https://github.com/aai-institute/tfl-training-probabilistic-model-checking
TfL course on probabilistic model checking using storm
https://github.com/aai-institute/tfl-training-probabilistic-model-checking
machine-learning markov-decision-processes model-checking probabilistic-models storm training transferlab
Last synced: 5 months ago
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TfL course on probabilistic model checking using storm
- Host: GitHub
- URL: https://github.com/aai-institute/tfl-training-probabilistic-model-checking
- Owner: aai-institute
- License: cc-by-sa-4.0
- Created: 2023-04-21T09:58:29.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2024-06-24T14:04:00.000Z (almost 2 years ago)
- Last Synced: 2024-06-24T15:46:23.506Z (almost 2 years ago)
- Topics: machine-learning, markov-decision-processes, model-checking, probabilistic-models, storm, training, transferlab
- Language: Jupyter Notebook
- Homepage: https://transferlab.appliedai.de/trainings/probabilistic-model-checking-with-storm/
- Size: 59.4 MB
- Stars: 1
- Watchers: 6
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# TransferLab Training: Verifying Systems in the Face of Uncertainty
Welcome to the TransferLab training: Probabilistic Verifying Systems in the Face of Uncertainty.
The content was created and presented 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.
## Course video
The event took place on 1st December 2023. The recorded Lecture is available on our [TranferLab website](https://transferlab.ai/trainings/verifying-systems-in-the-face-of-uncertainty/)
## Getting started
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.
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 within your local clone of the repository with
```shell
docker build -t tfl-training-probabilistic-model-checking .
```
2. You can then start the container e.g., with
```shell
docker run -it -p 8888:8888 tfl-training-probabilistic-model-checking jupyter notebook
```
3. Run the first notebook **welcome_run_me_first.ipynb** within jupyter. This will download 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
`/home/jovyan/tfl-training-probabilistic-model-checking`. If you want to do
that, the easiest way is to override the entrypoint or to mount somewhere else
and create a symbolic link. For details on that see the `Dockerfile` and
`entrypoint.sh`.
## License
This work is licensed under a
[Creative Commons Attribution-ShareAlike 4.0 International License][cc-by-sa].
[![CC BY-SA 4.0][cc-by-sa-image]][cc-by-sa]
[cc-by-sa]: http://creativecommons.org/licenses/by-sa/4.0/
[cc-by-sa-image]: https://licensebuttons.net/l/by-sa/4.0/88x31.png
[cc-by-sa-shield]: https://img.shields.io/badge/License-CC%20BY--SA%204.0-lightgrey.svg