Lab Tutorials

LRZ Cloud Computing

4 min read

The following information is useful for those who can access Linux cluster at LRZ.de

Access Cloud Computing

Msense Lab also has two cloud computing systems, Lora and Emma. Emma is accessible from the external internet, while Lora is walled by the intranet.

Tips

For convenience, it’s better that you create aliases for those servers. For example, in your .zshrc or .bashrc, put similar lines: alias lora="10.195.2.##" (please change the ip accordingly)

1. Via SSH

# access to Lora
ssh -Y your_account@lora

2. Via VNC

  1. Via VNC First, you need to tunnel to the server and initiate the VNC server (this only needs to be done once). The following commands should be run on the server side.

2.1 Setup VNC password

vncpasswd

You’ll be prompted to enter and verify your password. This password is required when connecting with your VNC client.

2.2 Start VNC server

vncserver

It will show you “New ‘X’ desktop is lora:#“. Remember this, we will first kill it and edit the file - xstartup and restart it again.

2.3 Edit VNC xstartup file

First, kill the VNC service

vncserver -kill :# (replace the number)

Now edit the xstartup

nano ~/.vnc/xstartup

Edit/Put the following text in the file:

#!/bin/sh
xrdb $HOME/.Xresources

# Start Xfce
startxfce4 &

2.4 Start VNC server localhost

vncserver -localhost -geometry 1024x768

Depending on your preference, you can specify the dimension of the virtual desktop. If you want to change it, you have to kill the service first, and restart it again (see above instructions). And remember which port number it started. You can check it in the ~/.vnc/ folder, by finding the lora:#.pd file.

2.5 Local connection

Now you can exit the server, and start a local command window, type:

# Access to Lora and replace the # with your VNC port number
ssh -L 590#:localhost:590# -C -N -l your_account 10.195.?.?

Replace the correct server IP, depending on your server.

Next, you can start your local VNC (under Mac) using “Finder ⇒ Go ⇒ Connect to Server”, type in:

vnc://localhost:590#

The connection password is the password you set earlier on.

3. Via Code Server

The code server is configured individually. If your account is set up, you can first establish the tunnel:

ssh -Y -N -L 808@:localhost:808@ your_account@lora_ip

Please replace the port number, account name, and IP above. And leave this command window open.

Next, open your browser and type localhost:808@. A code server interface will appear. Code Server is convenient when you don’t have a local installation of Visual Studio Code. However, if you accidentally close the brower tab, you may lose some working that is running.

4. Via Visual Studio Code

You need to install ‘remote-SSH’ and ‘Remote Explorer’ plugins. After you install these plugins, by click the left icon of ‘Remote Explorer’, add remote SSH, using the option 1 ‘Via SSH’ to connect to the server.

When the VS Code connects to the server, it will show the connection in the lower-left corner. The first time it opens nothing. You need to select ‘Open Folder’ to open a desired destination.

5. Mapping the remote server locally

This is desirable when checking remote MRI results like local files. It won’t do any remote computation. And it requires changing the security level in your local Mac computer.

First, install MacFUSE and SSHFS

brew install macfuse
brew tap gromgit/fuse
brew install gromgit/fuse/sshfs

Second, create a local mount point

mkdir ~/dss

Third, mount the remote linux server folder:

sshfs username@server:/path/remote ~/dss

Access Linux Cluster

It requires a Linux cluster LRZ account and 2FA. Details can be found in the LRZ website.

# connect to linux cluster at LRZ
 
ssh -Y your_account@cool.hpc.lrz.de

1. Resource

Overview of cluster specifications and limits

https://doku.lrz.de/job-processing-on-the-linux-cluster-10745970.html

Status here: https://doku.lrz.de/high-performance-computing-10613431.html

2. Install singularity

When logged in to the Linux cluster (on the login nodes), you can follow this recipe (this has been done):

module rm intel-mpi intel-mkl intel  
module load user_spack  
spack install squashfs squashfuse  
spack load squashfs squashfuse  
spack install singularity ~suid  
spack -c"modules:tcl:blacklist_implicits:False" module tcl refresh --upstream-modules squashfs singularity

Usage later is then simple (new terminal/or in a Slurm Job):


module use spack/modules/x86_avx2/linux-sles15-haswell   
module load singularity squashfs

3. Slurm partition and job settings

The following information needs to be updated; please check lrz.

job settings
**cm2_std:** 
--clusters=cm2  
--partition=cm2_std  
--qos=cm2_std

**cm2_inter**:
--clusters=inter  
--partition=cm2_inter

**cm2_inter_large_mem**
56 cpu, memory 120 G
--clusters=inter  
--partition=cm2_inter_large_mem  
--mem=<memory_per_node_MB>M

Common Slurm commands for job management: 56 cpu 56 G

squeue -M cm2_tiny -u $USER  
scancel -M cm2_tiny <JOB-ID>  
sacct -M cm2_tiny -X -u $USER --starttime=2021-01-01T00:00:01

squeue -M inter -u $USER  
scancel -M inter <JOB-ID>  
sacct -M inter -X -u $USER --starttime=2021-01-01T00:00:01

4. Interactive

salloc -N 1 -M inter -p cm4_inter -t 8:00:00

show CPUs and Memory

lscpu | grep '^CPU(s):'
free -h

submit to slurm

sbatch sing_cluster.sh

Graph View