Frequently asked questions

How can I parse and report my own attributes?

See the parsing documentation.

How can I combine the results from multiple experiments?

exp = Experiment('/new/path/to/combined-results')

Some runs failed. How can I rerun them?

If the failed runs were never started, for example, due to grid node failures, you can simply run the “start” experiment step again. It will skip all runs that have already been started. Afterwards, run “fetch” and make reports as usual.

Lab detects which runs have already been started by checking if the driver.log file exists. So if you have failed runs that were already started, but you want to rerun them anyway, go to their run directories, remove the driver.log files and then run the “start” experiment step again as above.

I forgot to parse something. How can I run only the parsers again?

See the parsing documentation for how to write parsers. Once you have fixed your existing parsers or added new parsers, add exp.add_parse_again_step() to your experiment script and then call

./ parse-again

How can I compute a new attribute from multiple runs?

Consider for example the IPC quality score. It is often computed over the list of runs for each task. Since filters only work on individual runs, we can’t compute the score with a single filter, but it is possible by using two filters as shown below: store_costs saves the list of costs per task in a dictionary whereas add_quality uses the stored costs to compute IPC quality scores and adds them to the runs.

class QualityFilters:
    """Compute the IPC quality score.

    >>> from downward.reports.absolute import AbsoluteReport
    >>> filters = QualityFilters()
    >>> report = AbsoluteReport(filter=[filters.store_costs, filters.add_quality])


    def __init__(self):
        self.tasks_to_costs = defaultdict(list)

    def _get_task(self, run):
        return (run["domain"], run["problem"])

    def _compute_quality(self, cost, all_costs):
        if cost is None:
            return 0.0
        assert all_costs
        min_cost = min(all_costs)
        if cost == 0:
            assert min_cost == 0
            return 1.0
        return min_cost / cost

    def store_costs(self, run):
        cost = run.get("cost")
        if cost is not None:
            assert run["coverage"]
        return True

    def add_quality(self, run):
        run["quality"] = self._compute_quality(
            run.get("cost"), self.tasks_to_costs[self._get_task(run)]
        return run

How can I make reports and plots for results obtained without Lab?

See for an example.

Which experiment class should I use for which Fast Downward revision?

  • Before CMake: use DownwardExperiment in Lab 1.x

  • With CMake and optional validation: use FastDownwardExperiment in Lab 1.x

  • With CMake and mandatory validation: use FastDownwardExperiment in Lab 2.x

  • New translator exit codes (issue739): use FastDownwardExperiment in Lab >= 3.x

How can I contribute to Lab?

If you’d like to contribute a feature or a bugfix to Lab or Downward Lab, please see

How can I customize Lab?

Lab tries to be easily customizable. That means that you shouldn’t have to make any changes to the Lab code itself, but rather you should be able to inherit from Lab classes and implement custom behaviour in your subclasses. If this doesn’t work in your case, let’s discuss how we can improve things in a GitHub issue.

That said, it can sometimes be easiest to quickly patch Lab. In this case, or when you want to run the latest Lab development version, you can clone the Lab repo and install it (preferable in a virtual environment):

git clone /path/to/lab
pip install --editable /path/to/lab

The --editable flag installs the project in “editable mode”, which makes any changes under /path/to/lab appear immediately in the virtual environment.

Which best practices do you recommend for working with Lab?

  • automate as much as possible but not too much

  • use fixed solver revisions (“3a27ea77f” instead of “main”)

  • use Python virtual environments

  • pin versions of all Python dependencies in requirements.txt

  • collect common experiment code in project module

  • copy experiment scripts for new experiments, don’t change them

  • make evaluation locally rather than on remote cluster

  • collect exploratory results from multiple experiments

  • rerun experiments for camera-ready copy in single experiment and with single code revision