Brief review

doc.zih.tu-dresden.de/docs/jobs_and_resources/alpha_centauri.md

-# Alpha Centauri - Multi-GPU sub-cluster
+# Alpha Centauri - Multi-GPU Sub-Cluster
 
-The sub-cluster "AlphaCentauri" had been installed for AI-related computations (ScaDS.AI).
+The sub-cluster "Alpha Centauri" had been installed for AI-related computations (ScaDS.AI).
 It has 34 nodes, each with:
 
-- 8 x NVIDIA A100-SXM4 (40 GB RAM)
-- 2 x AMD EPYC CPU 7352 (24 cores) @ 2.3 GHz with multithreading enabled
-- 1 TB RAM 3.5 TB `/tmp` local NVMe device
-- Hostnames: `taurusi[8001-8034]`
-- Slurm partition `alpha` for batch jobs and `alpha-interactive` for interactive jobs
+* 8 x NVIDIA A100-SXM4 (40 GB RAM)
+* 2 x AMD EPYC CPU 7352 (24 cores) @ 2.3 GHz with multi-threading enabled
+* 1 TB RAM 3.5 TB `/tmp` local NVMe device
+* Hostnames: `taurusi[8001-8034]`
+* Slurm partition `alpha` for batch jobs and `alpha-interactive` for interactive jobs
 
 !!! note
 
@@ -23,8 +23,8 @@ The software for the `alpha` partition is available in `modenv/hiera` module env
 
 To check the available modules for `modenv/hiera`, use the command
 
-```bash
-module spider <module_name>
+```console
+marie@alpha$ module spider <module_name>
 ```
 
 For example, to check whether PyTorch is available in version 1.7.1:
@@ -95,11 +95,11 @@ Successfully installed torchvision-0.10.0
 
 ### JupyterHub
 
-[JupyterHub](../access/jupyterhub.md) can be used to run Jupyter notebooks on AlphaCentauri
+[JupyterHub](../access/jupyterhub.md) can be used to run Jupyter notebooks on Alpha Centauri
 sub-cluster. As a starting configuration, a "GPU (NVIDIA Ampere A100)" preset can be used
 in the advanced form. In order to use latest software, it is recommended to choose
 `fosscuda-2020b` as a standard environment. Already installed modules from `modenv/hiera`
-can be pre-loaded in "Preload modules (modules load):" field.
+can be preloaded in "Preload modules (modules load):" field.
 
 ### Containers
 
@@ -109,6 +109,6 @@ Detailed information about containers can be found [here](../software/containers
 Nvidia
 [NGC](https://developer.nvidia.com/blog/how-to-run-ngc-deep-learning-containers-with-singularity/)
 containers can be used as an effective solution for machine learning related tasks. (Downloading
-containers requires registration).  Nvidia-prepared containers with software solutions for specific
+containers requires registration). Nvidia-prepared containers with software solutions for specific
 scientific problems can simplify the deployment of deep learning workloads on HPC. NGC containers
 have shown consistent performance compared to directly run code.

doc.zih.tu-dresden.de/wordlist.aspell

@@ -47,6 +47,7 @@ ecryptfs
 engl
 english
 env
+EPYC
 Espresso
 ESSL
 fastfs
@@ -78,6 +79,7 @@ HDFS
 HDFView
 Horovod
 hostname
+Hostnames
 HPC
 HPL
 html
@@ -133,11 +135,13 @@ natively
 NCCL
 Neptun
 NFS
+NGC
 NRINGS
 NUMA
 NUMAlink
 NumPy
 Nutzungsbedingungen
+Nvidia
 NVMe
 NWChem
 OME
@@ -169,6 +173,8 @@ PMI
 png
 PowerAI
 ppc
+Preload
+preloaded
 PSOCK
 Pthreads
 pymdownx
@@ -220,6 +226,7 @@ stdout
 subdirectories
 subdirectory
 SUSE
+SXM
 TBB
 TCP
 TensorBoard