add compose, fix readme still for old PM4Py

README.md

@@ -27,43 +27,30 @@ Click on the `launch binder` links for either the R or the Python notebook. You
 
 ### Local notebooks
 
-#### Docker
+#### Docker Compose
 
-Build a Docker image with the provided Dockerfile:
+Build and run a Docker image with Compose:
 
 ```
-docker build -t fmannhardt/starter-predictive-process-monitoring .
+docker compose up -d
 ```
 
-And start the Docker container running Jupyter on [localhost:8888](http://localhost:8888?token=processmining):
+#### Standalone
 
-```
-docker run --rm -ti -e JUPYTER_TOKEN=processmining -p 8888:8888 fmannhardt/starter-predictive-process-monitoring
-```
+Please make sure to have installed the following requirements:
 
-or use the Jupyter Lab interface:
+**Python**
 
 ```
-docker run --rm -ti -e JUPYTER_TOKEN=processmining -p 8888:8888 fmannhardt/starter-predictive-process-monitoring sh -c "jupyter lab --ip 0.0.0.0 --no-browser"
+pip install -r requirements.txt
 ```
 
-#### Standalone
+Make sure to install GraphViz for the visualization as per PM4Py documentation https://processintelligence.solutions/static/api/2.7.11/install.html
 
 You should be able to run the Jupyter notebooks directly in a Jupyter environment using:
 ```
 jupyter lab
 ```
 
-Please make sure to have installed the following requirements:
 
-**Python**
 
-```
-pip install -r requirements.txt
-```
-
-Make sure to install GraphViz for the visualization. On Windows with Chocolately this should work:
-```
-choco install graphviz
-```
-Consult the [PM4Py documentation](https://pm4py.fit.fraunhofer.de/install) for further details for other environments.

compose.yml

@@ -0,0 +1,11 @@
+services:
+  jupyter:
+    image: fmannhardt/starter-predictive-process-monitoring
+    build: .
+    environment:
+      - JUPYTER_TOKEN=processmining
+    ports:
+      - "8888:8888"
+    command: sh -c "jupyter lab --ip 0.0.0.0 --no-browser"
+    volumes:
+      - ./python:/home/rstudio/python