diff --git a/doc.zih.tu-dresden.de/docs/archive/deep_learning.md b/doc.zih.tu-dresden.de/docs/archive/deep_learning.md
deleted file mode 100644
index 7747d6f83c7fe1466532e0171a07001cf6998f62..0000000000000000000000000000000000000000
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@@ -1,329 +0,0 @@
-# Deep learning
-
-**Prerequisites**: To work with Deep Learning tools you obviously need [Login](../access/ssh_login.md)
-for the ZIH system system and basic knowledge about Python, Slurm manager.
-
-**Aim** of this page is to introduce users on how to start working with Deep learning software on
-both the ml environment and the scs5 environment of the system.
-
-## Deep Learning Software
-
-### TensorFlow
-
-[TensorFlow](https://www.tensorflow.org/guide/) is a free end-to-end open-source software library
-for dataflow and differentiable programming across a range of tasks.
-
-TensorFlow is available in both [ml environment and scs5 environment](modules.md#module-environments)
-under the module name "TensorFlow". For example:
-
-```Bash
-module load TensorFlow
-```
-
-There are numerous different possibilities on how to work with [TensorFlow](tensorflow.md) on
-ZIH system. On this page, for all examples default, scs5 partition is used. Generally, the easiest way
-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](custom_easy_build_environment.md), [Containers](tensorflow_container_on_hpcda.md) and see
-[the example](https://www.tensorflow.org/install/pip). For examples of using TensorFlow for ml partition
-with module system see [TensorFlow page](../software/tensorflow.md).
-
-Note: If you are going used manually installed TensorFlow release we recommend use only stable
-versions.
-
-## Keras
-
-[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
-name "Keras".
-
-On this page for all examples default scs5 partition used. There are numerous different
-possibilities on how to work with [TensorFlow](../software/tensorflow.md) and Keras
-on ZIH system. 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](../software/keras.md).
-
-It can either use TensorFlow as its backend. As mentioned in Keras documentation Keras capable of
-running on Theano backend. However, due to the fact that Theano has been abandoned by the
-developers, we don't recommend use Theano anymore. If you wish to use Theano backend you need to
-install it manually. To use the TensorFlow backend, please don't forget to load the corresponding
-TensorFlow module. TensorFlow should be loaded automatically as a dependency.
-
-Test case: Keras with TensorFlow on MNIST data
-
-Go to a directory on ZIH system, get Keras for the examples and go to the examples:
-
-```Bash
-git clone https://github.com/fchollet/keras.git
-cd keras/examples/
-```
-
-If you do not specify Keras backend, then TensorFlow is used as a default
-
-Job-file (schedule job with sbatch, check the status with 'squeue -u \<Username>'):
-
-```Bash
-#!/bin/bash
-#SBATCH --gres=gpu:1 # 1 - using one gpu, 2 - for using 2 gpus
-#SBATCH --mem=8000
-#SBATCH -p gpu2 # select the type of nodes (options: haswell, smp, sandy, west, gpu, ml) K80 GPUs on Haswell node
-#SBATCH --time=00:30:00
-#SBATCH -o HLR_<name_of_your_script>.out # save output under HLR_${SLURMJOBID}.out
-#SBATCH -e HLR_<name_of_your_script>.err # save error messages under HLR_${SLURMJOBID}.err
-
-module purge # purge if you already have modules loaded
-module load modenv/scs5 # load scs5 environment
-module load Keras # load Keras module
-module load TensorFlow # load TensorFlow module
-
-# if you see 'broken pipe error's (might happen in interactive session after the second srun command) uncomment line below
-# module load h5py
-
-python mnist_cnn.py
-```
-
-Keep in mind that you need to put the bash script to the same folder as an executable file or
-specify the path.
-
-Example output:
-
-```Bash
-x_train shape: (60000, 28, 28, 1) 60000 train samples 10000 test samples Train on 60000 samples,
-validate on 10000 samples Epoch 1/12
-
-128/60000 [..............................] - ETA: 12:08 - loss: 2.3064 - acc: 0.0781 256/60000
-[..............................] - ETA: 7:04 - loss: 2.2613 - acc: 0.1523 384/60000
-[..............................] - ETA: 5:22 - loss: 2.2195 - acc: 0.2005
-
-...
-
-60000/60000 [==============================] - 128s 2ms/step - loss: 0.0296 - acc: 0.9905 -
-val_loss: 0.0268 - val_acc: 0.9911 Test loss: 0.02677746053306255 Test accuracy: 0.9911
-```
-
-## Data Sets
-
-There are many different data sets designed for research purposes. If you would like to download some
-of them, first of all, keep in mind that many machine learning libraries have direct access to
-public data sets without downloading it (for example
-[TensorFlow data sets](https://www.tensorflow.org/datasets).
-
-If you still need to download some data sets, first of all, be careful with the size of the data sets
-which you would like to download (some of them have a size of few Terabytes). Don't download what
-you really not need to use! Use login nodes only for downloading small files (hundreds of the
-megabytes). For downloading huge files use [DataMover](../data_transfer/data_mover.md).
-For example, you can use command `dtwget` (it is an analogue of the general wget
-command). This command submits a job to the data transfer machines. If you need to download or
-allocate massive files (more than one terabyte) please contact the support before.
-
-### The ImageNet Data Set
-
-The [ImageNet](http://www.image-net.org/) project is a large visual database designed for use in
-visual object recognition software research. In order to save space in the filesystem by avoiding
-to have multiple duplicates of this lying around, we have put a copy of the ImageNet database
-(ILSVRC2012 and ILSVR2017) under `/scratch/imagenet` which you can use without having to download it
-again. For the future, the ImageNet data set will be available in `/warm_archive`. ILSVR2017 also
-includes a data set for recognition objects from a video. Please respect the corresponding
-[Terms of Use](https://image-net.org/download.php).
-
-## Jupyter Notebook
-
-Jupyter notebooks are a great way for interactive computing in your web browser. Jupyter allows
-working with data cleaning and transformation, numerical simulation, statistical modelling, data
-visualization and of course with machine learning.
-
-There are two general options on how to work Jupyter notebooks using HPC: remote Jupyter server and
-JupyterHub.
-
-These sections show how to run and set up a remote Jupyter server within a sbatch GPU job and which
-modules and packages you need for that.
-
-**Note:** On ZIH system, there is a [JupyterHub](../access/jupyterhub.md), where you do not need the
-manual server setup described below and can simply run your Jupyter notebook on HPC nodes. Keep in
-mind, that, with JupyterHub, you can't work with some special instruments. However, general data
-analytics tools are available.
-
-The remote Jupyter server is able to offer more freedom with settings and approaches.
-
-### Preparation phase (optional)
-
-On ZIH system, start an interactive session for setting up the
-environment:
-
-```Bash
-srun --pty -n 1 --cpus-per-task=2 --time=2:00:00 --mem-per-cpu=2500 --x11=first bash -l -i
-```
-
-Create a new subdirectory in your home, e.g. Jupyter
-
-```Bash
-mkdir Jupyter cd Jupyter
-```
-
-There are two ways how to run Anaconda. The easiest way is to load the Anaconda module. The second
-one is to download Anaconda in your home directory.
-
-1. Load Anaconda module (recommended):
-
-```Bash
-module load modenv/scs5 module load Anaconda3
-```
-
-1. Download latest Anaconda release (see example below) and change the rights to make it an
-executable script and run the installation script:
-
-```Bash
-wget https://repo.continuum.io/archive/Anaconda3-2019.03-Linux-x86_64.sh chmod 744
-Anaconda3-2019.03-Linux-x86_64.sh ./Anaconda3-2019.03-Linux-x86_64.sh
-
-(during installation you have to confirm the license agreement)
-```
-
-Next step will install the anaconda environment into the home
-directory (/home/userxx/anaconda3). Create a new anaconda environment with the name "jnb".
-
-```Bash
-conda create --name jnb
-```
-
-### Set environmental variables
-
-In shell activate previously created python environment (you can
-deactivate it also manually) and install Jupyter packages for this python environment:
-
-```Bash
-source activate jnb conda install jupyter
-```
-
-If you need to adjust the configuration, you should create the template. Generate config files for
-Jupyter notebook server:
-
-```Bash
-jupyter notebook --generate-config
-```
-
-Find a path of the configuration file, usually in the home under `.jupyter` directory, e.g.
-`/home//.jupyter/jupyter_notebook_config.py`
-
-Set a password (choose easy one for testing), which is needed later on to log into the server
-in browser session:
-
-```Bash
-jupyter notebook password Enter password: Verify password:
-```
-
-You get a message like that:
-
-```Bash
-[NotebookPasswordApp] Wrote *hashed password* to
-/home/<zih_user>/.jupyter/jupyter_notebook_config.json
-```
-
-I order to create an SSL certificate for secure connections, you can create a self-signed
-certificate:
-
-```Bash
-openssl req -x509 -nodes -days 365 -newkey rsa:1024 -keyout mykey.key -out mycert.pem
-```
-
-Fill in the form with decent values.
-
-Possible entries for your Jupyter config (`.jupyter/jupyter_notebook*config.py*`).
-
-```Bash
-c.NotebookApp.certfile = u'<path-to-cert>/mycert.pem' c.NotebookApp.keyfile =
-u'<path-to-cert>/mykey.key'
-
-# set ip to '*' otherwise server is bound to localhost only c.NotebookApp.ip = '*'
-c.NotebookApp.open_browser = False
-
-# copy hashed password from the jupyter_notebook_config.json c.NotebookApp.password = u'<your
-hashed password here>' c.NotebookApp.port = 9999 c.NotebookApp.allow_remote_access = True
-```
-
-Note: `<path-to-cert>` - path to key and certificate files, for example:
-(`/home/\<username>/mycert.pem`)
-
-### Slurm job file to run the Jupyter server on ZIH system with GPU (1x K80) (also works on K20)
-
-```Bash
-#!/bin/bash -l #SBATCH --gres=gpu:1 # request GPU #SBATCH --partition=gpu2 # use GPU partition
-SBATCH --output=notebook_output.txt #SBATCH --nodes=1 #SBATCH --ntasks=1 #SBATCH --time=02:30:00
-SBATCH --mem=4000M #SBATCH -J "jupyter-notebook" # job-name #SBATCH -A <name_of_your_project>
-
-unset XDG_RUNTIME_DIR # might be required when interactive instead of sbatch to avoid
-'Permission denied error' srun jupyter notebook
-```
-
-Start the script above (e.g. with the name jnotebook) with sbatch command:
-
-```Bash
-sbatch jnotebook.slurm
-```
-
-If you have a question about sbatch script see the article about [Slurm](../jobs_and_resources/slurm.md).
-
-Check by the command: `tail notebook_output.txt` the status and the **token** of the server. It
-should look like this:
-
-```Bash
-https://(taurusi2092.taurus.hrsk.tu-dresden.de or 127.0.0.1):9999/
-```
-
-You can see the **server node's hostname** by the command: `squeue -u <username>`.
-
-Remote connect to the server
-
-There are two options on how to connect to the server:
-
-1. You can create an ssh tunnel if you have problems with the
-solution above. Open the other terminal and configure ssh
-tunnel: (look up connection values in the output file of Slurm job, e.g.) (recommended):
-
-```Bash
-node=taurusi2092 #see the name of the node with squeue -u <your_login>
-localport=8887 #local port on your computer remoteport=9999
-#pay attention on the value. It should be the same value as value in the notebook_output.txt ssh
--fNL ${localport}:${node}:${remoteport} <zih_user>@taurus.hrsk.tu-dresden.de #configure
-of the ssh tunnel for connection to your remote server pgrep -f "ssh -fNL ${localport}"
-#verify that tunnel is alive
-```
-
-2. On your client (local machine) you now can connect to the server. You need to know the **node's
- hostname**, the **port** of the server and the **token** to login (see paragraph above).
-
-You can connect directly if you know the IP address (just ping the node's hostname while logged on
-ZIH system).
-
-```Bash
-#comand on remote terminal taurusi2092$> host taurusi2092 # copy IP address from output # paste
-IP to your browser or call on local terminal e.g. local$> firefox https://<IP>:<PORT> # https
-important to use SSL cert
-```
-
-To login into the Jupyter notebook site, you have to enter the **token**.
-(`https://localhost:8887`). Now you can create and execute notebooks on ZIH system with GPU support.
-
-If you would like to use [JupyterHub](../access/jupyterhub.md) after using a remote manually configured
-Jupyter server (example above) you need to change the name of the configuration file
-(`/home//.jupyter/jupyter_notebook_config.py`) to any other.
-
-### F.A.Q
-
-**Q:** - I have an error to connect to the Jupyter server (e.g. "open failed: administratively
-prohibited: open failed")
-
-**A:** - Check the settings of your Jupyter config file. Is it all necessary lines not commented, the
-right path to cert and key files, right hashed password from .json file? Check is the used local
-port [available](https://en.wikipedia.org/wiki/List_of_TCP_and_UDP_port_numbers)
-Check local settings e.g. (`/etc/ssh/sshd_config`, `/etc/hosts`).
-
-**Q:** I have an error during the start of the interactive session (e.g. PMI2_Init failed to
-initialize. Return code: 1)
-
-**A:** Probably you need to provide `--mpi=none` to avoid ompi errors ().
-`srun --mpi=none --reservation \<...> -A \<...> -t 90 --mem=4000 --gres=gpu:1
---partition=gpu2-interactive --pty bash -l`
diff --git a/doc.zih.tu-dresden.de/docs/archive/install_jupyter.md b/doc.zih.tu-dresden.de/docs/archive/install_jupyter.md
new file mode 100644
index 0000000000000000000000000000000000000000..993870eadf24901e765e18f90a32cae41e2270a9
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@@ -0,0 +1,181 @@
+# Jupyter Installation
+
+Jupyter notebooks are a great way for interactive computing in your web browser. Jupyter allows
+working with data cleaning and transformation, numerical simulation, statistical modelling, data
+visualization and of course with machine learning.
+
+There are two general options on how to work Jupyter notebooks using HPC: remote Jupyter server and
+JupyterHub.
+
+These sections show how to run and set up a remote Jupyter server within a sbatch GPU job and which
+modules and packages you need for that.
+
+**Note:** On ZIH system, there is a [JupyterHub](../access/jupyterhub.md), where you do not need the
+manual server setup described below and can simply run your Jupyter notebook on HPC nodes. Keep in
+mind, that, with JupyterHub, you can't work with some special instruments. However, general data
+analytics tools are available.
+
+The remote Jupyter server is able to offer more freedom with settings and approaches.
+
+## Preparation phase (optional)
+
+On ZIH system, start an interactive session for setting up the
+environment:
+
+```Bash
+srun --pty -n 1 --cpus-per-task=2 --time=2:00:00 --mem-per-cpu=2500 --x11=first bash -l -i
+```
+
+Create a new subdirectory in your home, e.g. Jupyter
+
+```Bash
+mkdir Jupyter cd Jupyter
+```
+
+There are two ways how to run Anaconda. The easiest way is to load the Anaconda module. The second
+one is to download Anaconda in your home directory.
+
+1. Load Anaconda module (recommended):
+
+```Bash
+module load modenv/scs5 module load Anaconda3
+```
+
+1. Download latest Anaconda release (see example below) and change the rights to make it an
+executable script and run the installation script:
+
+```Bash
+wget https://repo.continuum.io/archive/Anaconda3-2019.03-Linux-x86_64.sh chmod 744
+Anaconda3-2019.03-Linux-x86_64.sh ./Anaconda3-2019.03-Linux-x86_64.sh
+
+(during installation you have to confirm the license agreement)
+```
+
+Next step will install the anaconda environment into the home
+directory (/home/userxx/anaconda3). Create a new anaconda environment with the name "jnb".
+
+```Bash
+conda create --name jnb
+```
+
+## Set environmental variables
+
+In shell activate previously created python environment (you can
+deactivate it also manually) and install Jupyter packages for this python environment:
+
+```Bash
+source activate jnb conda install jupyter
+```
+
+If you need to adjust the configuration, you should create the template. Generate config files for
+Jupyter notebook server:
+
+```Bash
+jupyter notebook --generate-config
+```
+
+Find a path of the configuration file, usually in the home under `.jupyter` directory, e.g.
+`/home//.jupyter/jupyter_notebook_config.py`
+
+Set a password (choose easy one for testing), which is needed later on to log into the server
+in browser session:
+
+```Bash
+jupyter notebook password Enter password: Verify password:
+```
+
+You get a message like that:
+
+```Bash
+[NotebookPasswordApp] Wrote *hashed password* to
+/home/<zih_user>/.jupyter/jupyter_notebook_config.json
+```
+
+I order to create an SSL certificate for secure connections, you can create a self-signed
+certificate:
+
+```Bash
+openssl req -x509 -nodes -days 365 -newkey rsa:1024 -keyout mykey.key -out mycert.pem
+```
+
+Fill in the form with decent values.
+
+Possible entries for your Jupyter config (`.jupyter/jupyter_notebook*config.py*`).
+
+```Bash
+c.NotebookApp.certfile = u'<path-to-cert>/mycert.pem' c.NotebookApp.keyfile =
+u'<path-to-cert>/mykey.key'
+
+# set ip to '*' otherwise server is bound to localhost only c.NotebookApp.ip = '*'
+c.NotebookApp.open_browser = False
+
+# copy hashed password from the jupyter_notebook_config.json c.NotebookApp.password = u'<your
+hashed password here>' c.NotebookApp.port = 9999 c.NotebookApp.allow_remote_access = True
+```
+
+Note: `<path-to-cert>` - path to key and certificate files, for example:
+(`/home/\<username>/mycert.pem`)
+
+## Slurm job file to run the Jupyter server on ZIH system with GPU (1x K80) (also works on K20)
+
+```Bash
+#!/bin/bash -l #SBATCH --gres=gpu:1 # request GPU #SBATCH --partition=gpu2 # use GPU partition
+SBATCH --output=notebook_output.txt #SBATCH --nodes=1 #SBATCH --ntasks=1 #SBATCH --time=02:30:00
+SBATCH --mem=4000M #SBATCH -J "jupyter-notebook" # job-name #SBATCH -A <name_of_your_project>
+
+unset XDG_RUNTIME_DIR # might be required when interactive instead of sbatch to avoid
+'Permission denied error' srun jupyter notebook
+```
+
+Start the script above (e.g. with the name jnotebook) with sbatch command:
+
+```Bash
+sbatch jnotebook.slurm
+```
+
+If you have a question about sbatch script see the article about [Slurm](../jobs_and_resources/slurm.md).
+
+Check by the command: `tail notebook_output.txt` the status and the **token** of the server. It
+should look like this:
+
+```Bash
+https://(taurusi2092.taurus.hrsk.tu-dresden.de or 127.0.0.1):9999/
+```
+
+You can see the **server node's hostname** by the command: `squeue -u <username>`.
+
+Remote connect to the server
+
+There are two options on how to connect to the server:
+
+1. You can create an ssh tunnel if you have problems with the
+solution above. Open the other terminal and configure ssh
+tunnel: (look up connection values in the output file of Slurm job, e.g.) (recommended):
+
+```Bash
+node=taurusi2092 #see the name of the node with squeue -u <your_login>
+localport=8887 #local port on your computer remoteport=9999
+#pay attention on the value. It should be the same value as value in the notebook_output.txt ssh
+-fNL ${localport}:${node}:${remoteport} <zih_user>@taurus.hrsk.tu-dresden.de #configure
+of the ssh tunnel for connection to your remote server pgrep -f "ssh -fNL ${localport}"
+#verify that tunnel is alive
+```
+
+2. On your client (local machine) you now can connect to the server. You need to know the **node's
+ hostname**, the **port** of the server and the **token** to login (see paragraph above).
+
+You can connect directly if you know the IP address (just ping the node's hostname while logged on
+ZIH system).
+
+```Bash
+#comand on remote terminal taurusi2092$> host taurusi2092 # copy IP address from output # paste
+IP to your browser or call on local terminal e.g. local$> firefox https://<IP>:<PORT> # https
+important to use SSL cert
+```
+
+To login into the Jupyter notebook site, you have to enter the **token**.
+(`https://localhost:8887`). Now you can create and execute notebooks on ZIH system with GPU support.
+
+If you would like to use [JupyterHub](../access/jupyterhub.md) after using a remote manually configured
+Jupyter server (example above) you need to change the name of the configuration file
+(`/home//.jupyter/jupyter_notebook_config.py`) to any other.