Pyproject.toml

Jenkinsfile

@@ -192,7 +192,7 @@ for(b = 0; b < builderNodes.size(); b++) {
                     ${env.PYTHON} -m venv ${WORKSPACE}/venv
                     . ${WORKSPACE}/venv/bin/activate
                     pip install -U pip
-                    pip install numpy scipy pybind11 pytest flaky pytest-cov wheel flake8 bitarray psutil ${cupy}
+                    pip install numpy scipy pybind11 pytest flaky pytest-cov wheel flake8 bitarray psutil build pkgconfig ${cupy}
                     """;
                 }
 
@@ -263,77 +263,18 @@ for(b = 0; b < builderNodes.size(); b++) {
 
                 buildStep("Building Python wheels (${NODE_NAME})") {
                     dir("genn") {
-                        if(isUnix()) {
-                            // Create virtualenv, install numpy and pybind11; and make Python wheel
-                            echo "Creating Python wheels";
-                            script = """
+                        script = """
                             . ${WORKSPACE}/venv/bin/activate
 
-                            python setup.py clean --all 1>> "${outputFilename}" 2>&1
-                            python setup.py bdist_wheel -d . 1>> "${outputFilename}" 2>&1
-                            """
-
-                            def wheelStatusCode = sh script:script, returnStatus:true
-                            if(wheelStatusCode != 0) {
-                                setBuildStatus("Building Python wheels (" + env.NODE_NAME + ")", "FAILURE");
-                            }
-                        }
-                        else {
-                            // Build set of dynamic libraries for single-threaded CPU backend
-                            echo "Creating dynamic libraries";
-                            msbuildCommand = """
-                            CALL %VC_VARS_BAT%
-                            msbuild genn.sln /m /verbosity:quiet /p:Configuration=Release_DLL /t:single_threaded_cpu_backend >> "${outputFilename}" 2>&1
-                            """;
-
-                            // If node has suitable CUDA, also build CUDA backend
-                            if("cuda10" in nodeLabel || "cuda11" in nodeLabel || "cuda12" in nodeLabel) {
-                                msbuildCommand += """
-                                msbuild genn.sln /m /verbosity:quiet  /p:Configuration=Release_DLL /t:cuda_backend >> "${outputFilename}" 2>&1
-                                """;
-                            }
-                            // If this node has OpenCL, also build OpenCL backend
-                            if(nodeLabel.contains("opencl")) {
-                                msbuildCommand += """
-                                msbuild genn.sln /m /verbosity:quiet  /p:Configuration=Release_DLL /t:opencl_backend >> "${outputFilename}" 2>&1
-                                """;
-                            }
-
-                            def msbuildStatusCode = bat script:msbuildCommand, returnStatus:true
-                            if(msbuildStatusCode != 0) {
-                                setBuildStatus("Building Python wheels (" + env.NODE_NAME + ")", "FAILURE");
-                            }
-
-                            // Remove existing virtualenv
-                            bat script:"rmdir /S /Q virtualenv", returnStatus:true;
-
-                            echo "Creating Python wheels";
-                            script = """
-                            CALL %VC_VARS_BAT%
-                            CALL %ANACONDA_ACTIVATE_BAT%
-
-                            ${env.PYTHON} -m venv virtualenv
-                            pushd virtualenv\\Scripts
-                            call activate
-                            popd
-
-                            pip install --upgrade pip
-                            pip install wheel "numpy>=1.17" pybind11
-
-                            copy /Y lib\\genn*Release_DLL.* pygenn
-
-                            python setup.py clean --all
-                            python setup.py bdist_wheel -d . >> "${outputFilename}" 2>&1
+                            python -m build  --wheel . 1>> "${outputFilename}" 2>&1
                             """
-
-                            def wheelStatusCode = bat script:script, returnStatus:true
-                            if(wheelStatusCode != 0) {
-                                setBuildStatus("Building Python wheels (" + env.NODE_NAME + ")", "FAILURE");
-                            }
+                        def wheelStatusCode = sh script:script, returnStatus:true;
+                        if(wheelStatusCode != 0) {
+                            setBuildStatus("Building Python wheels (" + env.NODE_NAME + ")", "FAILURE");
                         }
 
                         // Archive wheel itself
-                        archive "*.whl"
+                        archive "dist/*.whl"
                     }
                 }
 

README.md

@@ -4,40 +4,11 @@
 
 # GPU-enhanced Neuronal Networks (GeNN)
 
-GeNN is a GPU-enhanced Neuronal Network simulation environment based on code generation for Nvidia CUDA.
+GeNN is a GPU-enhanced Neuronal Network simulation environment based on code generation for NVIDIA CUDA and AMD HIP.
 
 ## Installation
 
-You can download GeNN either as a zip file of a stable release, checkout the development
-version using the Git version control system or use our Docker container.
-
-### Downloading a release
-Point your browser to https://github.com/genn-team/genn/releases
-and download a release from the list by clicking the relevant source
-code button. After downloading continue to install GeNN as described in the [GitHub installing section](#installing-genn) below.
-
-### Obtaining a Git snapshot
-
-If it is not yet installed on your system, download and install Git
-(http://git-scm.com/). Then clone the GeNN repository from Github
-```bash
-git clone https://github.com/genn-team/genn.git
-```
-The github url of GeNN in the command above can be copied from the
-HTTPS clone URL displayed on the GeNN Github page (https://github.com/genn-team/genn).
-
-This will clone the entire repository, including all open branches.
-By default git will check out the master branch which contains the
-source version upon which the next release will be based. There are other 
-branches in the repository that are used for specific development 
-purposes and are opened and closed without warning.
-
-### Installing GeNN
-
-In future we plan on providing binary builds of GeNN via conda. However, for now, GeNN
-needs to be installed from source.
-
-#### Pre-installation
+### Pre-installation
 
 1.  Install the C++ compiler on the machine, if not already present.
     For Windows, Visual Studio 2019 or above is required. The Microsoft Visual Studio 
@@ -53,38 +24,47 @@ needs to be installed from source.
     https://developer.nvidia.com/cuda-downloads
     Be sure to pick CUDA and C++ compiler versions which are compatible
     with each other. The latest C++ compiler need not necessarily be
-    compatible with the latest CUDA toolkit.
+    compatible with the latest CUDA toolkit. Similarly, if your machine 
+    has an AMD GPU and you haven't installed HIP yet, follow the instructions at
+    https://rocm.docs.amd.com/projects/HIP/en/latest/install/install.html.
 3.  GeNN uses the ``CUDA_PATH`` environment variable to determine which 
     version of CUDA to build against. On Windows, this is set automatically when 
     installing CUDA. However, if you choose, you can verify which version is 
-    selected by running ``echo $CUDA_PATH`` in a command prompt.
+    selected by running ``echo %CUDA_PATH%`` in a command prompt.
     However, on Linux, you need to set ``CUDA_PATH`` manually with:
     ``export CUDA_PATH=/usr/local/cuda``
     assuming CUDA is installed in /usr/local/cuda (the standard location 
-    on Ubuntu Linux). Again, to make this change persistent, this can
-    be added to your login script (e.g. ``.profile`` or ``.bashrc``)
-4.  Either download the latest release of GeNN and extract into your 
-    home directory or clone using git from https://github.com/genn-team/genn
-5.  On Linux, install the development version of libffi. For example, on Ubuntu you can do this
+    on Ubuntu Linux). Similarly, if you are using HIP, you need to set the 
+    ``HIP_PATH`` variable manually and also specify your platform with either
+    ``export HIP_PLATFORM='nvidia'`` if you wish to use HIP with an NVIDIA GPU
+    or ``export HIP_PLATFORM='amd'`` if you wish to use an AMD GPU.
+    To make any of these changes persistent, these commands should be added to your login 
+    script (e.g. ``.profile`` or ``.bashrc``).
+4.  On Linux, install the development version of libffi. For example, on Ubuntu you can do this
     by running ``sudo apt-get install libffi-dev``.
-6.  Install the pybind11, psutil and numpy packages with pip i.e. ``pip install pybind11 psutil numpy``.
-
-
-#### Building with setup.py
-From the GeNN directory, the GeNN libraries and python package can be built
-with ``python setup.py install``. If you wish to create an editable install
-(most useful if you are intending to modify GeNN yourself) you can also used
-``python setup.py develop``. On Linux (or Windows if you have a debug version
-of the python libraries installed) you can build a debug version of GeNN with
-``python setup.py build_ext --debug develop``.
-
-#### Building with pip
-From the GeNN directory, the GeNN libraries and python package can be built
-with ``pip install .``. If you wish to create an editable install
-(most useful if you are intending to modify GeNN yourself) you can also used
-``pip install --editable .``.
 
-### Docker
+### Installation using pip
+The easiest way to install GeNN is directly from github using pip. 
+First of all make sure pip is up to date using :
+``pip install -U pip``
+Then, to install the latest development version you can use:
+``pip install https://github.com/genn-team/genn/archive/refs/heads/master.zip`` or, to install the 5.3.0 release, you can use: 
+``pip install https://github.com/genn-team/genn/archive/refs/tags/5.3.0.zip``.
+
+### Creating an editable install with pip
+If you want to develop GeNN yourself or run userprojects from the GeNN repository, it is helpful to create an 'editable' install. 
+The easiest way to do this to first 'clone' GeNN from github using ``git clone https://github.com/genn-team/genn.git``.
+Then, navigate to the GeNN directory and install using ``pip install -e .``. If you wish to install the additional dependencies needed 
+to run the userprojects, you can do so using ``pip install -e .[userproject]``.
+
+### Building with setup.py (LEGACY)
+Although it is not recommended, in order to build special development versions you sometimes need to install the old fashioned way!
+1.  Manually install PyGeNN's build dependencies using pip i.e. ``pip install pybind11 psutil pkgconfig setuptools>=61``.
+2.  Clone GeNN using git i.e. using ``git clone https://github.com/genn-team/genn.git``
+3.  From the GeNN directory, build PyGeNN using ``python setup.py develop``. 
+    You can build a debug version of GeNN with ``python setup.py build_ext --debug develop``.
+
+## Docker
 You can also use GeNN through our CUDA-enabled docker container which comes with GeNN pre-installed.
 To work with such CUDA-enabled containers, you need to first install CUDA on your host system as described above and then install docker and the NVIDIA Container Toolkit as described in https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker.
 You can then build the GeNN container yourself or download it from Dockerhub.

docs/installation.rst

@@ -23,36 +23,49 @@ Pre-installation
     https://developer.nvidia.com/cuda-downloads.
     Be sure to pick CUDA and C++ compiler versions which are compatible
     with each other. The latest C++ compiler need not necessarily be
-    compatible with the latest CUDA toolkit.
+    compatible with the latest CUDA toolkit. Similarly, if your machine 
+    has an AMD GPU and you haven't installed HIP yet, follow the instructions at
+    https://rocm.docs.amd.com/projects/HIP/en/latest/install/install.html.
 3.  GeNN uses the ``CUDA_PATH`` environment variable to determine which 
     version of CUDA to build against. On Windows, this is set automatically when 
     installing CUDA. However, if you choose, you can verify which version is 
     selected by running ``echo %CUDA_PATH%`` in a command prompt.
     However, on Linux, you need to set ``CUDA_PATH`` manually with:
     ``export CUDA_PATH=/usr/local/cuda``
     assuming CUDA is installed in /usr/local/cuda (the standard location 
-    on Ubuntu Linux). Again, to make this change persistent, this can
-    be added to your login script (e.g. ``.profile``, ``.bash_profile`` or ``.bashrc``)
-4.  Either download the latest release of GeNN and extract it into your 
-    home directory or clone using git from https://github.com/genn-team/genn
-5.  On Linux, install the development version of libffi. For example, on Ubuntu you can do this
+    on Ubuntu Linux). Similarly, if you are using HIP, you need to set the 
+    ``HIP_PATH`` variable manually and also specify your platform with either
+    ``export HIP_PLATFORM='nvidia'`` if you wish to use HIP with an NVIDIA GPU
+    or ``export HIP_PLATFORM='amd'`` if you wish to use an AMD GPU.
+    To make any of these changes persistent, these commands should be added to your login 
+    script (e.g. ``.profile`` or ``.bashrc``).
+4.  On Linux, install the development version of libffi. For example, on Ubuntu you can do this
     by running ``sudo apt-get install libffi-dev``.
-6.  Install the pybind11, psutil and numpy packages with pip, i.e. ``pip install pybind11 psutil numpy``.
 
 ----------------------
-Building with setup.py
+Installation using pip
 ----------------------
-From the GeNN directory, the GeNN libraries and python package can be built
-with ``python setup.py install``. If you wish to create an editable install
-(most useful if you are intending to modify GeNN yourself) you can also use
-``python setup.py develop``. On Linux (or Windows if you have a debug version
-of the python libraries installed) you can build a debug version of GeNN with
-``python setup.py build_ext --debug develop``.
-
------------------
-Building with pip
------------------
-From the GeNN directory, the GeNN libraries and python package can be built
-with ``pip install .``. If you wish to create an editable install
-(most useful if you are intending to modify GeNN yourself) you can also use
-``pip install --editable .``.
+The easiest way to install GeNN is directly from github using pip. 
+First of all make sure pip is up to date using :
+``pip install -U pip``
+Then, to install the latest development version you can use:
+``pip install https://github.com/genn-team/genn/archive/refs/heads/master.zip`` or, to install the 5.3.0 release, you can use: 
+``pip install https://github.com/genn-team/genn/archive/refs/tags/5.3.0.zip``.
+
+-------------------------------------
+Creating an editable install with pip
+-------------------------------------
+If you want to develop GeNN yourself or run userprojects from the GeNN repository, it is helpful to create an 'editable' install. 
+The easiest way to do this to first 'clone' GeNN from github using ``git clone https://github.com/genn-team/genn.git``.
+Then, navigate to the GeNN directory and install using ``pip install -e .``. If you wish to install the additional dependencies needed 
+to run the userprojects, you can do so using ``pip install -e .[userproject]``.
+
+-------------------------------
+Building with setup.py (LEGACY)
+-------------------------------
+Although it is not recommended, in order to build special development versions you sometimes need to install the old fashioned way!
+
+1.  Manually install PyGeNN's build dependencies using pip i.e. ``pip install pybind11 psutil pkgconfig setuptools>=61``.
+2.  Clone GeNN using git i.e. using ``git clone https://github.com/genn-team/genn.git``
+3.  From the GeNN directory, build PyGeNN using ``python setup.py develop``. 
+    You can build a debug version of GeNN with ``python setup.py build_ext --debug develop``.

pyproject.toml

@@ -0,0 +1,3 @@
+[build-system]
+requires = ["setuptools>=61", "pybind11", "psutil", "pkgconfig"]
+build-backend = "setuptools.build_meta"

setup.py

@@ -1,4 +1,5 @@
 import os
+import pkgconfig
 import sys
 from copy import deepcopy
 from platform import system, uname
@@ -59,13 +60,12 @@
     else:
         genn_lib_suffix = "_dynamic"
 
-genn_path = os.path.dirname(os.path.abspath(__file__))
-
-pygenn_path = os.path.join(genn_path, "pygenn")
+abs_genn_path = os.path.dirname(os.path.abspath(__file__))
+genn_include = os.path.join(".", "include", "genn", "genn")
+genn_third_party_include = os.path.join(".", "include", "genn", "third_party")
+pygenn_path = os.path.join(".", "pygenn")
 pygenn_src = os.path.join(pygenn_path, "src")
 pygenn_include = os.path.join(pygenn_path, "include")
-genn_include = os.path.join(genn_path, "include", "genn", "genn")
-genn_third_party_include = os.path.join(genn_path, "include", "genn", "third_party")
 
 # Always package LibGeNN
 if WIN:
@@ -104,6 +104,11 @@
                                         os.path.join(pygenn_path, "libffi" + genn_lib_suffix + ".dll")]
 # Otherwise
 else:
+    # Add whatever configuration libffi requires
+    ffi_config = pkgconfig.parse("libffi")
+    for k, v in ffi_config.items():
+        genn_extension_kwargs[k].extend(v)
+
     # --- Linux/macOS libffi linkage ---
     genn_extension_kwargs["libraries"].append("ffi")
     # Add GeNN library to dependencies
@@ -260,7 +265,7 @@
         package_data.append("libgenn_" + module_stem + "_backend" + genn_lib_suffix + ".so")
 
     # Add backend include directory to both SWIG and C++ compiler options
-    backend_include_dir = os.path.join(genn_path, "include", "genn", "backends", module_stem)
+    backend_include_dir = os.path.join(".", "include", "genn", "backends", module_stem)
     backend_extension_kwargs["libraries"].insert(0, "genn_" + module_stem + "_backend" + genn_lib_suffix)
     backend_extension_kwargs["include_dirs"].append(backend_include_dir)
 
@@ -274,16 +279,16 @@
         # If compiler is MSVC
         if WIN:
             # **NOTE** ensure pygenn_path has trailing slash to make MSVC happy
-            out_dir = os.path.join(pygenn_path, "")
+            out_dir = os.path.join(abs_genn_path, "pygenn", "")
             check_call(["msbuild", "genn.sln", f"/t:{module_stem}_backend",
                         f"/p:Configuration={genn_lib_suffix[1:]}",
                         "/m", "/verbosity:quiet",
                         f"/p:OutDir={out_dir}"],
-                        cwd=genn_path)
+                        cwd=abs_genn_path)
         else:
             # Define make arguments
             make_arguments = ["make", f"{module_stem}_backend", "DYNAMIC=1",
-                              f"LIBRARY_DIRECTORY={pygenn_path}",
+                              f"LIBRARY_DIRECTORY={os.path.join(abs_genn_path, 'pygenn')}",
                               f"--jobs={cpu_count(logical=False)}"]
             if debug_build:
                 make_arguments.append("DEBUG=1")
@@ -292,10 +297,10 @@
                 make_arguments.append("COVERAGE=1")
 
             # Build
-            check_call(make_arguments, cwd=genn_path)
+            check_call(make_arguments, cwd=abs_genn_path)
 
 # Read version from txt file
-with open(os.path.join(genn_path, "version.txt")) as version_file:
+with open(os.path.join(abs_genn_path, "version.txt")) as version_file:
     version = version_file.read().strip()
 
 setup(