diff --git a/doc.zih.tu-dresden.de/docs/software/DeepLearning.md b/doc.zih.tu-dresden.de/docs/software/DeepLearning.md
index 3d2874d0727d8800070df3420897cb02dac88be0..e9f5854c43c32d6e6cfcf303edd999cc9b2dd17f 100644
--- a/doc.zih.tu-dresden.de/docs/software/DeepLearning.md
+++ b/doc.zih.tu-dresden.de/docs/software/DeepLearning.md
@@ -1,372 +1,335 @@
# Deep learning
+**Prerequisites**: To work with Deep Learning tools you obviously need [Login](../access/Login.md)
+for the Taurus 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 Taurus system.
-**Prerequisites**: To work with Deep Learning tools you obviously need
-\<a href="Login" target="\_blank">access\</a> for the Taurus system and
-basic knowledge about Python, SLURM manager.
+## Deep Learning Software
-**Aim** \<span style="font-size: 1em;">of this page is to introduce
-users on how to start working with Deep learning software on both the
-\</span>\<span style="font-size: 1em;">ml environment and the scs5
-environment of the Taurus system.\</span>
+### TensorFlow
-## Deep Learning Software
+[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 main partitions
+[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:
+
+```Bash
+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)
+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
+[the example](https://www.tensorflow.org/install/pip). For examples of using TensorFlow for ml partition
+with module system see [TensorFlow page for HPC-DA](TensorFlow.md).
-Please refer to our [List of Modules](SoftwareModulesList) page for a
-daily-updated list of the respective software versions that are
-currently installed.
-
-## TensorFlow
-
-\<a href="<https://www.tensorflow.org/guide/>"
-target="\_blank">TensorFlow\</a> 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 main partitions [ml environment and scs5
-environment](RuntimeEnvironment#Module_Environments) under the module
-name "TensorFlow". However, for purposes of machine learning and deep
-learning, we recommend using Ml partition (\<a href="HPCDA"
-target="\_blank">HPC-DA\</a>). For example:
-
- module load TensorFlow
-
-There are numerous different possibilities on how to work with \<a
-href="TensorFlow" target="\_blank">Tensorflow\</a> on Taurus. On this
-page, for all examples default, scs5 partition is used. Generally, the
-easiest way is using the \<a
-href="RuntimeEnvironment#Module_Environments" target="\_blank">Modules
-system\</a> 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),
-[Containers](TensorFlowContainerOnHPCDA) and see [the
-example](https://www.tensorflow.org/install/pip). For examples of using
-TensorFlow for ml partition with module system see \<a href="TensorFlow"
-target="\_self">TensorFlow page for HPC-DA.\</a>
-
-Note: If you are going used manually installed Tensorflow release we
-recommend use only stable versions.
+Note: If you are going used manually installed Tensorflow release we recommend use only stable
+versions.
## Keras
-\<a href="<https://keras.io/>" target="\_blank">Keras\</a>\<span
-style="font-size: 1em;"> is a high-level neural network API, written in
-Python and capable of running on top of \</span>\<a
-href="<https://github.com/tensorflow/tensorflow>"
-target="\_top">TensorFlow\</a>\<span style="font-size: 1em;">. Keras is
-available in both environments \</span> [ml environment and scs5
-environment](RuntimeEnvironment#Module_Environments)\<span
-style="font-size: 1em;"> under the module name "Keras".\</span>
+[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 \<a
-href="TensorFlow" target="\_blank">Tensorflow\</a> and Keras on Taurus.
-Generally, the easiest way is using the \<a
-href="RuntimeEnvironment#Module_Environments" target="\_blank">Modules
-system\</a> and Python virtual environment (test case) to see Tensorflow
-part above. \<span style="font-size: 1em;">For examples of using Keras
-for ml partition with the module system see the \</span> [Keras page for
-HPC-DA](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
+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).
-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.
+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.
-\<span style="color: #222222; font-size: 1.385em;">Test case: Keras with
-TensorFlow on MNIST data\</span>
+Test case: Keras with TensorFlow on MNIST data
-Go to a directory on Taurus, get Keras for the examples and go to the
-examples:
+Go to a directory on Taurus, get Keras for the examples and go to the examples:
- git clone <a href='https://github.com/fchollet/keras.git'>https://github.com/fchollet/keras.git</a><br />cd keras/examples/
+```Bash
+git clone https://github.com/fchollet/keras.git'>https://github.com/fchollet/keras.git
+cd keras/examples/
+```
-If you do not specify Keras backend, then TensorFlow is used as a
-default
+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>'):
+Job-file (schedule job with sbatch, check the status with 'squeue -u \<Username>'):
- #!/bin/bash<br />#SBATCH --gres=gpu:1 # 1 - using one gpu, 2 - for using 2 gpus<br />#SBATCH --mem=8000<br />#SBATCH -p gpu2 # select the type of nodes (opitions: haswell, <code>smp</code>, <code>sandy</code>, <code>west</code>, <code>gpu, ml) </code><b>K80</b> GPUs on Haswell node<br />#SBATCH --time=00:30:00<br />#SBATCH -o HLR_<name_of_your_script>.out # save output under HLR_${SLURMJOBID}.out<br />#SBATCH -e HLR_<name_of_your_script>.err # save error messages under HLR_${SLURMJOBID}.err<br />
- module purge # purge if you already have modules loaded<br />module load modenv/scs5 # load scs5 environment<br />module load Keras # load Keras module<br />module load TensorFlow # load TensorFlow module<br />
+```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 (opitions: 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
- # if you see 'broken pipe error's (might happen in interactive session after the second srun command) uncomment line below<br /># module load h5py<br /><br />python mnist_cnn.py
+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
-Keep in mind that you need to put the bash script to the same folder as
-an executable file or specify the path.
+# 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:
- x_train shape: (60000, 28, 28, 1)
- 60000 train samples
- 10000 test samples
- Train on 60000 samples, validate on 10000 samples
- Epoch 1/12
+```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
+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
+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
+```
## Datasets
-There are many different datasets 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 datasets
-without downloading it (for example [TensorFlow
-Datasets](https://www.tensorflow.org/datasets)\<span style="font-size:
-1em; color: #444444;">). \</span>
-
-\<span style="font-size: 1em; color: #444444;">If you still need to
-download some datasets, first of all, be careful with the size of the
-datasets 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 \</span>\<a href="DataMover"
-target="\_blank">Datamover\</a>\<span style="font-size: 1em; color:
-#444444;">. For example, you can use command **\<span
-class="WYSIWYG_TT">dtwget \</span>**(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.\</span>
+There are many different datasets 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 datasets without downloading it (for example
+[TensorFlow Datasets](https://www.tensorflow.org/datasets).
+
+If you still need to download some datasets, first of all, be careful with the size of the datasets
+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_moving/DataMover.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 dataset
-\<span style="font-size: 1em;">The \</span> [ **ImageNet**
-](http://www.image-net.org/)\<span style="font-size: 1em;">project is a
-large visual database designed for use in visual object recognition
-software research. In order to save space in the file system by avoiding
-to have multiple duplicates of this lying around, we have put a copy of
-the ImageNet database (ILSVRC2012 and ILSVR2017) under\</span>**
-/scratch/imagenet**\<span style="font-size: 1em;"> which you can use
-without having to download it again. For the future, the Imagenet
-dataset will be available in **/warm_archive.**ILSVR2017 also includes a
-dataset for recognition objects from a video. Please respect the
-corresponding \</span> [Terms of
-Use](http://image-net.org/download-faq)\<span style="font-size:
-1em;">.\</span>
-
-## 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.
-
-\<span style="font-size: 1em; color: #444444;">%RED%Note:<span
-class="twiki-macro ENDCOLOR"></span> On Taurus, there is a \</span>\<a
-href="JupyterHub" target="\_self">jupyterhub\</a>\<span
-style="font-size: 1em; color: #444444;">, 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.\</span>
-
-The remote Jupyter server is able to offer more freedom with settings
-and approaches.
+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 file system 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 dataset will be available in `/warm_archive`. ILSVR2017 also
+includes a dataset 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 Taurus, there is a [JupyterHub](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.
Note: Jupyterhub is could be under construction
### Preparation phase (optional)
-\<span style="font-size: 1em;">On Taurus, start an interactive session
-for setting up the environment:\</span>
+On Taurus, start an interactive session for setting up the
+environment:
- srun --pty -n 1 --cpus-per-task=2 --time=2:00:00 --mem-per-cpu=2500 --x11=first bash -l -i
+```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
- mkdir Jupyter
- cd 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.
+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):
+1. Load Anaconda module (recommended):
- module load modenv/scs5
- module load Anaconda3
+```Bash
+module load modenv/scs5 module load Anaconda3
+```
-2\. Download latest Anaconda release (see example below) and change the
-rights to make it an executable script and run the installation script:
+1. Download latest Anaconda release (see example below) and change the rights to make it an
+executable script and run the installation script:
- 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
+```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 licence agreement)
+(during installation you have to confirm the licence agreement)
+```
-\<span style="font-size: 1em;">Next step will install the anaconda
-environment into the home directory (/home/userxx/anaconda3). Create a
-new anaconda environment with the name "jnb".\</span>
+Next step will install the anaconda environment into the home
+directory (/home/userxx/anaconda3). Create a new anaconda environment with the name "jnb".
- conda create --name jnb
+```Bash
+conda create --name jnb
+```
### Set environmental variables on Taurus
-\<span style="font-size: 1em;">In shell activate previously created
-python environment (you can deactivate it also manually) and Install
-jupyter packages for this python environment:\</span>
+In shell activate previously created python environment (you can
+deactivate it also manually) and Install jupyter packages for this python environment:
- source activate jnb
- conda install jupyter
+```Bash
+source activate jnb conda install jupyter
+```
-\<span style="font-size: 1em;">If you need to adjust the config, you
-should create the template. Generate config files for jupyter notebook
-server:\</span>
+If you need to adjust the config, you should create the template. Generate config files for jupyter
+notebook server:
- jupyter notebook --generate-config
+```Bash
+jupyter notebook --generate-config
+```
-Find a path of the configuration file, usually in the home under
-.jupyter directory, e.g.\<br
-/>/home//.jupyter/jupyter_notebook_config.py
+Find a path of the configuration file, usually in the home under `.jupyter` directory, e.g.
+`/home//.jupyter/jupyter_notebook_config.py`
-\<br />Set a password (choose easy one for testing), which is needed
-later on to log into the server in browser session:
+Set a password (choose easy one for testing), which is needed later on to log into the server
+in browser session:
- jupyter notebook password
- Enter password:
- Verify password:
+```Bash
+jupyter notebook password Enter password: Verify password:
+```
you will get a message like that:
- [NotebookPasswordApp] Wrote *hashed password* to /home/<zih_user>/.jupyter/jupyter_notebook_config.json
+```Bash
+[NotebookPasswordApp] Wrote *hashed password* to
+/home/<zih_user>/.jupyter/jupyter_notebook_config.json
+```
-I order to create an SSL certificate for https connections, you can
-create a self-signed certificate:
+I order to create an SSL certificate for https connections, you can create a self-signed
+certificate:
- openssl req -x509 -nodes -days 365 -newkey rsa:1024 -keyout mykey.key -out mycert.pem
+```Bash
+openssl req -x509 -nodes -days 365 -newkey rsa:1024 -keyout mykey.key -out mycert.pem
+```
-fill in the form with decent values
+fill in the form with decent values.
-Possible entries for your jupyter config
-(\_.jupyter/jupyter_notebook*config.py*). Uncomment below lines:
+Possible entries for your jupyter config (`.jupyter/jupyter_notebook*config.py*`). Uncomment below
+lines:
- c.NotebookApp.certfile = u'<path-to-cert>/mycert.pem'
- c.NotebookApp.keyfile = u'<path-to-cert>/mykey.key'
+```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
+# 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
+# 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')
+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 Taurus with GPU (1x K80) (also works on K20)
- #!/bin/bash -l
- #SBATCH --gres=gpu:1 # request GPU
- #SBATCH --partition=gpu2 # use GPU partition
- #SBATCH --output=notebok_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>
+```Bash
+#!/bin/bash -l #SBATCH --gres=gpu:1 # request GPU #SBATCH --partition=gpu2 # use GPU partition
+SBATCH --output=notebok_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
+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:
+Start the script above (e.g. with the name jnotebook) with sbatch command:
- sbatch jnotebook.slurm
+```Bash
+sbatch jnotebook.slurm
+```
-If you have a question about sbatch script see the article about \<a
-href="Slurm" target="\_blank">SLURM\</a>
+If you have a question about sbatch script see the article about [Slurm](../jobs/Slurm.md).
-Check by the command: '\<span>tail notebook_output.txt'\</span> the
-status and the **token** of the server. It should look like this:
+Check by the command: `tail notebook_output.txt` the status and the **token** of the server. It
+should look like this:
- https://(taurusi2092.taurus.hrsk.tu-dresden.de or 127.0.0.1):9999/
+```Bash
+https://(taurusi2092.taurus.hrsk.tu-dresden.de or 127.0.0.1):9999/
+```
-\<span style="font-size: 1em;">You can see the \</span>**server node's
-hostname**\<span style="font-size: 1em;">by the command:
-'\</span>\<span>squeue -u \<username>'\</span>\<span style="font-size:
-1em;">.\</span>
+You can see the **server node's hostname** by the command: `squeue -u <username>`.
-\<span style="color: #222222; font-size: 1.231em;">Remote connect to the
-server\</span>
+Remote connect to the server
There are two options on how to connect to the server:
-\<span style="font-size: 1em;">1. You can create an ssh tunnel if you
-have problems with the solution above.\</span> \<span style="font-size:
-1em;">Open the other terminal and configure ssh tunnel: \</span>\<span
-style="font-size: 1em;">(look up connection values in the output file of
-slurm job, e.g.)\</span> (recommended):
-
- 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 ${localport}" #verify that tunnel is alive
-
-\<span style="font-size: 1em;">2. On your client (local machine) you now
-can connect to the server. You need to know the\</span>** node's
-hostname**\<span style="font-size: 1em;">, the \</span> **port** \<span
-style="font-size: 1em;"> of the server and the \</span> **token** \<span
-style="font-size: 1em;"> to login (see paragraph above).\</span>
-
-You can connect directly if you know the IP address (just ping the
-node's hostname while logged on Taurus).
-
- #comand on remote terminal
- taurusi2092$> 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 Taurus with GPU support.
-
-%RED%Note:<span class="twiki-macro ENDCOLOR"></span> If you would like
-to use \<a href="JupyterHub" target="\_self">jupyterhub\</a> after using
-a remote manually configurated jupyter server (example above) you need
-to change the name of the configuration file
-(/home//.jupyter/jupyter_notebook_config.py) to any other.
+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 ${localport}"
+#verify that tunnel is alive
+```
+
+2. On your client (local machine) you now can connect to the server. You need to know the **node's
+ hostname**, the **port** of the server and the **token** to login (see paragraph above).
+
+You can connect directly if you know the IP address (just ping the node's hostname while logged on
+Taurus).
+
+```Bash
+#comand on remote terminal taurusi2092$> 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 Taurus with GPU support.
+
+If you would like to use [JupyterHub](JupyterHub.md) after using a remote manually configurated
+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")
+**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 \<span style="font-size: 1em;">jupyter
-config file. Is it all necessary lines uncommented, the right path to
-cert and key files, right hashed password from .json file? Check is the
-used local port \<a
-href="<https://en.wikipedia.org/wiki/List_of_TCP_and_UDP_port_numbers>"
-target="\_blank">available\</a>? Check local settings e.g.
-(/etc/ssh/sshd_config, /etc/hosts)\</span>
+**A:** - Check the settings of your jupyter config file. Is it all necessary lines uncommented, 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)
+**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 ().
-\<span style="font-size: 1em;">srun --mpi=none --reservation \<...> -A
-\<...> -t 90 --mem=4000 --gres=gpu:1 --partition=gpu2-interactive --pty
-bash -l\</span>
+**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`