Package evaluation to test ScikitLearn on Julia 1.12.7-DEV.42 (6f510b6086*) started at 2026-06-19T20:42:24.847 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Activating project at `~/.julia/environments/v1.12` Set-up completed after 8.24s ################################################################################ # Installation # Installing ScikitLearn... Resolving package versions... Installed PyCall ─ v1.96.4 Installed Conda ── v1.10.3 Updating `~/.julia/environments/v1.12/Project.toml` [3646fa90] + ScikitLearn v0.7.0 Updating `~/.julia/environments/v1.12/Manifest.toml` [34da2185] + Compat v4.18.1 [8f4d0f93] + Conda v1.10.3 [a8cc5b0e] + Crayons v4.1.1 [9a962f9c] + DataAPI v1.16.0 [a93c6f00] + DataFrames v1.8.2 ⌅ [864edb3b] + DataStructures v0.18.22 [e2d170a0] + DataValueInterfaces v1.0.0 [ffbed154] + DocStringExtensions v0.9.5 [842dd82b] + InlineStrings v1.4.5 [41ab1584] + InvertedIndices v1.3.1 [92d709cd] + IrrationalConstants v0.2.6 [c8e1da08] + IterTools v1.10.0 [82899510] + IteratorInterfaceExtensions v1.0.0 [682c06a0] + JSON v1.6.1 [b964fa9f] + LaTeXStrings v1.4.0 ⌅ [2ab3a3ac] + LogExpFunctions v0.3.29 [1914dd2f] + MacroTools v0.5.16 [e1d29d7a] + Missings v1.2.0 ⌅ [bac558e1] + OrderedCollections v1.8.2 ⌅ [d96e819e] + Parameters v0.12.3 [69de0a69] + Parsers v2.8.6 [2dfb63ee] + PooledArrays v1.4.3 [aea7be01] + PrecompileTools v1.3.4 [21216c6a] + Preferences v1.5.2 [08abe8d2] + PrettyTables v3.3.2 [438e738f] + PyCall v1.96.4 [189a3867] + Reexport v1.2.2 [3646fa90] + ScikitLearn v0.7.0 [6e75b9c4] + ScikitLearnBase v0.5.0 [91c51154] + SentinelArrays v1.4.10 [a2af1166] + SortingAlgorithms v1.2.2 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.8.0 ⌅ [2913bbd2] + StatsBase v0.33.21 [892a3eda] + StringManipulation v0.4.4 [ec057cc2] + StructUtils v2.8.2 [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 v1.7.0 [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 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [ca575930] + NetworkOptions v1.3.0 [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 [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 v8.15.0+0 [29816b5a] + LibSSH2_jll v1.11.3+1 [14a3606d] + MozillaCACerts_jll v2025.11.4 [4536629a] + OpenBLAS_jll v0.3.29+0 [458c3c95] + OpenSSL_jll v3.5.6+0 [bea87d4a] + SuiteSparse_jll v7.8.3+2 [83775a58] + Zlib_jll v1.3.1+2 [8e850b90] + libblastrampoline_jll v5.15.0+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/8f06b0cfa4c514c7b9546756dbae91fcfbc92dc9/build.log` Building PyCall → `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/9816a3826b0ebf49ab4926e2b18842ad8b5c8f04/build.log` Installation completed after 48.05s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompiling packages... 6630.0 ms ✓ ElasticPDMats 24896.9 ms ✓ GaussianMixtures 37508.1 ms ✓ GaussianProcesses 3 dependencies successfully precompiled in 75 seconds. 178 already precompiled. 24 dependencies precompiled but different versions are currently loaded (ArgTools, Base64, Dates, Downloads, JuliaSyntaxHighlighting, LibCURL, LibCURL_jll, LibGit2, LibGit2_jll, LibSSH2_jll, Logging, Markdown, MozillaCACerts_jll, NetworkOptions, OpenSSL_jll, Pkg, Printf, StyledStrings, TOML, Tar, UUIDs, Zlib_jll, nghttp2_jll and p7zip_jll). Restart julia to access the new versions. Otherwise, 80 dependents of these packages may trigger further precompilation to work with the unexpected versions. Precompilation completed after 91.72s ################################################################################ # Testing # Testing ScikitLearn Status `/tmp/jl_XJCOYi/Project.toml` [34da2185] Compat v4.18.1 [8f4d0f93] Conda v1.10.3 [a93c6f00] DataFrames v1.8.2 [7806a523] DecisionTree v0.12.4 [cc18c42c] GaussianMixtures v0.3.14 [891a1506] GaussianProcesses v0.12.6 [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 v1.1.0 [ce6b1742] RDatasets v0.8.1 [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_XJCOYi/Manifest.toml` [47edcb42] ADTypes v1.22.0 [1520ce14] AbstractTrees v0.4.5 [79e6a3ab] Adapt v4.6.1 [66dad0bd] AliasTables v1.1.3 [7d9fca2a] Arpack v0.5.4 [4fba245c] ArrayInterface v7.25.0 [336ed68f] CSV v0.10.16 [324d7699] CategoricalArrays v1.1.1 [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 v4.18.1 [8f4d0f93] Conda v1.10.3 [187b0558] ConstructionBase v1.6.0 [a8cc5b0e] Crayons v4.1.1 [9a962f9c] DataAPI v1.16.0 [a93c6f00] DataFrames v1.8.2 ⌅ [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 v1.16.0 [a0c0ee7d] DifferentiationInterface v0.7.18 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.127 [ffbed154] DocStringExtensions v0.9.5 [fdbdab4c] ElasticArrays v1.2.12 [2904ab23] ElasticPDMats v0.2.4 [4e289a0a] EnumX v1.0.7 [e2ba6199] ExprTools v0.1.10 [442a2c76] FastGaussQuadrature v1.3.0 [5789e2e9] FileIO v1.19.0 [48062228] FilePathsBase v0.9.24 [1a297f60] FillArrays v1.16.0 [6a86dc24] FiniteDiff v2.31.0 ⌅ [53c48c17] FixedPointNumbers v0.8.6 [f6369f11] ForwardDiff v1.4.1 [cc18c42c] GaussianMixtures v0.3.14 [891a1506] GaussianProcesses v0.12.6 [34004b35] HypergeometricFunctions v0.3.28 [842dd82b] InlineStrings v1.4.5 [41ab1584] InvertedIndices v1.3.1 [92d709cd] IrrationalConstants v0.2.6 [c8e1da08] IterTools v1.10.0 [82899510] IteratorInterfaceExtensions v1.0.0 [692b3bcd] JLLWrappers v1.8.0 [682c06a0] JSON v1.6.1 [b964fa9f] LaTeXStrings v1.4.0 ⌃ [d3d80556] LineSearches v7.5.1 ⌅ [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 v1.1.4 [b8a86587] NearestNeighbors v0.4.27 ⌅ [429524aa] Optim v1.13.3 ⌅ [bac558e1] OrderedCollections v1.8.2 ⌃ [90014a1f] PDMats v0.11.35 ⌅ [d96e819e] Parameters v0.12.3 [69de0a69] Parsers v2.8.6 [2dfb63ee] PooledArrays v1.4.3 [85a6dd25] PositiveFactorizations v0.2.4 [aea7be01] PrecompileTools v1.3.4 [21216c6a] Preferences v1.5.2 [08abe8d2] PrettyTables v3.3.2 [92933f4c] ProgressMeter v1.11.0 [43287f4e] PtrArrays v1.4.0 [438e738f] PyCall v1.96.4 [d330b81b] PyPlot v2.11.6 [1fd47b50] QuadGK v2.11.3 [df47a6cb] RData v1.1.0 [ce6b1742] RDatasets v0.8.1 [3cdcf5f2] RecipesBase v1.3.4 [189a3867] Reexport v1.2.2 [ae029012] Requires v1.3.1 [79098fc4] Rmath v0.9.0 [3646fa90] ScikitLearn v0.7.0 [6e75b9c4] ScikitLearnBase v0.5.0 [6c6a2e73] Scratch v1.3.0 [91c51154] SentinelArrays v1.4.10 [efcf1570] Setfield v1.1.2 [b85f4697] SoftGlobalScope v1.1.0 [a2af1166] SortingAlgorithms v1.2.2 [276daf66] SpecialFunctions v2.8.0 [90137ffa] StaticArrays v1.9.18 [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 v0.4.4 [ec057cc2] StructUtils v2.8.2 [dc5dba14] TZJData v1.5.0+2025b [3783bdb8] TableTraits v1.0.1 [bd369af6] Tables v1.12.1 [f269a46b] TimeZones v1.22.2 [3bb67fe8] TranscodingStreams v0.11.3 [3a884ed6] UnPack v1.0.2 [81def892] VersionParsing v1.3.0 [ea10d353] WeakRefStrings v1.4.3 [76eceee3] WorkerUtilities v1.6.1 ⌅ [68821587] Arpack_jll v3.5.2+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 v1.7.0 [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 v1.12.1 [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 v8.15.0+0 [e37daf67] LibGit2_jll v1.9.0+0 [29816b5a] LibSSH2_jll v1.11.3+1 [14a3606d] MozillaCACerts_jll v2025.11.4 [4536629a] OpenBLAS_jll v0.3.29+0 [05823500] OpenLibm_jll v0.8.7+0 [458c3c95] OpenSSL_jll v3.5.6+0 [bea87d4a] SuiteSparse_jll v7.8.3+2 [83775a58] Zlib_jll v1.3.1+2 [8e850b90] libblastrampoline_jll v5.15.0+0 [8e850ede] nghttp2_jll v1.64.0+1 [3f19e933] p7zip_jll v17.7.0+0 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... [ 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 ---------------------------|----------------- annotated-types-0.6.0 | py313h06a4308_1 26 KB anaconda anyio-4.12.1 | py313h06a4308_0 311 KB anaconda click-8.4.1 | py313h06a4308_0 352 KB anaconda conda-26.5.3 | py313h06a4308_0 1.4 MB anaconda conda-index-0.11.0 | py313h06a4308_0 110 KB anaconda conda-lockfiles-0.2.0 | py313h06a4308_0 34 KB anaconda conda-pypi-0.10.1 | py313h06a4308_0 287 KB anaconda conda-rattler-solver-0.1.0 | py313h06a4308_0 129 KB anaconda conda-self-0.2.0 | py313h06a4308_0 37 KB anaconda filelock-3.29.4 | py313h06a4308_0 96 KB anaconda h11-0.16.0 | py313h06a4308_1 60 KB anaconda httpcore-1.0.9 | py313h06a4308_0 122 KB anaconda httpx-0.28.1 | py313h06a4308_1 215 KB anaconda jinja2-3.1.6 | py313h06a4308_0 344 KB anaconda markupsafe-3.0.2 | py313h5eee18b_0 27 KB anaconda py-rattler-0.23.1 | py313h04fe016_0 13.4 MB anaconda pydantic-2.13.4 | py313h06a4308_0 1.2 MB anaconda pydantic-core-2.46.4 | py313h6e1b9ff_0 2.1 MB anaconda pyproject_hooks-1.2.0 | py313h06a4308_1 30 KB anaconda python-build-1.3.0 | py313h06a4308_1 72 KB anaconda python-installer-1.0.0 | pyhd3eb1b0_0 318 KB anaconda typing-extensions-4.15.0 | py313h06a4308_0 11 KB anaconda typing-inspection-0.4.2 | py313h06a4308_0 31 KB anaconda typing_extensions-4.15.0 | py313h06a4308_0 95 KB anaconda unearth-0.17.5 | py313h06a4308_1 351 KB anaconda ------------------------------------------------------------ Total: 21.0 MB The following NEW packages will be INSTALLED: annotated-types anaconda/linux-64::annotated-types-0.6.0-py313h06a4308_1 anyio anaconda/linux-64::anyio-4.12.1-py313h06a4308_0 click anaconda/linux-64::click-8.4.1-py313h06a4308_0 conda-index anaconda/linux-64::conda-index-0.11.0-py313h06a4308_0 conda-lockfiles anaconda/linux-64::conda-lockfiles-0.2.0-py313h06a4308_0 conda-pypi anaconda/linux-64::conda-pypi-0.10.1-py313h06a4308_0 conda-rattler-sol~ anaconda/linux-64::conda-rattler-solver-0.1.0-py313h06a4308_0 conda-self anaconda/linux-64::conda-self-0.2.0-py313h06a4308_0 filelock anaconda/linux-64::filelock-3.29.4-py313h06a4308_0 h11 anaconda/linux-64::h11-0.16.0-py313h06a4308_1 httpcore anaconda/linux-64::httpcore-1.0.9-py313h06a4308_0 httpx anaconda/linux-64::httpx-0.28.1-py313h06a4308_1 jinja2 anaconda/linux-64::jinja2-3.1.6-py313h06a4308_0 markupsafe anaconda/linux-64::markupsafe-3.0.2-py313h5eee18b_0 py-rattler anaconda/linux-64::py-rattler-0.23.1-py313h04fe016_0 pydantic anaconda/linux-64::pydantic-2.13.4-py313h06a4308_0 pydantic-core anaconda/linux-64::pydantic-core-2.46.4-py313h6e1b9ff_0 pyproject_hooks anaconda/linux-64::pyproject_hooks-1.2.0-py313h06a4308_1 python-build anaconda/linux-64::python-build-1.3.0-py313h06a4308_1 python-installer anaconda/noarch::python-installer-1.0.0-pyhd3eb1b0_0 typing-extensions anaconda/linux-64::typing-extensions-4.15.0-py313h06a4308_0 typing-inspection anaconda/linux-64::typing-inspection-0.4.2-py313h06a4308_0 typing_extensions anaconda/linux-64::typing_extensions-4.15.0-py313h06a4308_0 unearth anaconda/linux-64::unearth-0.17.5-py313h06a4308_1 The following packages will be UPDATED: conda conda-forge::conda-26.3.2-py313h78bf2~ --> anaconda::conda-26.5.3-py313h06a4308_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 ---------------------------|----------------- libgcc-15.2.0 | h767d61c_6 804 KB conda-forge libgcc-ng-15.2.0 | h69a702a_6 29 KB conda-forge libgomp-15.2.0 | h767d61c_6 438 KB conda-forge libstdcxx-15.2.0 | h8f9b012_6 3.7 MB conda-forge libstdcxx-ng-12.3.0 | hc0a3c3a_7 3.3 MB conda-forge ------------------------------------------------------------ Total: 8.3 MB The following NEW packages will be INSTALLED: libstdcxx-ng conda-forge/linux-64::libstdcxx-ng-12.3.0-hc0a3c3a_7 The following packages will be REVISED: libgcc 15.2.0-he0feb66_19 --> 15.2.0-h767d61c_6 libgcc-ng 15.2.0-h69a702a_19 --> 15.2.0-h69a702a_6 libgomp 15.2.0-he0feb66_19 --> 15.2.0-h767d61c_6 libstdcxx 15.2.0-h934c35e_19 --> 15.2.0-h8f9b012_6 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 [ Info: Running `conda install -q -y 'scikit-learn>=1.2,<1.3'` in root environment Channels: - conda-forge 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 =3.13 * is installable and it requires │ └─ python =3.13 *, 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=3.13 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:307 [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:1777 [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:1777 [inlined] [8] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:17 [inlined] [9] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:307 [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:283 [13] _start() @ Base ./client.jl:550 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'`,["LANG=C.UTF-8", "PYTHONIOENCODING=UTF-8", "PATH=/usr/local/bin:/usr/local/sbin:/usr/bin:/usr/sbin:/bin:/sbin:/opt/julia/bin", "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_XJCOYi", "PKGEVAL=true", "OPENBLAS_NUM_THREADS=1", "HOME=/home/pkgeval", "CI=true", "JULIA_PKG_PRECOMPILE_AUTO=0", "JULIA_PKGEVAL=true", "JULIA_DEPOT_PATH=/home/pkgeval/.julia:/usr/local/share/julia:", "R_HOME=*", "JULIA_NUM_THREADS=1"]), ProcessExited(1)) [1] Stacktrace: [1] pipeline_error @ ./process.jl:597 [inlined] [2] run(::Cmd; wait::Bool) @ Base ./process.jl:512 [3] run @ ./process.jl:509 [inlined] [4] runconda(args::Cmd, env::String) @ Conda ~/.julia/packages/Conda/05AVg/src/Conda.jl:182 [5] add(pkg::String, env::String; channel::String, satisfied_skip_solve::Bool, args::Cmd) @ Conda ~/.julia/packages/Conda/05AVg/src/Conda.jl:343 [6] add (repeats 2 times) @ ~/.julia/packages/Conda/05AVg/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:307 [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:1777 [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:1777 [inlined] [15] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:17 [inlined] [16] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:307 [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:283 [20] _start() @ Base ./client.jl:550 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:307 [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:1777 [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:1777 [inlined] [10] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:17 [inlined] [11] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:307 [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:283 [15] _start() @ Base ./client.jl:550 [ 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 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 =3.13 * is installable and it requires │ └─ python =3.13 *, 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=3.13 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:307 [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:1777 [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:1777 [inlined] [8] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:22 [inlined] [9] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:307 [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:283 [13] _start() @ Base ./client.jl:550 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'`,["LANG=C.UTF-8", "PYTHONIOENCODING=UTF-8", "PATH=/usr/local/bin:/usr/local/sbin:/usr/bin:/usr/sbin:/bin:/sbin:/opt/julia/bin", "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_XJCOYi", "PKGEVAL=true", "OPENBLAS_NUM_THREADS=1", "HOME=/home/pkgeval", "CI=true", "JULIA_PKG_PRECOMPILE_AUTO=0", "JULIA_PKGEVAL=true", "JULIA_DEPOT_PATH=/home/pkgeval/.julia:/usr/local/share/julia:", "R_HOME=*", "JULIA_NUM_THREADS=1"]), ProcessExited(1)) [1] Stacktrace: [1] pipeline_error @ ./process.jl:597 [inlined] [2] run(::Cmd; wait::Bool) @ Base ./process.jl:512 [3] run @ ./process.jl:509 [inlined] [4] runconda(args::Cmd, env::String) @ Conda ~/.julia/packages/Conda/05AVg/src/Conda.jl:182 [5] add(pkg::String, env::String; channel::String, satisfied_skip_solve::Bool, args::Cmd) @ Conda ~/.julia/packages/Conda/05AVg/src/Conda.jl:343 [6] add (repeats 2 times) @ ~/.julia/packages/Conda/05AVg/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:307 [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:1777 [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:1777 [inlined] [15] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:22 [inlined] [16] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:307 [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:283 [20] _start() @ Base ./client.jl:550 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:307 [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:1777 [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:1777 [inlined] [10] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:22 [inlined] [11] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:307 [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:283 [15] _start() @ Base ./client.jl:550 ┌ 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 [ 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 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 =3.13 * is installable and it requires │ └─ python =3.13 *, 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=3.13 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:307 [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:1777 [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:1777 [inlined] [8] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:35 [inlined] [9] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:307 [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:283 [13] _start() @ Base ./client.jl:550 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'`,["LANG=C.UTF-8", "PYTHONIOENCODING=UTF-8", "PATH=/usr/local/bin:/usr/local/sbin:/usr/bin:/usr/sbin:/bin:/sbin:/opt/julia/bin", "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_XJCOYi", "PKGEVAL=true", "OPENBLAS_NUM_THREADS=1", "HOME=/home/pkgeval", "CI=true", "JULIA_PKG_PRECOMPILE_AUTO=0", "JULIA_PKGEVAL=true", "JULIA_DEPOT_PATH=/home/pkgeval/.julia:/usr/local/share/julia:", "R_HOME=*", "JULIA_NUM_THREADS=1"]), ProcessExited(1)) [1] Stacktrace: [1] pipeline_error @ ./process.jl:597 [inlined] [2] run(::Cmd; wait::Bool) @ Base ./process.jl:512 [3] run @ ./process.jl:509 [inlined] [4] runconda(args::Cmd, env::String) @ Conda ~/.julia/packages/Conda/05AVg/src/Conda.jl:182 [5] add(pkg::String, env::String; channel::String, satisfied_skip_solve::Bool, args::Cmd) @ Conda ~/.julia/packages/Conda/05AVg/src/Conda.jl:343 [6] add (repeats 2 times) @ ~/.julia/packages/Conda/05AVg/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:307 [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:1777 [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:1777 [inlined] [15] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:35 [inlined] [16] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:307 [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:283 [20] _start() @ Base ./client.jl:550 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:307 [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:1777 [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:1777 [inlined] [10] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:35 [inlined] [11] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:307 [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:283 [15] _start() @ Base ./client.jl:550 [ 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 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 =3.13 * is installable and it requires │ └─ python =3.13 *, 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=3.13 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:307 [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:1777 [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:1777 [inlined] [9] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:38 [inlined] [10] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:307 [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:283 [14] _start() @ Base ./client.jl:550 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'`,["LANG=C.UTF-8", "PYTHONIOENCODING=UTF-8", "PATH=/usr/local/bin:/usr/local/sbin:/usr/bin:/usr/sbin:/bin:/sbin:/opt/julia/bin", "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_XJCOYi", "PKGEVAL=true", "OPENBLAS_NUM_THREADS=1", "HOME=/home/pkgeval", "CI=true", "JULIA_PKG_PRECOMPILE_AUTO=0", "JULIA_PKGEVAL=true", "JULIA_DEPOT_PATH=/home/pkgeval/.julia:/usr/local/share/julia:", "R_HOME=*", "JULIA_NUM_THREADS=1"]), ProcessExited(1)) [1] Stacktrace: [1] pipeline_error @ ./process.jl:597 [inlined] [2] run(::Cmd; wait::Bool) @ Base ./process.jl:512 [3] run @ ./process.jl:509 [inlined] [4] runconda(args::Cmd, env::String) @ Conda ~/.julia/packages/Conda/05AVg/src/Conda.jl:182 [5] add(pkg::String, env::String; channel::String, satisfied_skip_solve::Bool, args::Cmd) @ Conda ~/.julia/packages/Conda/05AVg/src/Conda.jl:343 [6] add (repeats 2 times) @ ~/.julia/packages/Conda/05AVg/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:307 [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:1777 [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:1777 [inlined] [16] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:38 [inlined] [17] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:307 [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:283 [21] _start() @ Base ./client.jl:550 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:307 [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:1777 [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:1777 [inlined] [11] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:38 [inlined] [12] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:307 [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:283 [16] _start() @ Base ./client.jl:550 Testing ../examples/Classifier_Comparison.ipynb Precompiling packages... 15306.8 ms ✓ PyPlot 1 dependency successfully precompiled in 16 seconds. 39 already precompiled. 35 dependencies precompiled but different versions are currently loaded (ArgTools, Base64, Conda, Dates, Downloads, InteractiveUtils, JSON, JuliaSyntaxHighlighting, LaTeXStrings, LibCURL, LibCURL_jll, LibSSH2_jll, Logging, MacroTools, Markdown, MozillaCACerts_jll, NetworkOptions, OpenSSL_jll, Parsers, PrecompileTools, Preferences, Printf, PyCall, Reexport, Serialization, Statistics, StructUtils, StyledStrings, TOML, Test, UUIDs, Unicode, VersionParsing, Zlib_jll and nghttp2_jll). Restart julia to access the new versions. Otherwise, 5 dependents of these packages may trigger further precompilation to work with the unexpected versions. [ Info: Installing matplotlib via the Conda matplotlib package... [ Info: Running `conda install -q -y matplotlib` 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: - matplotlib The following packages will be downloaded: package | build ---------------------------|----------------- alsa-lib-1.2.16.1 | hb03c661_0 578 KB conda-forge brotli-1.2.0 | hed03a55_1 20 KB conda-forge brotli-bin-1.2.0 | hb03c661_1 21 KB conda-forge cairo-1.18.4 | he90730b_1 966 KB conda-forge contourpy-1.3.3 | py313hc8edb43_4 314 KB conda-forge cycler-0.12.1 | pyhcf101f3_2 14 KB conda-forge cyrus-sasl-2.1.28 | hac629b4_1 205 KB conda-forge dbus-1.16.2 | h24cb091_1 437 KB conda-forge double-conversion-3.4.0 | hecca717_0 70 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-2.18.1 | h27c8c51_0 275 KB conda-forge fonts-conda-ecosystem-1 | 0 4 KB conda-forge fonts-conda-forge-1 | hc364b38_1 4 KB conda-forge fonttools-4.63.0 | py313h3dea7bd_0 2.9 MB conda-forge freetype-2.14.3 | ha770c72_0 170 KB conda-forge fribidi-1.0.16 | hb03c661_0 60 KB conda-forge graphite2-1.3.15 | hecca717_0 98 KB conda-forge harfbuzz-14.2.1 | h6083320_0 2.3 MB conda-forge kiwisolver-1.5.0 | py313hc8edb43_0 75 KB conda-forge lcms2-2.19.1 | h0c24ade_1 246 KB conda-forge lerc-4.1.0 | hdb68285_0 255 KB conda-forge libbrotlicommon-1.2.0 | hb03c661_1 78 KB conda-forge libbrotlidec-1.2.0 | hb03c661_1 34 KB conda-forge libbrotlienc-1.2.0 | hb03c661_1 291 KB conda-forge libclang13-22.1.8 |default_h746c552_2 12.3 MB conda-forge libcups-2.3.3 | h7a8fb5f_6 4.3 MB conda-forge libdeflate-1.25 | h17f619e_0 72 KB conda-forge libdrm-2.4.127 | hb03c661_0 304 KB conda-forge libegl-1.7.0 | ha4b6fd6_3 45 KB conda-forge libegl-devel-1.7.0 | ha4b6fd6_3 31 KB conda-forge libfreetype-2.14.3 | ha770c72_0 8 KB conda-forge libfreetype6-2.14.3 | h73754d4_0 376 KB conda-forge libgl-1.7.0 | ha4b6fd6_3 130 KB conda-forge libgl-devel-1.7.0 | ha4b6fd6_3 113 KB conda-forge libglib-2.88.1 | h0d30a3d_2 4.5 MB conda-forge libglvnd-1.7.0 | ha4b6fd6_3 130 KB conda-forge libglx-1.7.0 | ha4b6fd6_3 75 KB conda-forge libglx-devel-1.7.0 | ha4b6fd6_3 27 KB conda-forge libjpeg-turbo-3.1.4.1 | hb03c661_0 619 KB conda-forge libllvm22-22.1.8 | hf7376ad_1 42.3 MB conda-forge libntlm-1.8 | hb9d3cd8_0 33 KB conda-forge libopengl-1.7.0 | ha4b6fd6_3 51 KB conda-forge libpciaccess-0.19 | hb03c661_0 28 KB conda-forge libpng-1.6.58 | h421ea60_0 310 KB conda-forge libpq-18.4 | hd5a49e9_0 2.6 MB conda-forge libraqm-0.10.5 | h75b3fb1_0 29 KB conda-forge libtiff-4.7.1 | h9d88235_1 425 KB conda-forge libvulkan-loader-1.4.341.0 | h5279c79_0 195 KB conda-forge libwebp-base-1.6.0 | hd42ef1d_0 419 KB conda-forge libxcb-1.17.0 | h8a09558_0 387 KB conda-forge libxcrypt-4.4.36 | hd590300_1 98 KB conda-forge libxkbcommon-1.13.2 | hca5e8e5_0 831 KB conda-forge libxslt-1.1.43 | h711ed8c_1 240 KB conda-forge matplotlib-3.11.0 | py313h78bf25f_0 15 KB conda-forge matplotlib-base-3.11.0 | py313hd23ff06_0 8.6 MB conda-forge munkres-1.1.4 | pyhd8ed1ab_1 15 KB conda-forge openjpeg-2.5.4 | h55fea9a_0 347 KB conda-forge openldap-2.6.13 | hbde042b_0 768 KB conda-forge pcre2-10.47 | haa7fec5_0 1.2 MB conda-forge pillow-12.2.0 | py313h80991f8_0 1.0 MB conda-forge pixman-0.46.4 | h54a6638_1 440 KB conda-forge pthread-stubs-0.4 | hb9d3cd8_1002 8 KB conda-forge pyparsing-3.3.2 | pyhcf101f3_0 108 KB conda-forge pyside6-6.11.1 | py313hcd51b16_1 13.2 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-6.11.1 | pl5321h16c4a6b_0 57.4 MB conda-forge six-1.17.0 | pyhe01879c_1 18 KB conda-forge tornado-6.5.7 | py313h07c4f96_0 865 KB conda-forge wayland-1.25.0 | hd6090a7_0 326 KB conda-forge xcb-util-0.4.1 | h4f16b4b_2 20 KB conda-forge xcb-util-cursor-0.1.6 | hb03c661_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-2.47 | h280c20c_1 430 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-libx11-1.8.13 | he1eb515_0 820 KB conda-forge xorg-libxau-1.0.12 | hb03c661_1 15 KB conda-forge xorg-libxcomposite-0.4.7 | hb03c661_0 14 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 | hb03c661_1 20 KB conda-forge xorg-libxext-1.3.7 | hb03c661_0 49 KB conda-forge xorg-libxfixes-6.0.2 | hb03c661_0 20 KB conda-forge xorg-libxi-1.8.3 | hb03c661_0 47 KB conda-forge xorg-libxrandr-1.5.5 | hb03c661_0 30 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-1.1.7 | hb03c661_0 18 KB conda-forge xorg-xorgproto-2025.1 | hb03c661_0 557 KB conda-forge zlib-ng-2.3.3 | hceb46e0_1 120 KB conda-forge ------------------------------------------------------------ Total: 170.1 MB The following NEW packages will be INSTALLED: alsa-lib conda-forge/linux-64::alsa-lib-1.2.16.1-hb03c661_0 brotli conda-forge/linux-64::brotli-1.2.0-hed03a55_1 brotli-bin conda-forge/linux-64::brotli-bin-1.2.0-hb03c661_1 cairo conda-forge/linux-64::cairo-1.18.4-he90730b_1 contourpy conda-forge/linux-64::contourpy-1.3.3-py313hc8edb43_4 cycler conda-forge/noarch::cycler-0.12.1-pyhcf101f3_2 cyrus-sasl conda-forge/linux-64::cyrus-sasl-2.1.28-hac629b4_1 dbus conda-forge/linux-64::dbus-1.16.2-h24cb091_1 double-conversion conda-forge/linux-64::double-conversion-3.4.0-hecca717_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-2.18.1-h27c8c51_0 fonts-conda-ecosy~ conda-forge/noarch::fonts-conda-ecosystem-1-0 fonts-conda-forge conda-forge/noarch::fonts-conda-forge-1-hc364b38_1 fonttools conda-forge/linux-64::fonttools-4.63.0-py313h3dea7bd_0 freetype conda-forge/linux-64::freetype-2.14.3-ha770c72_0 fribidi conda-forge/linux-64::fribidi-1.0.16-hb03c661_0 graphite2 conda-forge/linux-64::graphite2-1.3.15-hecca717_0 harfbuzz conda-forge/linux-64::harfbuzz-14.2.1-h6083320_0 kiwisolver conda-forge/linux-64::kiwisolver-1.5.0-py313hc8edb43_0 lcms2 conda-forge/linux-64::lcms2-2.19.1-h0c24ade_1 lerc conda-forge/linux-64::lerc-4.1.0-hdb68285_0 libbrotlicommon conda-forge/linux-64::libbrotlicommon-1.2.0-hb03c661_1 libbrotlidec conda-forge/linux-64::libbrotlidec-1.2.0-hb03c661_1 libbrotlienc conda-forge/linux-64::libbrotlienc-1.2.0-hb03c661_1 libclang13 conda-forge/linux-64::libclang13-22.1.8-default_h746c552_2 libcups conda-forge/linux-64::libcups-2.3.3-h7a8fb5f_6 libdeflate conda-forge/linux-64::libdeflate-1.25-h17f619e_0 libdrm conda-forge/linux-64::libdrm-2.4.127-hb03c661_0 libegl conda-forge/linux-64::libegl-1.7.0-ha4b6fd6_3 libegl-devel conda-forge/linux-64::libegl-devel-1.7.0-ha4b6fd6_3 libfreetype conda-forge/linux-64::libfreetype-2.14.3-ha770c72_0 libfreetype6 conda-forge/linux-64::libfreetype6-2.14.3-h73754d4_0 libgl conda-forge/linux-64::libgl-1.7.0-ha4b6fd6_3 libgl-devel conda-forge/linux-64::libgl-devel-1.7.0-ha4b6fd6_3 libglib conda-forge/linux-64::libglib-2.88.1-h0d30a3d_2 libglvnd conda-forge/linux-64::libglvnd-1.7.0-ha4b6fd6_3 libglx conda-forge/linux-64::libglx-1.7.0-ha4b6fd6_3 libglx-devel conda-forge/linux-64::libglx-devel-1.7.0-ha4b6fd6_3 libjpeg-turbo conda-forge/linux-64::libjpeg-turbo-3.1.4.1-hb03c661_0 libllvm22 conda-forge/linux-64::libllvm22-22.1.8-hf7376ad_1 libntlm conda-forge/linux-64::libntlm-1.8-hb9d3cd8_0 libopengl conda-forge/linux-64::libopengl-1.7.0-ha4b6fd6_3 libpciaccess conda-forge/linux-64::libpciaccess-0.19-hb03c661_0 libpng conda-forge/linux-64::libpng-1.6.58-h421ea60_0 libpq conda-forge/linux-64::libpq-18.4-hd5a49e9_0 libraqm conda-forge/linux-64::libraqm-0.10.5-h75b3fb1_0 libtiff conda-forge/linux-64::libtiff-4.7.1-h9d88235_1 libvulkan-loader conda-forge/linux-64::libvulkan-loader-1.4.341.0-h5279c79_0 libwebp-base conda-forge/linux-64::libwebp-base-1.6.0-hd42ef1d_0 libxcb conda-forge/linux-64::libxcb-1.17.0-h8a09558_0 libxcrypt conda-forge/linux-64::libxcrypt-4.4.36-hd590300_1 libxkbcommon conda-forge/linux-64::libxkbcommon-1.13.2-hca5e8e5_0 libxslt conda-forge/linux-64::libxslt-1.1.43-h711ed8c_1 matplotlib conda-forge/linux-64::matplotlib-3.11.0-py313h78bf25f_0 matplotlib-base conda-forge/linux-64::matplotlib-base-3.11.0-py313hd23ff06_0 munkres conda-forge/noarch::munkres-1.1.4-pyhd8ed1ab_1 openjpeg conda-forge/linux-64::openjpeg-2.5.4-h55fea9a_0 openldap conda-forge/linux-64::openldap-2.6.13-hbde042b_0 pcre2 conda-forge/linux-64::pcre2-10.47-haa7fec5_0 pillow conda-forge/linux-64::pillow-12.2.0-py313h80991f8_0 pixman conda-forge/linux-64::pixman-0.46.4-h54a6638_1 pthread-stubs conda-forge/linux-64::pthread-stubs-0.4-hb9d3cd8_1002 pyparsing conda-forge/noarch::pyparsing-3.3.2-pyhcf101f3_0 pyside6 conda-forge/linux-64::pyside6-6.11.1-py313hcd51b16_1 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-6.11.1-pl5321h16c4a6b_0 six conda-forge/noarch::six-1.17.0-pyhe01879c_1 tornado conda-forge/linux-64::tornado-6.5.7-py313h07c4f96_0 wayland conda-forge/linux-64::wayland-1.25.0-hd6090a7_0 xcb-util conda-forge/linux-64::xcb-util-0.4.1-h4f16b4b_2 xcb-util-cursor conda-forge/linux-64::xcb-util-cursor-0.1.6-hb03c661_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-2.47-h280c20c_1 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-libx11-1.8.13-he1eb515_0 xorg-libxau conda-forge/linux-64::xorg-libxau-1.0.12-hb03c661_1 xorg-libxcomposite conda-forge/linux-64::xorg-libxcomposite-0.4.7-hb03c661_0 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-hb03c661_1 xorg-libxext conda-forge/linux-64::xorg-libxext-1.3.7-hb03c661_0 xorg-libxfixes conda-forge/linux-64::xorg-libxfixes-6.0.2-hb03c661_0 xorg-libxi conda-forge/linux-64::xorg-libxi-1.8.3-hb03c661_0 xorg-libxrandr conda-forge/linux-64::xorg-libxrandr-1.5.5-hb03c661_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-1.1.7-hb03c661_0 xorg-xorgproto conda-forge/linux-64::xorg-xorgproto-2025.1-hb03c661_0 zlib-ng conda-forge/linux-64::zlib-ng-2.3.3-hceb46e0_1 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 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 =3.13 * is installable and it requires │ └─ python =3.13 *, 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=3.13 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:2873 [5] include_string @ ./loading.jl:2883 [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:1777 [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:1777 [inlined] [18] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:64 [inlined] [19] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:307 [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:283 [23] _start() @ Base ./client.jl:550 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'`,["LANG=C.UTF-8", "PYTHONIOENCODING=UTF-8", "PATH=/usr/local/bin:/usr/local/sbin:/usr/bin:/usr/sbin:/bin:/sbin:/opt/julia/bin", "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_XJCOYi", "PKGEVAL=true", "OPENBLAS_NUM_THREADS=1", "HOME=/home/pkgeval", "CI=true", "JULIA_PKG_PRECOMPILE_AUTO=0", "JULIA_PKGEVAL=true", "JULIA_DEPOT_PATH=/home/pkgeval/.julia:/usr/local/share/julia:", "R_HOME=*", "JULIA_NUM_THREADS=1"]), ProcessExited(1)) [1] Stacktrace: [1] pipeline_error @ ./process.jl:597 [inlined] [2] run(::Cmd; wait::Bool) @ Base ./process.jl:512 [3] run @ ./process.jl:509 [inlined] [4] runconda(args::Cmd, env::String) @ Conda ~/.julia/packages/Conda/05AVg/src/Conda.jl:182 [5] add(pkg::String, env::String; channel::String, satisfied_skip_solve::Bool, args::Cmd) @ Conda ~/.julia/packages/Conda/05AVg/src/Conda.jl:343 [6] add (repeats 2 times) @ ~/.julia/packages/Conda/05AVg/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:2873 [12] include_string @ ./loading.jl:2883 [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:1777 [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:1777 [inlined] [25] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:64 [inlined] [26] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:307 [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:283 [30] _start() @ Base ./client.jl:550 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:2873 [7] include_string @ ./loading.jl:2883 [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:1777 [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:1777 [inlined] [20] macro expansion @ ~/.julia/packages/ScikitLearn/sqLdT/test/runtests.jl:64 [inlined] [21] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:307 [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:283 [25] _start() @ Base ./client.jl:550 Test Summary: | Pass Error Total Time ScikitLearnTests | 57 5 62 5m45.3s models | 9 9 30.3s base | 1 1 1m13.0s pipeline | 1 1 19.3s crossvalidation | 38 38 15.1s utils | 10 10 0.9s quickstart | 1 1 1m22.8s DataFrames | 1 1 20.9s Notebook examples | 1 1 1m42.6s RNG of the outermost testset: Xoshiro(0xa08a56737ed323c8, 0xf495e49630db4a69, 0x98cb9bffbae7cd97, 0x6830882484013a9e, 0x5399c93453f4a148) 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 371.64s 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:2538 [3] test @ /opt/julia/share/julia/stdlib/v1.12/Pkg/src/Operations.jl:2387 [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:552 [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:169 [6] test(pkgs::Vector{String}; kwargs::@Kwargs{julia_args::Cmd}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.12/Pkg/src/API.jl:157 [7] test @ /opt/julia/share/julia/stdlib/v1.12/Pkg/src/API.jl:157 [inlined] [8] #test#81 @ /opt/julia/share/julia/stdlib/v1.12/Pkg/src/API.jl:156 [inlined] [9] top-level scope @ /PkgEval.jl/scripts/evaluate.jl:223 [10] include(mod::Module, _path::String) @ Base ./Base.jl:306 [11] exec_options(opts::Base.JLOptions) @ Base ./client.jl:317 [12] _start() @ Base ./client.jl:550 in expression starting at /PkgEval.jl/scripts/evaluate.jl:214 PkgEval failed after 549.3s: package tests unexpectedly errored