Sake.re: reproducing experiments and analyses

See the prerequisite and be sure to be familiar with Sake.re. In particular, you will need to retrieve the description of the experiment that you want to reproduce.

Here we distinguish between reproducing an experiment, i.e. generating the results of a particular experiment (through multi-agent simulation), and reproducing an analysis, i.e. performing the analysis of these results.

Replaying experiments

By replaying, we mean reruning the experiment in conditions as close as possible to the initial processing. If one wants to rerun an experiment, with different conditions, it is good to replay it first and then to alter the conditions. For this, see Rerunning experiments.

with docker

The process is summarised by:

$ . params.sh
$ docker build -f ${OSVERS}.dkr -t lazylav:${OSVERS} .
$ docker build -f ${LABEL}.dkr -t lazylav:${LABEL} .
$ docker run --name ${LABEL} -v `pwd`/results:/workdir/results lazylav:${LABEL} process
The results will be found under the local results directory.

Below is a more detailed explanation. Each experiment comes with two 'docker files' specifying the runtime environment of the experiments. They can be downloaded by clicking on the first (gray) one (here , ${OSVERS}.dkr) declares the operating system, Java and Lazy lavender versions. It may be shared across experiments: if two experiments have the same one, this step can be skipped. The image corresponding to this environment is built by running (you may have to rename the files unfortunately):

$ docker build -f ${OSVERS}.dkr -t lazylav:${OSVERS} .
The second (blue) docker file (here ) sets the actual Lazy lavender version for the experiment and records the commands implementing the experiment. Its image may be built by:
$ docker build -f ${LABEL}.dkr -t lazylav:${LABEL} .
Once the images have been built, it is possible to replay the experiment. This is achieved by running:
$ docker run --name ${LABEL} -v `pwd`/results:/workdir/results lazylav:${LABEL} process
The resulting output will be found in the results directory. Since some of these experiments may take some time, it may be useful to monitor them through:
$ docker ps -a

It is also possible to run the analysis on the fly, by running:

$ docker run -p 7777:7777 --name ${LABEL} -v `pwd`/results:/workdir/results lazylav:${LABEL} full

with repository

First, clone the experiment to reproduce:

$ git clone --recurse-submodules cakes@felapton.inrialpes.fr:${LABEL}.git ${LABEL}
$ cd ${LABEL}
$ git checkout DESIGNED
$ ant -f lazylav/build.xml compileall
$ bash script.sh

Results should be found in the results directory.

with ZIP archive

First, download the zip then:

$ unzip ${LABEL}.zip
$ cd ${LABEL}
$ rm -rf results                   # if results are already there

Prepare the software:

$ git submodule update --init
$ ant -f lazylav/build.xml compileall

Finally run the experiments:

$ bash script.sh

Results should be found in the results directory.

Reruning experiments

By rerunning, we mean performing the same experiment under different conditions. In general, rerunning an experiment when the conditions are very diferent amounts to retrieving experiment descriptions and designing new experiments from them. Here we consider only very specific alterations, namely software upgrade.

with docker

Because most of the conditions are fixed within the Docker image (and we do not plan to alter Docker files), the only opportunity that we have is to run the Docker container with an up-to-date software, i.e. Lazy lavender. This is particularly useful to check that a modification in software did not entail changes in the results.

The dockerfile for building the image for rerunning is the same as for the initial experiment. This ensures that we are indeed rerunning the same experiment. The new image is built with a specific argument (--build-arg version=latest:

$ docker build -f example-NOOR.dkr -t lazylav:rerun-example-NOOR --build-arg version=latest .
Beware: Docker files generated between 2017-12 and 2018-06 contain an instruction to edit the `runexp.sh` script for avoiding it to pull the last version of Lazy lavender. This is now dealt with more elegantly, but this breaks the process. Hence, it is necessary to suppress the snippet `-e '/git pull/{s/^[^#]/# /}'` from the Docker file before building it.

Finally, the execution is launched exactly as before:

$ docker run --name rerun-example-NOOR -v `pwd`/results:/workdir/results lazylav:rerun-example-NOOR process

with repository

Performing the same with repositories amount to upgrade the software with the last version. This may be achieved with:

$ git submodule foreach 'git checkout master'

Reproducing analyses

First ensure that the results are available in the results directory. These results may have been generated by you as above or may be retrieved from git archives linked from the web site. It is possible to just move the results directory of the archives to the expected place.

with docker

Simply build the image as above and connect to the Jupyter server:

$ . params.sh
$ docker build -f busternb.dkr -t lazylav:busternb .
$ docker build -f ${LABEL}.dkr -t lazylav:${LABEL} .
$ docker run -p 7777:7777 --name ${LABEL} -v `pwd`/results:/workdir/results lazylav:${LABEL} analyse

Then you may open the browser at http://localhost:7777 with the given token to have access to the results.

with repository

You must have python, jupyter notebook and some extensions installed. If not already installed, this can be achieved with:

$ python3 -m pip install notebook
$ python3 -m pip install jupyter_contrib_nbextensions
$ jupyter nbextension enable python-markdown/main
$ jupyter nbextension enable hide_input_all/main
$ jupyter nbextension enable collapsible_headings/main
$ jupyter nbextension enable livemdpreview/livemdpreview

Clone the repository:

$ git clone --recurse-submodules cakes@felapton.inrialpes.fr:${LABEL}.git ${LABEL}
$ cd ${LABEL}
$ git checkout ANALYSED

After ensuring that the results are in the results directory, launch jupyter:

$ jupyter trust notebook.ipynb
$ jupyter notebook

Then open the notebook.ipynb notebook from the browser (usually http://localhost:8888).

Alternatively, it is possible to checkout the PERFORMED tag, but the notebook.ipynb may remain to be elaborated.

with ZIP archive

You must have installed the same python and jupyter as above. Then upload zip and software:

$ unzip ${LABEL}.zip
$ cd ${LABEL}
$ git submodule update --init

Finally run jupyter:

$ jupyter trust notebook.ipynb
$ jupyter notebook


If you have followed the instructions above, you may be able to publish the reproduced, or non reproduced results. You may also want to record the design of an alternative experiment.


The Docker icons are from Icons8.