Last modified by ldomide on 2024/04/08 12:55
From version 41.1
edited by ldomide
on 2024/04/08 12:55
Change comment: There is no comment for this version
To version 30.1
edited by dionperd
on 2023/03/22 15:45
Change comment: There is no comment for this version

Summary

Details

Page properties
Author
... ... @@ -1,1 +1,1 @@
1 -XWiki.ldomide
1 +XWiki.dionperd
Content
... ... @@ -20,44 +20,39 @@
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 -== ==
23 +== Use our Jupyter Hub setup online ==
24 24  
25 -== Running TVB-MULTISCALE at EBRAINS JupyterLab ==
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/]]
26 26  
27 -TVB-multiscale is made available at [[EBRAINS JupyterLab>>https://lab.ebrains.eu/]].
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.
28 28  
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.
29 +**[[image:https://lh6.googleusercontent.com/ytx9eYpMcL3cCScX2_Sxm4CeBW0xbKW3xKsfO2zSId10bW0gw1kiN2_SkexyYBCsF-sKsu0MaJC4cZvGVfQPjMoPBLiePbkvXOZd8BgY3Q0kFzSkRCqQ183lgDQv_6PYoqS3s7uJ||height="149" width="614"]]**
30 30  
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:
31 +Currently, the users can access 2 folders: //TVB-*-Examples// and //Contributed-Notebooks//.
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||rel="noopener noreferrer" target="_blank"]]).
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]])
34 34  
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).
35 +**[[image:https://lh6.googleusercontent.com/nnsM0mhXQinmQsJwZwwwe5Sx7f-tZc8t4ELnCh9DwksyVEPUE-jixJTkhoP4l25VKwlDGoXACWtnuxQM9NMOCYbQOzDesgMDlT3sntow___vsEqRVd4OwqMY4BPyBiLJ32BnUbmM||height="267" width="614"]]**
36 36  
37 -3. Select `Lab` (on the left), and navigate to the destination where you just copied the folder.
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).
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 -
42 42  == Running TVB-MULTISCALE locally ==
43 43  
44 -See more on Github [[https:~~/~~/github.com/the-virtual-brain/tvb-multiscale>>url:https://github.com/the-virtual-brain/tvb-multiscale]] .
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]]
45 45  
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]]
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]]
47 47  
48 48  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.
49 49  
50 -== ==
47 +== Running TVB-MULTISCALE jobs on CSCS infrastructure from HBP collab ==
51 51  
52 -== Running TVB-MULTISCALE jobs on HPC infrastructure from HBP collab ==
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]]
53 53  
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 -
56 56  * Create a collab space of your own
57 57  * 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/]]
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.
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.
59 59  ** 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
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.
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.
61 61  
62 62  Few technical details about what we do in these notebooks:
63 63  
... ... @@ -71,10 +71,10 @@
71 71  
72 72  >tr = unicore_client.Transport(oauth.get_token())
73 73  >r = unicore_client.Registry(tr, unicore_client._HBP_REGISTRY_URL)
74 -># we used "DAINT-CSCS", but you should change it to another supercomputer where you have a project active
69 +># use "DAINT-CSCS" change if another supercomputer is prepared for usage
75 75  >client = r.site('DAINT-CSCS')
76 76  
77 -2. Prepare job submission
72 +1. Prepare job submission
78 78  
79 79  In this step we have to prepare a JSON object which will be used in the job submission process.
80 80  
... ... @@ -91,7 +91,7 @@
91 91  >my_job['Resources'] = { 
92 92  > "CPUs": "1"}
93 93  
94 -3. Actual job submission
89 +1. Actual job submission
95 95  
96 96  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.
97 97  
... ... @@ -98,7 +98,7 @@
98 98  >job = site_client.new_job(job_description=my_job, inputs=['/path1', '/path2'])
99 99  >job.properties
100 100  
101 -4. Wait until job is completed and check the results
96 +1. Wait until job is completed and check the results
102 102  
103 103  Wait until the job is completed using the following command
104 104