Last modified by ldomide on 2024/04/08 12:55
From version 29.1
edited by dionperd
on 2022/11/07 19:52
Change comment: There is no comment for this version
To version 41.1
edited by ldomide
on 2024/04/08 12:55
Change comment: There is no comment for this version

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1 -XWiki.dionperd
1 +XWiki.ldomide
Content
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5 5  = (% style="color:inherit" %)TVB Co-Simulation {{html}}<iframe width="302" height="170" src="https://www.youtube.com/embed/6hEuvxD7IDk?list=PLVtblERyzDeLcVv4BbW3BvmO8D-qVZxKf" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>{{/html}}  (%%) =
6 6  
7 7  
8 -(% style="color:inherit" %)Multiscale: TVB, NEST, (%%)ANNarchy, NETPYNE , Elephant, PySpike
8 +(% style="color:inherit" %)Multiscale: TVB, NEST, (%%)ANNarchy, NetPyNE , Elephant, PySpike
9 9  
10 10  (% style="color:inherit" %)Authors: (%%)D. Perdikis, A. Blickensdörfer, V. Bragin, L. Domide, J. Mersmann, M. Schirner, P. Ritter(% style="color:inherit" %)
11 11  )))
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20 20  * TVB Dedicated Wiki [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/the-virtual-brain/>>url:https://wiki.ebrains.eu/bin/view/Collabs/the-virtual-brain/]]
21 21  * TVB in HBP User Story [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/user-story-tvb/>>url:https://wiki.ebrains.eu/bin/view/Collabs/user-story-tvb/]]
22 22  
23 -== Use our Jupyter Hub setup online ==
23 +== ==
24 24  
25 -We have setup a Jupyter Hub service with tvb-multiscale as backed already prepared. You will only need an HBP account for accessing this: [[https:~~/~~/tvb-multiscale.apps.hbp.eu/>>https://tvb-multiscale.apps.hbp.eu/]]
25 +== Running TVB-MULTISCALE at EBRAINS JupyterLab ==
26 26  
27 -This JupyterHub installation works smoothly with HBP Collab user credentials (login only once at HBP and get access here too). We use a custom Docker Hub tvb-multiscale image as a backend, and thus a ready to use environment is available immediately, without the need of any local installation or download. This should be the ideal env for demos, presentations or even workshops with tvb-multiscale.
27 +TVB-multiscale is made available at [[EBRAINS JupyterLab>>https://lab.ebrains.eu/]].
28 28  
29 -**[[image:https://lh6.googleusercontent.com/ytx9eYpMcL3cCScX2_Sxm4CeBW0xbKW3xKsfO2zSId10bW0gw1kiN2_SkexyYBCsF-sKsu0MaJC4cZvGVfQPjMoPBLiePbkvXOZd8BgY3Q0kFzSkRCqQ183lgDQv_6PYoqS3s7uJ||height="149" width="614"]]**
29 +All the user has to do is log in with their EBRAINS credentials, and start a Python console or a Jupyter notebook using the kernel "EBRAINS-23.09" (or a more recent version), where TVB-multiscale can be imported (e.g., via "import tvb_multiscale"). All necessary TVB-multiscale dependencies (NEST, ANNarchy, NetPyNE (NEURON), Elephant, Pyspike) are also installed and available.
30 30  
31 -Currently, the users can access 2 folders: //TVB-*-Examples// and //Contributed-Notebooks//.
31 +This collab contains various examples of using TVB-Multiscale with all three supported spiking simulators. We suggest copying the contents of this collab to your Library or to any collab owned by you, and running them there (note that the user's drive offers persistent storage, i.e. users will find their files after logging out and in again), as follows:
32 32  
33 -The notebooks under **TVB-*-Examples** are public, shared by everyone accessing the instance. Periodically, we will clean all changes under TVB-*-Examples folder (by redeploying the pod image), and show the original example notebooks submitted on our Github repo. If users intend to contribute here, they are encouraged to submit changes through Pull Requests ([[https:~~/~~/github.com/the-virtual-brain/tvb-multiscale>>url:https://github.com/the-virtual-brain/tvb-multiscale]])
33 +~1. Select `Drive` on the left of the current page (or use [[this link>>https://wiki.ebrains.eu/bin/view/Collabs/the-virtual-brain-multiscale/Drive||rel="noopener noreferrer" target="_blank"]]).
34 34  
35 -**[[image:https://lh6.googleusercontent.com/nnsM0mhXQinmQsJwZwwwe5Sx7f-tZc8t4ELnCh9DwksyVEPUE-jixJTkhoP4l25VKwlDGoXACWtnuxQM9NMOCYbQOzDesgMDlT3sntow___vsEqRVd4OwqMY4BPyBiLJ32BnUbmM||height="267" width="614"]]**
35 +2. Check the `tvb-multiscale-collab` folder checkbox, and copy it to your `My Library` ("copy" icon will appear above the files/folders list).
36 36  
37 -Folder **Contributed-Notebooks** is not shared. Here, users can experiment with their own private examples. This folder is persisted on restarts in the user HBP Collab personal space. Thus, users will be able to access their work even after a redeploy. (e.g. during a workshop every participant could have in here his own exercise solution).
37 +3. Select `Lab` (on the left), and navigate to the destination where you just copied the folder.
38 38  
39 +4. Enter the `tvb-multiscale-collab` folder, and open either of example notebooks. Ensure you select the appropriate ipykernel (EBRAINS-23.09 or a more recent one)
40 +
41 +
39 39  == Running TVB-MULTISCALE locally ==
40 40  
41 -See more on Github [[https:~~/~~/github.com/the-virtual-brain/tvb-multiscale>>url:https://github.com/the-virtual-brain/tvb-multiscale]]  and check this notebook example: [[https:~~/~~/drive.ebrains.eu/f/b3ea5740fcc34f12af7a/?dl=1>>url:https://drive.ebrains.eu/f/b3ea5740fcc34f12af7a/?dl=1]]
44 +See more on Github [[https:~~/~~/github.com/the-virtual-brain/tvb-multiscale>>url:https://github.com/the-virtual-brain/tvb-multiscale]] .
42 42  
43 -This notebook will be ok to download and try yourself locally, after you have also prepared and launched locally a Docker env: [[https:~~/~~/hub.docker.com/r/thevirtualbrain/tvb-multiscale>>https://hub.docker.com/r/thevirtualbrain/tvb-multiscale]]
46 +Documented notebooks and other examples will be ok to download and try yourself locally, after you have also prepared and launched locally a Docker env: [[https:~~/~~/hub.docker.com/r/thevirtualbrain/tvb-multiscale>>https://hub.docker.com/r/thevirtualbrain/tvb-multiscale]]
44 44  
45 45  This is the path recommended for people working closely with tvb-multiscale. They are able to download it in their local work env and code freely and fast with it.
46 46  
47 -== Running TVB-MULTISCALE jobs on CSCS infrastructure from HBP collab ==
50 +== ==
48 48  
49 -The CSCS and HBP Collab deployment of tvb-multiscale is a good example to show how tvb-multiscale can run with an HPC backend. This will be efficient when the simulation jobs are very large. From our experience, with small jobs, the stage-in/out time is considerable, and then the user might be better with just a local run. Also, this deployment requires that **the user have an active CSCS personal account**. More details on how to use this deployment can be found in this movie: [[https:~~/~~/drive.google.com/open?id=1osF263FK_NjhZcBJfpSy-F7qkbYs3Q-E>>url:https://drive.google.com/open?id=1osF263FK_NjhZcBJfpSy-F7qkbYs3Q-E]]
52 +== Running TVB-MULTISCALE jobs on HPC infrastructure from HBP collab ==
50 50  
54 +tvb-multiscale can run with an HPC backend. This will be efficient when the simulation jobs are very large. From our experience, with small jobs, the stage-in/out time is considerable, and then the user might be better with just a local run. Also, such a deployment requires that **the user have an active HPC personal account and allocation project active**. More details on how to use this deployment can be found in this movie: [[https:~~/~~/drive.google.com/open?id=1osF263FK_NjhZcBJfpSy-F7qkbYs3Q-E>>url:https://drive.google.com/open?id=1osF263FK_NjhZcBJfpSy-F7qkbYs3Q-E]]
55 +
51 51  * Create a collab space of your own
52 52  * Clone and run in your HBP Collab Hub ([[https:~~/~~/lab.ebrains.eu/>>url:https://lab.ebrains.eu/]]) the notebooks from here: [[https:~~/~~/drive.ebrains.eu/d/245e6c13082f45bcacfa/>>url:https://drive.ebrains.eu/d/245e6c13082f45bcacfa/]]
53 -** test_tvb-nest_installation.ipynb  Run the cosimulate_tvb_nest.sh script on the CSCS Daint supercomputer. In this example, basically we are running the //installation_test.py// file which is in the docker folder.
58 +** test_tvb-nest_installation.ipynb  Run the cosimulate_tvb_nest.sh script on the HPC supercomputer where you have an account active. In this example, basically we are running the //installation_test.py// file which is in the docker folder.
54 54  ** run_custom_cosimulation.ipynb For this example we are using the //cosimulate_with_staging.sh// script in order to pull the tvb-multiscale docker image and we are using a custom simulation script (from Github page) which will be uploaded in the staging in phase
55 -** run_custom_cosimulation_from_notebook.ipynb  This example is running the same simulation as the example above but instead of using an external file with the simulation code we will build a simulation file from a few notebook cells and we will pass this file to the CSCS server.
60 +** run_custom_cosimulation_from_notebook.ipynb  This example is running the same simulation as the example above but instead of using an external file with the simulation code we will build a simulation file from a few notebook cells and we will pass this file to the HPC.
56 56  
57 57  Few technical details about what we do in these notebooks:
58 58  
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66 66  
67 67  >tr = unicore_client.Transport(oauth.get_token())
68 68  >r = unicore_client.Registry(tr, unicore_client._HBP_REGISTRY_URL)
69 -># use "DAINT-CSCS" change if another supercomputer is prepared for usage
74 +># we used "DAINT-CSCS", but you should change it to another supercomputer where you have a project active
70 70  >client = r.site('DAINT-CSCS')
71 71  
72 -1. Prepare job submission
77 +2. Prepare job submission
73 73  
74 74  In this step we have to prepare a JSON object which will be used in the job submission process.
75 75  
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86 86  >my_job['Resources'] = { 
87 87  > "CPUs": "1"}
88 88  
89 -1. Actual job submission
94 +3. Actual job submission
90 90  
91 91  In order to submit a job we have to use the JSON built in the previous step and also if we have some local files, we have to give their paths as a list of strings (inputs argument) so the UNICORE library will upload them in the job's working directory in the staging in phase, before launching the job.
92 92  
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93 93  >job = site_client.new_job(job_description=my_job, inputs=['/path1', '/path2'])
94 94  >job.properties
95 95  
96 -1. Wait until job is completed and check the results
101 +4. Wait until job is completed and check the results
97 97  
98 98  Wait until the job is completed using the following command
99 99