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For larger changes, or when you need to change the dependencies of Kernel Tuner, please follow the instructions below to setup the full development environment.
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Full development environment
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----------------------------
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The following steps help you set up a full development environment. **These steps are only needed for core developers of Kernel Tuner who need to test against multiple Python versions
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or change dependencies of Kernel Tuner.**
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Local setup
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^^^^^^^^^^^
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Steps with :bash:`sudo` access (e.g. on a local device):
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#. Clone the git repository to the desired location: :bash:`git clone https://github.com/KernelTuner/kernel_tuner.git`, and :bash:`cd` to it.
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#. Prepare your system for building Python versions.
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* On Ubuntu, run :bash:`sudo apt update && sudo apt upgrade`, and :bash:`sudo apt install -y make build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev xz-utils tk-dev libffi-dev liblzma-dev python-openssl git`.
* On Linux, run :bash:`curl https://pyenv.run | bash` (remember to add the output to :bash:`.bash_profile` and :bash:`.bashrc` as specified).
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* On macOS, run :bash:`brew update && brew install pyenv`.
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* After installation, restart your shell.
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#. Install the required Python versions:
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* On some systems, additional packages may be needed to build Python versions. For example on Ubuntu: :bash:`sudo apt install build-essential zlib1g-dev libncurses5-dev libgdbm-dev libnss3-dev libssl-dev libreadline-dev libffi-dev libsqlite3-dev wget libbz2-dev liblzma-dev lzma`.
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* Install the Python versions with: :bash:`pyenv install 3.8 3.9 3.10 3.11`. The reason we're installing all these versions as opposed to just one, is so we can test against all supported Python versions.
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#. Set the Python versions so they can be found: :bash:`pyenv local 3.8 3.9 3.10 3.11` (replace :bash:`local` with :bash:`global` when not using the virtualenv).
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#. Setup a local virtual environment in the folder: :bash:`pyenv virtualenv 3.11 kerneltuner` (or whatever environment name and Python version you prefer).
* Use :bash:`curl -sSL https://install.python-poetry.org | python3 -` to install Poetry.
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* Make sure to add Poetry to :bash:`PATH` as instructed at the end of the installation.
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* Add the poetry export plugin with :bash:`poetry self add poetry-plugin-export`.
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#. Make sure that non-Python dependencies are installed if applicable, such as CUDA, OpenCL or HIP. This is described in :ref:`Installation <installation>`.
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#. Apply changes:
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* Re-open the shell for changes to take effect.
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* Activate the environment with :bash:`pyenv activate kerneltuner`.
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* Make sure :bash:`which python` and :bash:`which pip` point to the expected Python location and version.
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* Update Pip with :bash:`pip install --upgrade pip`.
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#. Install the project, dependencies and extras: :bash:`poetry install --with test,docs -E cuda -E opencl -E hip`, leaving out :bash:`-E cuda`, :bash:`-E opencl` or :bash:`-E hip` if this does not apply on your system. To go all-out, use :bash:`--all-extras`
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* Depending on the environment, it may be necessary or convenient to install extra packages such as :bash:`cupy-cuda11x` / :bash:`cupy-cuda12x`, and :bash:`cuda-python`. These are currently not defined as dependencies for kernel-tuner, but can be part of tests.
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* Do not forget to make sure the paths are set correctly. If you're using CUDA, the desired CUDA version should be in :bash:`$PATH`, :bash:`$LD_LIBARY_PATH` and :bash:`$CPATH`.
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* Re-open the shell for changes to take effect.
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#. Check if the environment is setup correctly by running :bash:`pytest` and :bash:`nox`. All tests should pass, except if one or more extras has been left out in the previous step, then these tests will skip gracefully.
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* [Note]: sometimes, changing the NVIDIA driver privileges is required to read program counters and energy measurements. Check if :bash:`cat /proc/driver/nvidia/params | grep RmProfilingAdminOnly` is set to 1. If so, `follow these steps <https://developer.nvidia.com/nvidia-development-tools-solutions-err_nvgpuctrperm-permission-issue-performance-counters>`__
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Cluster setup
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^^^^^^^^^^^^^
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Steps without :bash:`sudo` access (e.g. on a cluster):
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#. Clone the git repository to the desired location: :bash:`git clone https://github.com/KernelTuner/kernel_tuner.git`.
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#. Install Conda with `Mamba <https://mamba.readthedocs.io/en/latest/mamba-installation.html>`__ (for better performance) or `Miniconda <https://docs.conda.io/projects/conda/en/latest/user-guide/install>`__ (for traditional minimal Conda).
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* [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 :bash:`.condarc` file:
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.. code-block:: bash
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envs_dirs:
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- /path/to/directory
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* [Optional] both Mamba and Miniconda can be automatically activated via :bash:`~/.bashrc`. Do not forget to add these (usually provided at the end of the installation).
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* Exit the shell and re-enter to make sure Conda is available. :bash:`cd` to the kernel tuner directory.
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* [Optional] if you have limited user folder space, the Pip cache can be pointed elsewhere with the environment variable :bash:`PIP_CACHE_DIR`. The cache location can be checked with :bash:`pip cache dir`. On Linux, to point the entire :bash:`~/.cache` default elsewhere, use the :bash:`XDG_CACHE_HOME` environment variable.
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* [Optional] update Conda if available before continuing: :bash:`conda update -n base -c conda-forge conda`.
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#. Setup a virtual environment: :bash:`conda create --name kerneltuner python=3.11` (or whatever Python version and environment name you prefer).
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#. Activate the virtual environment: :bash:`conda activate kerneltuner`.
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* [Optional] to use the correct environment by default, execute :bash:`conda config --set auto_activate_base false`, and add `conda activate kerneltuner` to your :bash:`.bash_profile` or :bash:`.bashrc`.
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#. 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 :ref:`Installation <installation>`.
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* Do not forget to make sure the paths are set correctly. If you're using CUDA, the desired CUDA version should be in :bash:`$PATH`, :bash:`$LD_LIBARY_PATH` and :bash:`$CPATH`.
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* [Optional] the loading of modules and setting of paths is likely convenient to put in your :bash:`.bash_profile` or :bash:`.bashrc`.
* Use :bash:`curl -sSL https://install.python-poetry.org | python3 -` to install Poetry.
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* Add the poetry export plugin with :bash:`poetry self add poetry-plugin-export`.
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#. Install the project, dependencies and extras: :bash:`poetry install --with test,docs -E cuda -E opencl -E hip`, leaving out :bash:`-E cuda`, :bash:`-E opencl` or :bash:`-E hip` if this does not apply on your system. To go all-out, use :bash:`--all-extras`.
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* If you run into "keyring" or other seemingly weird issues, this is a known issue with Poetry on some systems. Do: :bash:`pip install keyring`, :bash:`python3 -m keyring --disable`.
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* Depending on the environment, it may be necessary or convenient to install extra packages such as :bash:`cupy-cuda11x` / :bash:`cupy-cuda12x`, and :bash:`cuda-python`. These are currently not defined as dependencies for kernel-tuner, but can be part of tests.
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* Verify that your development environment has no missing installs or updates with :bash:`poetry install --sync --dry-run --with test`.
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#. Check if the environment is setup correctly by running :bash:`pytest`. All tests should pass, except if you're not on a GPU node, or one or more extras has been left out in the previous step, then these tests will skip gracefully.
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#. Set Nox to use the correct backend and location:
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* Run :bash:`conda -- create-settings-file` to automatically create a settings file.
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* In this settings file :bash:`noxsettings.toml`, change the :bash:`venvbackend`:
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* If you used Mamba in step 2, to :bash:`mamba`.
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* If you used Miniconda or Anaconda in step 2, to :bash:`conda`.
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* If you used Venv in step 2, to :bash:`venv`.
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* If you used Virtualenv in step 2, this is already the default.
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* Be sure to adjust this when changing backends.
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* The settings file also has :bash:`envdir`, which allows you to `change the directory Nox caches environments in <https://nox.thea.codes/en/stable/usage.html#opt-envdir>`_, particularly helpful if you have a diskquota on your user directory.
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#. [Optional] Run the tests on Nox as described below.
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Running tests
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-------------
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To run the tests you can use :bash:`nox` (to run against all supported Python versions in isolated environments) and :bash:`pytest` (to run against the local Python version, see below) in the top-level directory.
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For full coverage, make Nox use the additional tests (such as cupy and cuda-python) with :bash:`nox -- additional-tests`.
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The Nox isolated environments can take up to 1 gigabyte in size, so users tight on diskspace can run :bash:`nox` with the :bash:`small-disk` option. This removes the other environment caches before each session is ran (note that this will take longer to run). A better option would be to change the location environments are stored in with :bash:`envdir` in the :bash:`noxsettings.toml` file.
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Please note that the command-line options can be combined, e.g. :bash:`nox -- additional-tests skip-hip small-disk`.
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If you do not have fully compatible hardware or environment, you can use the following options:
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* :bash:`nox -- skip-cuda` to skip tests involving CUDA.
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* :bash:`nox -- skip-hip` to skip tests involving HIP.
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* :bash:`nox -- skip-opencl` to skip tests involving OpenCL.
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* :bash:`nox -- skip-gpu` to skip all tests on the GPU (the same as :bash:`nox -- skip-cuda skip-hip skip-opencl`), especially helpful if you don't have a GPU locally.
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Contributions you make to the Kernel Tuner should not break any of the tests even if you cannot run them locally!
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Running with :bash:`pytest` will test against your local Python version and PIP packages.
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In this case, tests that require PyCuda and/or a CUDA capable GPU will be skipped automatically if these are not installed/present.
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The same holds for tests that require PyOpenCL, Cupy, and CUDA.
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It is also possible to invoke PyTest from the 'Testing' tab in Visual Studio Code to visualize the testing in your IDE.
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The examples can be seen as *integration tests* for the Kernel Tuner.
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Note that these will also use the installed package.
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Building documentation
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----------------------
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Documentation is located in the ``doc/`` directory. This is where you can type
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``make html`` to generate the html pages in the ``doc/build/html`` directory.
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The source files used for building the documentation are located in
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``doc/source``.
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To locally inspect the documentation before committing you can browse through
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the documentation pages generated locally in ``doc/build/html``.
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To make sure you have all the dependencies required to build the documentation, at least those in ``--with docs``.
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Pandoc is also required, you can install pandoc on Ubuntu using ``sudo apt install pandoc`` and on Mac using ``brew install pandoc``.
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For different setups please see `pandoc's install documentation <https://pandoc.org/installing.html>`__.
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The documentation pages hosted online are built automatically using GitHub actions.
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The documentation pages corresponding to the master branch are hosted in /latest/.
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The documentation of the last release is in /stable/. When a new release
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is published the documentation for that release will be stored in a directory
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created for that release and /stable/ will be updated to point to the last
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release. This process is again fully automated using GitHub actions.
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To build the documentation locally:
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* :bash:`pip install -r doc/requirements.txt`
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* :bash:`cd doc`
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* :bash:`make html`
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These instructions should be enough for most small contributions.
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For larger changes, or when you need to change the dependencies of Kernel Tuner, please see the documentation on setting up a full development environment.
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