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Dask runner

smac.runner.dask_runner #

DaskParallelRunner #

DaskParallelRunner(
    single_worker: AbstractRunner,
    patience: int = 5,
    dask_client: Client | None = None,
)

Bases: AbstractRunner

Interface to submit and collect a job in a distributed fashion. DaskParallelRunner is intended to comply with the bridge design pattern. Nevertheless, to reduce the amount of code within single-vs-parallel implementations, DaskParallelRunner wraps a BaseRunner object which is then executed in parallel on n_workers.

This class then is constructed by passing an AbstractRunner that implements a run method, and is capable of doing so in a serial fashion. Next, this wrapper class uses dask to initialize N number of AbstractRunner that actively wait of a TrialInfo to produce a RunInfo object.

To be more precise, the work model is then:

  1. The intensifier dictates "what" to run (a configuration/instance/seed) via a TrialInfo object.
  2. An abstract runner takes this TrialInfo object and launches the task via submit_run. In the case of DaskParallelRunner, n_workers receive a pickle-object of DaskParallelRunner.single_worker, each with a run method coming from DaskParallelRunner.single_worker.run()
  3. TrialInfo objects are run in a distributed fashion, and their results are available locally to each worker. The result is collected by iter_results and then passed to SMBO.
  4. Exceptions are also locally available to each worker and need to be collected.

Dask works with Future object which are managed via the DaskParallelRunner.client.

Parameters#

single_worker : AbstractRunner A runner to run in a distributed fashion. Will be distributed using n_workers. patience: int, default to 5 How much to wait for workers (seconds) to be available if one fails. dask_client: Client | None, defaults to None User-created dask client, which can be used to start a dask cluster and then attach SMAC to it. This will not be closed automatically and will have to be closed manually if provided explicitly. If none is provided (default), a local one will be created for you and closed upon completion.

Source code in smac/runner/dask_runner.py 
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def __init__(
    self,
    single_worker: AbstractRunner,
    patience: int = 5,
    dask_client: Client | None = None,
):
    super().__init__(
        scenario=single_worker._scenario,
        required_arguments=single_worker._required_arguments,
    )

    # The single worker to hold on to and call run on
    self._single_worker = single_worker

    # The list of futures that dask will use to indicate in progress runs
    self._pending_trials: list[Future] = []

    # Dask related variables
    self._scheduler_file: Path | None = None
    self._patience = patience

    self._client: Client
    self._close_client_at_del: bool

    if dask_client is None:
        dask.config.set({"distributed.worker.daemon": False})
        self._close_client_at_del = True
        self._client = Client(
            n_workers=self._scenario.n_workers,
            processes=True,
            threads_per_worker=1,
            local_directory=str(self._scenario.output_directory),
        )

        if self._scenario.output_directory is not None:
            self._scheduler_file = Path(self._scenario.output_directory, ".dask_scheduler_file")
            self._client.write_scheduler_file(scheduler_file=str(self._scheduler_file))
    else:
        # We just use their set up
        self._client = dask_client
        self._close_client_at_del = False

meta property #

meta: dict[str, Any]

Returns the meta-data of the created object.

__del__ #

__del__() -> None

Makes sure that when this object gets deleted, the client is terminated. This is only done if the client was created by the dask runner.

Source code in smac/runner/dask_runner.py 
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def __del__(self) -> None:
    """Makes sure that when this object gets deleted, the client is terminated. This
    is only done if the client was created by the dask runner.
    """
    if self._close_client_at_del:
        self.close()

close #

close(force: bool = False) -> None

Closes the client.

Source code in smac/runner/dask_runner.py 
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def close(self, force: bool = False) -> None:
    """Closes the client."""
    if self._close_client_at_del or force:
        self._client.close()

count_available_workers #

count_available_workers() -> int

Total number of workers available. This number is dynamic as more resources can be allocated.

Source code in smac/runner/dask_runner.py 
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def count_available_workers(self) -> int:
    """Total number of workers available. This number is dynamic as more resources
    can be allocated.
    """
    return sum(self._client.nthreads().values()) - len(self._pending_trials)

run_wrapper #

run_wrapper(
    trial_info: TrialInfo,
    **dask_data_to_scatter: dict[str, Any]
) -> tuple[TrialInfo, TrialValue]

Wrapper around run() to execute and check the execution of a given config. This function encapsulates common handling/processing, so that run() implementation is simplified.

Parameters#

trial_info : RunInfo Object that contains enough information to execute a configuration run in isolation. dask_data_to_scatter: dict[str, Any] When a user scatters data from their local process to the distributed network, this data is distributed in a round-robin fashion grouping by number of cores. Roughly speaking, we can keep this data in memory and then we do not have to (de-)serialize the data every time we would like to execute a target function with a big dataset. For example, when your target function has a big dataset shared across all the target function, this argument is very useful.

Returns#

info : TrialInfo An object containing the configuration launched. value : TrialValue Contains information about the status/performance of config.

Source code in smac/runner/abstract_runner.py 
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def run_wrapper(
    self, trial_info: TrialInfo, **dask_data_to_scatter: dict[str, Any]
) -> tuple[TrialInfo, TrialValue]:
    """Wrapper around run() to execute and check the execution of a given config.
    This function encapsulates common
    handling/processing, so that run() implementation is simplified.

    Parameters
    ----------
    trial_info : RunInfo
        Object that contains enough information to execute a configuration run in isolation.
    dask_data_to_scatter: dict[str, Any]
        When a user scatters data from their local process to the distributed network,
        this data is distributed in a round-robin fashion grouping by number of cores.
        Roughly speaking, we can keep this data in memory and then we do not have to (de-)serialize the data
        every time we would like to execute a target function with a big dataset.
        For example, when your target function has a big dataset shared across all the target function,
        this argument is very useful.

    Returns
    -------
    info : TrialInfo
        An object containing the configuration launched.
    value : TrialValue
        Contains information about the status/performance of config.
    """
    start = time.time()
    cpu_time = time.process_time()
    try:
        status, cost, runtime, cpu_time, additional_info = self.run(
            config=trial_info.config,
            instance=trial_info.instance,
            budget=trial_info.budget,
            seed=trial_info.seed,
            **dask_data_to_scatter,
        )
    except Exception as e:
        status = StatusType.CRASHED
        cost = self._crash_cost
        cpu_time = time.process_time() - cpu_time
        runtime = time.time() - start

        # Add context information to the error message
        exception_traceback = traceback.format_exc()
        error_message = repr(e)
        additional_info = {
            "traceback": exception_traceback,
            "error": error_message,
        }

    end = time.time()

    # Catch NaN or inf
    if not np.all(np.isfinite(cost)):
        logger.warning(
            "Target function returned infinity or nothing at all. Result is treated as CRASHED"
            f" and cost is set to {self._crash_cost}."
        )

        if "traceback" in additional_info:
            logger.warning(f"Traceback: {additional_info['traceback']}\n")

        status = StatusType.CRASHED

    if status == StatusType.CRASHED:
        cost = self._crash_cost

    trial_value = TrialValue(
        status=status,
        cost=cost,
        time=runtime,
        cpu_time=cpu_time,
        additional_info=additional_info,
        starttime=start,
        endtime=end,
    )

    return trial_info, trial_value

submit_trial #

submit_trial(
    trial_info: TrialInfo,
    **dask_data_to_scatter: dict[str, Any]
) -> None

This function submits a configuration embedded in a trial_info object, and uses one of the workers to produce a result locally to each worker.

The execution of a configuration follows this procedure:

. The SMBO/intensifier generates a TrialInfo.#
. SMBO calls submit_trial so that a worker launches the trial_info.#
. submit_trial internally calls self.run(). It does so via a call to run_wrapper which contains common#

code that any run method will otherwise have to implement.

All results will be only available locally to each worker, so the main node needs to collect them.

Parameters#

trial_info : TrialInfo An object containing the configuration launched.

dict[str, Any]

When a user scatters data from their local process to the distributed network, this data is distributed in a round-robin fashion grouping by number of cores. Roughly speaking, we can keep this data in memory and then we do not have to (de-)serialize the data every time we would like to execute a target function with a big dataset. For example, when your target function has a big dataset shared across all the target function, this argument is very useful.

Source code in smac/runner/dask_runner.py 
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def submit_trial(self, trial_info: TrialInfo, **dask_data_to_scatter: dict[str, Any]) -> None:
    """This function submits a configuration embedded in a ``trial_info`` object, and uses one of
    the workers to produce a result locally to each worker.

    The execution of a configuration follows this procedure:

    #. The SMBO/intensifier generates a `TrialInfo`.
    #. SMBO calls `submit_trial` so that a worker launches the `trial_info`.
    #. `submit_trial` internally calls ``self.run()``. It does so via a call to `run_wrapper` which contains common
       code that any `run` method will otherwise have to implement.

    All results will be only available locally to each worker, so the main node needs to collect them.

    Parameters
    ----------
    trial_info : TrialInfo
        An object containing the configuration launched.

    dask_data_to_scatter: dict[str, Any]
        When a user scatters data from their local process to the distributed network,
        this data is distributed in a round-robin fashion grouping by number of cores.
        Roughly speaking, we can keep this data in memory and then we do not have to (de-)serialize the data
        every time we would like to execute a target function with a big dataset.
        For example, when your target function has a big dataset shared across all the target function,
        this argument is very useful.
    """
    # Check for resources or block till one is available
    if self.count_available_workers() <= 0:
        logger.debug("No worker available. Waiting for one to be available...")
        wait(self._pending_trials, return_when="FIRST_COMPLETED")
        self._process_pending_trials()

    # Check again to make sure that there are resources
    if self.count_available_workers() <= 0:
        logger.warning("No workers are available. This could mean workers crashed. Waiting for new workers...")
        time.sleep(self._patience)
        if self.count_available_workers() <= 0:
            raise RuntimeError(
                "Tried to execute a job, but no worker was ever available."
                "This likely means that a worker crashed or no workers were properly configured."
            )

    # At this point we can submit the job
    trial = self._client.submit(self._single_worker.run_wrapper, trial_info=trial_info, **dask_data_to_scatter)
    self._pending_trials.append(trial)