executor

Base classes and functions for handling Task execution.

Executors run a Task as a subprocess and handle all communication with other services, e.g., the eLog. They accept specific handlers to override default stream parsing.

Event handlers/hooks are implemented as standalone functions which can be added to an Executor.

Classes:

Name	Description
BaseExecutor	Abstract base class from which all Executors are derived.
Executor	Default Executor implementing all basic functionality and IPC.
MPIExecutor	Runs exactly as the Executor but submits the Task using MPI.

Exceptions

BaseExecutor

Bases: ABC

ABC to manage Task execution and communication with user services.

When running in a workflow, "tasks" (not the class instances) are submitted as Executors. The Executor manages environment setup, the actual Task submission, and communication regarding Task results and status with third party services like the eLog.

Attributes:

Methods:

Name	Description
add_hook	str, hook: Callable[[None], None]) -> None: Create a new hook to be called each time a specific event occurs.
add_default_hooks	Populate the event hooks with the default functions.
update_environment	Dict[str, str], update_path: str): Update the environment that is passed to the Task subprocess.
execute_task	Run the task as a subprocess.

Source code in lute/execution/executor.py

class BaseExecutor(ABC):
    """ABC to manage Task execution and communication with user services.

    When running in a workflow, "tasks" (not the class instances) are submitted
    as `Executors`. The Executor manages environment setup, the actual Task
    submission, and communication regarding Task results and status with third
    party services like the eLog.

    Attributes:

    Methods:
        add_hook(event: str, hook: Callable[[None], None]) -> None: Create a
            new hook to be called each time a specific event occurs.

        add_default_hooks() -> None: Populate the event hooks with the default
            functions.

        update_environment(env: Dict[str, str], update_path: str): Update the
            environment that is passed to the Task subprocess.

        execute_task(): Run the task as a subprocess.
    """

    class Hooks:
        """A container class for the Executor's event hooks.

        There is a corresponding function (hook) for each event/signal. Each
        function takes two parameters - a reference to the Executor (self) and
        a reference to the Message (msg) which includes the corresponding
        signal.
        """

        def no_pickle_mode(
            self: Self, msg: Message, proc: Optional[subprocess.Popen] = None
        ) -> None: ...

        def task_started(
            self: Self, msg: Message, proc: Optional[subprocess.Popen] = None
        ) -> None: ...

        def task_failed(
            self: Self, msg: Message, proc: Optional[subprocess.Popen] = None
        ) -> None: ...

        def task_stopped(
            self: Self, msg: Message, proc: Optional[subprocess.Popen] = None
        ) -> None: ...

        def task_done(
            self: Self, msg: Message, proc: Optional[subprocess.Popen] = None
        ) -> None: ...

        def task_cancelled(
            self: Self, msg: Message, proc: Optional[subprocess.Popen] = None
        ) -> None: ...

        def task_result(
            self: Self, msg: Message, proc: Optional[subprocess.Popen] = None
        ) -> bool: ...

        def task_log(
            self: Self, msg: Message, proc: Optional[subprocess.Popen] = None
        ) -> bool: ...

    def __init__(
        self,
        task_name: str,
        communicators: List[Communicator],
        poll_interval: float = 0.05,
    ) -> None:
        """The Executor will manage the subprocess in which `task_name` is run.

        Args:
            task_name (str): The name of the Task to be submitted. Must match
                the Task's class name exactly. The parameter specification must
                also be in a properly named model to be identified.

            communicators (List[Communicator]): A list of one or more
                communicators which manage information flow to/from the Task.
                Subclasses may have different defaults, and new functionality
                can be introduced by composing Executors with communicators.

            poll_interval (float): Time to wait between reading/writing to the
                managed subprocess. In seconds.
        """
        result: TaskResult = TaskResult(
            task_name=task_name, task_status=TaskStatus.PENDING, summary="", payload=""
        )
        task_parameters: Optional[TaskParameters] = None
        task_env: Dict[str, str] = os.environ.copy()
        self._communicators: List[Communicator] = communicators
        communicator_desc: List[str] = []
        for comm in self._communicators:
            comm.stage_communicator()
            communicator_desc.append(str(comm))

        self._analysis_desc: DescribedAnalysis = DescribedAnalysis(
            task_result=result,
            task_parameters=task_parameters,
            task_env=task_env,
            poll_interval=poll_interval,
            communicator_desc=communicator_desc,
        )
        self._tasklets: TaskletDict = {"before": None, "after": None}
        self._shell_source_script: Optional[str] = None

    def add_tasklet(
        self,
        tasklet: Callable,
        args: List[Any],
        when: str = "after",
        set_result: bool = False,
        set_summary: bool = False,
    ) -> None:
        """Add/register a tasklet to be run by the Executor.

        Adds a tasklet to be run by the Executor in addition to the main Task.
        The tasklet can be run before or after the main Task has been run.

        Args:
            tasklet (Callable[[Any], Any]): The tasklet (function) to run.

            args (List[Any]): A list of all the arguments to be passed to the
                tasklet. Arguments can include substitutions for parameters to
                be extracted from the TaskParameters object. The same jinja-like
                syntax used in configuration file substiutions is used to specify
                a parameter substitution in an argument. E.g. if a Task to be
                run has a parameter `input_file`, the parameter can be substituted
                in the tasklet argument using: `"{{ input_file  }}"`. Note that
                substitutions must be passed as strings. Conversions will be done
                during substitution if required.

            when (str): When to run the tasklet. Either `before` or `after` the
                main Task. Default is after.

            set_result (bool): Whether to use the output from the tasklet as the
                result of the main Task. Default is False.

            set_summary (bool): Whether to use the output from the tasklet as the
                summary of the main Task. Default is False.
        """
        if when not in ("before", "after"):
            logger.error("Can only run tasklet `before` or `after` Task! Ignoring...")
            return
        tasklet_tuple: Tuple[Callable[[Any], Any], List[Any], bool, bool]
        tasklet_tuple = (tasklet, args, set_result, set_summary)
        if self._tasklets[when] is None:
            self._tasklets[when] = [tasklet_tuple]
        else:
            self._tasklets[when].append(tasklet_tuple)

    def _sub_tasklet_parameters(self, args: List[Any]) -> List[Any]:
        """Substitute tasklet arguments using TaskParameters members."""
        sub_pattern = r"\{\{[^}{]*\}\}"
        new_args: List[Any] = []
        for arg in args:
            new_arg: Any = arg
            matches: List[str] = re.findall(sub_pattern, arg)
            for m in matches:
                param_name: str = m[2:-2].strip()  # Remove {{}}
                params: Any = self._analysis_desc.task_parameters
                if "." in param_name:
                    # Iterate so we can substitute e.g. {{ lute_config.run }}
                    hier_param_list: List[str] = param_name.split(".")
                    for idx, param in enumerate(hier_param_list):
                        if hasattr(params, param):
                            if idx != len(hier_param_list) - 1:
                                params = getattr(params, param)
                            param_name = param
                        else:
                            break
                if hasattr(params, param_name):
                    pattern: str = m.replace("{{", r"\{\{").replace("}}", r"\}\}")
                    sub: Any = getattr(params, param_name)
                    new_arg = re.sub(pattern, str(sub), new_arg)
                if new_arg.isnumeric():
                    new_arg = int(new_arg)
                else:
                    try:
                        new_arg = float(new_arg)
                    except ValueError:
                        pass
            new_args.append(new_arg)
        return new_args

    def _run_tasklets(self, *, when: str) -> None:
        """Run all tasklets of the specified kind."""
        if when not in self._tasklets.keys():
            logger.error(f"Ignore request to run tasklets of unknown kind: {when}")
            return
        for tasklet_spec in self._tasklets[when]:
            tasklet: Callable[[Any], Any]
            args: List[Any]
            set_result: bool
            set_summary: bool
            tasklet, args, set_result, set_summary = tasklet_spec
            args = self._sub_tasklet_parameters(args)
            logger.debug(f"Running {tasklet} with {args}")
            output: Any
            try:
                output = tasklet(*args)  # Many don't return anything
            except Exception as err:
                logger.error(f"Tasklet failed! Error: {err}")
                output = None
            # We set result payloads or summaries now, but the processing is done
            # by process_results method called sometime after the last tasklet
            if set_result and output is not None:
                if isinstance(self._analysis_desc.task_result.payload, list):
                    # We have multiple payloads to process, append to list
                    self._analysis_desc.task_result.payload.append(output)
                elif self._analysis_desc.task_result.payload != "":
                    # We have one payload already, convert to list and append
                    tmp: Any = self._analysis_desc.task_result.payload
                    self._analysis_desc.task_result.payload = []
                    self._analysis_desc.task_result.payload.append(tmp)
                    self._analysis_desc.task_result.payload.append(output)
                else:
                    # Payload == "" - i.e. hasn't been set
                    self._analysis_desc.task_result.payload = output
            if set_summary and output is not None:
                if isinstance(self._analysis_desc.task_result.summary, list):
                    # We have multiple summary objects to process, append to list
                    self._analysis_desc.task_result.summary.append(output)
                elif self._analysis_desc.task_result.summary != "":
                    # We have one summary already, convert to list and append
                    tmp: Any = self._analysis_desc.task_result.summary
                    self._analysis_desc.task_result.summary = []
                    self._analysis_desc.task_result.summary.append(tmp)
                    self._analysis_desc.task_result.summary.append(output)
                else:
                    # Summary == "" - i.e. hasn't been set
                    self._analysis_desc.task_result.summary = output

    def add_hook(
        self,
        event: str,
        hook: Callable[[Self, Message, Optional[subprocess.Popen]], None],
    ) -> None:
        """Add a new hook.

        Each hook is a function called any time the Executor receives a signal
        for a particular event, e.g. Task starts, Task ends, etc. Calling this
        method will remove any hook that currently exists for the event. I.e.
        only one hook can be called per event at a time. Creating hooks for
        events which do not exist is not allowed.

        Args:
            event (str): The event for which the hook will be called.

            hook (Callable[[None], None]) The function to be called during each
                occurrence of the event.
        """
        if event.upper() in LUTE_SIGNALS:
            setattr(self.Hooks, event.lower(), hook)

    @abstractmethod
    def add_default_hooks(self) -> None:
        """Populate the set of default event hooks."""

        ...

    def update_environment(
        self, env: Dict[str, str], update_path: str = "prepend"
    ) -> None:
        """Update the stored set of environment variables.

        These are passed to the subprocess to setup its environment.

        Args:
            env (Dict[str, str]): A dictionary of "VAR":"VALUE" pairs of
                environment variables to be added to the subprocess environment.
                If any variables already exist, the new variables will
                overwrite them (except PATH, see below).

            update_path (str): If PATH is present in the new set of variables,
                this argument determines how the old PATH is dealt with. There
                are three options:
                * "prepend" : The new PATH values are prepended to the old ones.
                * "append" : The new PATH values are appended to the old ones.
                * "overwrite" : The old PATH is overwritten by the new one.
                "prepend" is the default option. If PATH is not present in the
                current environment, the new PATH is used without modification.
        """
        if "PATH" in env:
            sep: str = os.pathsep
            if update_path == "prepend":
                env["PATH"] = (
                    f"{env['PATH']}{sep}{self._analysis_desc.task_env['PATH']}"
                )
            elif update_path == "append":
                env["PATH"] = (
                    f"{self._analysis_desc.task_env['PATH']}{sep}{env['PATH']}"
                )
            elif update_path == "overwrite":
                pass
            else:
                raise ValueError(
                    (
                        f"{update_path} is not a valid option for `update_path`!"
                        " Options are: prepend, append, overwrite."
                    )
                )
        os.environ.update(env)
        self._analysis_desc.task_env.update(env)

    def shell_source(self, env: str) -> None:
        """Source a script.

        Unlike `update_environment` this method sources a new file.

        We prepend a token to each environment variable. This allows the initial
        part of the Task to be run using the appropriate environment.

        The environment variables containing the token will be swapped in using
        their appropriate form prior to the actual execution of Task code.

        Args:
            env (str): Path to the script to source.
        """
        self._shell_source_script = env

    def _shell_source(self) -> None:
        """Actually shell source step.

        This is run prior to Task execution.
        """
        if self._shell_source_script is None:
            logger.error("Called _shell_source without defining source script!")
            return
        if not os.path.exists(self._shell_source_script):
            logger.error(f"Cannot source environment from {self._shell_source_script}!")
            return

        script: str = (
            f"set -a\n"
            f'source "{self._shell_source_script}" >/dev/null\n'
            f'{sys.executable} -c "import os; print(dict(os.environ))"\n'
        )
        logger.info(f"Sourcing file {self._shell_source_script}")
        o, e = subprocess.Popen(
            ["bash", "-c", script], stdout=subprocess.PIPE
        ).communicate()
        tmp_environment: Dict[str, str] = eval(o)
        new_environment: Dict[str, str] = {}
        for key, value in tmp_environment.items():
            # Make sure LUTE vars are available
            if "LUTE_" in key or key in ("RUN", "EXPERIMENT"):
                new_environment[key] = value
            else:
                new_environment[f"LUTE_TENV_{key}"] = value
        self._analysis_desc.task_env = new_environment

    def _pre_task(self) -> None:
        """Any actions to be performed before task submission.

        This method may or may not be used by subclasses. It may be useful
        for logging etc.
        """
        # This prevents the Executors in managed_tasks.py from all acquiring
        # resources like sockets.
        for communicator in self._communicators:
            communicator.delayed_setup()
            # Not great, but experience shows we need a bit of time to setup
            # network.
            time.sleep(0.1)
        # Propagate any env vars setup by Communicators - only update LUTE_ vars
        tmp: Dict[str, str] = {
            key: os.environ[key] for key in os.environ if "LUTE_" in key
        }
        self._analysis_desc.task_env.update(tmp)

    def _submit_task(self, cmd: str) -> subprocess.Popen:
        proc: subprocess.Popen = subprocess.Popen(
            cmd.split(),
            stdout=subprocess.PIPE,
            stderr=subprocess.PIPE,
            env=self._analysis_desc.task_env,
        )
        os.set_blocking(proc.stdout.fileno(), False)
        os.set_blocking(proc.stderr.fileno(), False)
        return proc

    @abstractmethod
    def _task_loop(self, proc: subprocess.Popen) -> None:
        """Actions to perform while the Task is running.

        This function is run in the body of a loop until the Task signals
        that its finished.
        """
        ...

    @abstractmethod
    def _finalize_task(self, proc: subprocess.Popen) -> None:
        """Any actions to be performed after the Task has ended.

        Examples include a final clearing of the pipes, retrieving results,
        reporting to third party services, etc.
        """
        ...

    def _submit_cmd(self, executable_path: str, params: str) -> str:
        """Return a formatted command for launching Task subprocess.

        May be overridden by subclasses.

        The default submission uses the Executor environment. This ensures that
        all necessary packages (e.g. Pydantic for validation) are available to
        the startup scripts. If a Task has a different environment it will be
        swapped prior to execution.

        Args:
            executable_path (str): Path to the LUTE subprocess script.

            params (str): String of formatted command-line arguments.

        Returns:
            cmd (str): Appropriately formatted command for this Executor.
        """
        cmd: str = ""
        if __debug__:
            cmd = f"{sys.executable} -B {executable_path} {params}"
        else:
            cmd = f"{sys.executable} -OB {executable_path} {params}"

        return cmd

    def execute_task(self) -> None:
        """Run the requested Task as a subprocess."""
        self._pre_task()
        lute_path: Optional[str] = os.getenv("LUTE_PATH")
        if lute_path is None:
            logger.debug("Absolute path to subprocess_task.py not found.")
            lute_path = os.path.abspath(f"{os.path.dirname(__file__)}/../..")
            self.update_environment({"LUTE_PATH": lute_path})
        executable_path: str = f"{lute_path}/subprocess_task.py"
        config_path: str = self._analysis_desc.task_env["LUTE_CONFIGPATH"]
        params: str = f"-c {config_path} -t {self._analysis_desc.task_result.task_name}"

        if self._shell_source_script is not None:
            self._shell_source()
        cmd: str = self._submit_cmd(executable_path, params)
        proc: subprocess.Popen = self._submit_task(cmd)

        while self._task_is_running(proc):
            self._task_loop(proc)
            time.sleep(self._analysis_desc.poll_interval)

        os.set_blocking(proc.stdout.fileno(), True)
        os.set_blocking(proc.stderr.fileno(), True)

        self._finalize_task(proc)
        proc.stdout.close()
        proc.stderr.close()
        proc.wait()
        if ret := proc.returncode:
            logger.warning(f"Task failed with return code: {ret}")
            self._analysis_desc.task_result.task_status = TaskStatus.FAILED
            self.Hooks.task_failed(self, msg=Message())
        elif self._analysis_desc.task_result.task_status == TaskStatus.RUNNING:
            # Ret code is 0, no exception was thrown, task forgot to set status
            self._analysis_desc.task_result.task_status = TaskStatus.COMPLETED
            logger.debug(f"Task did not change from RUNNING status. Assume COMPLETED.")
            self.Hooks.task_done(self, msg=Message())
        if self._tasklets["after"] is not None:
            # Tasklets before results processing since they may create result
            self._run_tasklets(when="after")

        try:
            self.process_results()
        except Exception as err:
            logger.critical(
                f"Unable to process results! Downstream Tasks may fail! {err}"
            )

        try:
            self._store_configuration()
        except Exception as err:
            logger.critical(
                f"Unable to store configuration! Downstream Tasks may fail! {err}"
            )

        for comm in self._communicators:
            comm.clear_communicator()

        if self._analysis_desc.task_result.task_status == TaskStatus.FAILED:
            logger.info("Exiting after Task failure. Result recorded.")
            sys.exit(-1)

    def _store_configuration(self) -> None:
        """Store configuration and results in the LUTE database."""
        record_analysis_db(copy.deepcopy(self._analysis_desc))

    def _task_is_running(self, proc: subprocess.Popen) -> bool:
        """Whether a subprocess is running.

        Args:
            proc (subprocess.Popen): The subprocess to determine the run status
                of.

        Returns:
            bool: Is the subprocess task running.
        """
        # Add additional conditions - don't want to exit main loop
        # if only stopped
        task_status: TaskStatus = self._analysis_desc.task_result.task_status
        is_running: bool = task_status != TaskStatus.COMPLETED
        is_running &= task_status != TaskStatus.CANCELLED
        is_running &= task_status != TaskStatus.TIMEDOUT
        return proc.poll() is None and is_running

    def _stop(self, proc: subprocess.Popen) -> None:
        """Stop the Task subprocess."""
        os.kill(proc.pid, signal.SIGTSTP)
        self._analysis_desc.task_result.task_status = TaskStatus.STOPPED

    def _continue(self, proc: subprocess.Popen) -> None:
        """Resume a stopped Task subprocess."""
        os.kill(proc.pid, signal.SIGCONT)
        self._analysis_desc.task_result.task_status = TaskStatus.RUNNING

    def _set_result_from_parameters(self) -> None:
        """Use TaskParameters object to set TaskResult fields.

        A result may be defined in terms of specific parameters. This is most
        useful for ThirdPartyTasks which would not otherwise have an easy way of
        reporting what the TaskResult is. There are two options for specifying
        results from parameters:
            1. A single parameter (Field) of the model has an attribute
               `is_result`. This is a bool indicating that this parameter points
               to a result. E.g. a parameter `output` may set `is_result=True`.
            2. The `TaskParameters.Config` has a `result_from_params` attribute.
               This is an appropriate option if the result is determinable for
               the Task, but it is not easily defined by a single parameter. The
               TaskParameters.Config.result_from_param can be set by a custom
               validator, e.g. to combine the values of multiple parameters into
               a single result. E.g. an `out_dir` and `out_file` parameter used
               together specify the result. Currently only string specifiers are
               supported.

        A TaskParameters object specifies that it contains information about the
        result by setting a single config option:
                        TaskParameters.Config.set_result=True
        In general, this method should only be called when the above condition is
        met, however, there are minimal checks in it as well.
        """
        # This method shouldn't be called unless appropriate
        # But we will add extra guards here
        if self._analysis_desc.task_parameters is None:
            logger.debug(
                "Cannot set result from TaskParameters. TaskParameters is None!"
            )
            return
        if (
            not hasattr(self._analysis_desc.task_parameters.Config, "set_result")
            or not self._analysis_desc.task_parameters.Config.set_result
        ):
            logger.debug(
                "Cannot set result from TaskParameters. `set_result` not specified!"
            )
            return

        # First try to set from result_from_params (faster)
        if hasattr(self._analysis_desc.task_parameters, "_result_from_params"):
            result_from_params: str = (
                self._analysis_desc.task_parameters._result_from_params
            )
            logger.info(f"TaskResult specified as {result_from_params}.")
            self._analysis_desc.task_result.payload = result_from_params
            del self._analysis_desc.task_parameters._result_from_params
        else:
            # Iterate parameters to find the one that is the result
            schema: Dict[str, Any] = self._analysis_desc.task_parameters.schema()
            for param, value in self._analysis_desc.task_parameters.dict().items():
                if param == "_result_from_params":
                    continue
                param_attrs: Dict[str, Any]
                if isinstance(value, TemplateParameters):
                    # Extract TemplateParameters if needed
                    value = value.params
                    extra_models: List[str] = schema["definitions"].keys()
                    for model in extra_models:
                        if model in ("AnalysisHeader", "TemplateConfig"):
                            continue
                        if param in schema["definitions"][model]["properties"]:
                            param_attrs = schema["definitions"][model]["properties"][
                                param
                            ]
                            break
                    else:
                        if isinstance(
                            self._analysis_desc.task_parameters, ThirdPartyParameters
                        ):
                            param_attrs = self._analysis_desc.task_parameters._unknown_template_params[
                                param
                            ]
                        else:
                            raise ValueError(
                                f"No parameter schema for {param}. Check model!"
                            )
                else:
                    param_attrs = schema["properties"][param]
                if "is_result" in param_attrs:
                    is_result: bool = param_attrs["is_result"]
                    if isinstance(is_result, bool) and is_result:
                        logger.info(f"TaskResult specified as {value}.")
                        self._analysis_desc.task_result.payload = value
                    else:
                        logger.debug(
                            (
                                f"{param} specified as result! But specifier is of "
                                f"wrong type: {type(is_result)}!"
                            )
                        )
                    break  # We should only have 1 result-like parameter!

        # If we get this far and haven't changed the payload we should complain
        if self._analysis_desc.task_result.payload == "":
            task_name: str = self._analysis_desc.task_result.task_name
            logger.debug(
                (
                    f"{task_name} specified result be set from {task_name}Parameters,"
                    " but no result provided! Check model definition!"
                )
            )
        # Now check for impl_schemas and pass to result.impl_schemas
        # Currently unused
        impl_schemas: Optional[str] = (
            self._analysis_desc.task_parameters.Config.impl_schemas
        )
        self._analysis_desc.task_result.impl_schemas = impl_schemas
        # If we set_result but didn't get schema information we should complain
        if self._analysis_desc.task_result.impl_schemas is None:
            task_name: str = self._analysis_desc.task_result.task_name
            logger.debug(
                (
                    f"{task_name} specified result be set from {task_name}Parameters,"
                    " but no schema provided! Check model definition!"
                )
            )

    def process_results(self) -> None:
        """Perform any necessary steps to process TaskResults object.

        Processing will depend on subclass. Examples of steps include, moving
        files, converting file formats, compiling plots/figures into an HTML
        file, etc.
        """
        self._process_results()

    @abstractmethod
    def _process_results(self) -> None: ...

Hooks

A container class for the Executor's event hooks.

There is a corresponding function (hook) for each event/signal. Each function takes two parameters - a reference to the Executor (self) and a reference to the Message (msg) which includes the corresponding signal.

Source code in lute/execution/executor.py

class Hooks:
    """A container class for the Executor's event hooks.

    There is a corresponding function (hook) for each event/signal. Each
    function takes two parameters - a reference to the Executor (self) and
    a reference to the Message (msg) which includes the corresponding
    signal.
    """

    def no_pickle_mode(
        self: Self, msg: Message, proc: Optional[subprocess.Popen] = None
    ) -> None: ...

    def task_started(
        self: Self, msg: Message, proc: Optional[subprocess.Popen] = None
    ) -> None: ...

    def task_failed(
        self: Self, msg: Message, proc: Optional[subprocess.Popen] = None
    ) -> None: ...

    def task_stopped(
        self: Self, msg: Message, proc: Optional[subprocess.Popen] = None
    ) -> None: ...

    def task_done(
        self: Self, msg: Message, proc: Optional[subprocess.Popen] = None
    ) -> None: ...

    def task_cancelled(
        self: Self, msg: Message, proc: Optional[subprocess.Popen] = None
    ) -> None: ...

    def task_result(
        self: Self, msg: Message, proc: Optional[subprocess.Popen] = None
    ) -> bool: ...

    def task_log(
        self: Self, msg: Message, proc: Optional[subprocess.Popen] = None
    ) -> bool: ...

__init__(task_name, communicators, poll_interval=0.05)

The Executor will manage the subprocess in which task_name is run.

Parameters:

Name	Type	Description	Default
task_name	str	The name of the Task to be submitted. Must match the Task's class name exactly. The parameter specification must also be in a properly named model to be identified.	required
communicators	List[Communicator]	A list of one or more communicators which manage information flow to/from the Task. Subclasses may have different defaults, and new functionality can be introduced by composing Executors with communicators.	required
poll_interval	float	Time to wait between reading/writing to the managed subprocess. In seconds.	0.05

Source code in lute/execution/executor.py

def __init__(
    self,
    task_name: str,
    communicators: List[Communicator],
    poll_interval: float = 0.05,
) -> None:
    """The Executor will manage the subprocess in which `task_name` is run.

    Args:
        task_name (str): The name of the Task to be submitted. Must match
            the Task's class name exactly. The parameter specification must
            also be in a properly named model to be identified.

        communicators (List[Communicator]): A list of one or more
            communicators which manage information flow to/from the Task.
            Subclasses may have different defaults, and new functionality
            can be introduced by composing Executors with communicators.

        poll_interval (float): Time to wait between reading/writing to the
            managed subprocess. In seconds.
    """
    result: TaskResult = TaskResult(
        task_name=task_name, task_status=TaskStatus.PENDING, summary="", payload=""
    )
    task_parameters: Optional[TaskParameters] = None
    task_env: Dict[str, str] = os.environ.copy()
    self._communicators: List[Communicator] = communicators
    communicator_desc: List[str] = []
    for comm in self._communicators:
        comm.stage_communicator()
        communicator_desc.append(str(comm))

    self._analysis_desc: DescribedAnalysis = DescribedAnalysis(
        task_result=result,
        task_parameters=task_parameters,
        task_env=task_env,
        poll_interval=poll_interval,
        communicator_desc=communicator_desc,
    )
    self._tasklets: TaskletDict = {"before": None, "after": None}
    self._shell_source_script: Optional[str] = None

_continue(proc)

Resume a stopped Task subprocess.

Source code in lute/execution/executor.py

def _continue(self, proc: subprocess.Popen) -> None:
    """Resume a stopped Task subprocess."""
    os.kill(proc.pid, signal.SIGCONT)
    self._analysis_desc.task_result.task_status = TaskStatus.RUNNING

_finalize_task(proc) abstractmethod

Any actions to be performed after the Task has ended.

Examples include a final clearing of the pipes, retrieving results, reporting to third party services, etc.

Source code in lute/execution/executor.py

@abstractmethod
def _finalize_task(self, proc: subprocess.Popen) -> None:
    """Any actions to be performed after the Task has ended.

    Examples include a final clearing of the pipes, retrieving results,
    reporting to third party services, etc.
    """
    ...

_pre_task()

Any actions to be performed before task submission.

This method may or may not be used by subclasses. It may be useful for logging etc.

Source code in lute/execution/executor.py

def _pre_task(self) -> None:
    """Any actions to be performed before task submission.

    This method may or may not be used by subclasses. It may be useful
    for logging etc.
    """
    # This prevents the Executors in managed_tasks.py from all acquiring
    # resources like sockets.
    for communicator in self._communicators:
        communicator.delayed_setup()
        # Not great, but experience shows we need a bit of time to setup
        # network.
        time.sleep(0.1)
    # Propagate any env vars setup by Communicators - only update LUTE_ vars
    tmp: Dict[str, str] = {
        key: os.environ[key] for key in os.environ if "LUTE_" in key
    }
    self._analysis_desc.task_env.update(tmp)

_run_tasklets(*, when)

Run all tasklets of the specified kind.

Source code in lute/execution/executor.py

def _run_tasklets(self, *, when: str) -> None:
    """Run all tasklets of the specified kind."""
    if when not in self._tasklets.keys():
        logger.error(f"Ignore request to run tasklets of unknown kind: {when}")
        return
    for tasklet_spec in self._tasklets[when]:
        tasklet: Callable[[Any], Any]
        args: List[Any]
        set_result: bool
        set_summary: bool
        tasklet, args, set_result, set_summary = tasklet_spec
        args = self._sub_tasklet_parameters(args)
        logger.debug(f"Running {tasklet} with {args}")
        output: Any
        try:
            output = tasklet(*args)  # Many don't return anything
        except Exception as err:
            logger.error(f"Tasklet failed! Error: {err}")
            output = None
        # We set result payloads or summaries now, but the processing is done
        # by process_results method called sometime after the last tasklet
        if set_result and output is not None:
            if isinstance(self._analysis_desc.task_result.payload, list):
                # We have multiple payloads to process, append to list
                self._analysis_desc.task_result.payload.append(output)
            elif self._analysis_desc.task_result.payload != "":
                # We have one payload already, convert to list and append
                tmp: Any = self._analysis_desc.task_result.payload
                self._analysis_desc.task_result.payload = []
                self._analysis_desc.task_result.payload.append(tmp)
                self._analysis_desc.task_result.payload.append(output)
            else:
                # Payload == "" - i.e. hasn't been set
                self._analysis_desc.task_result.payload = output
        if set_summary and output is not None:
            if isinstance(self._analysis_desc.task_result.summary, list):
                # We have multiple summary objects to process, append to list
                self._analysis_desc.task_result.summary.append(output)
            elif self._analysis_desc.task_result.summary != "":
                # We have one summary already, convert to list and append
                tmp: Any = self._analysis_desc.task_result.summary
                self._analysis_desc.task_result.summary = []
                self._analysis_desc.task_result.summary.append(tmp)
                self._analysis_desc.task_result.summary.append(output)
            else:
                # Summary == "" - i.e. hasn't been set
                self._analysis_desc.task_result.summary = output

_set_result_from_parameters()

Use TaskParameters object to set TaskResult fields.

A result may be defined in terms of specific parameters. This is most useful for ThirdPartyTasks which would not otherwise have an easy way of reporting what the TaskResult is. There are two options for specifying results from parameters: 1. A single parameter (Field) of the model has an attribute is_result. This is a bool indicating that this parameter points to a result. E.g. a parameter output may set is_result=True. 2. The TaskParameters.Config has a result_from_params attribute. This is an appropriate option if the result is determinable for the Task, but it is not easily defined by a single parameter. The TaskParameters.Config.result_from_param can be set by a custom validator, e.g. to combine the values of multiple parameters into a single result. E.g. an out_dir and out_file parameter used together specify the result. Currently only string specifiers are supported.

A TaskParameters object specifies that it contains information about the result by setting a single config option: TaskParameters.Config.set_result=True In general, this method should only be called when the above condition is met, however, there are minimal checks in it as well.

Source code in lute/execution/executor.py

def _set_result_from_parameters(self) -> None:
    """Use TaskParameters object to set TaskResult fields.

    A result may be defined in terms of specific parameters. This is most
    useful for ThirdPartyTasks which would not otherwise have an easy way of
    reporting what the TaskResult is. There are two options for specifying
    results from parameters:
        1. A single parameter (Field) of the model has an attribute
           `is_result`. This is a bool indicating that this parameter points
           to a result. E.g. a parameter `output` may set `is_result=True`.
        2. The `TaskParameters.Config` has a `result_from_params` attribute.
           This is an appropriate option if the result is determinable for
           the Task, but it is not easily defined by a single parameter. The
           TaskParameters.Config.result_from_param can be set by a custom
           validator, e.g. to combine the values of multiple parameters into
           a single result. E.g. an `out_dir` and `out_file` parameter used
           together specify the result. Currently only string specifiers are
           supported.

    A TaskParameters object specifies that it contains information about the
    result by setting a single config option:
                    TaskParameters.Config.set_result=True
    In general, this method should only be called when the above condition is
    met, however, there are minimal checks in it as well.
    """
    # This method shouldn't be called unless appropriate
    # But we will add extra guards here
    if self._analysis_desc.task_parameters is None:
        logger.debug(
            "Cannot set result from TaskParameters. TaskParameters is None!"
        )
        return
    if (
        not hasattr(self._analysis_desc.task_parameters.Config, "set_result")
        or not self._analysis_desc.task_parameters.Config.set_result
    ):
        logger.debug(
            "Cannot set result from TaskParameters. `set_result` not specified!"
        )
        return

    # First try to set from result_from_params (faster)
    if hasattr(self._analysis_desc.task_parameters, "_result_from_params"):
        result_from_params: str = (
            self._analysis_desc.task_parameters._result_from_params
        )
        logger.info(f"TaskResult specified as {result_from_params}.")
        self._analysis_desc.task_result.payload = result_from_params
        del self._analysis_desc.task_parameters._result_from_params
    else:
        # Iterate parameters to find the one that is the result
        schema: Dict[str, Any] = self._analysis_desc.task_parameters.schema()
        for param, value in self._analysis_desc.task_parameters.dict().items():
            if param == "_result_from_params":
                continue
            param_attrs: Dict[str, Any]
            if isinstance(value, TemplateParameters):
                # Extract TemplateParameters if needed
                value = value.params
                extra_models: List[str] = schema["definitions"].keys()
                for model in extra_models:
                    if model in ("AnalysisHeader", "TemplateConfig"):
                        continue
                    if param in schema["definitions"][model]["properties"]:
                        param_attrs = schema["definitions"][model]["properties"][
                            param
                        ]
                        break
                else:
                    if isinstance(
                        self._analysis_desc.task_parameters, ThirdPartyParameters
                    ):
                        param_attrs = self._analysis_desc.task_parameters._unknown_template_params[
                            param
                        ]
                    else:
                        raise ValueError(
                            f"No parameter schema for {param}. Check model!"
                        )
            else:
                param_attrs = schema["properties"][param]
            if "is_result" in param_attrs:
                is_result: bool = param_attrs["is_result"]
                if isinstance(is_result, bool) and is_result:
                    logger.info(f"TaskResult specified as {value}.")
                    self._analysis_desc.task_result.payload = value
                else:
                    logger.debug(
                        (
                            f"{param} specified as result! But specifier is of "
                            f"wrong type: {type(is_result)}!"
                        )
                    )
                break  # We should only have 1 result-like parameter!

    # If we get this far and haven't changed the payload we should complain
    if self._analysis_desc.task_result.payload == "":
        task_name: str = self._analysis_desc.task_result.task_name
        logger.debug(
            (
                f"{task_name} specified result be set from {task_name}Parameters,"
                " but no result provided! Check model definition!"
            )
        )
    # Now check for impl_schemas and pass to result.impl_schemas
    # Currently unused
    impl_schemas: Optional[str] = (
        self._analysis_desc.task_parameters.Config.impl_schemas
    )
    self._analysis_desc.task_result.impl_schemas = impl_schemas
    # If we set_result but didn't get schema information we should complain
    if self._analysis_desc.task_result.impl_schemas is None:
        task_name: str = self._analysis_desc.task_result.task_name
        logger.debug(
            (
                f"{task_name} specified result be set from {task_name}Parameters,"
                " but no schema provided! Check model definition!"
            )
        )

_shell_source()

Actually shell source step.

This is run prior to Task execution.

Source code in lute/execution/executor.py

def _shell_source(self) -> None:
    """Actually shell source step.

    This is run prior to Task execution.
    """
    if self._shell_source_script is None:
        logger.error("Called _shell_source without defining source script!")
        return
    if not os.path.exists(self._shell_source_script):
        logger.error(f"Cannot source environment from {self._shell_source_script}!")
        return

    script: str = (
        f"set -a\n"
        f'source "{self._shell_source_script}" >/dev/null\n'
        f'{sys.executable} -c "import os; print(dict(os.environ))"\n'
    )
    logger.info(f"Sourcing file {self._shell_source_script}")
    o, e = subprocess.Popen(
        ["bash", "-c", script], stdout=subprocess.PIPE
    ).communicate()
    tmp_environment: Dict[str, str] = eval(o)
    new_environment: Dict[str, str] = {}
    for key, value in tmp_environment.items():
        # Make sure LUTE vars are available
        if "LUTE_" in key or key in ("RUN", "EXPERIMENT"):
            new_environment[key] = value
        else:
            new_environment[f"LUTE_TENV_{key}"] = value
    self._analysis_desc.task_env = new_environment

_stop(proc)

Stop the Task subprocess.

Source code in lute/execution/executor.py

def _stop(self, proc: subprocess.Popen) -> None:
    """Stop the Task subprocess."""
    os.kill(proc.pid, signal.SIGTSTP)
    self._analysis_desc.task_result.task_status = TaskStatus.STOPPED

_store_configuration()

Store configuration and results in the LUTE database.

Source code in lute/execution/executor.py

def _store_configuration(self) -> None:
    """Store configuration and results in the LUTE database."""
    record_analysis_db(copy.deepcopy(self._analysis_desc))

_sub_tasklet_parameters(args)

Substitute tasklet arguments using TaskParameters members.

Source code in lute/execution/executor.py

def _sub_tasklet_parameters(self, args: List[Any]) -> List[Any]:
    """Substitute tasklet arguments using TaskParameters members."""
    sub_pattern = r"\{\{[^}{]*\}\}"
    new_args: List[Any] = []
    for arg in args:
        new_arg: Any = arg
        matches: List[str] = re.findall(sub_pattern, arg)
        for m in matches:
            param_name: str = m[2:-2].strip()  # Remove {{}}
            params: Any = self._analysis_desc.task_parameters
            if "." in param_name:
                # Iterate so we can substitute e.g. {{ lute_config.run }}
                hier_param_list: List[str] = param_name.split(".")
                for idx, param in enumerate(hier_param_list):
                    if hasattr(params, param):
                        if idx != len(hier_param_list) - 1:
                            params = getattr(params, param)
                        param_name = param
                    else:
                        break
            if hasattr(params, param_name):
                pattern: str = m.replace("{{", r"\{\{").replace("}}", r"\}\}")
                sub: Any = getattr(params, param_name)
                new_arg = re.sub(pattern, str(sub), new_arg)
            if new_arg.isnumeric():
                new_arg = int(new_arg)
            else:
                try:
                    new_arg = float(new_arg)
                except ValueError:
                    pass
        new_args.append(new_arg)
    return new_args

_submit_cmd(executable_path, params)

Return a formatted command for launching Task subprocess.

May be overridden by subclasses.

The default submission uses the Executor environment. This ensures that all necessary packages (e.g. Pydantic for validation) are available to the startup scripts. If a Task has a different environment it will be swapped prior to execution.

Parameters:

Name	Type	Description	Default
executable_path	str	Path to the LUTE subprocess script.	required
params	str	String of formatted command-line arguments.	required

Returns:

Name	Type	Description
cmd	str	Appropriately formatted command for this Executor.

Source code in lute/execution/executor.py

def _submit_cmd(self, executable_path: str, params: str) -> str:
    """Return a formatted command for launching Task subprocess.

    May be overridden by subclasses.

    The default submission uses the Executor environment. This ensures that
    all necessary packages (e.g. Pydantic for validation) are available to
    the startup scripts. If a Task has a different environment it will be
    swapped prior to execution.

    Args:
        executable_path (str): Path to the LUTE subprocess script.

        params (str): String of formatted command-line arguments.

    Returns:
        cmd (str): Appropriately formatted command for this Executor.
    """
    cmd: str = ""
    if __debug__:
        cmd = f"{sys.executable} -B {executable_path} {params}"
    else:
        cmd = f"{sys.executable} -OB {executable_path} {params}"

    return cmd

_task_is_running(proc)

Whether a subprocess is running.

Parameters:

Name	Type	Description	Default
proc	Popen	The subprocess to determine the run status of.	required

Returns:

Name	Type	Description
bool	bool	Is the subprocess task running.

Source code in lute/execution/executor.py

def _task_is_running(self, proc: subprocess.Popen) -> bool:
    """Whether a subprocess is running.

    Args:
        proc (subprocess.Popen): The subprocess to determine the run status
            of.

    Returns:
        bool: Is the subprocess task running.
    """
    # Add additional conditions - don't want to exit main loop
    # if only stopped
    task_status: TaskStatus = self._analysis_desc.task_result.task_status
    is_running: bool = task_status != TaskStatus.COMPLETED
    is_running &= task_status != TaskStatus.CANCELLED
    is_running &= task_status != TaskStatus.TIMEDOUT
    return proc.poll() is None and is_running

_task_loop(proc) abstractmethod

Actions to perform while the Task is running.

This function is run in the body of a loop until the Task signals that its finished.

Source code in lute/execution/executor.py

@abstractmethod
def _task_loop(self, proc: subprocess.Popen) -> None:
    """Actions to perform while the Task is running.

    This function is run in the body of a loop until the Task signals
    that its finished.
    """
    ...

add_default_hooks() abstractmethod

Populate the set of default event hooks.

Source code in lute/execution/executor.py

@abstractmethod
def add_default_hooks(self) -> None:
    """Populate the set of default event hooks."""

    ...

add_hook(event, hook)

Add a new hook.

Each hook is a function called any time the Executor receives a signal for a particular event, e.g. Task starts, Task ends, etc. Calling this method will remove any hook that currently exists for the event. I.e. only one hook can be called per event at a time. Creating hooks for events which do not exist is not allowed.

Parameters:

Name	Type	Description	Default
event	str	The event for which the hook will be called.	required

Source code in lute/execution/executor.py

def add_hook(
    self,
    event: str,
    hook: Callable[[Self, Message, Optional[subprocess.Popen]], None],
) -> None:
    """Add a new hook.

    Each hook is a function called any time the Executor receives a signal
    for a particular event, e.g. Task starts, Task ends, etc. Calling this
    method will remove any hook that currently exists for the event. I.e.
    only one hook can be called per event at a time. Creating hooks for
    events which do not exist is not allowed.

    Args:
        event (str): The event for which the hook will be called.

        hook (Callable[[None], None]) The function to be called during each
            occurrence of the event.
    """
    if event.upper() in LUTE_SIGNALS:
        setattr(self.Hooks, event.lower(), hook)

add_tasklet(tasklet, args, when='after', set_result=False, set_summary=False)

Add/register a tasklet to be run by the Executor.

Adds a tasklet to be run by the Executor in addition to the main Task. The tasklet can be run before or after the main Task has been run.

Parameters:

Name	Type	Description	Default
tasklet	Callable[[Any], Any]	The tasklet (function) to run.	required
args	List[Any]	A list of all the arguments to be passed to the tasklet. Arguments can include substitutions for parameters to be extracted from the TaskParameters object. The same jinja-like syntax used in configuration file substiutions is used to specify a parameter substitution in an argument. E.g. if a Task to be run has a parameter input_file, the parameter can be substituted in the tasklet argument using: "{{ input_file }}". Note that substitutions must be passed as strings. Conversions will be done during substitution if required.	required
when	str	When to run the tasklet. Either before or after the main Task. Default is after.	'after'
set_result	bool	Whether to use the output from the tasklet as the result of the main Task. Default is False.	False
set_summary	bool	Whether to use the output from the tasklet as the summary of the main Task. Default is False.	False

Source code in lute/execution/executor.py

def add_tasklet(
    self,
    tasklet: Callable,
    args: List[Any],
    when: str = "after",
    set_result: bool = False,
    set_summary: bool = False,
) -> None:
    """Add/register a tasklet to be run by the Executor.

    Adds a tasklet to be run by the Executor in addition to the main Task.
    The tasklet can be run before or after the main Task has been run.

    Args:
        tasklet (Callable[[Any], Any]): The tasklet (function) to run.

        args (List[Any]): A list of all the arguments to be passed to the
            tasklet. Arguments can include substitutions for parameters to
            be extracted from the TaskParameters object. The same jinja-like
            syntax used in configuration file substiutions is used to specify
            a parameter substitution in an argument. E.g. if a Task to be
            run has a parameter `input_file`, the parameter can be substituted
            in the tasklet argument using: `"{{ input_file  }}"`. Note that
            substitutions must be passed as strings. Conversions will be done
            during substitution if required.

        when (str): When to run the tasklet. Either `before` or `after` the
            main Task. Default is after.

        set_result (bool): Whether to use the output from the tasklet as the
            result of the main Task. Default is False.

        set_summary (bool): Whether to use the output from the tasklet as the
            summary of the main Task. Default is False.
    """
    if when not in ("before", "after"):
        logger.error("Can only run tasklet `before` or `after` Task! Ignoring...")
        return
    tasklet_tuple: Tuple[Callable[[Any], Any], List[Any], bool, bool]
    tasklet_tuple = (tasklet, args, set_result, set_summary)
    if self._tasklets[when] is None:
        self._tasklets[when] = [tasklet_tuple]
    else:
        self._tasklets[when].append(tasklet_tuple)

execute_task()

Run the requested Task as a subprocess.

Source code in lute/execution/executor.py

def execute_task(self) -> None:
    """Run the requested Task as a subprocess."""
    self._pre_task()
    lute_path: Optional[str] = os.getenv("LUTE_PATH")
    if lute_path is None:
        logger.debug("Absolute path to subprocess_task.py not found.")
        lute_path = os.path.abspath(f"{os.path.dirname(__file__)}/../..")
        self.update_environment({"LUTE_PATH": lute_path})
    executable_path: str = f"{lute_path}/subprocess_task.py"
    config_path: str = self._analysis_desc.task_env["LUTE_CONFIGPATH"]
    params: str = f"-c {config_path} -t {self._analysis_desc.task_result.task_name}"

    if self._shell_source_script is not None:
        self._shell_source()
    cmd: str = self._submit_cmd(executable_path, params)
    proc: subprocess.Popen = self._submit_task(cmd)

    while self._task_is_running(proc):
        self._task_loop(proc)
        time.sleep(self._analysis_desc.poll_interval)

    os.set_blocking(proc.stdout.fileno(), True)
    os.set_blocking(proc.stderr.fileno(), True)

    self._finalize_task(proc)
    proc.stdout.close()
    proc.stderr.close()
    proc.wait()
    if ret := proc.returncode:
        logger.warning(f"Task failed with return code: {ret}")
        self._analysis_desc.task_result.task_status = TaskStatus.FAILED
        self.Hooks.task_failed(self, msg=Message())
    elif self._analysis_desc.task_result.task_status == TaskStatus.RUNNING:
        # Ret code is 0, no exception was thrown, task forgot to set status
        self._analysis_desc.task_result.task_status = TaskStatus.COMPLETED
        logger.debug(f"Task did not change from RUNNING status. Assume COMPLETED.")
        self.Hooks.task_done(self, msg=Message())
    if self._tasklets["after"] is not None:
        # Tasklets before results processing since they may create result
        self._run_tasklets(when="after")

    try:
        self.process_results()
    except Exception as err:
        logger.critical(
            f"Unable to process results! Downstream Tasks may fail! {err}"
        )

    try:
        self._store_configuration()
    except Exception as err:
        logger.critical(
            f"Unable to store configuration! Downstream Tasks may fail! {err}"
        )

    for comm in self._communicators:
        comm.clear_communicator()

    if self._analysis_desc.task_result.task_status == TaskStatus.FAILED:
        logger.info("Exiting after Task failure. Result recorded.")
        sys.exit(-1)

process_results()

Perform any necessary steps to process TaskResults object.

Processing will depend on subclass. Examples of steps include, moving files, converting file formats, compiling plots/figures into an HTML file, etc.

Source code in lute/execution/executor.py

def process_results(self) -> None:
    """Perform any necessary steps to process TaskResults object.

    Processing will depend on subclass. Examples of steps include, moving
    files, converting file formats, compiling plots/figures into an HTML
    file, etc.
    """
    self._process_results()

shell_source(env)

Source a script.

Unlike update_environment this method sources a new file.

We prepend a token to each environment variable. This allows the initial part of the Task to be run using the appropriate environment.

The environment variables containing the token will be swapped in using their appropriate form prior to the actual execution of Task code.

Parameters:

Name	Type	Description	Default
env	str	Path to the script to source.	required

Source code in lute/execution/executor.py

def shell_source(self, env: str) -> None:
    """Source a script.

    Unlike `update_environment` this method sources a new file.

    We prepend a token to each environment variable. This allows the initial
    part of the Task to be run using the appropriate environment.

    The environment variables containing the token will be swapped in using
    their appropriate form prior to the actual execution of Task code.

    Args:
        env (str): Path to the script to source.
    """
    self._shell_source_script = env

update_environment(env, update_path='prepend')

Update the stored set of environment variables.

These are passed to the subprocess to setup its environment.

Parameters:

Name	Type	Description	Default
env	Dict[str, str]	A dictionary of "VAR":"VALUE" pairs of environment variables to be added to the subprocess environment. If any variables already exist, the new variables will overwrite them (except PATH, see below).	required
update_path	str	If PATH is present in the new set of variables, this argument determines how the old PATH is dealt with. There are three options: * "prepend" : The new PATH values are prepended to the old ones. * "append" : The new PATH values are appended to the old ones. * "overwrite" : The old PATH is overwritten by the new one. "prepend" is the default option. If PATH is not present in the current environment, the new PATH is used without modification.	'prepend'

Source code in lute/execution/executor.py

def update_environment(
    self, env: Dict[str, str], update_path: str = "prepend"
) -> None:
    """Update the stored set of environment variables.

    These are passed to the subprocess to setup its environment.

    Args:
        env (Dict[str, str]): A dictionary of "VAR":"VALUE" pairs of
            environment variables to be added to the subprocess environment.
            If any variables already exist, the new variables will
            overwrite them (except PATH, see below).

        update_path (str): If PATH is present in the new set of variables,
            this argument determines how the old PATH is dealt with. There
            are three options:
            * "prepend" : The new PATH values are prepended to the old ones.
            * "append" : The new PATH values are appended to the old ones.
            * "overwrite" : The old PATH is overwritten by the new one.
            "prepend" is the default option. If PATH is not present in the
            current environment, the new PATH is used without modification.
    """
    if "PATH" in env:
        sep: str = os.pathsep
        if update_path == "prepend":
            env["PATH"] = (
                f"{env['PATH']}{sep}{self._analysis_desc.task_env['PATH']}"
            )
        elif update_path == "append":
            env["PATH"] = (
                f"{self._analysis_desc.task_env['PATH']}{sep}{env['PATH']}"
            )
        elif update_path == "overwrite":
            pass
        else:
            raise ValueError(
                (
                    f"{update_path} is not a valid option for `update_path`!"
                    " Options are: prepend, append, overwrite."
                )
            )
    os.environ.update(env)
    self._analysis_desc.task_env.update(env)

Executor

Bases: BaseExecutor

Basic implementation of an Executor which manages simple IPC with Task.

Attributes:

Methods:

Name	Description
add_hook	str, hook: Callable[[None], None]) -> None: Create a new hook to be called each time a specific event occurs.
add_default_hooks	Populate the event hooks with the default functions.
update_environment	Dict[str, str], update_path: str): Update the environment that is passed to the Task subprocess.
execute_task	Run the task as a subprocess.

Source code in lute/execution/executor.py

class Executor(BaseExecutor):
    """Basic implementation of an Executor which manages simple IPC with Task.

    Attributes:

    Methods:
        add_hook(event: str, hook: Callable[[None], None]) -> None: Create a
            new hook to be called each time a specific event occurs.

        add_default_hooks() -> None: Populate the event hooks with the default
            functions.

        update_environment(env: Dict[str, str], update_path: str): Update the
            environment that is passed to the Task subprocess.

        execute_task(): Run the task as a subprocess.
    """

    def __init__(
        self,
        task_name: str,
        communicators: List[Communicator] = [
            PipeCommunicator(Party.EXECUTOR),
            SocketCommunicator(Party.EXECUTOR),
        ],
        poll_interval: float = 0.05,
    ) -> None:
        super().__init__(
            task_name=task_name,
            communicators=communicators,
            poll_interval=poll_interval,
        )
        self.add_default_hooks()

    def add_default_hooks(self) -> None:
        """Populate the set of default event hooks."""

        def no_pickle_mode(
            self: Executor, msg: Message, proc: Optional[subprocess.Popen] = None
        ) -> None:
            for idx, communicator in enumerate(self._communicators):
                if isinstance(communicator, PipeCommunicator):
                    self._communicators[idx] = PipeCommunicator(
                        Party.EXECUTOR, use_pickle=False
                    )

        self.add_hook("no_pickle_mode", no_pickle_mode)

        def task_started(self: Executor, msg: Message, proc: subprocess.Popen) -> None:
            if isinstance(msg.contents, TaskParameters):
                self._analysis_desc.task_parameters = msg.contents
                # Run "before" tasklets
                if self._tasklets["before"] is not None:
                    self._run_tasklets(when="before")
                # Need to continue since Task._signal_start raises SIGSTOP
                self._continue(proc)
                if hasattr(self._analysis_desc.task_parameters.Config, "set_result"):
                    # Tasks may mark a parameter as the result
                    # If so, setup the result now.
                    self._set_result_from_parameters()
            logger.info(
                f"Executor: {self._analysis_desc.task_result.task_name} started"
            )
            self._analysis_desc.task_result.task_status = TaskStatus.RUNNING
            elog_data: Dict[str, str] = {
                f"{self._analysis_desc.task_result.task_name} status": "RUNNING",
            }
            post_elog_run_status(elog_data)

        self.add_hook("task_started", task_started)

        def task_failed(
            self: Executor, msg: Message, proc: Optional[subprocess.Popen] = None
        ) -> None:
            elog_data: Dict[str, str] = {
                f"{self._analysis_desc.task_result.task_name} status": "FAILED",
            }
            post_elog_run_status(elog_data)

        self.add_hook("task_failed", task_failed)

        def task_stopped(
            self: Executor, msg: Message, proc: Optional[subprocess.Popen] = None
        ) -> None:
            elog_data: Dict[str, str] = {
                f"{self._analysis_desc.task_result.task_name} status": "STOPPED",
            }
            post_elog_run_status(elog_data)

        self.add_hook("task_stopped", task_stopped)

        def task_done(
            self: Executor, msg: Message, proc: Optional[subprocess.Popen] = None
        ) -> None:
            elog_data: Dict[str, str] = {
                f"{self._analysis_desc.task_result.task_name} status": "COMPLETED",
            }
            post_elog_run_status(elog_data)

        self.add_hook("task_done", task_done)

        def task_cancelled(
            self: Executor, msg: Message, proc: Optional[subprocess.Popen] = None
        ) -> None:
            elog_data: Dict[str, str] = {
                f"{self._analysis_desc.task_result.task_name} status": "CANCELLED",
            }
            post_elog_run_status(elog_data)

        self.add_hook("task_cancelled", task_cancelled)

        def task_result(
            self: Executor, msg: Message, proc: Optional[subprocess.Popen] = None
        ) -> bool:
            if isinstance(msg.contents, TaskResult):
                self._analysis_desc.task_result = msg.contents
                is_printable_type: Callable[[Any], bool] = lambda x: isinstance(
                    x, dict
                ) or isinstance(x, str)
                if is_printable_type(self._analysis_desc.task_result.summary):
                    logger.info(self._analysis_desc.task_result.summary)
                elif isinstance(self._analysis_desc.task_result.summary, list):
                    for item in self._analysis_desc.task_result.summary:
                        if is_printable_type(item):
                            logger.info(item)

                logger.info(self._analysis_desc.task_result.task_status)
            elog_data: Dict[str, str] = {
                f"{self._analysis_desc.task_result.task_name} status": "COMPLETED",
            }
            post_elog_run_status(elog_data)

            return True

        self.add_hook("task_result", task_result)

        def task_log(
            self: Executor, msg: Message, proc: Optional[subprocess.Popen] = None
        ) -> bool:
            if isinstance(msg.contents, str):
                # This should be log formatted already
                print(msg.contents)
                return True
            return False

        self.add_hook("task_log", task_log)

    def _task_loop(self, proc: subprocess.Popen) -> None:
        """Actions to perform while the Task is running.

        This function is run in the body of a loop until the Task signals
        that its finished.
        """
        # Some hooks may ask that the rest of the task loop be skipped (continued)
        should_continue: Optional[bool]
        for communicator in self._communicators:
            while True:
                msg: Message = communicator.read(proc)
                if msg.signal is not None and msg.signal.upper() in LUTE_SIGNALS:
                    hook: Callable[[Executor, Message], None] = getattr(
                        self.Hooks, msg.signal.lower()
                    )
                    should_continue = hook(self, msg, proc)
                    if should_continue:
                        continue

                if msg.contents is not None:
                    if isinstance(msg.contents, str) and msg.contents != "":
                        logger.info(msg.contents)
                    elif not isinstance(msg.contents, str):
                        logger.info(msg.contents)
                if not communicator.has_messages:
                    break

    def _finalize_task(self, proc: subprocess.Popen) -> None:
        """Any actions to be performed after the Task has ended.

        Examples include a final clearing of the pipes, retrieving results,
        reporting to third party services, etc.
        """
        self._task_loop(proc)  # Perform a final read.

    def _process_results(self) -> None:
        """Performs result processing.

        Actions include:
        - For `ElogSummaryPlots`, will save the summary plot to the appropriate
            directory for display in the eLog.
        """
        task_result: TaskResult = self._analysis_desc.task_result
        self._process_result_payload(task_result.payload)
        self._process_result_summary(task_result.summary)

    def _process_result_payload(self, payload: Any) -> None:
        if self._analysis_desc.task_parameters is None:
            logger.error("Please run Task before using this method!")
            return
        new_payload: Optional[str]
        if isinstance(payload, ElogSummaryPlots):
            new_payload = self._process_elog_plot(payload)
            if new_payload is not None:
                self._analysis_desc.task_result.payload = new_payload
        elif isinstance(payload, list) or isinstance(payload, tuple):
            new_payload = ""
            for item in payload:
                if isinstance(item, ElogSummaryPlots):
                    ret: Optional[str] = self._process_elog_plot(item)
                    if ret is not None:
                        new_payload = ";".join(filter(None, (new_payload, ret)))
            if new_payload != "":
                self._analysis_desc.task_result.payload = new_payload
        elif isinstance(payload, str):
            # May be a path to a file...
            schemas: Optional[str] = self._analysis_desc.task_result.impl_schemas
            # Should also check `impl_schemas` to determine what to do with path

    def _process_elog_plot(self, plots: ElogSummaryPlots) -> Optional[str]:
        """Process an ElogSummaryPlots

        Writes out the eLog summary plot for display and returns the path of
        where the plots were written out so they can be stored as the result
        payload.

        ElogSummaryPlots objects already convert the plots to a byte stream
        which can be directly written to an HTML file.

        Args:
            plots (ElogSummaryPlots): The plots dataclass.

        Returns:
            path (str): Path the plots were written out to.
        """
        if self._analysis_desc.task_parameters is None:
            logger.error("Please run Task before using this method!")
            return
        # ElogSummaryPlots has figures and a display name
        # display name also serves as a path.
        expmt: str = self._analysis_desc.task_parameters.lute_config.experiment
        base_path: str = f"/sdf/data/lcls/ds/{expmt[:3]}/{expmt}/stats/summary"
        full_path: str = f"{base_path}/{plots.display_name}"
        if not os.path.isdir(full_path):
            os.makedirs(full_path)

        path: str = f"{full_path}/report.html"
        with open(f"{full_path}/report.html", "wb") as f:
            f.write(plots.figures)

        return path

    def _process_result_summary(self, summary: Any) -> None:
        """Process an object destined for the results summary.

        Args:
            summary (Any): The object to be set as a summary. If a dictionary
                it is assumed to be a set of key/value pairs to be written out
                as run parameters in the eLog. If a list each item is processed
                individually.
        """
        if self._analysis_desc.task_parameters is None:
            logger.error("Please run Task before using this method!")
            return
        if isinstance(summary, dict):
            # Assume dict is key: value pairs of eLog run parameters to post
            self._analysis_desc.task_result.summary = self._process_summary_run_params(
                summary
            )
        elif isinstance(summary, list) or isinstance(summary, tuple):
            new_summary_str: str = ""
            for item in summary:
                if isinstance(item, dict):
                    ret: str = self._process_summary_run_params(item)
                    new_summary_str = ";".join(filter(None, (new_summary_str, ret)))
                elif isinstance(item, ElogSummaryPlots):
                    plot_path: Optional[str] = self._process_elog_plot(item)
                    new_summary_str = ";".join(
                        filter(None, (new_summary_str, plot_path))
                    )
            self._analysis_desc.task_result.summary = new_summary_str
        elif isinstance(summary, str):
            ...
        else:
            ...

    def _process_summary_run_params(self, params: Dict[str, str]) -> str:
        """Process a dictionary of run parameters to be posted to the eLog.

        Args:
            params (Dict[str, str]): Key/value pairs to be posted as run parameters.

        Returns:
            summary_str (str): New string of key/value pairs to be stored in
                summary field of the database.
        """
        if self._analysis_desc.task_parameters is None:
            logger.error("Please run Task before using this method!")
            return ""
        exp: str = self._analysis_desc.task_parameters.lute_config.experiment
        run: int = int(self._analysis_desc.task_parameters.lute_config.run)
        logger.debug("Posting eLog run parameters.")
        try:
            post_elog_run_table(exp, run, params)
        except Exception as err:
            logger.error(f"Unable to post run parameters! Error: {err}")
        post_elog_run_status(params)
        summary_str: str = ";".join(f"{key}: {value}" for key, value in params.items())
        return summary_str

_finalize_task(proc)

Any actions to be performed after the Task has ended.

Examples include a final clearing of the pipes, retrieving results, reporting to third party services, etc.

Source code in lute/execution/executor.py

def _finalize_task(self, proc: subprocess.Popen) -> None:
    """Any actions to be performed after the Task has ended.

    Examples include a final clearing of the pipes, retrieving results,
    reporting to third party services, etc.
    """
    self._task_loop(proc)  # Perform a final read.

_process_elog_plot(plots)

Process an ElogSummaryPlots

Writes out the eLog summary plot for display and returns the path of where the plots were written out so they can be stored as the result payload.

ElogSummaryPlots objects already convert the plots to a byte stream which can be directly written to an HTML file.

Parameters:

Name	Type	Description	Default
plots	ElogSummaryPlots	The plots dataclass.	required

Returns:

Name	Type	Description
path	str	Path the plots were written out to.

Source code in lute/execution/executor.py

def _process_elog_plot(self, plots: ElogSummaryPlots) -> Optional[str]:
    """Process an ElogSummaryPlots

    Writes out the eLog summary plot for display and returns the path of
    where the plots were written out so they can be stored as the result
    payload.

    ElogSummaryPlots objects already convert the plots to a byte stream
    which can be directly written to an HTML file.

    Args:
        plots (ElogSummaryPlots): The plots dataclass.

    Returns:
        path (str): Path the plots were written out to.
    """
    if self._analysis_desc.task_parameters is None:
        logger.error("Please run Task before using this method!")
        return
    # ElogSummaryPlots has figures and a display name
    # display name also serves as a path.
    expmt: str = self._analysis_desc.task_parameters.lute_config.experiment
    base_path: str = f"/sdf/data/lcls/ds/{expmt[:3]}/{expmt}/stats/summary"
    full_path: str = f"{base_path}/{plots.display_name}"
    if not os.path.isdir(full_path):
        os.makedirs(full_path)

    path: str = f"{full_path}/report.html"
    with open(f"{full_path}/report.html", "wb") as f:
        f.write(plots.figures)

    return path

_process_result_summary(summary)

Process an object destined for the results summary.

Parameters:

Name	Type	Description	Default
summary	Any	The object to be set as a summary. If a dictionary it is assumed to be a set of key/value pairs to be written out as run parameters in the eLog. If a list each item is processed individually.	required

Source code in lute/execution/executor.py

def _process_result_summary(self, summary: Any) -> None:
    """Process an object destined for the results summary.

    Args:
        summary (Any): The object to be set as a summary. If a dictionary
            it is assumed to be a set of key/value pairs to be written out
            as run parameters in the eLog. If a list each item is processed
            individually.
    """
    if self._analysis_desc.task_parameters is None:
        logger.error("Please run Task before using this method!")
        return
    if isinstance(summary, dict):
        # Assume dict is key: value pairs of eLog run parameters to post
        self._analysis_desc.task_result.summary = self._process_summary_run_params(
            summary
        )
    elif isinstance(summary, list) or isinstance(summary, tuple):
        new_summary_str: str = ""
        for item in summary:
            if isinstance(item, dict):
                ret: str = self._process_summary_run_params(item)
                new_summary_str = ";".join(filter(None, (new_summary_str, ret)))
            elif isinstance(item, ElogSummaryPlots):
                plot_path: Optional[str] = self._process_elog_plot(item)
                new_summary_str = ";".join(
                    filter(None, (new_summary_str, plot_path))
                )
        self._analysis_desc.task_result.summary = new_summary_str
    elif isinstance(summary, str):
        ...
    else:
        ...

_process_results()

Performs result processing.

Actions include: - For ElogSummaryPlots, will save the summary plot to the appropriate directory for display in the eLog.

Source code in lute/execution/executor.py

def _process_results(self) -> None:
    """Performs result processing.

    Actions include:
    - For `ElogSummaryPlots`, will save the summary plot to the appropriate
        directory for display in the eLog.
    """
    task_result: TaskResult = self._analysis_desc.task_result
    self._process_result_payload(task_result.payload)
    self._process_result_summary(task_result.summary)

_process_summary_run_params(params)

Process a dictionary of run parameters to be posted to the eLog.

Parameters:

Name	Type	Description	Default
params	Dict[str, str]	Key/value pairs to be posted as run parameters.	required

Returns:

Name	Type	Description
summary_str	str	New string of key/value pairs to be stored in summary field of the database.

Source code in lute/execution/executor.py

def _process_summary_run_params(self, params: Dict[str, str]) -> str:
    """Process a dictionary of run parameters to be posted to the eLog.

    Args:
        params (Dict[str, str]): Key/value pairs to be posted as run parameters.

    Returns:
        summary_str (str): New string of key/value pairs to be stored in
            summary field of the database.
    """
    if self._analysis_desc.task_parameters is None:
        logger.error("Please run Task before using this method!")
        return ""
    exp: str = self._analysis_desc.task_parameters.lute_config.experiment
    run: int = int(self._analysis_desc.task_parameters.lute_config.run)
    logger.debug("Posting eLog run parameters.")
    try:
        post_elog_run_table(exp, run, params)
    except Exception as err:
        logger.error(f"Unable to post run parameters! Error: {err}")
    post_elog_run_status(params)
    summary_str: str = ";".join(f"{key}: {value}" for key, value in params.items())
    return summary_str

_task_loop(proc)

Actions to perform while the Task is running.

This function is run in the body of a loop until the Task signals that its finished.

Source code in lute/execution/executor.py

def _task_loop(self, proc: subprocess.Popen) -> None:
    """Actions to perform while the Task is running.

    This function is run in the body of a loop until the Task signals
    that its finished.
    """
    # Some hooks may ask that the rest of the task loop be skipped (continued)
    should_continue: Optional[bool]
    for communicator in self._communicators:
        while True:
            msg: Message = communicator.read(proc)
            if msg.signal is not None and msg.signal.upper() in LUTE_SIGNALS:
                hook: Callable[[Executor, Message], None] = getattr(
                    self.Hooks, msg.signal.lower()
                )
                should_continue = hook(self, msg, proc)
                if should_continue:
                    continue

            if msg.contents is not None:
                if isinstance(msg.contents, str) and msg.contents != "":
                    logger.info(msg.contents)
                elif not isinstance(msg.contents, str):
                    logger.info(msg.contents)
            if not communicator.has_messages:
                break

add_default_hooks()

Populate the set of default event hooks.

Source code in lute/execution/executor.py

def add_default_hooks(self) -> None:
    """Populate the set of default event hooks."""

    def no_pickle_mode(
        self: Executor, msg: Message, proc: Optional[subprocess.Popen] = None
    ) -> None:
        for idx, communicator in enumerate(self._communicators):
            if isinstance(communicator, PipeCommunicator):
                self._communicators[idx] = PipeCommunicator(
                    Party.EXECUTOR, use_pickle=False
                )

    self.add_hook("no_pickle_mode", no_pickle_mode)

    def task_started(self: Executor, msg: Message, proc: subprocess.Popen) -> None:
        if isinstance(msg.contents, TaskParameters):
            self._analysis_desc.task_parameters = msg.contents
            # Run "before" tasklets
            if self._tasklets["before"] is not None:
                self._run_tasklets(when="before")
            # Need to continue since Task._signal_start raises SIGSTOP
            self._continue(proc)
            if hasattr(self._analysis_desc.task_parameters.Config, "set_result"):
                # Tasks may mark a parameter as the result
                # If so, setup the result now.
                self._set_result_from_parameters()
        logger.info(
            f"Executor: {self._analysis_desc.task_result.task_name} started"
        )
        self._analysis_desc.task_result.task_status = TaskStatus.RUNNING
        elog_data: Dict[str, str] = {
            f"{self._analysis_desc.task_result.task_name} status": "RUNNING",
        }
        post_elog_run_status(elog_data)

    self.add_hook("task_started", task_started)

    def task_failed(
        self: Executor, msg: Message, proc: Optional[subprocess.Popen] = None
    ) -> None:
        elog_data: Dict[str, str] = {
            f"{self._analysis_desc.task_result.task_name} status": "FAILED",
        }
        post_elog_run_status(elog_data)

    self.add_hook("task_failed", task_failed)

    def task_stopped(
        self: Executor, msg: Message, proc: Optional[subprocess.Popen] = None
    ) -> None:
        elog_data: Dict[str, str] = {
            f"{self._analysis_desc.task_result.task_name} status": "STOPPED",
        }
        post_elog_run_status(elog_data)

    self.add_hook("task_stopped", task_stopped)

    def task_done(
        self: Executor, msg: Message, proc: Optional[subprocess.Popen] = None
    ) -> None:
        elog_data: Dict[str, str] = {
            f"{self._analysis_desc.task_result.task_name} status": "COMPLETED",
        }
        post_elog_run_status(elog_data)

    self.add_hook("task_done", task_done)

    def task_cancelled(
        self: Executor, msg: Message, proc: Optional[subprocess.Popen] = None
    ) -> None:
        elog_data: Dict[str, str] = {
            f"{self._analysis_desc.task_result.task_name} status": "CANCELLED",
        }
        post_elog_run_status(elog_data)

    self.add_hook("task_cancelled", task_cancelled)

    def task_result(
        self: Executor, msg: Message, proc: Optional[subprocess.Popen] = None
    ) -> bool:
        if isinstance(msg.contents, TaskResult):
            self._analysis_desc.task_result = msg.contents
            is_printable_type: Callable[[Any], bool] = lambda x: isinstance(
                x, dict
            ) or isinstance(x, str)
            if is_printable_type(self._analysis_desc.task_result.summary):
                logger.info(self._analysis_desc.task_result.summary)
            elif isinstance(self._analysis_desc.task_result.summary, list):
                for item in self._analysis_desc.task_result.summary:
                    if is_printable_type(item):
                        logger.info(item)

            logger.info(self._analysis_desc.task_result.task_status)
        elog_data: Dict[str, str] = {
            f"{self._analysis_desc.task_result.task_name} status": "COMPLETED",
        }
        post_elog_run_status(elog_data)

        return True

    self.add_hook("task_result", task_result)

    def task_log(
        self: Executor, msg: Message, proc: Optional[subprocess.Popen] = None
    ) -> bool:
        if isinstance(msg.contents, str):
            # This should be log formatted already
            print(msg.contents)
            return True
        return False

    self.add_hook("task_log", task_log)

MPIExecutor

Bases: Executor

Runs first-party Tasks that require MPI.

This Executor is otherwise identical to the standard Executor, except it uses mpirun for Task submission. Currently this Executor assumes a job has been submitted using SLURM as a first step. It will determine the number of MPI ranks based on the resources requested. As a fallback, it will try to determine the number of local cores available for cases where a job has not been submitted via SLURM. On S3DF, the second determination mechanism should accurately match the environment variable provided by SLURM indicating resources allocated.

This Executor will submit the Task to run with a number of processes equal to the total number of cores available minus 1. A single core is reserved for the Executor itself. Note that currently this means that you must submit on 3 cores or more, since MPI requires a minimum of 2 ranks, and the number of ranks is determined from the cores dedicated to Task execution.

Methods:

Name	Description
_submit_cmd	Run the task as a subprocess using mpirun.

Source code in lute/execution/executor.py

class MPIExecutor(Executor):
    """Runs first-party Tasks that require MPI.

    This Executor is otherwise identical to the standard Executor, except it
    uses `mpirun` for `Task` submission. Currently this Executor assumes a job
    has been submitted using SLURM as a first step. It will determine the number
    of MPI ranks based on the resources requested. As a fallback, it will try
    to determine the number of local cores available for cases where a job has
    not been submitted via SLURM. On S3DF, the second determination mechanism
    should accurately match the environment variable provided by SLURM indicating
    resources allocated.

    This Executor will submit the Task to run with a number of processes equal
    to the total number of cores available minus 1. A single core is reserved
    for the Executor itself. Note that currently this means that you must submit
    on 3 cores or more, since MPI requires a minimum of 2 ranks, and the number
    of ranks is determined from the cores dedicated to Task execution.

    Methods:
        _submit_cmd: Run the task as a subprocess using `mpirun`.
    """

    def _submit_cmd(self, executable_path: str, params: str) -> str:
        """Override submission command to use `mpirun`

        Args:
            executable_path (str): Path to the LUTE subprocess script.

            params (str): String of formatted command-line arguments.

        Returns:
            cmd (str): Appropriately formatted command for this Executor.
        """
        py_cmd: str = ""
        nprocs: int = max(
            int(os.environ.get("SLURM_NPROCS", len(os.sched_getaffinity(0)))) - 1, 1
        )
        mpi_cmd: str = f"mpirun -np {nprocs}"
        if __debug__:
            py_cmd = f"python -B -u -m mpi4py.run {executable_path} {params}"
        else:
            py_cmd = f"python -OB -u -m mpi4py.run {executable_path} {params}"

        cmd: str = f"{mpi_cmd} {py_cmd}"
        return cmd

_submit_cmd(executable_path, params)

Override submission command to use mpirun

Parameters:

Name	Type	Description	Default
executable_path	str	Path to the LUTE subprocess script.	required
params	str	String of formatted command-line arguments.	required

Returns:

Name	Type	Description
cmd	str	Appropriately formatted command for this Executor.

Source code in lute/execution/executor.py

def _submit_cmd(self, executable_path: str, params: str) -> str:
    """Override submission command to use `mpirun`

    Args:
        executable_path (str): Path to the LUTE subprocess script.

        params (str): String of formatted command-line arguments.

    Returns:
        cmd (str): Appropriately formatted command for this Executor.
    """
    py_cmd: str = ""
    nprocs: int = max(
        int(os.environ.get("SLURM_NPROCS", len(os.sched_getaffinity(0)))) - 1, 1
    )
    mpi_cmd: str = f"mpirun -np {nprocs}"
    if __debug__:
        py_cmd = f"python -B -u -m mpi4py.run {executable_path} {params}"
    else:
        py_cmd = f"python -OB -u -m mpi4py.run {executable_path} {params}"

    cmd: str = f"{mpi_cmd} {py_cmd}"
    return cmd