diff --git a/.build/Dockerfile b/.build/Dockerfile
index 846e3cc..f920a0f 100755
--- a/.build/Dockerfile
+++ b/.build/Dockerfile
@@ -1,4 +1,9 @@
-# This adaptive Dockerfile is generated by 'generate-Dockerfile.sh' from parts within src/
+# This Dockerfile is generated by 'generate-Dockerfile.sh' from elements within 'src/'
+
+# **Please do not change this file directly!**
+# To adapt this Dockerfile, adapt 'generate-Dockerfile.sh' or 'src/Dockerfile.usefulpackages'.
+# More information can be found in the documentation.
+
 
 # Use NVIDIA CUDA as base image and run the same installation as in the other packages.
 # The version of cudatoolkit must match those of the base image, see Dockerfile.pytorch
@@ -412,12 +417,6 @@ LABEL authors="Christoph Schranz <christoph.schranz@salzburgresearch.at>, Mathem
 
 USER root
 
-# Install elasticsearch libs
-USER root
-RUN apt-get update \
- && curl -sL https://repo1.maven.org/maven2/org/elasticsearch/elasticsearch-hadoop/6.8.1/elasticsearch-hadoop-6.8.1.jar
-RUN pip install --no-cache-dir elasticsearch==7.1.0
-
 RUN pip install --no-cache-dir ipyleaflet plotly==4.8.* "ipywidgets>=7.5"
 
 # Install important packages and Graphviz
diff --git a/README.md b/README.md
index e9de060..6c6b2ef 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
 # GPU-Jupyter
 #### Leverage Jupyter Notebooks with the power of your NVIDIA GPU and perform GPU calculations using Tensorflow and Pytorch in collaborative notebooks. 
 
-![Jupyterlab Overview](/extra/jupyterlab-overview.png)
+![Jupyterlab Overview](https://raw.githubusercontent.com/iot-salzburg/gpu-jupyter/master/extra/jupyterlab-overview.png)
 
 First of all, thanks to [docker-stacks](https://github.com/jupyter/docker-stacks) 
 for creating and maintaining a robost  Python, R and Julia toolstack for Data Analytics/Science 
@@ -14,8 +14,8 @@ The image of this repository is available on [Dockerhub](https://hub.docker.com/
 1. [Requirements](#requirements)
 2. [Quickstart](#quickstart)
 3. [Tracing](#tracing)
-4. [Deployment](#deployment-in-the-docker-swarm)
-5. [Configuration](#configuration)
+4. [Configuration](#configuration)
+5. [Deployment](#deployment-in-the-docker-swarm)
 6. [Issues and Contributing](#issues-and-contributing)
 
 
@@ -58,58 +58,41 @@ The image of this repository is available on [Dockerhub](https://hub.docker.com/
 
 ## Quickstart
 
-First of all, it is necessary to generate the `Dockerfile` based on the NIVIDA base image and the 
-[docker-stacks](https://github.com/jupyter/docker-stacks).
+First of all, it is necessary to generate the `Dockerfile` that is based on 
+the NIVIDA base image and the [docker-stacks](https://github.com/jupyter/docker-stacks).
 As soon as you have access to your GPU within Docker containers 
-(make sure the command `docker run --gpus all nvidia/cuda:10.1-base-ubuntu18.04 nvidia-smi` shows your
-GPU statistics), you can generate a Dockerfile, build and run it.
-The following commands will start *GPU-Jupyter* on [localhost:8848](http://localhost:8848) with the default 
-password `asdf`.
+(make sure the command `docker run --gpus all nvidia/cuda:10.1-base-ubuntu18.04 nvidia-smi` 
+shows your GPU statistics), you can generate the Dockerfile, build and run it.
+The following commands will start *GPU-Jupyter* on [localhost:8848](http://localhost:8848) 
+with the default password `asdf`.
 
   ```bash
-  ./generate-Dockerfile.sh
+  # generate a Dockerfile with python and without Julia and R
+  ./generate-Dockerfile.sh --no-datascience-notebook  
   docker build -t gpu-jupyter .build/  # will take a while
   docker run -d -p [port]:8888 gpu-jupyter  # starts gpu-jupyter WITHOUT GPU support
   ``` 
 
-To run the container with GPU support, a local data volume and , run:
+To run the container WITH GPU support, a local data volume and some other configurations, run:
   ```bash
-docker run -d -it -p 8848:8888 -v $(pwd)/data:/home/jovyan/work -e GRANT_SUDO=yes -e JUPYTER_ENABLE_LAB=yes --user root --restart always --name gpu-jupyter_1 gpu-jupyter 
+docker run --gpus all -d -it -p 8848:8888 -v $(pwd)/data:/home/jovyan/work -e GRANT_SUDO=yes -e JUPYTER_ENABLE_LAB=yes --user root --restart always --name gpu-jupyter_1 gpu-jupyter 
 ``` 
 
+### Start via Docker Compose
 
-## Parameter
-
-The script `generate-Dockerfile.sh` has multiple parameters:
-
-* `-c|--commit`: specify a commit or `"latest"` for the `docker-stacks`, the default commit is a working one.
-
-* `-s|--slim`: Generate a slim Dockerfile. 
-As some installations are not needed by everyone, there is the possibility to skip some installations 
-to reduce the size of the image.
-Here the `docker-stack` `scipy-notebook` is used instead of `datascience-notebook` that comes with Julia and R. 
-Moreover, none of the packages within `src/Dockerfile.usefulpackages` is installed.
-
-* `--no-datascience-notebook`: As the name suggests, the `docker-stack` `datascience-notebook` is not installed
-on top of the `scipy-notebook`, but the packages within `src/Dockerfile.usefulpackages` are.
-
-* `--no-useful-packages`: On top of the `docker-stack` `datascience-notebook`, the essential `gpulibs` are installed
-but not the packages within `src/Dockerfile.usefulpackages`.
-
-
-The script `start-local.sh` is a wrapper for a quick configuration of the underlying `docker-compose.yml`.
-It is equal to these commands:
+The script `start-local.sh` is a wrapper for a quick configuration of the 
+underlying `docker-compose.yml`:
 
   ```bash
-  docker build -t gpu-jupyter .build/
-  docker run -d -p [port]:8888 gpu-jupyter
+  ./start-local.sh -p 8848  # the default port is 8888
   ```
 
+
 ## Tracing
   
 With these commands we can see if everything worked well:
 ```bash
-bash show-local.sh  # a env-var safe wrapper for a 'docker-compose logs -f'
+bash show-local.sh  # a env-var safe wrapper for 'docker-compose logs -f'
 docker ps
 docker logs [service-name]
 ```
@@ -120,8 +103,107 @@ In order to stop the local deployment, run:
   ./stop-local.sh
   ```
  
+
+## Configuration
+
+### Configuration of the Dockerfile-Generation
+
+The script `generate-Dockerfile.sh` generates a Dockerfile within the `.build/`
+directory. 
+This implies that this Dockerfile is overwritten by each generation.
+The Dockerfile-generation script `generate-Dockerfile.sh`
+has the following parameters (note that 2, 3 and 4 are exclusive): 
+
+* `-c|--commit`: specify a commit or `"latest"` for the `docker-stacks`, 
+the default commit is a working one.
+
+* `-s|--slim`: Generate a slim Dockerfile. 
+As some installations are not needed by everyone, there is the possibility to skip some 
+installations to reduce the size of the image.
+Here the `docker-stack` `scipy-notebook` is used instead of `datascience-notebook` 
+that comes with Julia and R. 
+Moreover, none of the packages within `src/Dockerfile.usefulpackages` is installed.
+
+* `--no-datascience-notebook`: As the name suggests, the `docker-stack` `datascience-notebook` 
+is not installed
+on top of the `scipy-notebook`, but the packages within `src/Dockerfile.usefulpackages` are.
+
+* `--no-useful-packages`: On top of the `docker-stack` `datascience-notebook` (Julia and R), 
+the essential `gpulibs` are installed, but not the packages within `src/Dockerfile.usefulpackages`.
+
+
+### Custom Installations
+
+**As `.build/Dockerfile` is overwritten, it is suggested to append custom installations either
+within `src/Dockerfile.usefulpackages` or in `generate-Dockerfile.sh`.**
+If you think some package is missing in the default stack, please let us know!
+
+
+
+### Set the Password
+
+Please set a new password using `src/jupyter_notebook_config.json`.
+Therefore, hash your password in the form (password)(salt) using a sha1 hash generator, e.g., the sha1 generator of [sha1-online.com](http://www.sha1-online.com/). 
+The input with the default password `asdf` is appended by a arbitrary salt `e49e73b0eb0e` to `asdfe49e73b0eb0e` and should yield the hash string as shown in the config below.
+**Never give away your own unhashed password!**
+
+Then update the config file as shown below and restart the service.
+
+```json
+{
+  "NotebookApp": {
+    "password": "sha1:e49e73b0eb0e:32edae7a5fd119045e699a0bd04f90819ca90cd6"
+  }
+}
+```
+
+
+### Updates
  
- ## Deployment in the Docker Swarm
+#### Update CUDA to another version
+
+Please check version compatibilities for [CUDA and Pytorch](https://pytorch.org/get-started/locally/)
+ respectively [CUDA and Tensorflow](https://www.tensorflow.org/install/gpu) previously. 
+To update CUDA to another version, change in `Dockerfile.header`
+the line:
+
+    FROM nvidia/cuda:10.1-base-ubuntu18.04
+    
+and in the `Dockerfile.pytorch` the line:
+
+    cudatoolkit=10.1
+
+Then re-generate and re-run the image, as closer described above:
+
+```bash
+./generate-Dockerfile.sh
+./start-local.sh -p 8848
+```
+
+#### Update Docker-Stack
+
+The [docker-stacks](https://github.com/jupyter/docker-stacks) are used as  a
+submodule within `.build/docker-stacks`. Per default, the head of the commit is reset to a commit on which `gpu-jupyter` runs stable. 
+To update the generated Dockerfile to a specific commit, run:
+
+```bash
+./generate-Dockerfile.sh --commit c1c32938438151c7e2a22b5aa338caba2ec01da2
+```
+
+To update the generated Dockerfile to the latest commit, run:
+
+```bash
+./generate-Dockerfile.sh --commit latest
+```
+
+A new build can last some time and may consume a lot of data traffic. Note, that the latest version may result in
+a version conflict!
+More info to submodules can be found in
+ [this tutorial](https://www.vogella.com/tutorials/GitSubmodules/article.html).
+
+
+
+## Deployment in the Docker Swarm
  
 A Jupyter instance often requires data from other services. 
 If that data-source is containerized in Docker and sharing a port for communication shouldn't be allowed, e.g., for security reasons,
@@ -184,10 +266,14 @@ Finally, *GPU-Jupyter* can be deployed in the Docker Swarm with the shared netwo
 ```
 where:
 * **-p:** port specifies the port on which the service will be available.
-* **-n:** docker-network is the name of the attachable network from the previous step, e.g., here it is **elk_datastack**.
-* **-r:** registry port is the port that is published by the registry service, see [Set up Docker Swarm and Registry](set-up-docker-swarm-and-registry).
+* **-n:** docker-network is the name of the attachable network from the previous step, 
+e.g., here it is **elk_datastack**.
+* **-r:** registry port is the port that is published by the registry service, default is `5000`.
 
-Now, *gpu-jupyter* will be accessable here on [localhost:8848](http://localhost:8848) with the default password `asdf` and shares the network with the other data-source, i.e., all ports of the data-source will be accessable within *GPU-Jupyter*, even if they aren't routed it the source's `docker-compose` file.
+Now, *gpu-jupyter* will be accessible here on [localhost:8848](http://localhost:8848) 
+with the default password `asdf` and shares the network with the other data-source, i.e., 
+all ports of the data-source will be accessible within *GPU-Jupyter*, 
+even if they aren't routed it the source's `docker-compose` file.
 
 Check if everything works well using:
 ```bash
@@ -200,70 +286,12 @@ In order to remove the service from the swarm, use:
 ./remove-from-swarm.sh
 ```
 
-## Configuration
-
-Please set a new password using `src/jupyter_notebook_config.json`.
-Therefore, hash your password in the form (password)(salt) using a sha1 hash generator, e.g., the sha1 generator of [sha1-online.com](http://www.sha1-online.com/). 
-The input with the default password `asdf` is appended by a arbitrary salt `e49e73b0eb0e` to `asdfe49e73b0eb0e` and should yield the hash string as shown in the config below.
-**Never give away your own unhashed password!**
-
-Then update the config file as shown below and restart the service.
-
-```json
-{
-  "NotebookApp": {
-    "password": "sha1:e49e73b0eb0e:32edae7a5fd119045e699a0bd04f90819ca90cd6"
-  }
-}
-```
-
-### Updates
- 
-#### Update CUDA to another version
-
-Please check version compatibilities for [CUDA and Pytorch](https://pytorch.org/get-started/locally/)
- respectively [CUDA and Tensorflow](https://www.tensorflow.org/install/gpu) previously. 
-To update CUDA to another version, change in `Dockerfile.header`
-the line:
-
-    FROM nvidia/cuda:10.1-base-ubuntu18.04
-    
-and in the `Dockerfile.pytorch` the line:
-
-    cudatoolkit=10.1
-
-Then re-generate and re-run the image, as closer described above:
-
-```bash
-./generate-Dockerfile.sh
-./start-local.sh -p 8848
-```
-
-#### Update Docker-Stack
-
-The [docker-stacks](https://github.com/jupyter/docker-stacks) are used as  a
-submodule within `.build/docker-stacks`. Per default, the head of the commit is reset to a commit on which `gpu-jupyter` runs stable. 
-To update the generated Dockerfile to a specific commit, run:
-
-```bash
-./generate-Dockerfile.sh --commit c1c32938438151c7e2a22b5aa338caba2ec01da2
-```
-
-To update the generated Dockerfile to the latest commit, run:
-
-```bash
-./generate-Dockerfile.sh --commit latest
-```
-
-A new build can last some time and may consume a lot of data traffic. Note, that the latest version may result in
-a version conflict!
-More info to submodules can be found in
- [this tutorial](https://www.vogella.com/tutorials/GitSubmodules/article.html).
-
 
 ## Issues and Contributing
 
-This project has the intention to create a robust image for CUDA-based GPU-applications, which is built on top of the [docker-stacks](https://github.com/jupyter/docker-stacks). You are free to help to improve this project, by:
+This project has the intention to create a robust image for CUDA-based GPU-applications, 
+which is built on top of the [docker-stacks](https://github.com/jupyter/docker-stacks). 
+You are free to help to improve this project, by:
 
 * [filing a new issue](https://github.com/iot-salzburg/gpu-jupyter/issues/new)
 * [open a pull request](https://help.github.com/articles/using-pull-requests/)
diff --git a/generate-Dockerfile.sh b/generate-Dockerfile.sh
index 266161f..d9916fc 100755
--- a/generate-Dockerfile.sh
+++ b/generate-Dockerfile.sh
@@ -40,7 +40,12 @@ else
 fi
 
 # Write the contents into the DOCKERFILE and start with the header
-echo "# This adaptive Dockerfile is generated by 'generate-Dockerfile.sh' from parts within src/
+echo "# This Dockerfile is generated by 'generate-Dockerfile.sh' from elements within 'src/'
+
+# **Please do not change this file directly!**
+# To adapt this Dockerfile, adapt 'generate-Dockerfile.sh' or 'src/Dockerfile.usefulpackages'.
+# More information can be found in the README under configuration.
+
 " > $DOCKERFILE
 cat src/Dockerfile.header >> $DOCKERFILE
 
diff --git a/src/Dockerfile.usefulpackages b/src/Dockerfile.usefulpackages
index 7782bb2..9595344 100644
--- a/src/Dockerfile.usefulpackages
+++ b/src/Dockerfile.usefulpackages
@@ -2,12 +2,6 @@ LABEL authors="Christoph Schranz <christoph.schranz@salzburgresearch.at>, Mathem
 
 USER root
 
-# Install elasticsearch libs
-USER root
-RUN apt-get update \
- && curl -sL https://repo1.maven.org/maven2/org/elasticsearch/elasticsearch-hadoop/6.8.1/elasticsearch-hadoop-6.8.1.jar
-RUN pip install --no-cache-dir elasticsearch==7.1.0
-
 RUN pip install --no-cache-dir ipyleaflet plotly==4.8.* "ipywidgets>=7.5"
 
 # Install important packages and Graphviz