mirror of
https://github.com/ARM-software/workload-automation.git
synced 2025-01-19 12:24:32 +00:00
324 lines
11 KiB
Python
324 lines
11 KiB
Python
# Copyright 2013-2015 ARM Limited
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# pylint: disable=no-member
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"""
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This module defines the classes used to handle result
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processing inside Workload Automation. There will be a
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:class:`wlauto.core.workload.WorkloadResult` object generated for
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every workload iteration executed. This object will have a list of
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:class:`wlauto.core.workload.WorkloadMetric` objects. This list will be
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populated by the workload itself and may also be updated by instrumentation
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(e.g. to add power measurements). Once the result object has been fully
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populated, it will be passed into the ``process_iteration_result`` method of
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:class:`ResultProcessor`. Once the entire run has completed, a list containing
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result objects from all iterations will be passed into ``process_results``
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method of :class`ResultProcessor`.
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Which result processors will be active is defined by the ``result_processors``
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list in the ``~/.workload_automation/config.py``. Only the result_processors
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who's names appear in this list will be used.
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A :class:`ResultsManager` keeps track of active results processors.
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"""
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import logging
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import traceback
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from copy import copy
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from contextlib import contextmanager
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from datetime import datetime
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from wlauto.core.extension import Extension
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from wlauto.exceptions import WAError
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from wlauto.utils.types import numeric
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from wlauto.utils.misc import enum_metaclass
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class ResultManager(object):
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"""
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Keeps track of result processors and passes on the results onto the individual processors.
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"""
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def __init__(self):
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self.logger = logging.getLogger('ResultsManager')
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self.processors = []
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self._bad = []
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def install(self, processor):
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self.logger.debug('Installing results processor %s', processor.name)
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self.processors.append(processor)
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def uninstall(self, processor):
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if processor in self.processors:
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self.logger.debug('Uninstalling results processor %s', processor.name)
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self.processors.remove(processor)
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else:
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self.logger.warning('Attempting to uninstall results processor %s, which is not installed.',
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processor.name)
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def initialize(self, context):
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# Errors aren't handled at this stage, because this gets executed
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# before workload execution starts and we just want to propagte them
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# and terminate (so that error can be corrected and WA restarted).
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for processor in self.processors:
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processor.initialize(context)
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def add_result(self, result, context):
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with self._manage_processors(context):
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for processor in self.processors:
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with self._handle_errors(processor):
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processor.process_iteration_result(result, context)
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for processor in self.processors:
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with self._handle_errors(processor):
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processor.export_iteration_result(result, context)
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def process_run_result(self, result, context):
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with self._manage_processors(context):
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for processor in self.processors:
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with self._handle_errors(processor):
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processor.process_run_result(result, context)
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for processor in self.processors:
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with self._handle_errors(processor):
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processor.export_run_result(result, context)
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def finalize(self, context):
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with self._manage_processors(context):
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for processor in self.processors:
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with self._handle_errors(processor):
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processor.finalize(context)
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def validate(self):
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for processor in self.processors:
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processor.validate()
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@contextmanager
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def _manage_processors(self, context, finalize_bad=True):
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yield
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for processor in self._bad:
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if finalize_bad:
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processor.finalize(context)
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self.uninstall(processor)
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self._bad = []
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@contextmanager
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def _handle_errors(self, processor):
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try:
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yield
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except KeyboardInterrupt, e:
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raise e
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except WAError, we:
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self.logger.error('"{}" result processor has encountered an error'.format(processor.name))
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self.logger.error('{}("{}")'.format(we.__class__.__name__, we.message))
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self._bad.append(processor)
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except Exception, e: # pylint: disable=W0703
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self.logger.error('"{}" result processor has encountered an error'.format(processor.name))
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self.logger.error('{}("{}")'.format(e.__class__.__name__, e))
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self.logger.error(traceback.format_exc())
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self._bad.append(processor)
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class ResultProcessor(Extension):
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"""
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Base class for result processors. Defines an interface that should be implemented
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by the subclasses. A result processor can be used to do any kind of post-processing
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of the results, from writing them out to a file, to uploading them to a database,
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performing calculations, generating plots, etc.
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"""
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def initialize(self, context):
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pass
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def process_iteration_result(self, result, context):
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pass
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def export_iteration_result(self, result, context):
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pass
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def process_run_result(self, result, context):
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pass
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def export_run_result(self, result, context):
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pass
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def finalize(self, context):
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pass
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class RunResult(object):
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"""
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Contains overall results for a run.
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"""
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__metaclass__ = enum_metaclass('values', return_name=True)
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values = [
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'OK',
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'OKISH',
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'PARTIAL',
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'FAILED',
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'UNKNOWN',
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]
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@property
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def status(self):
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if not self.iteration_results or all([s.status == IterationResult.FAILED for s in self.iteration_results]):
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return self.FAILED
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elif any([s.status == IterationResult.FAILED for s in self.iteration_results]):
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return self.PARTIAL
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elif any([s.status == IterationResult.ABORTED for s in self.iteration_results]):
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return self.PARTIAL
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elif (any([s.status == IterationResult.PARTIAL for s in self.iteration_results]) or
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self.non_iteration_errors):
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return self.OKISH
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elif all([s.status == IterationResult.OK for s in self.iteration_results]):
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return self.OK
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else:
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return self.UNKNOWN # should never happen
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def __init__(self, run_info, output_directory=None):
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self.info = run_info
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self.iteration_results = []
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self.artifacts = []
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self.events = []
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self.non_iteration_errors = False
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self.output_directory = output_directory
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class RunEvent(object):
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"""
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An event that occured during a run.
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"""
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def __init__(self, message):
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self.timestamp = datetime.utcnow()
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self.message = message
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def to_dict(self):
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return copy(self.__dict__)
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def __str__(self):
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return '{} {}'.format(self.timestamp, self.message)
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__repr__ = __str__
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class IterationResult(object):
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"""
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Contains the result of running a single iteration of a workload. It is the
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responsibility of a workload to instantiate a IterationResult, populate it,
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and return it form its get_result() method.
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Status explanations:
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:NOT_STARTED: This iteration has not yet started.
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:RUNNING: This iteration is currently running and no errors have been detected.
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:OK: This iteration has completed and no errors have been detected
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:PARTIAL: One or more instruments have failed (the iteration may still be running).
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:FAILED: The workload itself has failed.
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:ABORTED: The user interupted the workload
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:SKIPPED: The iteration was skipped due to a previous failure
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"""
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__metaclass__ = enum_metaclass('values', return_name=True)
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values = [
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'NOT_STARTED',
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'RUNNING',
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'OK',
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'NONCRITICAL',
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'PARTIAL',
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'FAILED',
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'ABORTED',
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'SKIPPED',
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]
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def __init__(self, spec):
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self.spec = spec
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self.id = spec.id
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self.workload = spec.workload
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self.iteration = None
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self.status = self.NOT_STARTED
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self.output_directory = None
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self.events = []
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self.metrics = []
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self.artifacts = []
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def add_metric(self, name, value, units=None, lower_is_better=False):
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self.metrics.append(Metric(name, value, units, lower_is_better))
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def has_metric(self, name):
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for metric in self.metrics:
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if metric.name == name:
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return True
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return False
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def add_event(self, message):
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self.events.append(RunEvent(message))
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def to_dict(self):
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d = copy(self.__dict__)
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d['events'] = [e.to_dict() for e in self.events]
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return d
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def __iter__(self):
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return iter(self.metrics)
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def __getitem__(self, name):
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for metric in self.metrics:
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if metric.name == name:
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return metric
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raise KeyError('Metric {} not found.'.format(name))
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class Metric(object):
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"""
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This is a single metric collected from executing a workload.
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:param name: the name of the metric. Uniquely identifies the metric
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within the results.
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:param value: The numerical value of the metric for this execution of
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a workload. This can be either an int or a float.
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:param units: Units for the collected value. Can be None if the value
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has no units (e.g. it's a count or a standardised score).
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:param lower_is_better: Boolean flag indicating where lower values are
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better than higher ones. Defaults to False.
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"""
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def __init__(self, name, value, units=None, lower_is_better=False):
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self.name = name
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self.value = numeric(value)
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self.units = units
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self.lower_is_better = lower_is_better
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def to_dict(self):
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return self.__dict__
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def __str__(self):
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result = '{}: {}'.format(self.name, self.value)
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if self.units:
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result += ' ' + self.units
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result += ' ({})'.format('-' if self.lower_is_better else '+')
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return '<{}>'.format(result)
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__repr__ = __str__
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