diff --git a/doc.zih.tu-dresden.de/docs/modules/.gitkeep b/doc.zih.tu-dresden.de/docs/modules/.gitkeep
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/doc.zih.tu-dresden.de/docs/software/Cloud.md b/doc.zih.tu-dresden.de/docs/software/Cloud.md
index 9d9e550808df2e0c18f22bea3ff5bb3838bc4180..3819bdc56ceab064bfe46a722b3e1168324e6659 100644
--- a/doc.zih.tu-dresden.de/docs/software/Cloud.md
+++ b/doc.zih.tu-dresden.de/docs/software/Cloud.md
@@ -1,7 +1,7 @@
# Virtual machine on Taurus
The following instructions are primarily aimed at users who want to build their
-[Singularity](containers.md) containers on Taurus.
+[Singularity](Containers.md) containers on Taurus.
The Singularity container setup requires a Linux machine with root privileges, the same architecture
and a compatible kernel. If some of these requirements can not be fulfilled, then there is
diff --git a/doc.zih.tu-dresden.de/docs/software/containers.md b/doc.zih.tu-dresden.de/docs/software/Containers.md
similarity index 100%
rename from doc.zih.tu-dresden.de/docs/software/containers.md
rename to doc.zih.tu-dresden.de/docs/software/Containers.md
diff --git a/doc.zih.tu-dresden.de/docs/software/DeepLearning.md b/doc.zih.tu-dresden.de/docs/software/DeepLearning.md
index e9f5854c43c32d6e6cfcf303edd999cc9b2dd17f..5eee674f447668a9d1f7f4119505af3941a2beb0 100644
--- a/doc.zih.tu-dresden.de/docs/software/DeepLearning.md
+++ b/doc.zih.tu-dresden.de/docs/software/DeepLearning.md
@@ -14,7 +14,7 @@ both the ml environment and the scs5 environment of the Taurus system.
for dataflow and differentiable programming across a range of tasks.
TensorFlow is available in both main partitions
-[ml environment and scs5 environment](modules.md#module-environments)
+[ml environment and scs5 environment](Modules.md#module-environments)
under the module name "TensorFlow". However, for purposes of machine learning and deep learning, we
recommend using Ml partition [HPC-DA](../jobs/HPCDA.md). For example:
@@ -24,7 +24,7 @@ module load TensorFlow
There are numerous different possibilities on how to work with [TensorFlow](TensorFlow.md) on
Taurus. On this page, for all examples default, scs5 partition is used. Generally, the easiest way
-is using the [modules system](modules.md)
+is using the [modules system](Modules.md)
and Python virtual environment (test case). However, in some cases, you may need directly installed
Tensorflow stable or night releases. For this purpose use the
[EasyBuild](CustomEasyBuildEnvironment.md), [Containers](TensorFlowContainerOnHPCDA.md) and see
@@ -38,12 +38,12 @@ versions.
[Keras](https://keras.io/) is a high-level neural network API, written in Python and capable of
running on top of [TensorFlow](https://github.com/tensorflow/tensorflow) Keras is available in both
-environments [ml environment and scs5 environment](modules.md#module-environments) under the module
+environments [ml environment and scs5 environment](Modules.md#module-environments) under the module
name "Keras".
On this page for all examples default scs5 partition used. There are numerous different
possibilities on how to work with [TensorFlow](TensorFlow.md) and Keras
-on Taurus. Generally, the easiest way is using the [module system](modules.md) and Python
+on Taurus. Generally, the easiest way is using the [module system](Modules.md) and Python
virtual environment (test case) to see Tensorflow part above.
For examples of using Keras for ml partition with the module system see the
[Keras page for HPC-DA](Keras.md).
diff --git a/doc.zih.tu-dresden.de/docs/software/GetStartedWithHPCDA.md b/doc.zih.tu-dresden.de/docs/software/GetStartedWithHPCDA.md
index 4ac517d046a85700a5ce232da711f30f7b9402b5..1c14c5d346050d50992270cecbe7eb3ea9dab582 100644
--- a/doc.zih.tu-dresden.de/docs/software/GetStartedWithHPCDA.md
+++ b/doc.zih.tu-dresden.de/docs/software/GetStartedWithHPCDA.md
@@ -198,7 +198,7 @@ There are three main options on how to work with Tensorflow and PyTorch:
### Modules
-The easiest way is using the [modules system](modules.md) and Python virtual environment. Modules
+The easiest way is using the [modules system](Modules.md) and Python virtual environment. Modules
are a way to use frameworks, compilers, loader, libraries, and utilities. The module is a user
interface that provides utilities for the dynamic modification of a user's environment without
manual modifications. You could use them for srun , bath jobs (sbatch) and the Jupyterhub.
@@ -327,7 +327,7 @@ page of the container.
To use not a pure Tensorflow, PyTorch but also with some Python packages
you have to use the definition file to create the container
-(bootstrapping). For details please see the [Container](containers.md) page
+(bootstrapping). For details please see the [Container](Containers.md) page
from our wiki. Bootstrapping **has required root privileges** and
Virtual Machine (VM) should be used! There are two main options on how
to work with VM on Taurus: [VM tools](VMTools.md) - automotive algorithms
diff --git a/doc.zih.tu-dresden.de/docs/software/modules.md b/doc.zih.tu-dresden.de/docs/software/Modules.md
similarity index 100%
rename from doc.zih.tu-dresden.de/docs/software/modules.md
rename to doc.zih.tu-dresden.de/docs/software/Modules.md
diff --git a/doc.zih.tu-dresden.de/docs/software/overview.md b/doc.zih.tu-dresden.de/docs/software/Overview.md
similarity index 96%
rename from doc.zih.tu-dresden.de/docs/software/overview.md
rename to doc.zih.tu-dresden.de/docs/software/Overview.md
index dbb75ca856f493db73454fb45bbedc4955c6d7be..f856f706e2126c5cbf40939020dece5967e00212 100644
--- a/doc.zih.tu-dresden.de/docs/software/overview.md
+++ b/doc.zih.tu-dresden.de/docs/software/Overview.md
@@ -14,7 +14,7 @@ There are a lot of different possibilities to work with software on Taurus:
## Modules
Usage of software on HPC systems is managed by a **modules system**. Thus, it is crucial to
-be familiar with the [modules concept and commands](modules.md). Modules are a way to use
+be familiar with the [modules concept and commands](Modules.md). Modules are a way to use
frameworks, compilers, loader, libraries, and utilities. A module is a user interface that provides
utilities for the dynamic modification of a user's environment without manual modifications. You
could use them for `srun`, batch jobs (`sbatch`) and the Jupyterhub.
diff --git a/doc.zih.tu-dresden.de/docs/software/pika.md b/doc.zih.tu-dresden.de/docs/software/PIKA.md
similarity index 100%
rename from doc.zih.tu-dresden.de/docs/software/pika.md
rename to doc.zih.tu-dresden.de/docs/software/PIKA.md
diff --git a/doc.zih.tu-dresden.de/docs/software/PyTorch.md b/doc.zih.tu-dresden.de/docs/software/PyTorch.md
index e73b224881fae055861c8d52c50eb61760ea2d6b..90018e4efd20fef71e7637c516d713fe2b69a608 100644
--- a/doc.zih.tu-dresden.de/docs/software/PyTorch.md
+++ b/doc.zih.tu-dresden.de/docs/software/PyTorch.md
@@ -24,8 +24,8 @@ and users who are just starting their work with Taurus.
2\. The second way is using the Modules system and Python or conda virtual environment.
See [the Python page](Python.md) for the HPC-DA system.
-Note: The information on working with the PyTorch using Containers could
-be found [here](containers.md).
+Note: The information on working with the PyTorch using Containers could be found
+[here](Containers.md).
## Get started with PyTorch
diff --git a/doc.zih.tu-dresden.de/docs/software/Python.md b/doc.zih.tu-dresden.de/docs/software/Python.md
index d345e749df59efa54833908a621c98cfbc0472f7..92d7070a7e5d42ed74e0613ec2dabba9321085c7 100644
--- a/doc.zih.tu-dresden.de/docs/software/Python.md
+++ b/doc.zih.tu-dresden.de/docs/software/Python.md
@@ -14,8 +14,8 @@ Taurus system and basic knowledge about Python, Numpy and SLURM system.
There are three main options on how to
work with Keras and Tensorflow on the HPC-DA: 1. Modules; 2. [JupyterNotebook](JupyterHub.md);
-3.[Containers](containers.md). The main way is using the [Modules
-system](modules.md) and Python virtual environment.
+3.[Containers](Containers.md). The main way is using the
+[Modules system](Modules.md) and Python virtual environment.
Note: You could work with simple examples in your home directory but according to
[HPCStorageConcept2019](../data_management/HPCStorageConcept2019.md) please use **workspaces**
@@ -170,20 +170,22 @@ module.
Moreover, it is possible to install mpi4py in your local conda
environment:
- srun -p ml --time=04:00:00 -n 1 --pty --mem-per-cpu=8000 bash #allocate recources
- module load modenv/ml
- module load PythonAnaconda/3.6 #load module to use conda
- conda create --prefix=<location_for_your_environment> python=3.6 anaconda #create conda virtual environment
+```Bash
+srun -p ml --time=04:00:00 -n 1 --pty --mem-per-cpu=8000 bash #allocate recources
+module load modenv/ml
+module load PythonAnaconda/3.6 #load module to use conda
+conda create --prefix=<location_for_your_environment> python=3.6 anaconda #create conda virtual environment
- conda activate <location_for_your_environment> #activate your virtual environment
+conda activate <location_for_your_environment> #activate your virtual environment
- conda install -c conda-forge mpi4py #install mpi4py
+conda install -c conda-forge mpi4py #install mpi4py
- python #start python
+python #start python
- from mpi4py import MPI #verify your mpi4py
- comm = MPI.COMM_WORLD
- print("%d of %d" % (comm.Get_rank(), comm.Get_size()))
+from mpi4py import MPI #verify your mpi4py
+comm = MPI.COMM_WORLD
+print("%d of %d" % (comm.Get_rank(), comm.Get_size()))
+```
### Horovod
@@ -203,13 +205,14 @@ in some cases better results than pure TensorFlow and PyTorch.
#### Horovod as a module
-Horovod is available as a module with **TensorFlow** or **PyTorch**for
-**all** module environments. Please check the [software module
-list](modules.md) for the current version of the software.
+Horovod is available as a module with **TensorFlow** or **PyTorch**for **all** module environments.
+Please check the [software module list](Modules.md) for the current version of the software.
Horovod can be loaded like other software on the Taurus:
- ml av Horovod #Check available modules with Python
- module load Horovod #Loading of the module
+```Bash
+ml av Horovod #Check available modules with Python
+module load Horovod #Loading of the module
+```
#### Horovod installation
@@ -224,36 +227,42 @@ for your study and work projects** (see the Storage concept).
Setup:
- srun -N 1 --ntasks-per-node=6 -p ml --time=08:00:00 --pty bash #allocate a Slurm job allocation, which is a set of resources (nodes)
- module load modenv/ml #Load dependencies by using modules
- module load OpenMPI/3.1.4-gcccuda-2018b
- module load Python/3.6.6-fosscuda-2018b
- module load cuDNN/7.1.4.18-fosscuda-2018b
- module load CMake/3.11.4-GCCcore-7.3.0
- virtualenv --system-site-packages <location_for_your_environment> #create virtual environment
- source <location_for_your_environment>/bin/activate #activate virtual environment
+```Bash
+srun -N 1 --ntasks-per-node=6 -p ml --time=08:00:00 --pty bash #allocate a Slurm job allocation, which is a set of resources (nodes)
+module load modenv/ml #Load dependencies by using modules
+module load OpenMPI/3.1.4-gcccuda-2018b
+module load Python/3.6.6-fosscuda-2018b
+module load cuDNN/7.1.4.18-fosscuda-2018b
+module load CMake/3.11.4-GCCcore-7.3.0
+virtualenv --system-site-packages <location_for_your_environment> #create virtual environment
+source <location_for_your_environment>/bin/activate #activate virtual environment
+```
Or when you need to use conda:
- srun -N 1 --ntasks-per-node=6 -p ml --time=08:00:00 --pty bash #allocate a Slurm job allocation, which is a set of resources (nodes)
- module load modenv/ml #Load dependencies by using modules
- module load OpenMPI/3.1.4-gcccuda-2018b
- module load PythonAnaconda/3.6
- module load cuDNN/7.1.4.18-fosscuda-2018b
- module load CMake/3.11.4-GCCcore-7.3.0
-
- conda create --prefix=<location_for_your_environment> python=3.6 anaconda #create virtual environment
-
- conda activate <location_for_your_environment> #activate virtual environment
+```Bash
+srun -N 1 --ntasks-per-node=6 -p ml --time=08:00:00 --pty bash #allocate a Slurm job allocation, which is a set of resources (nodes)
+module load modenv/ml #Load dependencies by using modules
+module load OpenMPI/3.1.4-gcccuda-2018b
+module load PythonAnaconda/3.6
+module load cuDNN/7.1.4.18-fosscuda-2018b
+module load CMake/3.11.4-GCCcore-7.3.0
+
+conda create --prefix=<location_for_your_environment> python=3.6 anaconda #create virtual environment
+
+conda activate <location_for_your_environment> #activate virtual environment
+```
Install Pytorch (not recommended)
- cd /tmp
- git clone https://github.com/pytorch/pytorch #clone Pytorch from the source
- cd pytorch #go to folder
- git checkout v1.7.1 #Checkout version (example: 1.7.1)
- git submodule update --init #Update dependencies
- python setup.py install #install it with python
+```Bash
+cd /tmp
+git clone https://github.com/pytorch/pytorch #clone Pytorch from the source
+cd pytorch #go to folder
+git checkout v1.7.1 #Checkout version (example: 1.7.1)
+git submodule update --init #Update dependencies
+python setup.py install #install it with python
+```
##### Install Horovod for Pytorch with python and pip
@@ -261,22 +270,28 @@ In the example presented installation for the Pytorch without
TensorFlow. Adapt as required and refer to the horovod documentation for
details.
- HOROVOD_GPU_ALLREDUCE=MPI HOROVOD_WITHOUT_TENSORFLOW=1 HOROVOD_WITH_PYTORCH=1 HOROVOD_WITHOUT_MXNET=1 pip install --no-cache-dir horovod
+```Bash
+HOROVOD_GPU_ALLREDUCE=MPI HOROVOD_WITHOUT_TENSORFLOW=1 HOROVOD_WITH_PYTORCH=1 HOROVOD_WITHOUT_MXNET=1 pip install --no-cache-dir horovod
+```
##### Verify that Horovod works
- python #start python
- import torch #import pytorch
- import horovod.torch as hvd #import horovod
- hvd.init() #initialize horovod
- hvd.size()
- hvd.rank()
- print('Hello from:', hvd.rank())
+```Bash
+python #start python
+import torch #import pytorch
+import horovod.torch as hvd #import horovod
+hvd.init() #initialize horovod
+hvd.size()
+hvd.rank()
+print('Hello from:', hvd.rank())
+```
##### Horovod with NCCL
If you want to use NCCL instead of MPI you can specify that in the
install command after loading the NCCL module:
- module load NCCL/2.3.7-fosscuda-2018b
- HOROVOD_GPU_ALLREDUCE=NCCL HOROVOD_GPU_BROADCAST=NCCL HOROVOD_WITHOUT_TENSORFLOW=1 HOROVOD_WITH_PYTORCH=1 HOROVOD_WITHOUT_MXNET=1 pip install --no-cache-dir horovod
+```Bash
+module load NCCL/2.3.7-fosscuda-2018b
+HOROVOD_GPU_ALLREDUCE=NCCL HOROVOD_GPU_BROADCAST=NCCL HOROVOD_WITHOUT_TENSORFLOW=1 HOROVOD_WITH_PYTORCH=1 HOROVOD_WITHOUT_MXNET=1 pip install --no-cache-dir horovod
+```
diff --git a/doc.zih.tu-dresden.de/docs/software/VMTools.md b/doc.zih.tu-dresden.de/docs/software/VMTools.md
index 556270290641d83c618d29709cfc6b88d7c2c193..884926b697f0cf72920874047ef112c0373f4c80 100644
--- a/doc.zih.tu-dresden.de/docs/software/VMTools.md
+++ b/doc.zih.tu-dresden.de/docs/software/VMTools.md
@@ -1,7 +1,7 @@
# Singularity on Power9 / ml partition
Building Singularity containers from a recipe on Taurus is normally not possible due to the
-requirement of root (administrator) rights, see [Containers](containers.md). For obvious reasons
+requirement of root (administrator) rights, see [Containers](Containers.md). For obvious reasons
users on Taurus cannot be granted root permissions.
The solution is to build your container on your local Linux machine by executing something like
diff --git a/doc.zih.tu-dresden.de/docs/software/Visualization.md b/doc.zih.tu-dresden.de/docs/software/Visualization.md
index 2d7b0787d7e1652504c9d8f0d8baafa91f940b4d..79b3bdef27121a47cd6110277f8643aec0237d20 100644
--- a/doc.zih.tu-dresden.de/docs/software/Visualization.md
+++ b/doc.zih.tu-dresden.de/docs/software/Visualization.md
@@ -4,7 +4,7 @@
[ParaView](https://paraview.org) is an open-source, multi-platform data
analysis and visualization application. It is available on Taurus under
-the `ParaView` [modules](modules.md#modules-environment)
+the `ParaView` [modules](Modules.md#modules-environment)
```Bash
taurus$ module avail ParaView
diff --git a/doc.zih.tu-dresden.de/docs/use_of_hardware/AlphaCentauri.md b/doc.zih.tu-dresden.de/docs/use_of_hardware/AlphaCentauri.md
index 6c13dcee7742a53f12ff0f09c1cfa5eb15a22666..e7a1368f44b5c6ee48f359548a7216ac9427dedb 100644
--- a/doc.zih.tu-dresden.de/docs/use_of_hardware/AlphaCentauri.md
+++ b/doc.zih.tu-dresden.de/docs/use_of_hardware/AlphaCentauri.md
@@ -29,11 +29,11 @@ cluster:
1. **Modules**
1 **Virtual Environments (manual software installation)**
1. [JupyterHub](https://taurus.hrsk.tu-dresden.de/)
-1. [Containers](../software/containers.md)
+1. [Containers](../software/Containers.md)
### Modules
-The easiest way is using the [module system](../software/modules.md) and Python virtual environment.
+The easiest way is using the [module system](../software/Modules.md) and Python virtual environment.
Modules are a way to use frameworks, compilers, loader, libraries, and utilities. The software
environment for the **alpha** partition is available under the name **hiera**:
@@ -100,7 +100,7 @@ conda deactivate #Leave the virtual environment
New software for data analytics is emerging faster than we can install it. If you urgently need a
certain version we advise you to manually install it (the machine learning frameworks and required
-packages) in your virtual environment (or use a [container](../software/containers.md).
+packages) in your virtual environment (or use a [container](../software/Containers.md).
The **Virtualenv** example:
@@ -183,7 +183,7 @@ parameter).
On Taurus [Singularity](https://sylabs.io/) is used as a standard container
solution. It can be run on the `alpha` partition as well. Singularity enables users to have full
control of their environment. Detailed information about containers can be found
-[here](../software/containers.md).
+[here](../software/Containers.md).
Nvidia
[NGC](https://developer.nvidia.com/blog/how-to-run-ngc-deep-learning-containers-with-singularity/)
diff --git a/doc.zih.tu-dresden.de/mkdocs.yml b/doc.zih.tu-dresden.de/mkdocs.yml
index f94b619153bf0d925d1dea27b2afd3f10ccc0afa..21004dc1b0d13d306eff9651265f42eed7429303 100644
--- a/doc.zih.tu-dresden.de/mkdocs.yml
+++ b/doc.zih.tu-dresden.de/mkdocs.yml
@@ -13,17 +13,24 @@ nav:
- Login: access/Login.md
- Security Restrictions: access/SecurityRestrictions.md
- SSH with Putty: access/SSHMitPutty.md
- - Available Software and Usage:
- - Overview: software/overview.md
- - Modules: software/modules.md
- - JupyterHub: software/JupyterHub.md
- - JupyterHub for Teaching: software/JupyterHubForTeaching.md
+ - Environment and Software:
+ - Overview: software/Overview.md
+ - Environment:
+ - Modules: software/Modules.md
+ - Custom EasyBuild Modules: software/CustomEasyBuildEnvironment.md
+ - JupyterHub:
+ - Overview: software/JupyterHub.md
+ - JupyterHub for Teaching: software/JupyterHubForTeaching.md
- Containers:
- - Singularity: software/containers.md
+ - Singularity: software/Containers.md
- Singularity Recicpe Hints: software/SingularityRecipeHints.md
- Singularity Example Definitions: software/SingularityExampleDefinitions.md
- - Custom Easy Build Modules: software/CustomEasyBuildEnvironment.md
- - Mathematics: software/Mathematics.md
+ - VM tools: software/VMTools.md
+ - Applications:
+ - Bio Informatics: software/Bioinformatics.md
+ - Computational Fluid Dynamics (CFD): software/CFD.md
+ - NanoscaleSimulations: software/NanoscaleSimulations.md
+ - FEMSoftware: software/FEMSoftware.md
- Visualization: software/Visualization.md
- HPC-DA:
- Get started with HPC-DA: software/GetStartedWithHPCDA.md
@@ -39,14 +46,8 @@ nav:
- Dask: software/Dask.md
- Power AI: software/PowerAI.md
- PyTorch: software/PyTorch.md
- - Computational Fluid Dynamics (CFD): software/CFD.md
- - FAQs: software/modules-faq.md
- - Bio Informatics: software/Bioinformatics.md
- SCS5 Migration Hints: software/SCS5Software.md
- - NanoscaleSimulations: software/NanoscaleSimulations.md
- - FEMSoftware: software/FEMSoftware.md
- Cloud: software/Cloud.md
- - VM tools: software/VMTools.md
- Virtual Desktops: software/VirtualDesktops.md
- Software Development and Tools:
- Overview: software/SoftwareDevelopment.md
@@ -58,8 +59,9 @@ nav:
- Score-P: software/ScoreP.md
- PAPI Library: software/PapiLibrary.md
- Perf Tools: software/PerfTools.md
- - PIKA: software/pika.md
+ - PIKA: software/PIKA.md
- Vampir: software/Vampir.md
+ - Mathematics: software/Mathematics.md
- Data Management:
- Overview: data_management/DataManagement.md
- Announcement of Quotas: data_management/AnnouncementOfQuotas.md