69 lines
1.5 KiB
Nix
69 lines
1.5 KiB
Nix
{
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lib,
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buildPythonPackage,
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fetchFromGitHub,
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fetchpatch,
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linear-operator,
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scikit-learn,
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setuptools,
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setuptools-scm,
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wheel,
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torch,
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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 = "1.12";
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format = "pyproject";
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src = fetchFromGitHub {
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owner = "cornellius-gp";
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repo = pname;
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rev = "refs/tags/v${version}";
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hash = "sha256-8W0QSiXl+C86m5yaI9KfGN92uA2VGjGwQt6DI/1NaQE=";
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};
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patches = [
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(fetchpatch {
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# https://github.com/cornellius-gp/gpytorch/pull/2545
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name = "scipy-1.14-compatibility.patch";
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url = "https://github.com/cornellius-gp/gpytorch/commit/2562be472521b8aec366de2619e3130a96fab982.patch";
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excludes = [ "setup.py" ];
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hash = "sha256-znOFpN6go2iIxP24VjJLKF3Laxcr4xV/IyP2y36g4QY=";
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})
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];
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nativeBuildInputs = [
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setuptools
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setuptools-scm
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wheel
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];
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propagatedBuildInputs = [
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linear-operator
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scikit-learn
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torch
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];
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checkInputs = [ 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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# https://github.com/cornellius-gp/gpytorch/issues/2396
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"test_t_matmul_matrix"
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];
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meta = with lib; {
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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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license = licenses.mit;
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maintainers = with maintainers; [ veprbl ];
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};
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}
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