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Last modified by ldomide on 2024/04/08 12:55
From version 37.1
edited by vbragin
on 2024/03/02 12:30
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

Summary

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Author
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1 -XWiki.vbragin
1 +XWiki.ldomide
Content
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30 30  
31 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 -~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]]).
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 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  
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39 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 40  
41 41  
42 -== Use our Jupyter Hub setup online ((% style="color:#c0392b" %)DEPRECATED(%%)) ==
43 -
44 -(% style="color:#c0392b" %)**TVB-multiscale app is deprecated and will stop being available after the end of 2023!**
45 -
46 -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/]]
47 -
48 -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.
49 -
50 -**[[image:https://lh6.googleusercontent.com/ytx9eYpMcL3cCScX2_Sxm4CeBW0xbKW3xKsfO2zSId10bW0gw1kiN2_SkexyYBCsF-sKsu0MaJC4cZvGVfQPjMoPBLiePbkvXOZd8BgY3Q0kFzSkRCqQ183lgDQv_6PYoqS3s7uJ||height="149" width="614"]]**
51 -
52 -Currently, the users can access 2 folders: //TVB-*-Examples// and //Contributed-Notebooks//.
53 -
54 -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]])
55 -
56 -**[[image:https://lh6.googleusercontent.com/nnsM0mhXQinmQsJwZwwwe5Sx7f-tZc8t4ELnCh9DwksyVEPUE-jixJTkhoP4l25VKwlDGoXACWtnuxQM9NMOCYbQOzDesgMDlT3sntow___vsEqRVd4OwqMY4BPyBiLJ32BnUbmM||height="267" width="614"]]**
57 -
58 -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)
59 -
60 -
61 61  == Running TVB-MULTISCALE locally ==
62 62  
63 63  See more on Github [[https:~~/~~/github.com/the-virtual-brain/tvb-multiscale>>url:https://github.com/the-virtual-brain/tvb-multiscale]] .
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68 68  
69 69  == ==
70 70  
71 -== Running TVB-MULTISCALE jobs on CSCS infrastructure from HBP collab ==
52 +== Running TVB-MULTISCALE jobs on HPC infrastructure from HBP collab ==
72 72  
73 -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]]
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]]
74 74  
75 75  * Create a collab space of your own
76 76  * 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/]]
77 -** 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.
78 78  ** 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
79 -** 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.
80 80  
81 81  Few technical details about what we do in these notebooks:
82 82  
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90 90  
91 91  >tr = unicore_client.Transport(oauth.get_token())
92 92  >r = unicore_client.Registry(tr, unicore_client._HBP_REGISTRY_URL)
93 -># 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
94 94  >client = r.site('DAINT-CSCS')
95 95  
96 -1. Prepare job submission
77 +2. Prepare job submission
97 97  
98 98  In this step we have to prepare a JSON object which will be used in the job submission process.
99 99  
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110 110  >my_job['Resources'] = { 
111 111  > "CPUs": "1"}
112 112  
113 -1. Actual job submission
94 +3. Actual job submission
114 114  
115 115  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.
116 116  
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117 117  >job = site_client.new_job(job_description=my_job, inputs=['/path1', '/path2'])
118 118  >job.properties
119 119  
120 -1. Wait until job is completed and check the results
101 +4. Wait until job is completed and check the results
121 121  
122 122  Wait until the job is completed using the following command
123 123