Wiki source code of 02. Installing PyNN

Version 27.13 by shailesh on 2021/10/08 11:20

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8.1	3	tutorials under development for Linux, Mac OS, Windows, Jupyter Lab.
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4.1	5
5.1	6
4.1	7	== Learning objectives ==
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23.1	9	In this tutorial, you will learn how to install PyNN, together with the NEST, NEURON and Brian 2 simulators, on Linux / Mac OS / Windows / in EBRAINS Jupyter Lab.
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	13	Note: There will be a separate tutorial for each environment.
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4.1	16	== Audience ==
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5.1	18	This tutorial is intended for people with at least a basic knowledge of neuroscience (high school level or above) and basic familiarity with the Python programming language. It should also be helpful for people who already have advanced knowledge of neuroscience and neural simulation, who simply wish to learn how to use PyNN, and how it differs from other simulation tools they know.
4.1	19
	20	== Prerequisites ==
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24.1	22	To follow this tutorial, you will need a computer with Linux and a good network connection. You will need to know how to open the terminal application for your operating system.
4.1	23
	24	== Format ==
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27.1	26	These tutorials will be screencasts, in which the presenter runs commands in a terminal, and the viewer is expected to follow along. The intended duration is 10-15 minutes.
4.1	27
	28	== Script ==
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	32	Slide showing tutorial title, PyNN logo, link to PyNN service page.
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	34
	35	Hello, my name is X.
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12.1	37	This video is one of a series of tutorials for PyNN, which is Python software for modelling and simulating spiking neural networks.
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12.1	39	For a list of the other tutorials in this series, you can visit ebrains.eu/service/pynn, that's p-y-n-n.
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	43	Slide listing learning objectives
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22.17	46	In this tutorial, I will guide you through setting up PyNN, together with the NEST, NEURON and Brian 2 simulators, in a Linux environment. Note that we have a dedicated version of this tutorial for other environments, such as Mac OS, Windows and EBRAINS Jupyter Lab.
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	50	Slide listing prerequisites
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22.18	53	I shall be demonstrating the installation on a computer with Ubuntu 20.04 OS installed. The steps are likely to remain very similar for other versions of Ubuntu OS, and also not expected to vary significantly for other Linux distributions. In the latter case, you will find on the Internet about how to carry out the equivalent of the tasks demonstrated here using Ubuntu OS. Also, the tutorial will focus only on Python 3, as Python 2 has now been deprecated. It is recommended to use Python version 3.6 or higher. I would be using Python 3.8.10 in this tutorial, as it is the default version provided with Ubuntu 20.04.
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22.18	55	(% class="box errormessage" %)
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	57	Note:
	58	Having multiple versions of Python on your system can produce issues while installing NEST. The method shown below will install NEST for the default version of Python provided by your Ubuntu OS. E.g. for Ubuntu 18.04 this might be Python 3.6.9 and for Ubuntu 20.04 it will likely be 3.8.10. If you wish to associate the NEST installation with a different Python version installed on your system, please refer the NEST installation instructions to do so on their website.
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21.8	63	Screencast - terminal
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21.6	66	We shall make use of virtual environments in this tutorial. This allows multiple Python projects to coexist on the same computer, even when they might have different, and even conflicting, requirements. It helps isolate projects and thereby preventing unrequested changes in others, when any one of them is updated.
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21.8	68	We begin by creating a directory for our project.
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21.4	70	(% class="box infomessage" %)
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21.8	72	Screencast - terminal
	73	\\(% style="color:#000000" %)cd ~~
21.5	74	mkdir pynn_project
	75	cd pynn_project
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5.1	77
22.19	78	Next we shall create a virtual environment within this directory. Python 3 provides support for creating virtual environments. Since Python 3.6, the recommended method of creating a new virtual environment is as follows:
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	82	Note:
	83	For older versions of Python 3, you might require to manually install `python3-venv` package before being able to run the below command. To install, run:
	84	\\sudo apt-get install python3-venv
	85	\\More recent versions of Python 3 (e.g. v3.9) already have this pre-installed.
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21.8	88	(% class="box infomessage" %)
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	90	Screencast - terminal
22.14	91	\\(% style="color:#000000" %)python3 -m venv pynn_env
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27.10	94	(% class="box warningmessage" %)
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	96	Note:
	97	\\Observe that this command is as 'python3' and not simply 'python'. This is because Ubuntu 20, as default, understands only the former. You can find on the Internet various ways to have 'python' also refer to 'python3', but for the purposes of this tutorial we shall keep things simple and try to work with the bare minimum changes to the system.
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21.9	100	This will create a sub-directory named 'pynn_env' within our project directory, with several files and sub-directories. Let us take a look at the 'site-packages' directory.
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	104	Screencast - file explorer
21.11	105	\\(% style="color:#000000" %)<< show directory contents; especially lib/python3.9/site-packages >>
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21.9	108	As you see here, only a limited number of basic packages have currently been installed in this virtual environment. In the steps ahead, we shall install various other packages, and you shall see that these would be reflected here.
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21.11	110	To enter into this virtual environment, and thereby use its resources in isolation from other projects on your computer, we require to "activate" it. This is achieved by running the command:
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	114	Screencast - terminal
	115	\\(% style="color:#000000" %)source pynn_env/bin/activate
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22.1	118	Notice how this changes the command prompt to show the name of your virtual environment. In our case, we had named it 'pynn_env', and this is now reflected as a prefix to the command prompt. This confirms that we are now in our new virtual environment.
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	122	Note:
	123	\\You might be required to run the above command every time you open a new terminal window. Do verify that the terminal command prompt indicates the name of your virtual environment to confirm that you have indeed activated it.
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21.7	126	Now that we have our project's virtual environment setup, we are now ready to install PyNN and other simulators. In general, it is advisable to install the various simulators (especially NEURON and NEST) prior to installing PyNN, because PyNN will then auto compile NEURON's NMODL fles and NEST's extensions during installation. Alternatively, this would need to be done manually as described on the PyNN website. In this tutorial, we will adopt the easier approach and begin by installing the simulators. For the purposes of this tutorial, we shall demonstrate the installation of Brian2, NEURON and NEST simulators.
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22.17	128	We start here with the installation of Brian 2. Brian 2 can be installed simply using the pip command.
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21.10	132	Screencast - terminal
22.4	133	\\(% style="color:#000000" %)pip install brian2
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22.17	136	This will install Brian 2, along with all its dependencies such as 'cython', 'numpy', etc. We can now go back into our virtual environment's 'site-packages' directory to see how it is now populated with all these packages.
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	140	Screencast - file explorer
	141	\\(% style="color:#000000" %)<< show directory contents lib/python3.9/site-packages >>
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22.17	144	To confirm that we have properly installed Brian 2 on our computer, we can test as follows:
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22.4	148	Screencast - terminal
27.10	149	\\(% style="color:#000000" %)python
22.4	150	\\import brian2
22.7	151	\\exit()
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27.10	154	(% class="box warningmessage" %)
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	156	Note:
	157	\\You might remember that earlier in this tutorial we had to use the term 'python3' to run Python on our system. But here, as in the rest of this tutorial, we shall simply write 'python'. This is possible because once we have activated our virtual environment, this environment understands that both 'python' and 'python3' are equivalent.
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22.17	160	If there are no error messages here, and the import is successful, then we have completed installing Brian 2.
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22.20	162	We shall now move on to install the NEST simulator. Unlike Brian 2, NEST is not a Python package and therefore it cannot be installed via the 'pip' command.
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22.20	164	At the time of creating this tutorial, the lastest version of NEST is v3.1. This is currently supported by PyNN v0.10, and it is likely that other versions of NEST could potentially be incompatible with this version of PyNN. The installation is done by first adding the PPA repository for NEST and updating apt, followed by the installation of NEST itself.
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	168	Screencast - terminal
	169	\\(% style="color:#000000" %)sudo add-apt-repository ppa:nest-simulator/nest
	170	sudo apt-get update
	171	\\sudo apt-get install nest
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27.3	174	This installs the NEST module along with PyNEST, which is a Python interface for controlling the NEST kernel. This allows us to use NEST via Python. To confirm that we have properly installed NEST on our computer, we can test as follows:
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22.21	178	Screencast - terminal
27.2	179	\\(% style="color:#000000" %)nest
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27.3	182	This will display the NEST banner, which mentions the version amongst other info. Here, as we can see, we have now installed NEST v3.1 on our system. Let us next verify that this is indeed accessible via Python.
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	186	Screencast - terminal
27.10	187	\\(% style="color:#000000" %)python
22.21	188	\\import nest
	189	\\exit()
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27.5	192	(% class="box warningmessage" %)
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	194	Note:
27.7	195	\\I find that I receive a "no module named nest" error, when trying this right after installing NEST. But it succeeds after a restart. So if you do observe an error, close all programs and restart your computer, and try again. This time it should execute as expected.
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27.3	198	If there are no error messages here, and the import is successful, then we have completed installing NEST simulator, and are able to load it on Python.
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27.8	200	We next move on to the third simulator, NEURON. The installation for NEURON used to be more involved previously, but can now be easily completed using the 'pip' command:
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	204	Screencast - terminal
	205	\\(% style="color:#000000" %)pip install neuron
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27.8	208	This installs the NEURON simulator on your system. To confirm that we have properly installed NEURON, we can test as follows:
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	212	Screencast - terminal
	213	\\(% style="color:#000000" %)nrngui
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	216	This will display the NEURON banner, which mentions the version amongst other info. Here, as we can see, we have now installed NEURON v8.0.0 on our system. Let us next verify that this is indeed accessible via Python.
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	220	Screencast - terminal
27.10	221	\\(% style="color:#000000" %)python
27.8	222	\\from neuron import h
	223	\\exit()
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	230	Slide recap of learning objectives
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20.1	233	That is the end of this tutorial, in which I've demonstrated how to install PyNN, and other required simulators, in a Linux system. You are now ready to start modeling! To learn about model development in PyNN, do take a look at our next tutorial.
	234
	235	Als, we will be releasing a series of tutorials, throughout the rest of 2021 and 2022, to introduce these more advanced features of PyNN, so keep an eye on the EBRAINS website.
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27.12	239	Slide useful links
	240	\\[[(% style="color:#000000" %)https:~~/~~/realpython.com/python-virtual-environments-a-primer/>>https://realpython.com/python-virtual-environments-a-primer/]](%%)
	241	[[(% style="color:#000000" %)https:~~/~~/briansimulator.org/install/>>https://briansimulator.org/install/]](%%)
27.13	242	[[(% style="color:#000000" %)https:~~/~~/nest-simulator.readthedocs.io/en/v3.1/installation/index.html>>https://nest-simulator.readthedocs.io/en/v3.1/installation/index.html]](%%)
	243	[[https:~~/~~/neuron.yale.edu/neuron/>>https://neuron.yale.edu/neuron/]]
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21.1	249	Slide acknowledgements, contact information
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20.1	253	PyNN has been developed by many different people, with financial support from several different organisations. I'd like to mention in particular the CNRS and the European Commission, through the FACETS, BrainScaleS and Human Brain Project grants.
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20.1	256	For more information visit neuralensemble.org/PyNN. If you have questions you can contact us through the PyNN Github project, the NeuralEnsemble forum, EBRAINS support, or the EBRAINS Community.

Wiki source code of 02. Installing PyNN

PyNN: resources