diff --git a/doc.zih.tu-dresden.de/docs/software/data_analytics_with_python.md b/doc.zih.tu-dresden.de/docs/software/data_analytics_with_python.md
index d3250433b2fefea217dc8ad4e4fc04f301d86525..29063c85860e2916db6cfa75cbc0ef628e022cc4 100644
--- a/doc.zih.tu-dresden.de/docs/software/data_analytics_with_python.md
+++ b/doc.zih.tu-dresden.de/docs/software/data_analytics_with_python.md
@@ -215,111 +215,3 @@ comm = MPI.COMM_WORLD
print("%d of %d" % (comm.Get_rank(), comm.Get_size()))
```
-### Horovod
-
-[Horovod](https://github.com/horovod/horovod) is the open source distributed training
-framework for TensorFlow, Keras, PyTorch. It is supposed to make it easy
-to develop distributed deep learning projects and speed them up with
-TensorFlow.
-
-#### Why use Horovod?
-
-Horovod allows you to easily take a single-GPU TensorFlow and Pytorch
-program and successfully train it on many GPUs! In
-some cases, the MPI model is much more straightforward and requires far
-less code changes than the distributed code from TensorFlow for
-instance, with parameter servers. Horovod uses MPI and NCCL which gives
-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 can be loaded like other software on the Taurus:
-
-```Bash
-ml av Horovod #Check available modules with Python
-module load Horovod #Loading of the module
-```
-
-#### Horovod installation
-
-However, if it is necessary to use Horovod with **PyTorch** or use
-another version of Horovod it is possible to install it manually. To
-install Horovod you need to create a virtual environment and load the
-dependencies (e.g. MPI). Installing PyTorch can take a few hours and is
-not recommended
-
-**Note:** You could work with simple examples in your home directory but **please use workspaces
-for your study and work projects** (see the Storage concept).
-
-Setup:
-
-```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:
-
-```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)
-
-```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
-
-In the example presented installation for the Pytorch without
-TensorFlow. Adapt as required and refer to the horovod documentation for
-details.
-
-```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
-
-```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:
-
-```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/distributed_training.md b/doc.zih.tu-dresden.de/docs/software/distributed_training.md
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..442066e9fcb409b6ad4744ec2fe25096d11c1eaf 100644
--- a/doc.zih.tu-dresden.de/docs/software/distributed_training.md
+++ b/doc.zih.tu-dresden.de/docs/software/distributed_training.md
@@ -0,0 +1,114 @@
+# Distributed Training
+
+## Internal Distribution
+
+## External Distribution
+
+### Horovod
+
+[Horovod](https://github.com/horovod/horovod) is the open source distributed training
+framework for TensorFlow, Keras, PyTorch. It is supposed to make it easy
+to develop distributed deep learning projects and speed them up with
+TensorFlow.
+
+#### Why use Horovod?
+
+Horovod allows you to easily take a single-GPU TensorFlow and Pytorch
+program and successfully train it on many GPUs! In
+some cases, the MPI model is much more straightforward and requires far
+less code changes than the distributed code from TensorFlow for
+instance, with parameter servers. Horovod uses MPI and NCCL which gives
+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 can be loaded like other software on the Taurus:
+
+```Bash
+ml av Horovod #Check available modules with Python
+module load Horovod #Loading of the module
+```
+
+#### Horovod installation
+
+However, if it is necessary to use Horovod with **PyTorch** or use
+another version of Horovod it is possible to install it manually. To
+install Horovod you need to create a virtual environment and load the
+dependencies (e.g. MPI). Installing PyTorch can take a few hours and is
+not recommended
+
+**Note:** You could work with simple examples in your home directory but **please use workspaces
+for your study and work projects** (see the Storage concept).
+
+Setup:
+
+```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:
+
+```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)
+
+```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
+
+In the example presented installation for the Pytorch without
+TensorFlow. Adapt as required and refer to the horovod documentation for
+details.
+
+```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
+
+```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:
+
+```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
+```