 xorg-libxxf86vm conda-forge/linux-64::xorg-libxxf86vm-1.1.6-hb9d3cd8_0 Preparing transaction: ...working... done Verifying transaction: ...working... done Executing transaction: ...working... done ┌ Warning: `@pyimport foo` is deprecated in favor of `foo = pyimport("foo")`. │ caller = _pywrap_pyimport(o::PyCall.PyObject) at PyCall.jl:421 └ @ Core ~/.julia/packages/PyCall/1gn3u/src/PyCall.jl:421 [ Info: Installing sklearn via the Conda scikit-learn>=1.2,<1.3 package... [ Info: Running `conda config --add channels conda-forge --file /home/pkgeval/.julia/conda/3/x86_64/condarc-julia.yml --force` in root environment Warning: 'conda-forge' already in 'channels' list, moving to the top [ Info: Running `conda install -q -y 'scikit-learn>=1.2,<1.3'` in root environment Channels: - conda-forge - defaults Platform: linux-64 Collecting package metadata (repodata.json): ...working... done Solving environment: ...working... failed LibMambaUnsatisfiableError: Encountered problems while solving: - package scikit-learn-1.2.0-py310h209a8ca_0 requires python >=3.10,<3.11.0a0, but none of the providers can be installed Could not solve for environment specs The following packages are incompatible ├─ pin on python =3.12 * is installable and it requires │ └─ python =3.12 *, which can be installed; └─ scikit-learn >=1.2,<1.3 * is not installable because there are no viable options ├─ scikit-learn [1.2.0|1.2.1|1.2.2] would require │ └─ python >=3.10,<3.11.0a0 *, which conflicts with any installable versions previously reported; ├─ scikit-learn [1.2.0|1.2.1|1.2.2] would require │ └─ python >=3.11,<3.12.0a0 *, which conflicts with any installable versions previously reported; ├─ scikit-learn [1.2.0|1.2.1|1.2.2] would require │ └─ python >=3.8,<3.9.0a0 *, which conflicts with any installable versions previously reported; └─ scikit-learn [1.2.0|1.2.1|1.2.2] would require └─ python >=3.9,<3.10.0a0 *, which conflicts with any installable versions previously reported. Pins seem to be involved in the conflict. Currently pinned specs: - python=3.12 Notebook examples: Error During Test at /home/pkgeval/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:63 Got exception outside of a @test LoadError: UndefVarError: `CONDA` not defined in `ScikitLearn.Skcore` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] import_sklearn() @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/Skcore.jl:225 [2] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/examples/Classifier_Comparison.ipynb:In[1]:272 [3] eval(m::Module, e::Any) @ Core ./boot.jl:489 [4] include_string(mapexpr::typeof(identity), mod::Module, code::String, filename::String) @ Base ./loading.jl:2837 [5] include_string @ ./loading.jl:2847 [inlined] [6] my_include_string(m::Module, s::String, path::String, prev::String, softscope::Bool) @ NBInclude ~/.julia/packages/NBInclude/pfsyO/src/NBInclude.jl:30 [7] #3 @ ~/.julia/packages/NBInclude/pfsyO/src/NBInclude.jl:93 [inlined] [8] task_local_storage(body::NBInclude.var"#3#4"{Bool, Module, String, String, String, String}, key::Symbol, val::Bool) @ Base ./task.jl:298 [9] nbinclude(m::Module, path::String; renumber::Bool, counters::UnitRange{Int64}, regex::Regex, anshook::typeof(identity), softscope::Bool) @ NBInclude ~/.julia/packages/NBInclude/pfsyO/src/NBInclude.jl:92 [10] nbinclude(m::Module, path::String) @ NBInclude ~/.julia/packages/NBInclude/pfsyO/src/NBInclude.jl:57 [11] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:56 [12] eval(m::Module, e::Any) @ Core ./boot.jl:489 [13] (::var"#run_examples#run_examples##0"{Vector{String}})() @ Main ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:54 [14] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:12 [15] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [16] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:64 [inlined] [17] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [18] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:64 [inlined] [19] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [20] top-level scope @ none:6 [21] eval(m::Module, e::Any) @ Core ./boot.jl:489 [22] exec_options(opts::Base.JLOptions) @ Base ./client.jl:287 [23] _start() @ Base ./client.jl:554 in expression starting at /home/pkgeval/.julia/packages/ScikitLearn/sqLdT/examples/Classifier_Comparison.ipynb:In[1]:12 caused by: failed process: Process(setenv(`/home/pkgeval/.julia/conda/3/x86_64/bin/conda install -q -y 'scikit-learn>=1.2,<1.3'`,["PYTHONIOENCODING=UTF-8", "LANG=C.UTF-8", "PATH=/usr/local/bin:/usr/local/sbin:/usr/bin:/usr/sbin:/bin:/sbin", "OPENBLAS_MAIN_FREE=1", "CONDARC=/home/pkgeval/.julia/conda/3/x86_64/condarc-julia.yml", "CONDA_PREFIX=/home/pkgeval/.julia/conda/3/x86_64", "JULIA_CPU_THREADS=1", "JULIA_NUM_PRECOMPILE_TASKS=1", "DISPLAY=:1", "PYSIDE6_OPTION_PYTHON_ENUM=True", "JULIA_LOAD_PATH=@:/tmp/jl_vDHKmp", "PKGEVAL=true", "OPENBLAS_NUM_THREADS=1", "CI=true", "JULIA_PKG_PRECOMPILE_AUTO=0", "JULIA_PKGEVAL=true", "JULIA_DEPOT_PATH=/home/pkgeval/.julia:/usr/local/share/julia:", "JULIA_NUM_THREADS=1", "R_HOME=*", "HOME=/home/pkgeval"]), ProcessExited(1)) [1] Stacktrace: [1] pipeline_error @ ./process.jl:598 [inlined] [2] run(::Cmd; wait::Bool) @ Base ./process.jl:513 [3] run @ ./process.jl:510 [inlined] [4] runconda(args::Cmd, env::String) @ Conda ~/.julia/packages/Conda/zReqD/src/Conda.jl:182 [5] add(pkg::String, env::String; channel::String, satisfied_skip_solve::Bool, args::Cmd) @ Conda ~/.julia/packages/Conda/zReqD/src/Conda.jl:343 [6] add (repeats 2 times) @ ~/.julia/packages/Conda/zReqD/src/Conda.jl:326 [inlined] [7] pyimport_conda(modulename::String, condapkg::String, channel::String) @ PyCall ~/.julia/packages/PyCall/1gn3u/src/PyCall.jl:721 [8] import_sklearn() @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/Skcore.jl:219 [9] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/examples/Classifier_Comparison.ipynb:In[1]:272 [10] eval(m::Module, e::Any) @ Core ./boot.jl:489 [11] include_string(mapexpr::typeof(identity), mod::Module, code::String, filename::String) @ Base ./loading.jl:2837 [12] include_string @ ./loading.jl:2847 [inlined] [13] my_include_string(m::Module, s::String, path::String, prev::String, softscope::Bool) @ NBInclude ~/.julia/packages/NBInclude/pfsyO/src/NBInclude.jl:30 [14] #3 @ ~/.julia/packages/NBInclude/pfsyO/src/NBInclude.jl:93 [inlined] [15] task_local_storage(body::NBInclude.var"#3#4"{Bool, Module, String, String, String, String}, key::Symbol, val::Bool) @ Base ./task.jl:298 [16] nbinclude(m::Module, path::String; renumber::Bool, counters::UnitRange{Int64}, regex::Regex, anshook::typeof(identity), softscope::Bool) @ NBInclude ~/.julia/packages/NBInclude/pfsyO/src/NBInclude.jl:92 [17] nbinclude(m::Module, path::String) @ NBInclude ~/.julia/packages/NBInclude/pfsyO/src/NBInclude.jl:57 [18] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:56 [19] eval(m::Module, e::Any) @ Core ./boot.jl:489 [20] (::var"#run_examples#run_examples##0"{Vector{String}})() @ Main ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:54 [21] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:12 [22] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [23] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:64 [inlined] [24] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [25] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:64 [inlined] [26] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [27] top-level scope @ none:6 [28] eval(m::Module, e::Any) @ Core ./boot.jl:489 [29] exec_options(opts::Base.JLOptions) @ Base ./client.jl:287 [30] _start() @ Base ./client.jl:554 caused by: PyError (PyImport_ImportModule The Python package sklearn could not be imported by pyimport. Usually this means that you did not install sklearn in the Python version being used by PyCall. PyCall is currently configured to use the Julia-specific Python distribution installed by the Conda.jl package. To install the sklearn module, you can use `pyimport_conda("sklearn", PKG)`, where PKG is the Anaconda package that contains the module sklearn, or alternatively you can use the Conda package directly (via `using Conda` followed by `Conda.add` etcetera). Alternatively, if you want to use a different Python distribution on your system, such as a system-wide Python (as opposed to the Julia-specific Python), you can re-configure PyCall with that Python. As explained in the PyCall documentation, set ENV["PYTHON"] to the path/name of the python executable you want to use, run Pkg.build("PyCall"), and re-launch Julia. ) ModuleNotFoundError("No module named 'sklearn'") Stacktrace: [1] pyimport(name::String) @ PyCall ~/.julia/packages/PyCall/1gn3u/src/PyCall.jl:558 [2] pyimport_conda(modulename::String, condapkg::String, channel::String) @ PyCall ~/.julia/packages/PyCall/1gn3u/src/PyCall.jl:716 [3] import_sklearn() @ ScikitLearn.Skcore ~/.julia/packages/ScikitLearn/sqLdT/src/Skcore.jl:219 [4] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/examples/Classifier_Comparison.ipynb:In[1]:272 [5] eval(m::Module, e::Any) @ Core ./boot.jl:489 [6] include_string(mapexpr::typeof(identity), mod::Module, code::String, filename::String) @ Base ./loading.jl:2837 [7] include_string @ ./loading.jl:2847 [inlined] [8] my_include_string(m::Module, s::String, path::String, prev::String, softscope::Bool) @ NBInclude ~/.julia/packages/NBInclude/pfsyO/src/NBInclude.jl:30 [9] #3 @ ~/.julia/packages/NBInclude/pfsyO/src/NBInclude.jl:93 [inlined] [10] task_local_storage(body::NBInclude.var"#3#4"{Bool, Module, String, String, String, String}, key::Symbol, val::Bool) @ Base ./task.jl:298 [11] nbinclude(m::Module, path::String; renumber::Bool, counters::UnitRange{Int64}, regex::Regex, anshook::typeof(identity), softscope::Bool) @ NBInclude ~/.julia/packages/NBInclude/pfsyO/src/NBInclude.jl:92 [12] nbinclude(m::Module, path::String) @ NBInclude ~/.julia/packages/NBInclude/pfsyO/src/NBInclude.jl:57 [13] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:56 [14] eval(m::Module, e::Any) @ Core ./boot.jl:489 [15] (::var"#run_examples#run_examples##0"{Vector{String}})() @ Main ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:54 [16] top-level scope @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:12 [17] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [18] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:64 [inlined] [19] macro expansion @ /opt/julia/share/julia/stdlib/v1.12/Test/src/Test.jl:1776 [inlined] [20] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:64 [inlined] [21] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:306 [22] top-level scope @ none:6 [23] eval(m::Module, e::Any) @ Core ./boot.jl:489 [24] exec_options(opts::Base.JLOptions) @ Base ./client.jl:287 [25] _start() @ Base ./client.jl:554 Test Summary: | Pass Error Total Time ScikitLearnTests | 57 5 62 7m26.9s models | 9 9 30.2s base | 1 1 1m02.8s pipeline | 1 1 24.2s crossvalidation | 38 38 16.3s utils | 10 10 0.9s quickstart | 1 1 2m48.8s DataFrames | 1 1 25.5s Notebook examples | 1 1 1m57.6s RNG of the outermost testset: Xoshiro(0x2594da096d457344, 0x21987cc0c12516d7, 0x431765dc7ee9e9ca, 0x488d2a608a83c9c8, 0xd5af763df4e0cd33) ERROR: LoadError: Some tests did not pass: 57 passed, 0 failed, 5 errored, 0 broken. in expression starting at /home/pkgeval/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:10 Testing failed after 459.26s ERROR: LoadError: Package ScikitLearn errored during testing Stacktrace: [1] pkgerror(msg::String) @ Pkg.Types /opt/julia/share/julia/stdlib/v1.12/Pkg/src/Types.jl:68 [2] test(ctx::Pkg.Types.Context, pkgs::Vector{PackageSpec}; coverage::Bool, julia_args::Cmd, test_args::Cmd, test_fn::Nothing, force_latest_compatible_version::Bool, allow_earlier_backwards_compatible_versions::Bool, allow_reresolve::Bool) @ Pkg.Operations /opt/julia/share/julia/stdlib/v1.12/Pkg/src/Operations.jl:2458 [3] test @ /opt/julia/share/julia/stdlib/v1.12/Pkg/src/Operations.jl:2313 [inlined] [4] test(ctx::Pkg.Types.Context, pkgs::Vector{PackageSpec}; coverage::Bool, test_fn::Nothing, julia_args::Cmd, test_args::Cmd, force_latest_compatible_version::Bool, allow_earlier_backwards_compatible_versions::Bool, allow_reresolve::Bool, kwargs::@Kwargs{io::IOContext{IO}}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.12/Pkg/src/API.jl:511 [5] test(pkgs::Vector{PackageSpec}; io::IOContext{IO}, kwargs::@Kwargs{julia_args::Cmd}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.12/Pkg/src/API.jl:164 [6] test(pkgs::Vector{String}; kwargs::@Kwargs{julia_args::Cmd}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.12/Pkg/src/API.jl:152 [7] test @ /opt/julia/share/julia/stdlib/v1.12/Pkg/src/API.jl:152 [inlined] [8] #test#81 @ /opt/julia/share/julia/stdlib/v1.12/Pkg/src/API.jl:151 [inlined] [9] top-level scope @ /PkgEval.jl/scripts/evaluate.jl:219 [10] include(mod::Module, _path::String) @ Base ./Base.jl:305 [11] exec_options(opts::Base.JLOptions) @ Base ./client.jl:321 [12] _start() @ Base ./client.jl:554 in expression starting at /PkgEval.jl/scripts/evaluate.jl:210 PkgEval failed after 887.24s: package tests unexpectedly errored