kirillrdy
81 lines
1.8 KiB
Nix
81 lines
1.8 KiB
Nix
{
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lib,
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stdenv,
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buildPythonPackage,
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fetchFromGitHub,
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# build-system
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setuptools,
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setuptools-scm,
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# dependencies
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jaxtyping,
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linear-operator,
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mpmath,
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scikit-learn,
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scipy,
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torch,
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# tests
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pytestCheckHook,
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}:
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buildPythonPackage rec {
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pname = "gpytorch";
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version = "1.14.3";
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pyproject = true;
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src = fetchFromGitHub {
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owner = "cornellius-gp";
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repo = "gpytorch";
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tag = "v${version}";
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hash = "sha256-AuWVNAduh2y/sLIJAXg/9YgpFa21d1sbRHlcdG5cpJ8=";
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};
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build-system = [
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setuptools
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setuptools-scm
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];
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dependencies = [
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jaxtyping
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linear-operator
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mpmath
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scikit-learn
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scipy
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torch
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];
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nativeCheckInputs = [ pytestCheckHook ];
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pythonImportsCheck = [ "gpytorch" ];
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disabledTests = [
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# AssertionError on number of warnings emitted
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"test_deprecated_methods"
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# flaky numerical tests
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"test_classification_error"
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"test_matmul_matrix_broadcast"
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"test_optimization_optimal_error"
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# https://github.com/cornellius-gp/gpytorch/issues/2396
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"test_t_matmul_matrix"
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];
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disabledTestPaths = lib.optionals (stdenv.hostPlatform.isDarwin && stdenv.hostPlatform.isx86_64) [
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# Hang forever
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"test/examples/test_spectral_mixture_gp_regression.py"
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"test/kernels/test_spectral_mixture_kernel.py"
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"test/utils/test_nearest_neighbors.py"
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"test/variational/test_nearest_neighbor_variational_strategy.py"
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];
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meta = {
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description = "Highly efficient and modular implementation of Gaussian Processes, with GPU acceleration";
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homepage = "https://gpytorch.ai";
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downloadPage = "https://github.com/cornellius-gp/gpytorch";
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changelog = "https://github.com/cornellius-gp/gpytorch/releases/tag/${src.tag}";
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license = lib.licenses.mit;
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maintainers = with lib.maintainers; [ veprbl ];
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};
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}
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