Add specs recommendation based on workshop attendance

[cmd-ntrf]

No description provided.

[plstonge]

Main nodes

1. Minimum:

• mgmt = { type = "p4-6gb", count = 1 }
• login = { type = "p2-3gb", count = 1 }

2. One has to be careful about the total memory usage of all users compiling stuff simultaneously on the login node. Suggestions:

• login = { type = "p4-6gb", count = 1 } for a group of 20 participants
• login = { type = "p8-12gb", count = 1 } for a group of 40 participants or more

Compute nodes - for each type of workshops

• Jupyter Notebooks (1 core per user): node = { type = "p8-12gb", count = N }
• Introduction to Linux (optional): node = { type = "p2-3gb", count = 1 }
• Introduction to ARC (group of 20): node = { type = "p4-6gb", count = 4 }
• Introduction to ARC (group of 40): node = { type = "p4-6gb", count = 8 }
• Introduction to OpenMP (group of 30): node = { type = "p8-12gb", count = 8 }
• Introduction to MPI (group of 30): node = { type = "p4-6gb", count = 12 }

Storage

• Between 5 and 8 GB per user for home_size
• Between 8 and 32 GB for project_size
• Between 8 and 48 GB for scratch_size

[cmd-ntrf]

@plstonge said this regarding login node flavor:

installing an R package on the login node took too much memory when 20+ participants were doing the installation simultaneously. The login node almost crashed. One workaround consists of pre-installing the package before the workshop, but this is not the same as doing it as an exercise.

[mboisson]

A useful yaml configuration file that I used for a "Intro to ARC" workshop using the terminal :

jupyterhub::jupyterhub_config_hash:
        SbatchForm:
                runtime:
                        min: 3.5
                        def: 3.5
                        max: 5.0
                nprocs:
                        min: 1
                        def: 1
                        max: 1
                memory:
                        min: 1024
                        max: 2048
                        def: 2048
                oversubscribe:
                        def: true
                        lock: true
                ui:
                        def: 'terminal'
        SlurmFormSpawner:
                disable_form: true


jupyterhub::enable_otp_auth: false

#jupyterhub::kernel::venv::packages: ['numpy', 'pandas', 'matplotlib']

profile::cvmfs::client::lmod_default_modules: ['nixpkgs/16.09', 'imkl/2018.3.222', 'gcc/7.3.0', 'openmpi/3.1.2', 'ipython-kernel/3.7', 'rstudio-server', 'openrefine']``` 

with the option

  hieradata = file("config.yaml")

in the main.tf

[mboisson]

I think @ccoulombe has some interesting parameters for admin users as well.

[plstonge]

For a (Data Carpentry) Data Analysis with Python workshop, with 50 participants and up to 6 host/co-hosts (with Admin role on Jupyter). I had 7 nodes of 8c-12gb. The config.yaml used was:

jupyterhub::admin_groups: ['user51', 'user52', 'user53', 'user54', 'user55', 'user56']

jupyterhub::enable_otp_auth: false

jupyterhub::jupyterhub_config_hash:
        SbatchForm:
                runtime:
                        min: 3.5
                        def: 3.5
                        max: 5.0
                nprocs:
                        min: 1
                        def: 1
                        max: 1
                memory:
                        min: 1024
                        def: 1280
                        max: 1536
                oversubscribe:
                        def: true
                        lock: true
                ui:
                        def: 'notebook'
        SlurmFormSpawner:
                disable_form: true

jupyterhub::kernel::venv::packages: ['numpy', 'pandas', 'matplotlib', 'plotnine']

profile::cvmfs::client::lmod_default_modules: ['nixpkgs/16.09', 'imkl/2018.3.222', 'gcc/7.3.0', 'openmpi/3.1.2']