From d88adec5a25d070ad9db51e18d2ab24738be4ab3 Mon Sep 17 00:00:00 2001 From: GitHub Action Date: Wed, 11 Oct 2023 15:12:28 +0000 Subject: [PATCH] Add changes for d22bdc913974e98c99fac6e5fe165ee2669a4bee --- latest/contributing.html | 29 ++++++++++++++++++++++------- latest/searchindex.js | 2 +- 2 files changed, 23 insertions(+), 8 deletions(-) diff --git a/latest/contributing.html b/latest/contributing.html index c1ad9b130..2c07a994a 100644 --- a/latest/contributing.html +++ b/latest/contributing.html @@ -173,12 +173,20 @@

Local setup

Cluster setup

Steps without sudo access (e.g. on a cluster):

-
    +

    1. Clone the git repository to the desired location: git clone https://github.com/KernelTuner/kernel_tuner.git.

      2. -
    2. -
      Install Conda with Mamba (for better performance) or Miniconda (for traditional minimal Conda).
        -

      • [Optional] both Mamba and Miniconda can be automatically activated via ~/.bashrc. Do not forget to add these (usually mentioned at the end of the installation).

        • -

      • Exit the shell and re-enter to make sure Conda is available, cd to the kernel tuner directory.

        • +
      • +
        Install Conda with Mamba (for better performance) or Miniconda (for traditional minimal Conda).
          +
        • +
          [Optional] if you are under quotas or are otherwise restricted by disk space, you can instruct Conda to use a different directory for saving environments by adding the following to your .condarc file:
          envs_dirs:
+ - /path/to/directory
+
          +
          +
          +
          +
          • +

        • [Optional] both Mamba and Miniconda can be automatically activated via ~/.bashrc. Do not forget to add these (usually provided at the end of the installation).

          • +

        • Exit the shell and re-enter to make sure Conda is available. cd to the kernel tuner directory.

        • [Optional] update Conda if available before continuing: conda update -n base -c conda-forge conda.

        @@ -188,7 +196,12 @@

        Cluster setup
        Activate the virtual environment: conda activate kerneltuner.
        +
        +

        +
        • +
      • +
        Make sure that non-Python dependencies are loaded if applicable, such as CUDA, OpenCL or HIP. On most clusters it is possible to load (or unload) modules (e.g. CUDA, OpenCL / ROCM). For more information, see Installation.

        • Do not forget to make sure the paths are set correctly. If you’re using CUDA, the desired CUDA version should be in $PATH, $LD_LIBARY_PATH and $CPATH.

        • [Optional] the loading of modules and setting of paths is likely convenient to put in your .bash_profile or .bashrc.

        @@ -216,14 +229,16 @@

        Cluster setup

        Running tests

        To run the tests you can use nox (to run against all supported Python versions in isolated environments) and pytest (to run against the local Python version) in the top-level directory. +For full coverage, make Nox install and use the additional tests (such as cupy and cuda-python) with nox -- additional-tests. It’s also possible to invoke PyTest from the ‘Testing’ tab in Visual Studio Code. -The isolated environments can take up to 1 gigabyte in size, so users tight on diskspace can run nox with the small-disk option. This removes the other environment caches before each session is ran.

        +The isolated environments can take up to 1 gigabyte in size, so users tight on diskspace can run nox with the small-disk option. This removes the other environment caches before each session is ran (note that this will take longer to run).

        Note that tests that require PyCuda and/or a CUDA capable GPU will be skipped if these are not installed/present. The same holds for tests that require PyOpenCL, Cupy, Nvidia CUDA.

        Contributions you make to the Kernel Tuner should not break any of the tests even if you cannot run them locally.

        diff --git a/latest/searchindex.js b/latest/searchindex.js index f7146d47e..8690cd4bc 100644 --- a/latest/searchindex.js +++ b/latest/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["cache_files", "contents", "contributing", "convolution", "correctness", "design", "diffusion", "diffusion_opencl", "diffusion_use_optparam", "examples", "grid3d", "hostcode", "index", "install", "matrix_multiplication", "metrics", "observers", "optimization", "quickstart", "structs", "templates", "user-api", "vocabulary"], "filenames": ["cache_files.rst", "contents.rst", "contributing.rst", "convolution.ipynb", "correctness.rst", "design.rst", "diffusion.ipynb", "diffusion_opencl.ipynb", "diffusion_use_optparam.ipynb", "examples.rst", "grid3d.ipynb", "hostcode.rst", "index.rst", "install.rst", "matrix_multiplication.ipynb", "metrics.rst", "observers.rst", "optimization.rst", "quickstart.rst", "structs.rst", "templates.rst", "user-api.rst", "vocabulary.rst"], "titles": ["Cache files", "The 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kernel_tuner.util)": [[5, "kernel_tuner.util.write_file"]], "benchmarkobserver (class in kernel_tuner.observers)": [[16, "kernel_tuner.observers.BenchmarkObserver"]], "nvmlobserver (class in kernel_tuner.observers.nvml)": [[16, "kernel_tuner.observers.nvml.NVMLObserver"]], "pmtobserver (class in kernel_tuner.observers.pmt)": [[16, "kernel_tuner.observers.pmt.PMTObserver"]], "powersensorobserver (class in kernel_tuner.observers.powersensor)": [[16, "kernel_tuner.observers.powersensor.PowerSensorObserver"]], "after_finish() (kernel_tuner.observers.benchmarkobserver method)": [[16, "kernel_tuner.observers.BenchmarkObserver.after_finish"]], "after_start() (kernel_tuner.observers.benchmarkobserver method)": [[16, "kernel_tuner.observers.BenchmarkObserver.after_start"]], "before_start() (kernel_tuner.observers.benchmarkobserver method)": [[16, "kernel_tuner.observers.BenchmarkObserver.before_start"]], "during() (kernel_tuner.observers.benchmarkobserver method)": [[16, "kernel_tuner.observers.BenchmarkObserver.during"]], "get_results() (kernel_tuner.observers.benchmarkobserver method)": [[16, "kernel_tuner.observers.BenchmarkObserver.get_results"]], "register_configuration() (kernel_tuner.observers.benchmarkobserver method)": [[16, "kernel_tuner.observers.BenchmarkObserver.register_configuration"]], "register_device() (kernel_tuner.observers.benchmarkobserver method)": [[16, "kernel_tuner.observers.BenchmarkObserver.register_device"]], "firefly (class in kernel_tuner.strategies.firefly_algorithm)": [[17, "kernel_tuner.strategies.firefly_algorithm.Firefly"]], "acceptance_prob() (in module kernel_tuner.strategies.simulated_annealing)": [[17, "kernel_tuner.strategies.simulated_annealing.acceptance_prob"]], "compute_intensity() (kernel_tuner.strategies.firefly_algorithm.firefly method)": [[17, "kernel_tuner.strategies.firefly_algorithm.Firefly.compute_intensity"]], "disruptive_uniform_crossover() (in module kernel_tuner.strategies.genetic_algorithm)": [[17, "kernel_tuner.strategies.genetic_algorithm.disruptive_uniform_crossover"]], "distance_to() (kernel_tuner.strategies.firefly_algorithm.firefly method)": [[17, "kernel_tuner.strategies.firefly_algorithm.Firefly.distance_to"]], "generate_normalized_param_dicts() (in module kernel_tuner.strategies.bayes_opt)": [[17, "kernel_tuner.strategies.bayes_opt.generate_normalized_param_dicts"]], "kernel_tuner.strategies.basinhopping": [[17, "module-kernel_tuner.strategies.basinhopping"]], "kernel_tuner.strategies.bayes_opt": [[17, "module-kernel_tuner.strategies.bayes_opt"]], "kernel_tuner.strategies.brute_force": [[17, "module-kernel_tuner.strategies.brute_force"]], "kernel_tuner.strategies.diff_evo": [[17, "module-kernel_tuner.strategies.diff_evo"]], "kernel_tuner.strategies.dual_annealing": [[17, "module-kernel_tuner.strategies.dual_annealing"]], "kernel_tuner.strategies.firefly_algorithm": [[17, "module-kernel_tuner.strategies.firefly_algorithm"]], "kernel_tuner.strategies.genetic_algorithm": [[17, "module-kernel_tuner.strategies.genetic_algorithm"]], "kernel_tuner.strategies.greedy_ils": [[17, "module-kernel_tuner.strategies.greedy_ils"]], "kernel_tuner.strategies.greedy_mls": [[17, "module-kernel_tuner.strategies.greedy_mls"]], "kernel_tuner.strategies.minimize": [[17, "module-kernel_tuner.strategies.minimize"]], "kernel_tuner.strategies.mls": [[17, "module-kernel_tuner.strategies.mls"]], "kernel_tuner.strategies.ordered_greedy_mls": [[17, "module-kernel_tuner.strategies.ordered_greedy_mls"]], "kernel_tuner.strategies.pso": [[17, "module-kernel_tuner.strategies.pso"]], "kernel_tuner.strategies.random_sample": [[17, "module-kernel_tuner.strategies.random_sample"]], "kernel_tuner.strategies.simulated_annealing": [[17, "module-kernel_tuner.strategies.simulated_annealing"]], "move_towards() (kernel_tuner.strategies.firefly_algorithm.firefly method)": [[17, "kernel_tuner.strategies.firefly_algorithm.Firefly.move_towards"]], "mutate() (in module kernel_tuner.strategies.genetic_algorithm)": [[17, "kernel_tuner.strategies.genetic_algorithm.mutate"]], "neighbor() (in module kernel_tuner.strategies.simulated_annealing)": [[17, "kernel_tuner.strategies.simulated_annealing.neighbor"]], "normalize_parameter_space() (in module kernel_tuner.strategies.bayes_opt)": [[17, "kernel_tuner.strategies.bayes_opt.normalize_parameter_space"]], "prune_parameter_space() (in module kernel_tuner.strategies.bayes_opt)": [[17, "kernel_tuner.strategies.bayes_opt.prune_parameter_space"]], "single_point_crossover() (in module kernel_tuner.strategies.genetic_algorithm)": [[17, "kernel_tuner.strategies.genetic_algorithm.single_point_crossover"]], "tune() (in module kernel_tuner.strategies.basinhopping)": [[17, "kernel_tuner.strategies.basinhopping.tune"]], "tune() (in module kernel_tuner.strategies.bayes_opt)": [[17, "kernel_tuner.strategies.bayes_opt.tune"]], "tune() (in module kernel_tuner.strategies.brute_force)": [[17, "kernel_tuner.strategies.brute_force.tune"]], "tune() (in module kernel_tuner.strategies.diff_evo)": [[17, "kernel_tuner.strategies.diff_evo.tune"]], "tune() (in module kernel_tuner.strategies.dual_annealing)": [[17, "kernel_tuner.strategies.dual_annealing.tune"]], "tune() (in module kernel_tuner.strategies.firefly_algorithm)": [[17, "kernel_tuner.strategies.firefly_algorithm.tune"]], "tune() (in module kernel_tuner.strategies.genetic_algorithm)": [[17, "kernel_tuner.strategies.genetic_algorithm.tune"]], "tune() (in module kernel_tuner.strategies.greedy_ils)": [[17, "kernel_tuner.strategies.greedy_ils.tune"]], "tune() (in module kernel_tuner.strategies.greedy_mls)": [[17, "kernel_tuner.strategies.greedy_mls.tune"]], "tune() (in module kernel_tuner.strategies.minimize)": [[17, "kernel_tuner.strategies.minimize.tune"]], "tune() (in module kernel_tuner.strategies.mls)": [[17, "kernel_tuner.strategies.mls.tune"]], "tune() (in module kernel_tuner.strategies.ordered_greedy_mls)": [[17, "kernel_tuner.strategies.ordered_greedy_mls.tune"]], "tune() (in module kernel_tuner.strategies.pso)": [[17, "kernel_tuner.strategies.pso.tune"]], "tune() (in module kernel_tuner.strategies.random_sample)": [[17, "kernel_tuner.strategies.random_sample.tune"]], "tune() (in module kernel_tuner.strategies.simulated_annealing)": [[17, "kernel_tuner.strategies.simulated_annealing.tune"]], "two_point_crossover() (in module kernel_tuner.strategies.genetic_algorithm)": [[17, "kernel_tuner.strategies.genetic_algorithm.two_point_crossover"]], "uniform_crossover() (in module kernel_tuner.strategies.genetic_algorithm)": [[17, "kernel_tuner.strategies.genetic_algorithm.uniform_crossover"]], "weighted_choice() (in module kernel_tuner.strategies.genetic_algorithm)": [[17, "kernel_tuner.strategies.genetic_algorithm.weighted_choice"]]}}) \ No newline at end of file