From 3d7c8162a699a99102e922e8974d754513d5777e Mon Sep 17 00:00:00 2001
From: Elias Werner <eliwerner3@googlemail.com>
Date: Wed, 22 Sep 2021 13:57:57 +0200
Subject: [PATCH] change "here" in links to a keyword
---
.../docs/software/machine_learning.md | 21 ++++++++++++-------
1 file changed, 13 insertions(+), 8 deletions(-)
diff --git a/doc.zih.tu-dresden.de/docs/software/machine_learning.md b/doc.zih.tu-dresden.de/docs/software/machine_learning.md
index 0bff1da41..4fdbe6952 100644
--- a/doc.zih.tu-dresden.de/docs/software/machine_learning.md
+++ b/doc.zih.tu-dresden.de/docs/software/machine_learning.md
@@ -6,14 +6,15 @@ For machine learning purposes, we recommend to use the [Alpha](#alpha-partition)
## ML Partition
-The compute nodes of the ML partition are built on the base of [Power9](https://www.ibm.com/it-infrastructure/power/power9)
-architecture from IBM. The system was created for AI challenges, analytics and working with
+The compute nodes of the ML partition are built on the base of [Power9 architecture](https://www.ibm.com/it-infrastructure/power/power9)
+from IBM. The system was created for AI challenges, analytics and working with
data-intensive workloads and accelerated databases.
The main feature of the nodes is the ability to work with the
[NVIDIA Tesla V100](https://www.nvidia.com/en-gb/data-center/tesla-v100/) GPU with **NV-Link**
support that allows a total bandwidth with up to 300 gigabytes per second (GB/sec). Each node on the
-ml partition has 6x Tesla V-100 GPUs. You can find a detailed specification of the partition [here](../jobs_and_resources/power9.md).
+ml partition has 6x Tesla V-100 GPUs. You can find a detailed specification of the partition in our
+[Power9 documentation](../jobs_and_resources/power9.md).
!!! note
The ML partition is based on the Power9 architecture, which means that the software built
@@ -32,13 +33,14 @@ The following have been reloaded with a version change: 1) modenv/scs5 => moden
### Power AI
There are tools provided by IBM, that work on `ml` partition and are related to AI tasks.
-For more information see [here](power_ai.md).
+For more information see our [Power AI documentation](power_ai.md).
## Alpha partition
Another partition for machine learning tasks is Alpha. It is mainly dedicated to [ScaDS.AI](https://scads.ai/)
topics. Each node on Alpha has 2x AMD EPYC CPUs, 8x NVIDIA A100-SXM4 GPUs, 1TB RAM and 3.5TB local
-space (`/tmp`) on an NVMe device. You can find more details of the partition [here](../jobs_and_resources/alpha_centauri.md).
+space (`/tmp`) on an NVMe device. You can find more details of the partition in our [Alpha Centauri](../jobs_and_resources/alpha_centauri.md)
+documentation.
### Modules
@@ -60,14 +62,16 @@ The following have been reloaded with a version change: 1) modenv/ml => modenv/
Python users should use a [virtual environment](python_virtual_environments.md) when conducting
machine learning tasks via console.
-For more details on machine learning or data science with Python see [here](data_analytics_with_python.md).
+For more details on machine learning or data science with Python see the [Data Analytics with Python](data_analytics_with_python.md)
+section.
### R
R also supports machine learning via console. It does not require a virtual environment due to a
different package management.
-For more details on machine learning or data science with R see [here](data_analytics_with_r.md/#r-console).
+For more details on machine learning or data science with R see the [Data Analytics with R](data_analytics_with_r.md/#r-console)
+documentation.
## Machine Learning with Jupyter
@@ -86,7 +90,8 @@ or [RStudio](data_analytics_with_rstudio.md) for your machine learning and data
Some machine learning tasks require using containers. In the HPC domain, the [Singularity](https://singularity.hpcng.org/)
container system is a widely used tool. Docker containers can also be used by Singularity. You can
-find further information on working with containers on ZIH systems [here](containers.md)
+find further information on working with containers on ZIH systems in our [Containers](containers.md)
+documentation.
There are two sources for containers for Power9 architecture with
TensorFlow and PyTorch on the board:
--
GitLab