runners.py

#!/usr/bin/env python3
 
# disable doxygen check for this file
# @cond
 
 
 
from abc import ABC, abstractmethod
import time
from basf2 import B2DEBUG, B2ERROR, B2INFO
import multiprocessing
 
 
class Runner(ABC):
    """
    Abstract Base Class for Runner type object.
    """
    @abstractmethod
    def run(self):
        """
        """
 
 
class AlgorithmsRunner(Runner):
    """
    Base class for `AlgorithmsRunner` classes. Defines the necessary information that will be provided to every
    `AlgorithmsRunner` used by the `framework.CAF`
 
    An `AlgorithmsRunner` will be given a list of `framework.Algorithm` objects defined during the setup of a
    `framework.Calibration` instance. The `AlgorithmsRunner` describes how to run each of the `strategies.AlgorithmStrategy`
    objects. As an example, assume that a single `framework.Calibration` was given and list of two `framework.Algorithm`
    instances to run.
 
    In this example the chosen :py:meth:`AlgorithmsRunner.run()` is simple and just loops over the list of `caf.framework.Algorithm`
    calling each one's :py:meth:`caf.strategies.AlgorithmStrategy.run()` methods in order.    Thereby generating a localdb with the only communication between the `strategies.AlgorithmStrategy` instances coming from the
    database payloads being available from one algorithm to the next.
 
    But you could imagine a more complex situation. The `AlgorithmsRunner` might take the first `framework.Algorithm` and
    call its `AlgorithmStrategy.run` for only the first (exp,run) in the collected data. Then it might not commit the payloads
    to a localdb but instead pass some calculated values to the next algorithm to run on the same IoV. Then it might go back
    and re-run the first AlgorithmStrategy with new information and commit payloads this time. Then move onto the next IoV.
 
    Hopefully you can see that while the default provided `AlgorithmsRunner` and `AlgorithmStrategy` classes should be good for
    most situations, you have lot of freedom to define your own strategy if needed. By following the basic requirements for the
    interface to the `framework.CAF` you can easily plugin a different special case, or mix and match a custom class with
    default CAF ones.
 
    The run(self) method should be defined for every derived `AlgorithmsRunner`. It will be called once and only once for each
    iteration of (collector -> algorithm).
 
    Input files are automatically given via the `framework.Calibration.output_patterns` which constructs
    a list of all files in the collector output directories that match the output_patterns. If you have multiple types of
    output data it is your job to filter through the input files and assign them correctly.
 
    A list of local database paths are given to the `AlgorithmsRunner` based on the `framework.Calibration` dependencies and
    any overall database chain given to the Calibration before running.
    By default you can call the "setup_algorithm" transition of the `caf.state_machines.AlgorithmMachine` to automatically
    set a database chain based on this list.
    But you have freedom to not call this at all in `run`, or to implement a different method to deal with this.
    """
 
    FAILED = "FAILED"
    COMPLETED = "COMPLETED"
 
    def __init__(self, name):
        """
        """
        
        self.name = name
        
        self.input_files = []
        
        self.database_chain = []
        
        self.dependent_databases = []
        
        self.output_database_dir = ""
        
        self.results = {}
        
        self.final_state = None
        
        self.algorithms = None
        
        self.output_dir = ""
 
 
class SeqAlgorithmsRunner(AlgorithmsRunner):
    """
    """
 
    def __init__(self, name):
        """
        """
        super().__init__(name)
 
    def run(self, iov, iteration):
        """
        """
        from caf.strategies import AlgorithmStrategy
        B2INFO(f"SequentialAlgorithmsRunner begun for Calibration {self.name}.")
        # First we do the setup of algorithm strategies
        strategies = []
        for algorithm in self.algorithms:
            # Need to create an instance of the requested strategy and set the attributes
            strategy = algorithm.strategy(algorithm)
            # Now add all the necessary parameters for a strategy to run
            strategy_params = {}
            strategy_params["database_chain"] = self.database_chain
            strategy_params["dependent_databases"] = self.dependent_databases
            strategy_params["output_dir"] = self.output_dir
            strategy_params["output_database_dir"] = self.output_database_dir
            strategy_params["input_files"] = self.input_files
            strategy_params["ignored_runs"] = self.ignored_runs
            strategy.setup_from_dict(strategy_params)
            strategies.append(strategy)
 
        # We then fork off a copy of this python process so that we don't affect the original with logging changes
        ctx = multiprocessing.get_context("fork")
        for strategy in strategies:
            queue = multiprocessing.SimpleQueue()
            child = ctx.Process(target=SeqAlgorithmsRunner._run_strategy,
                                args=(strategy, iov, iteration, queue))
 
            self.results[strategy.algorithm.name] = []
            B2INFO(f"Starting subprocess of AlgorithmStrategy for {strategy.algorithm.name}.")
            B2INFO("Logging will be diverted into algorithm output.")
            child.start()
            final_state = None
            final_loop = False
 
            B2INFO(f"Collecting results for {strategy.algorithm.name}.")
            while True:
                # Do we have results?
                while not queue.empty():
                    output = queue.get()
                    B2DEBUG(29, f"Result from queue was {output}")
                    if output["type"] == "result":
                        self.results[strategy.algorithm.name].append(output["value"])
                    elif output["type"] == "final_state":
                        final_state = output["value"]
                    else:
                        raise RunnerError(f"Unknown result output: {output}")
 
                # Still alive but not results at the moment? Wait a few seconds before checking.
                if child.is_alive():
                    time.sleep(5)
                    continue
                else:
                    # Reached a good ending of strategy
                    if final_state:
                        # Check the exitcode for failed Process()
                        if child.exitcode == 0:
                            B2INFO(f"AlgorithmStrategy subprocess for {strategy.algorithm.name} exited")
                            break
                        else:
                            raise RunnerError(f"Error during subprocess of AlgorithmStrategy for {strategy.algorithm.name}")
                    # It might be possible that the subprocess has finished but all results weren't gathered yet.
                    else:
                        # Go around once more since all results should be in the queue waiting
                        if not final_loop:
                            final_loop = True
                            continue
                        else:
                            raise RunnerError(f"Strategy for {strategy.algorithm.name} "
                                              "exited subprocess but without a final state!")
 
            # Exit early and don't continue strategies as this one failed
            if final_state == AlgorithmStrategy.FAILED:
                B2ERROR(f"AlgorithmStrategy for {strategy.algorithm.name} failed. We wil not proceed with any more algorithms")
                self.final_state = self.FAILED
                break
 
            B2DEBUG(29, f"Finished subprocess of AlgorithmStrategy for {strategy.algorithm.name}")
 
        if self.final_state != self.FAILED:
            B2INFO(f"SequentialAlgorithmsRunner finished for Calibration {self.name}")
            self.final_state = self.COMPLETED
 
    @staticmethod
    def _run_strategy(strategy, iov, iteration, queue):
        """Runs the AlgorithmStrategy sends back the results"""
        strategy.run(iov, iteration, queue)
        # Get the return codes of the algorithm for the IoVs found by the Process
        B2INFO(f"Finished Strategy for {strategy.algorithm.name}.")
 
 
class RunnerError(Exception):
    """
    Base exception class for Runners    """
 
# @endcond