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--- en:hpc [2022/04/14 13:17] – created grikiete
+++ en:hpc [2022/04/15 06:09] – [Software] grikiete
@@ Line 3: / Line 3: @@
 A Distributed Computing Network (DCN) is a specially designed network of computers capable of running applications that can exchange data efficiently.
-VU MIF PST consists of a supercomputer from the clusters (the first number is the actual and available amount ):
+VU MIF PST consists of a supercomputer from the clusters (the first number is the actual and available amount):
-^Pavadinimas ^Mazgai ^CPU ^GPU ^RAM        ^HDD    ^Tinklas ^Pastabos|
+^Title ^Nodes ^CPU ^GPU ^RAM        ^HDD    ^Network ^Notes|
 ^main        ^35/36  ^48  ^0   ^384GiB     ^0      ^1Gbit/s, 2x10Gbit/s, 4xEDR(100Gbit/s) infiniband ^[[https://ark.intel.com/content/www/us/en/ark/products/192447/intel-xeon-gold-6252-processor-35-75m-cache-2-10-ghz.html|CPU]]|
 ^gpu         ^3/3    ^40  ^8   ^512GB/32GB  ^7TB   ^2x10Gbit/s, 4xEDR(100Gbit/s) infiniband ^[[https://ark.intel.com/content/www/us/en/ark/products/91753/intel-xeon-processor-e5-2698-v4-50m-cache-2-20-ghz.html|CPU]] [[https://en.wikipedia.org/wiki/Nvidia_DGX#DGX-1|NVIDIA DGX-1]]|
 ^power       ^2/2    ^32  ^4   ^1024GB/32GB ^1.8TB ^2x10Gbit/s, 4xEDR(100Gbit/s) infiniband ^[[https://www.ibm.com/products/power-systems-ac922|IBM Power System AC922]]|
-Iš viso	**40/41** mazgų, **1912** CPU cores su **17TB** RAM, **32** GPU su **1TB** RAM.
+Total	**40/41** nodes, **1912** CPU cores with **17TB** RAM, **32** GPU with **1TB** RAM.
-Toliau tekste procesorius = CPU = core - procesoriaus vienas branduolys (su visomis hypergijomis, jei jos yra įjungtos).
+The processor below = CPU = core - a single core of the processor (with all hyperthreads if they are turned on).
+====== Software ======
+In **main** and **gpu** partitions there are installed [[https://docs.qlustar.com/Qlustar/11.0/HPCstack/hpc-user-manual.html|Qlustar 11]] OS. It is based on Ubuntu 18.04 LTS. In **power** partition there is installed Ubuntu 18.04 LTS.
+You can check the list of OS package with the command ''dpkg -l'' (in login node **hpc** or in **power** nodes).
+With the command [[https://sylabs.io/guides/3.2/user-guide/index.html|singularity]] it is possible to make use of ready-made copies of container files in directories ''/apps/local/hpc'', ''/apps/local/nvidia'', ''/apps/local/intel'', ''/apps/local/lang'' or to download from singularity and docker online repositories. You can also create your own singularity containers using the MIF cloud service.
+With singularity you can prepare your container, for example:
+<code shell>
+$ singularity build --sandbox /tmp/python docker://python:3.8
+$ singularity exec -w /tmp/python pip install package
+$ singularity build python.sif /tmp/python
+$ rm -rf /tmp/python
+</code>
+Similarly, you can use R, Julia or other containers that do not require root privileges to install packages.
+If you want to add OS packages to the singularity container, you need root/superuser privileges. With fakeroot, we simulate them, and copy the required library ''libfakeroot-sysv.so'' into the container, for example:
+<code shell>
+$ singularity build --sandbox /tmp/python docker://ubuntu:18.04
+$ cp /libfakeroot-sysv.so /tmp/python/
+$ fakeroot -l /libfakeroot-sysv.so singularity exec -w /tmp/python apt-get update
+$ fakeroot -l /libfakeroot-sysv.so singularity exec -w /tmp/python apt-get install python3.8 ...
+$ fakeroot -l /libfakeroot-sysv.so singularity exec -w /tmp/python apt-get clean
+$ rm -rf /tmp/python/libfakeroot-sysv.so /tmp/python/var/lib/apt/lists (you can clean up more of what you don't need)
+$ singularity build python.sif /tmp/python
+$ rm -rf /tmp/python
+</code>
+There are ready-made scripts to run your **hadoop** tasks using the [[https://github.com/LLNL/magpie|Magpie]] set in the directory ''/apps/local/bigdata''.
+With [[https://hpc.mif.vu.lt/hub/|JupyterHub]] you can run calculations with the python command line in a web browser and use the [[https://jupyter.org|JupyterLab]] environment. If you install your own JupyterLab environment in your home directory, you need to install the additional ''batchspawner'' package - this will start your environment, example:
+<code shell>
+$ python3.7 -m pip install --upgrade pip setuptools wheel
+$ python3.7 -m pip install --ignore-installed batchspawner jupyterlab
+</code>
+Alternatively, you can use a container that you made via JupyterHub. In that container, you need to install the ''batchswapner'' and ''jupyterlab'' packages, and to create a script ''~/.local/bin/batchspawner-singleuser'' with execution permissions (''chmod +x ~/.local/bin/batchspawner-singleuser'').
+<code shell>
+#!/bin/sh
+exec singularity exec --nv myjupyterlab.sif batchspawner-singleuser "$@"
+</code>