import logging
import os
import shutil
from collections import OrderedDict
from copy import copy
from datetime import datetime
from wa.framework.configuration.core import JobSpec, Status
from wa.framework.configuration.execution import CombinedConfig
from wa.framework.exception import HostError
from wa.framework.run import RunState, RunInfo
from wa.framework.target.info import TargetInfo
from wa.utils.misc import touch, ensure_directory_exists
from wa.utils.serializer import write_pod, read_pod
from wa.utils.types import enum, numeric
logger = logging.getLogger('output')
class Output(object):
kind = None
@property
def resultfile(self):
return os.path.join(self.basepath, 'result.json')
@property
def event_summary(self):
num_events = len(self.events)
if num_events:
lines = self.events[0].message.split('\n')
message = '({} event(s)): {}'
if num_events > 1 or len(lines) > 1:
message += '[...]'
return message.format(num_events, lines[0])
return ''
@property
def status(self):
if self.result is None:
return None
return self.result.status
@status.setter
def status(self, value):
self.result.status = value
@property
def metrics(self):
if self.result is None:
return []
return self.result.metrics
@property
def artifacts(self):
if self.result is None:
return []
return self.result.artifacts
@property
def classifiers(self):
if self.result is None:
return OrderedDict()
return self.result.classifiers
@classifiers.setter
def classifiers(self, value):
if self.result is None:
msg ='Attempting to set classifiers before output has been set'
raise RuntimeError(msg)
self.result.classifiers = value
def __init__(self, path):
self.basepath = path
self.result = None
self.events = []
def reload(self):
try:
pod = read_pod(self.resultfile)
self.result = Result.from_pod(pod)
except Exception as e:
self.result = Result()
self.result.status = Status.UNKNOWN
self.add_event(str(e))
def write_result(self):
write_pod(self.result.to_pod(), self.resultfile)
def get_path(self, subpath):
return os.path.join(self.basepath, subpath.strip(os.sep))
def add_metric(self, name, value, units=None, lower_is_better=False,
classifiers=None):
self.result.add_metric(name, value, units, lower_is_better, classifiers)
def add_artifact(self, name, path, kind, description=None, classifiers=None):
if not os.path.exists(path):
path = self.get_path(path)
if not os.path.exists(path):
msg = 'Attempting to add non-existing artifact: {}'
raise HostError(msg.format(path))
path = os.path.relpath(path, self.basepath)
self.result.add_artifact(name, path, kind, description, classifiers)
def add_event(self, message):
self.result.add_event(message)
def get_metric(self, name):
return self.result.get_metric(name)
def get_artifact(self, name):
return self.result.get_artifact(name)
def get_artifact_path(self, name):
artifact = self.get_artifact(name)
return self.get_path(artifact.path)
class RunOutput(Output):
kind = 'run'
@property
def logfile(self):
return os.path.join(self.basepath, 'run.log')
@property
def metadir(self):
return os.path.join(self.basepath, '__meta')
@property
def infofile(self):
return os.path.join(self.metadir, 'run_info.json')
@property
def statefile(self):
return os.path.join(self.basepath, '.run_state.json')
@property
def configfile(self):
return os.path.join(self.metadir, 'config.json')
@property
def targetfile(self):
return os.path.join(self.metadir, 'target_info.json')
@property
def jobsfile(self):
return os.path.join(self.metadir, 'jobs.json')
@property
def raw_config_dir(self):
return os.path.join(self.metadir, 'raw_config')
@property
def failed_dir(self):
path = os.path.join(self.basepath, '__failed')
return ensure_directory_exists(path)
@property
def run_config(self):
if self._combined_config:
return self._combined_config.run_config
@property
def settings(self):
if self._combined_config:
return self._combined_config.settings
def __init__(self, path):
super(RunOutput, self).__init__(path)
self.info = None
self.state = None
self.result = None
self.target_info = None
self._combined_config = None
self.jobs = []
self.job_specs = []
if (not os.path.isfile(self.statefile) or
not os.path.isfile(self.infofile)):
msg = '"{}" does not exist or is not a valid WA output directory.'
raise ValueError(msg.format(self.basepath))
self.reload()
def reload(self):
super(RunOutput, self).reload()
self.info = RunInfo.from_pod(read_pod(self.infofile))
self.state = RunState.from_pod(read_pod(self.statefile))
if os.path.isfile(self.configfile):
self._combined_config = CombinedConfig.from_pod(read_pod(self.configfile))
if os.path.isfile(self.targetfile):
self.target_info = TargetInfo.from_pod(read_pod(self.targetfile))
if os.path.isfile(self.jobsfile):
self.job_specs = self.read_job_specs()
for job_state in self.state.jobs.itervalues():
job_path = os.path.join(self.basepath, job_state.output_name)
job = JobOutput(job_path, job_state.id,
job_state.label, job_state.iteration,
job_state.retries)
job.status = job_state.status
job.spec = self.get_job_spec(job.id)
if job.spec is None:
logger.warning('Could not find spec for job {}'.format(job.id))
self.jobs.append(job)
def write_info(self):
write_pod(self.info.to_pod(), self.infofile)
def write_state(self):
write_pod(self.state.to_pod(), self.statefile)
def write_config(self, config):
write_pod(config.to_pod(), self.configfile)
def read_config(self):
if not os.path.isfile(self.configfile):
return None
return CombinedConfig.from_pod(read_pod(self.configfile))
def set_target_info(self, ti):
self.target_info = ti
write_pod(ti.to_pod(), self.targetfile)
def write_job_specs(self, job_specs):
job_specs[0].to_pod()
js_pod = {'jobs': [js.to_pod() for js in job_specs]}
write_pod(js_pod, self.jobsfile)
def read_job_specs(self):
if not os.path.isfile(self.jobsfile):
return None
pod = read_pod(self.jobsfile)
return [JobSpec.from_pod(jp) for jp in pod['jobs']]
def move_failed(self, job_output):
name = os.path.basename(job_output.basepath)
attempt = job_output.retry + 1
failed_name = '{}-attempt{:02}'.format(name, attempt)
failed_path = os.path.join(self.failed_dir, failed_name)
if os.path.exists(failed_path):
raise ValueError('Path {} already exists'.format(failed_path))
shutil.move(job_output.basepath, failed_path)
job_output.basepath = failed_path
def get_job_spec(self, spec_id):
for spec in self.job_specs:
if spec.id == spec_id:
return spec
return None
class JobOutput(Output):
kind = 'job'
# pylint: disable=redefined-builtin
def __init__(self, path, id, label, iteration, retry):
super(JobOutput, self).__init__(path)
self.id = id
self.label = label
self.iteration = iteration
self.retry = retry
self.result = None
self.spec = None
self.reload()
class Result(object):
@staticmethod
def from_pod(pod):
instance = Result()
instance.status = Status(pod['status'])
instance.metrics = [Metric.from_pod(m) for m in pod['metrics']]
instance.artifacts = [Artifact.from_pod(a) for a in pod['artifacts']]
instance.events = [Event.from_pod(e) for e in pod['events']]
instance.classifiers = pod.get('classifiers', OrderedDict())
return instance
def __init__(self):
# pylint: disable=no-member
self.status = Status.NEW
self.metrics = []
self.artifacts = []
self.events = []
self.classifiers = OrderedDict()
def add_metric(self, name, value, units=None, lower_is_better=False,
classifiers=None):
metric = Metric(name, value, units, lower_is_better, classifiers)
logger.debug('Adding metric: {}'.format(metric))
self.metrics.append(metric)
def add_artifact(self, name, path, kind, description=None, classifiers=None):
artifact = Artifact(name, path, kind, description=description,
classifiers=classifiers)
logger.debug('Adding artifact: {}'.format(artifact))
self.artifacts.append(artifact)
def add_event(self, message):
self.events.append(Event(message))
def get_metric(self, name):
for metric in self.metrics:
if metric.name == name:
return metric
return None
def get_artifact(self, name):
for artifact in self.artifacts:
if artifact.name == name:
return artifact
raise HostError('Artifact "{}" not found'.format(name))
def to_pod(self):
return dict(
status=str(self.status),
metrics=[m.to_pod() for m in self.metrics],
artifacts=[a.to_pod() for a in self.artifacts],
events=[e.to_pod() for e in self.events],
classifiers=copy(self.classifiers),
)
ARTIFACT_TYPES = ['log', 'meta', 'data', 'export', 'raw']
ArtifactType = enum(ARTIFACT_TYPES)
class Artifact(object):
"""
This is an artifact generated during execution/post-processing of a
workload. Unlike metrics, this represents an actual artifact, such as a
file, generated. This may be "output", such as trace, or it could be "meta
data" such as logs. These are distinguished using the ``kind`` attribute,
which also helps WA decide how it should be handled. Currently supported
kinds are:
:log: A log file. Not part of the "output" as such but contains
information about the run/workload execution that be useful for
diagnostics/meta analysis.
:meta: A file containing metadata. This is not part of the "output", but
contains information that may be necessary to reproduce the
results (contrast with ``log`` artifacts which are *not*
necessary).
:data: This file contains new data, not available otherwise and should
be considered part of the "output" generated by WA. Most traces
would fall into this category.
:export: Exported version of results or some other artifact. This
signifies that this artifact does not contain any new data
that is not available elsewhere and that it may be safely
discarded without losing information.
:raw: Signifies that this is a raw dump/log that is normally processed
to extract useful information and is then discarded. In a sense,
it is the opposite of ``export``, but in general may also be
discarded.
.. note:: whether a file is marked as ``log``/``data`` or ``raw``
depends on how important it is to preserve this file,
e.g. when archiving, vs how much space it takes up.
Unlike ``export`` artifacts which are (almost) always
ignored by other exporters as that would never result
in data loss, ``raw`` files *may* be processed by
exporters if they decided that the risk of losing
potentially (though unlikely) useful data is greater
than the time/space cost of handling the artifact (e.g.
a database uploader may choose to ignore ``raw``
artifacts, where as a network filer archiver may choose
to archive them).
.. note: The kind parameter is intended to represent the logical
function of a particular artifact, not it's intended means of
processing -- this is left entirely up to the output
processors.
"""
@staticmethod
def from_pod(pod):
pod['kind'] = ArtifactType(pod['kind'])
return Artifact(**pod)
def __init__(self, name, path, kind, description=None, classifiers=None):
""""
:param name: Name that uniquely identifies this artifact.
:param path: The *relative* path of the artifact. Depending on the
``level`` must be either relative to the run or iteration
output directory. Note: this path *must* be delimited
using ``/`` irrespective of the
operating system.
:param kind: The type of the artifact this is (e.g. log file, result,
etc.) this will be used as a hint to output processors. This
must be one of ``'log'``, ``'meta'``, ``'data'``,
``'export'``, ``'raw'``.
:param description: A free-form description of what this artifact is.
:param classifiers: A set of key-value pairs to further classify this
metric beyond current iteration (e.g. this can be
used to identify sub-tests).
"""
self.name = name
self.path = path.replace('/', os.sep) if path is not None else path
try:
self.kind = ArtifactType(kind)
except ValueError:
msg = 'Invalid Artifact kind: {}; must be in {}'
raise ValueError(msg.format(kind, ARTIFACT_TYPES))
self.description = description
self.classifiers = classifiers or {}
def to_pod(self):
pod = copy(self.__dict__)
pod['kind'] = str(self.kind)
return pod
def __str__(self):
return self.path
def __repr__(self):
return '{} ({}): {}'.format(self.name, self.kind, self.path)
class Metric(object):
"""
This is a single metric collected from executing a workload.
:param name: the name of the metric. Uniquely identifies the metric
within the results.
:param value: The numerical value of the metric for this execution of a
workload. This can be either an int or a float.
:param units: Units for the collected value. Can be None if the value
has no units (e.g. it's a count or a standardised score).
:param lower_is_better: Boolean flag indicating where lower values are
better than higher ones. Defaults to False.
:param classifiers: A set of key-value pairs to further classify this
metric beyond current iteration (e.g. this can be used
to identify sub-tests).
"""
__slots__ = ['name', 'value', 'units', 'lower_is_better', 'classifiers']
@staticmethod
def from_pod(pod):
return Metric(**pod)
def __init__(self, name, value, units=None, lower_is_better=False,
classifiers=None):
self.name = name
self.value = numeric(value)
self.units = units
self.lower_is_better = lower_is_better
self.classifiers = classifiers or {}
def to_pod(self):
return dict(
name=self.name,
value=self.value,
units=self.units,
lower_is_better=self.lower_is_better,
classifiers=self.classifiers,
)
def __str__(self):
result = '{}: {}'.format(self.name, self.value)
if self.units:
result += ' ' + self.units
result += ' ({})'.format('-' if self.lower_is_better else '+')
return result
def __repr__(self):
text = self.__str__()
if self.classifiers:
return '<{} {}>'.format(text, self.classifiers)
else:
return '<{}>'.format(text)
class Event(object):
"""
An event that occured during a run.
"""
__slots__ = ['timestamp', 'message']
@staticmethod
def from_pod(pod):
instance = Event(pod['message'])
instance.timestamp = pod['timestamp']
return instance
@property
def summary(self):
lines = self.message.split('\n')
result = lines[0]
if len(lines) > 1:
result += '[...]'
return result
def __init__(self, message):
self.timestamp = datetime.utcnow()
self.message = message
def to_pod(self):
return dict(
timestamp=self.timestamp,
message=self.message,
)
def __str__(self):
return '[{}] {}'.format(self.timestamp, self.message)
__repr__ = __str__
def init_run_output(path, wa_state, force=False):
if os.path.exists(path):
if force:
logger.info('Removing existing output directory.')
shutil.rmtree(os.path.abspath(path))
else:
raise RuntimeError('path exists: {}'.format(path))
logger.info('Creating output directory.')
os.makedirs(path)
meta_dir = os.path.join(path, '__meta')
os.makedirs(meta_dir)
_save_raw_config(meta_dir, wa_state)
touch(os.path.join(path, 'run.log'))
info = RunInfo(
run_name=wa_state.run_config.run_name,
project=wa_state.run_config.project,
project_stage=wa_state.run_config.project_stage,
)
write_pod(info.to_pod(), os.path.join(meta_dir, 'run_info.json'))
write_pod(RunState().to_pod(), os.path.join(path, '.run_state.json'))
write_pod(Result().to_pod(), os.path.join(path, 'result.json'))
return RunOutput(path)
def init_job_output(run_output, job):
output_name = '{}-{}-{}'.format(job.id, job.spec.label, job.iteration)
path = os.path.join(run_output.basepath, output_name)
ensure_directory_exists(path)
write_pod(Result().to_pod(), os.path.join(path, 'result.json'))
job_output = JobOutput(path, job.id, job.label, job.iteration, job.retries)
job_output.spec = job.spec
job_output.status = job.status
run_output.jobs.append(job_output)
return job_output
def discover_wa_outputs(path):
for root, dirs, files in os.walk(path):
if '__meta' in dirs:
yield RunOutput(root)
def _save_raw_config(meta_dir, state):
raw_config_dir = os.path.join(meta_dir, 'raw_config')
os.makedirs(raw_config_dir)
for i, source in enumerate(state.loaded_config_sources):
if not os.path.isfile(source):
continue
basename = os.path.basename(source)
dest_path = os.path.join(raw_config_dir, 'cfg{}-{}'.format(i, basename))
shutil.copy(source, dest_path)