Merge pull request #14 from aai-institute/fariedabuzaid-patch-1

Update installation instructions

README.md

@@ -4,33 +4,27 @@ Welcome to the TransferLab training: Probabilistic Model Checking with Storm.
 The content was created by two major researchers in the field, Prof. [Joost-Pieter Katoen](https://moves.rwth-aachen.de/people/katoen/) and Assoc. Prof. [Sebastian Junges](https://sjunges.github.io). The course contains a mix of lectures and hands-on exercises covering
 the fundamentals of probabilistic model checking as well as practical applications using the model checker Storm. 
 
+## Getting started
 
-## During the training
+If you want to execute the notebooks, we recommend to use docker. You can
+eigther download a pre-build image from ghcr or build the image locally. 
 
-If you are currently participating in the training, you can find the agenda in
-the file `AGENDA.md`. Everything is already set up, so feel free to follow the
-trainer's presentation or to explore the notebooks and source code on your own.
+1. Option a) Pull the pre-build image from [ghcr.io](ghcr.io/aai-institute/tfl-training-probabilistic-model-checking:main)
+   ```shell
+   docker pull ghcr.io/aai-institute/tfl-training-probabilistic-model-checking:main
+   ```
+   Option b) Build the image with
 
-## After the training
-
-You have received this file as part of the training materials.
-
-There are multiple ways of viewing/executing the content. 
-
-1. If you just want to view the rendered notebooks, open `html/index.html` in
-your browser.
-2. If you want to execute the notebooks, we recommend to use docker. You can
-build the image locally. First, set the variable
-`PARTICIPANT_BUCKET_READ_SECRET` to the secret found in `config.yml`, and then
-build the image with
     ```shell
-    docker build --build-arg PARTICIPANT_BUCKET_READ_SECRET=$PARTICIPANT_BUCKET_READ_SECRET -t tfl-training-probabilistic-model-checking .
+    docker build --build-arg -t tfl-training-probabilistic-model-checking .
     ```
-    You can then start the container e.g., with
+
+2. You can then start the container e.g., with
     ```shell
     docker run -it -p 8888:8888 tfl-training-probabilistic-model-checking jupyter notebook
     ```
-4. The data will be downloaded on the fly when you run the notebooks.
+3. Run the first notebook **welcome_run_me_first.ipynb** within jupyter. This will dowload the data for 
+the workshop and finilize the setup.
 
 Note that there is some non-trivial logic in the entrypoint that may collide
 with mounting volumes to paths directly inside