class JobScheduler:
def __init__(self,
workflow,
dag,
cores,
local_cores=1,
dryrun=False,
touch=False,
cluster=None,
cluster_config=None,
cluster_sync=None,
drmaa=None,
jobname=None,
quiet=False,
printreason=False,
printshellcmds=False,
keepgoing=False,
max_jobs_per_second=None,
latency_wait=3,
benchmark_repeats=1,
greediness=1.0,
force_use_threads=False):
""" Create a new instance of KnapsackJobScheduler. """
self.cluster = cluster
self.cluster_config = cluster_config
self.cluster_sync = cluster_sync
self.dag = dag
self.workflow = workflow
self.dryrun = dryrun
self.quiet = quiet
self.keepgoing = keepgoing
self.running = set()
self.failed = set()
self.finished_jobs = 0
self.greediness = 1
self.resources = dict(self.workflow.global_resources)
use_threads = force_use_threads or (
os.name != "posix") or cluster or cluster_sync or drmaa
self._open_jobs = threading.Event()
self._lock = threading.Lock()
self._errors = False
self._finished = False
self._job_queue = None
self._submit_callback = self._noop
self._finish_callback = partial(self._proceed,
update_dynamic=not self.dryrun,
print_progress=not self.quiet
and not self.dryrun)
if dryrun:
self._executor = DryrunExecutor(workflow,
dag,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait)
self.job_reward = self.dryrun_job_reward
elif touch:
self._executor = TouchExecutor(workflow,
dag,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait)
elif cluster or cluster_sync or (drmaa is not None):
workers = min(max(1, sum(1 for _ in dag.local_needrun_jobs)),
local_cores)
self._local_executor = CPUExecutor(
workflow,
dag,
workers,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
use_threads=use_threads,
latency_wait=latency_wait,
benchmark_repeats=benchmark_repeats)
self.run = self.run_cluster_or_local
if cluster or cluster_sync:
constructor = SynchronousClusterExecutor if cluster_sync \
else GenericClusterExecutor
self._executor = constructor(
workflow,
dag,
None,
submitcmd=(cluster or cluster_sync),
cluster_config=cluster_config,
jobname=jobname,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
benchmark_repeats=benchmark_repeats,
max_jobs_per_second=max_jobs_per_second)
if workflow.immediate_submit:
self.job_reward = self.dryrun_job_reward
self._submit_callback = partial(
self._proceed,
update_dynamic=False,
print_progress=False,
update_resources=False,
)
else:
self._executor = DRMAAExecutor(
workflow,
dag,
None,
drmaa_args=drmaa,
jobname=jobname,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
benchmark_repeats=benchmark_repeats,
cluster_config=cluster_config,
max_jobs_per_second=max_jobs_per_second)
else:
# local execution or execution of cluster job
# calculate how many parallel workers the executor shall spawn
# each job has at least one thread, hence we need to have
# the minimum of given cores and number of jobs
workers = min(cores, max(1, len(dag)))
self._executor = CPUExecutor(
workflow,
dag,
workers,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
use_threads=use_threads,
latency_wait=latency_wait,
benchmark_repeats=benchmark_repeats,
)
self._open_jobs.set()
@property
def stats(self):
try:
return self._executor.stats
except AttributeError:
raise TypeError("Executor does not support stats")
def candidate(self, job):
""" Return whether a job is a candidate to be executed. """
return (job not in self.running and job not in self.failed
and (self.dryrun or
(not job.dynamic_input and not self.dag.dynamic(job))))
@property
def open_jobs(self):
""" Return open jobs. """
return filter(self.candidate, list(self.dag.ready_jobs))
def schedule(self):
""" Schedule jobs that are ready, maximizing cpu usage. """
try:
while True:
# work around so that the wait does not prevent keyboard interrupts
while not self._open_jobs.wait(1):
pass
# obtain needrun and running jobs in a thread-safe way
with self._lock:
needrun = list(self.open_jobs)
running = list(self.running)
# free the event
self._open_jobs.clear()
# handle errors
if not self.keepgoing and self._errors:
logger.info("Will exit after finishing "
"currently running jobs.")
if not running:
self._executor.shutdown()
logger.error(_ERROR_MSG_FINAL)
return False
continue
# normal shutdown because all jobs have been finished
if not needrun and not running:
self._executor.shutdown()
if self._errors:
logger.error(_ERROR_MSG_FINAL)
return not self._errors
# continue if no new job needs to be executed
if not needrun:
continue
logger.debug("Resources before job selection: {}".format(
self.resources))
logger.debug("Ready jobs ({}):\n\t".format(len(needrun)) +
"\n\t".join(map(str, needrun)))
# select jobs by solving knapsack problem
run = self.job_selector(needrun)
logger.debug("Selected jobs ({}):\n\t".format(len(run)) +
"\n\t".join(map(str, run)))
# update running jobs
with self._lock:
self.running.update(run)
logger.debug("Resources after job selection: {}".format(
self.resources))
# actually run jobs
for job in run:
self.run(job)
except (KeyboardInterrupt, SystemExit):
logger.info("Terminating processes on user request.")
self._executor.cancel()
with self._lock:
running = list(self.running)
for job in running:
job.cleanup()
return False
def run(self, job):
self._executor.run(job,
callback=self._finish_callback,
submit_callback=self._submit_callback,
error_callback=self._error)
def run_cluster_or_local(self, job):
executor = self._local_executor if self.workflow.is_local(
job.rule) else self._executor
executor.run(job,
callback=self._finish_callback,
submit_callback=self._submit_callback,
error_callback=self._error)
def _noop(self, job):
pass
def _free_resources(self, job):
for name, value in job.resources.items():
if name in self.resources:
value = self.calc_resource(name, value)
self.resources[name] += value
logger.debug("Releasing {} {} (now {}).".format(
value, name, self.resources[name]))
def _proceed(self,
job,
update_dynamic=True,
print_progress=False,
update_resources=True):
""" Do stuff after job is finished. """
with self._lock:
if update_resources:
self.finished_jobs += 1
self.running.remove(job)
self._free_resources(job)
self.dag.finish(job, update_dynamic=update_dynamic)
logger.job_finished(jobid=self.dag.jobid(job))
if print_progress:
self.progress()
if any(self.open_jobs) or not self.running:
# go on scheduling if open jobs are ready or no job is running
self._open_jobs.set()
def _error(self, job):
"""Clear jobs and stop the workflow.
If Snakemake is configured to restart jobs then the job might have
"restart_times" left and we just decrement and let the scheduler
try to run the job again.
"""
with self._lock:
self.running.remove(job)
self._free_resources(job)
self._open_jobs.set()
if job.restart_times > 0:
msg = (("Trying to restart job for rule {} with "
"wildcards {}").format(job.rule.name,
job.wildcards_dict))
logger.info(msg)
job.restart_times -= 1
else:
self._errors = True
self.failed.add(job)
if self.keepgoing:
logger.info("Job failed, going on with independent jobs.")
def job_selector(self, jobs):
"""
Using the greedy heuristic from
"A Greedy Algorithm for the General Multidimensional Knapsack
Problem", Akcay, Li, Xu, Annals of Operations Research, 2012
Args:
jobs (list): list of jobs
"""
with self._lock:
# each job is an item with one copy (0-1 MDKP)
n = len(jobs)
x = [0] * n # selected jobs
E = set(range(n)) # jobs still free to select
u = [1] * n
a = list(map(self.job_weight, jobs)) # resource usage of jobs
c = list(map(self.job_reward, jobs)) # job rewards
def calc_reward():
return [c_j * y_j for c_j, y_j in zip(c, y)]
b = [
self.resources[name] for name in self.workflow.global_resources
] # resource capacities
while True:
# Step 2: compute effective capacities
y = [(min((min(u[j], b_i // a_j_i) if a_j_i > 0 else u[j])
for b_i, a_j_i in zip(b, a[j])
if a_j_i) if j in E else 0) for j in range(n)]
if not any(y):
break
y = [(max(1, int(self.greediness * y_j)) if y_j > 0 else 0)
for y_j in y]
# Step 3: compute rewards on cumulative sums
reward = calc_reward()
j_sel = max(E, key=reward.__getitem__) # argmax
# Step 4: batch increment
y_sel = y[j_sel]
# Step 5: update information
x[j_sel] += y_sel
b = [b_i - (a_j_i * y_sel) for b_i, a_j_i in zip(b, a[j_sel])]
u[j_sel] -= y_sel
if not u[j_sel] or self.greediness == 1:
E.remove(j_sel)
if not E:
break
solution = [job for job, sel in zip(jobs, x) if sel]
# update resources
for name, b_i in zip(self.workflow.global_resources, b):
self.resources[name] = b_i
return solution
def calc_resource(self, name, value):
return min(value, self.workflow.global_resources[name])
def rule_weight(self, rule):
res = rule.resources
return [
self.calc_resource(name, res.get(name, 0))
for name in self.workflow.global_resources
]
def job_weight(self, job):
res = job.resources
return [
self.calc_resource(name, res.get(name, 0))
for name in self.workflow.global_resources
]
def job_reward(self, job):
return (self.dag.priority(job), self.dag.temp_input_count(job),
self.dag.downstream_size(job), job.inputsize)
def dryrun_job_reward(self, job):
return (self.dag.priority(job), self.dag.temp_input_count(job),
self.dag.downstream_size(job))
def progress(self):
""" Display the progress. """
logger.progress(done=self.finished_jobs, total=len(self.dag))
def __init__(self,
workflow,
dag,
cores,
local_cores=1,
dryrun=False,
touch=False,
cluster=None,
cluster_config=None,
cluster_sync=None,
drmaa=None,
jobname=None,
quiet=False,
printreason=False,
printshellcmds=False,
keepgoing=False,
max_jobs_per_second=None,
latency_wait=3,
benchmark_repeats=1,
greediness=1.0,
force_use_threads=False):
""" Create a new instance of KnapsackJobScheduler. """
self.cluster = cluster
self.cluster_config = cluster_config
self.cluster_sync = cluster_sync
self.dag = dag
self.workflow = workflow
self.dryrun = dryrun
self.quiet = quiet
self.keepgoing = keepgoing
self.running = set()
self.failed = set()
self.finished_jobs = 0
self.greediness = 1
self.resources = dict(self.workflow.global_resources)
use_threads = force_use_threads or (
os.name != "posix") or cluster or cluster_sync or drmaa
self._open_jobs = threading.Event()
self._lock = threading.Lock()
self._errors = False
self._finished = False
self._job_queue = None
self._submit_callback = self._noop
self._finish_callback = partial(self._proceed,
update_dynamic=not self.dryrun,
print_progress=not self.quiet
and not self.dryrun)
if dryrun:
self._executor = DryrunExecutor(workflow,
dag,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait)
self.job_reward = self.dryrun_job_reward
elif touch:
self._executor = TouchExecutor(workflow,
dag,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait)
elif cluster or cluster_sync or (drmaa is not None):
workers = min(max(1, sum(1 for _ in dag.local_needrun_jobs)),
local_cores)
self._local_executor = CPUExecutor(
workflow,
dag,
workers,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
use_threads=use_threads,
latency_wait=latency_wait,
benchmark_repeats=benchmark_repeats)
self.run = self.run_cluster_or_local
if cluster or cluster_sync:
constructor = SynchronousClusterExecutor if cluster_sync \
else GenericClusterExecutor
self._executor = constructor(
workflow,
dag,
None,
submitcmd=(cluster or cluster_sync),
cluster_config=cluster_config,
jobname=jobname,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
benchmark_repeats=benchmark_repeats,
max_jobs_per_second=max_jobs_per_second)
if workflow.immediate_submit:
self.job_reward = self.dryrun_job_reward
self._submit_callback = partial(
self._proceed,
update_dynamic=False,
print_progress=False,
update_resources=False,
)
else:
self._executor = DRMAAExecutor(
workflow,
dag,
None,
drmaa_args=drmaa,
jobname=jobname,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
benchmark_repeats=benchmark_repeats,
cluster_config=cluster_config,
max_jobs_per_second=max_jobs_per_second)
else:
# local execution or execution of cluster job
# calculate how many parallel workers the executor shall spawn
# each job has at least one thread, hence we need to have
# the minimum of given cores and number of jobs
workers = min(cores, max(1, len(dag)))
self._executor = CPUExecutor(
workflow,
dag,
workers,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
use_threads=use_threads,
latency_wait=latency_wait,
benchmark_repeats=benchmark_repeats,
)
self._open_jobs.set()
class JobScheduler:
def __init__(
self,
workflow,
dag,
cores,
local_cores=1,
dryrun=False,
touch=False,
cluster=None,
cluster_status=None,
cluster_config=None,
cluster_sync=None,
drmaa=None,
drmaa_log_dir=None,
kubernetes=None,
container_image=None,
tibanna=None,
tibanna_sfn=None,
precommand="",
tibanna_config=False,
jobname=None,
quiet=False,
printreason=False,
printshellcmds=False,
keepgoing=False,
max_jobs_per_second=None,
max_status_checks_per_second=100,
latency_wait=3,
greediness=1.0,
force_use_threads=False,
assume_shared_fs=True,
keepincomplete=False,
):
""" Create a new instance of KnapsackJobScheduler. """
from ratelimiter import RateLimiter
self.cluster = cluster
self.cluster_config = cluster_config
self.cluster_sync = cluster_sync
self.dag = dag
self.workflow = workflow
self.dryrun = dryrun
self.touch = touch
self.quiet = quiet
self.keepgoing = keepgoing
self.running = set()
self.failed = set()
self.finished_jobs = 0
self.greediness = 1
self.max_jobs_per_second = max_jobs_per_second
self.keepincomplete = keepincomplete
self.global_resources = {
name: (sys.maxsize if res is None else res)
for name, res in workflow.global_resources.items()
}
self.resources = dict(self.global_resources)
use_threads = (force_use_threads or (os.name != "posix") or cluster
or cluster_sync or drmaa)
self._open_jobs = threading.Semaphore(0)
self._lock = threading.Lock()
self._errors = False
self._finished = False
self._job_queue = None
self._submit_callback = self._noop
self._finish_callback = partial(
self._proceed,
update_dynamic=not self.dryrun,
print_progress=not self.quiet and not self.dryrun,
)
self._local_executor = None
if dryrun:
self._executor = DryrunExecutor(
workflow,
dag,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
)
elif touch:
self._executor = TouchExecutor(
workflow,
dag,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
)
elif cluster or cluster_sync or (drmaa is not None):
if not workflow.immediate_submit:
# No local jobs when using immediate submit!
# Otherwise, they will fail due to missing input
self._local_executor = CPUExecutor(
workflow,
dag,
local_cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
cores=local_cores,
keepincomplete=keepincomplete,
)
if cluster or cluster_sync:
if cluster_sync:
constructor = SynchronousClusterExecutor
else:
constructor = partial(
GenericClusterExecutor,
statuscmd=cluster_status,
max_status_checks_per_second=
max_status_checks_per_second,
)
self._executor = constructor(
workflow,
dag,
None,
submitcmd=(cluster or cluster_sync),
cluster_config=cluster_config,
jobname=jobname,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
assume_shared_fs=assume_shared_fs,
keepincomplete=keepincomplete,
)
if workflow.immediate_submit:
self._submit_callback = partial(
self._proceed,
update_dynamic=False,
print_progress=False,
update_resources=False,
handle_job_success=False,
)
else:
self._executor = DRMAAExecutor(
workflow,
dag,
None,
drmaa_args=drmaa,
drmaa_log_dir=drmaa_log_dir,
jobname=jobname,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
cluster_config=cluster_config,
assume_shared_fs=assume_shared_fs,
max_status_checks_per_second=max_status_checks_per_second,
keepincomplete=keepincomplete,
)
elif kubernetes:
self._local_executor = CPUExecutor(
workflow,
dag,
local_cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
cores=local_cores,
keepincomplete=keepincomplete,
)
self._executor = KubernetesExecutor(
workflow,
dag,
kubernetes,
container_image=container_image,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
cluster_config=cluster_config,
keepincomplete=keepincomplete,
)
elif tibanna:
self._local_executor = CPUExecutor(
workflow,
dag,
local_cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
use_threads=use_threads,
latency_wait=latency_wait,
cores=local_cores,
keepincomplete=keepincomplete,
)
self._executor = TibannaExecutor(
workflow,
dag,
cores,
tibanna_sfn,
precommand=precommand,
tibanna_config=tibanna_config,
container_image=container_image,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
keepincomplete=keepincomplete,
)
else:
self._executor = CPUExecutor(
workflow,
dag,
cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
use_threads=use_threads,
latency_wait=latency_wait,
cores=cores,
keepincomplete=keepincomplete,
)
if self.max_jobs_per_second and not self.dryrun:
max_jobs_frac = Fraction(
self.max_jobs_per_second).limit_denominator()
self.rate_limiter = RateLimiter(max_calls=max_jobs_frac.numerator,
period=max_jobs_frac.denominator)
else:
# essentially no rate limit
self.rate_limiter = DummyRateLimiter()
self._user_kill = None
signal.signal(signal.SIGTERM, self.exit_gracefully)
self._open_jobs.release()
@property
def stats(self):
try:
return self._executor.stats
except AttributeError:
raise TypeError("Executor does not support stats")
def candidate(self, job):
""" Return whether a job is a candidate to be executed. """
return (job not in self.running and job not in self.failed
and (self.dryrun or
(not job.dynamic_input and not self.dag.dynamic(job))))
@property
def open_jobs(self):
""" Return open jobs. """
return filter(self.candidate, list(job for job in self.dag.ready_jobs))
def schedule(self):
""" Schedule jobs that are ready, maximizing cpu usage. """
try:
while True:
# work around so that the wait does not prevent keyboard interrupts
# while not self._open_jobs.acquire(False):
# time.sleep(1)
self._open_jobs.acquire()
# obtain needrun and running jobs in a thread-safe way
with self._lock:
needrun = list(self.open_jobs)
running = list(self.running)
errors = self._errors
user_kill = self._user_kill
# handle errors
if user_kill or (not self.keepgoing and errors):
if user_kill == "graceful":
logger.info("Will exit after finishing "
"currently running jobs.")
if not running:
logger.info(
"Shutting down, this might take some time.")
self._executor.shutdown()
if not user_kill:
logger.error(_ERROR_MSG_FINAL)
return False
continue
# normal shutdown because all jobs have been finished
if not needrun and (not running
or self.workflow.immediate_submit):
self._executor.shutdown()
if errors:
logger.error(_ERROR_MSG_FINAL)
return not errors
# continue if no new job needs to be executed
if not needrun:
continue
# select jobs by solving knapsack problem (omit with dryrun)
if self.dryrun:
run = needrun
else:
logger.debug("Resources before job selection: {}".format(
self.resources))
logger.debug("Ready jobs ({}):\n\t".format(len(needrun)) +
"\n\t".join(map(str, needrun)))
run = self.job_selector(needrun)
logger.debug("Selected jobs ({}):\n\t".format(len(run)) +
"\n\t".join(map(str, run)))
logger.debug("Resources after job selection: {}".format(
self.resources))
# update running jobs
with self._lock:
self.running.update(run)
# actually run jobs
for job in run:
with self.rate_limiter:
self.run(job)
except (KeyboardInterrupt, SystemExit):
logger.info(
"Terminating processes on user request, this might take some time."
)
self._executor.cancel()
return False
def get_executor(self, job):
if self._local_executor is None:
return self._executor
else:
return self._local_executor if job.is_local else self._executor
def run(self, job):
self.get_executor(job).run(
job,
callback=self._finish_callback,
submit_callback=self._submit_callback,
error_callback=self._error,
)
def _noop(self, job):
pass
def _free_resources(self, job):
for name, value in job.resources.items():
if name in self.resources:
value = self.calc_resource(name, value)
self.resources[name] += value
def _proceed(
self,
job,
update_dynamic=True,
print_progress=False,
update_resources=True,
handle_job_success=True,
):
""" Do stuff after job is finished. """
with self._lock:
if handle_job_success:
# by calling this behind the lock, we avoid race conditions
try:
self.get_executor(job).handle_job_success(job)
except (RuleException, WorkflowError) as e:
# if an error occurs while processing job output,
# we do the same as in case of errors during execution
print_exception(e, self.workflow.linemaps)
self._handle_error(job)
return
try:
self.dag.finish(job, update_dynamic=update_dynamic)
except (RuleException, WorkflowError) as e:
# if an error occurs while processing job output,
# we do the same as in case of errors during execution
print_exception(e, self.workflow.linemaps)
self._handle_error(job)
return
if update_resources:
# normal jobs have len=1, group jobs have len>1
self.finished_jobs += len(job)
self.running.remove(job)
self._free_resources(job)
if print_progress:
if job.is_group():
for j in job:
logger.job_finished(jobid=j.jobid)
else:
logger.job_finished(jobid=job.jobid)
self.progress()
if (any(self.open_jobs) or not self.running
or self.workflow.immediate_submit):
# go on scheduling if open jobs are ready or no job is running
self._open_jobs.release()
def _error(self, job):
with self._lock:
self._handle_error(job)
def _handle_error(self, job):
"""Clear jobs and stop the workflow.
If Snakemake is configured to restart jobs then the job might have
"restart_times" left and we just decrement and let the scheduler
try to run the job again.
"""
self.get_executor(job).handle_job_error(job)
self.running.remove(job)
self._free_resources(job)
# attempt starts counting from 1, but the first attempt is not
# a restart, hence we subtract 1.
if job.restart_times > job.attempt - 1:
logger.info("Trying to restart job {}.".format(
self.dag.jobid(job)))
job.attempt += 1
else:
self._errors = True
self.failed.add(job)
if self.keepgoing:
logger.info("Job failed, going on with independent jobs.")
self._open_jobs.release()
def exit_gracefully(self, *args):
with self._lock:
self._user_kill = "graceful"
self._open_jobs.release()
def job_selector(self, jobs):
"""
Using the greedy heuristic from
"A Greedy Algorithm for the General Multidimensional Knapsack
Problem", Akcay, Li, Xu, Annals of Operations Research, 2012
Args:
jobs (list): list of jobs
"""
with self._lock:
# each job is an item with one copy (0-1 MDKP)
n = len(jobs)
x = [0] * n # selected jobs
E = set(range(n)) # jobs still free to select
u = [1] * n
a = list(map(self.job_weight, jobs)) # resource usage of jobs
c = list(map(self.job_reward, jobs)) # job rewards
def calc_reward():
return [c_j * y_j for c_j, y_j in zip(c, y)]
b = [self.resources[name]
for name in self.global_resources] # resource capacities
while True:
# Step 2: compute effective capacities
y = [(min((min(u[j], b_i // a_j_i) if a_j_i > 0 else u[j])
for b_i, a_j_i in zip(b, a[j])
if a_j_i) if j in E else 0) for j in range(n)]
if not any(y):
break
y = [(max(1, int(self.greediness * y_j)) if y_j > 0 else 0)
for y_j in y]
# Step 3: compute rewards on cumulative sums
reward = calc_reward()
j_sel = max(E, key=reward.__getitem__) # argmax
# Step 4: batch increment
y_sel = y[j_sel]
# Step 5: update information
x[j_sel] += y_sel
b = [b_i - (a_j_i * y_sel) for b_i, a_j_i in zip(b, a[j_sel])]
u[j_sel] -= y_sel
if not u[j_sel] or self.greediness == 1:
E.remove(j_sel)
if not E:
break
solution = [job for job, sel in zip(jobs, x) if sel]
# update resources
for name, b_i in zip(self.global_resources, b):
self.resources[name] = b_i
return solution
def calc_resource(self, name, value):
gres = self.global_resources[name]
if value > gres:
if name == "_cores":
name = "threads"
raise WorkflowError(
"Job needs {name}={res} but only {name}={gres} "
"are available. This is likely because two "
"jobs are connected via a pipe and have to run "
"simultaneously. Consider providing more "
"resources (e.g. via --cores).".format(name=name,
res=value,
gres=gres))
return value
def rule_weight(self, rule):
res = rule.resources
return [
self.calc_resource(name, res.get(name, 0))
for name in self.global_resources
]
def job_weight(self, job):
res = job.resources
return [
self.calc_resource(name, res.get(name, 0))
for name in self.global_resources
]
def job_reward(self, job):
if self.touch or self.dryrun or self.workflow.immediate_submit:
temp_size = 0
input_size = 0
else:
temp_size = self.dag.temp_size(job)
input_size = job.inputsize
# Usually, this should guide the scheduler to first schedule all jobs
# that remove the largest temp file, then the second largest and so on.
# Since the weight is summed up, it can in theory be that it sometimes
# prefers a set of many jobs that all depend on smaller temp files though.
# A real solution to the problem is therefore to use dummy jobs that
# ensure selection of groups of jobs that together delete the same temp
# file.
return (job.priority, temp_size, input_size)
def progress(self):
""" Display the progress. """
logger.progress(done=self.finished_jobs, total=len(self.dag))
def __init__(
self,
workflow,
dag,
cores,
local_cores=1,
dryrun=False,
touch=False,
cluster=None,
cluster_status=None,
cluster_config=None,
cluster_sync=None,
drmaa=None,
drmaa_log_dir=None,
kubernetes=None,
container_image=None,
tibanna=None,
tibanna_sfn=None,
precommand="",
tibanna_config=False,
jobname=None,
quiet=False,
printreason=False,
printshellcmds=False,
keepgoing=False,
max_jobs_per_second=None,
max_status_checks_per_second=100,
latency_wait=3,
greediness=1.0,
force_use_threads=False,
assume_shared_fs=True,
keepincomplete=False,
):
""" Create a new instance of KnapsackJobScheduler. """
from ratelimiter import RateLimiter
self.cluster = cluster
self.cluster_config = cluster_config
self.cluster_sync = cluster_sync
self.dag = dag
self.workflow = workflow
self.dryrun = dryrun
self.touch = touch
self.quiet = quiet
self.keepgoing = keepgoing
self.running = set()
self.failed = set()
self.finished_jobs = 0
self.greediness = 1
self.max_jobs_per_second = max_jobs_per_second
self.keepincomplete = keepincomplete
self.global_resources = {
name: (sys.maxsize if res is None else res)
for name, res in workflow.global_resources.items()
}
self.resources = dict(self.global_resources)
use_threads = (force_use_threads or (os.name != "posix") or cluster
or cluster_sync or drmaa)
self._open_jobs = threading.Semaphore(0)
self._lock = threading.Lock()
self._errors = False
self._finished = False
self._job_queue = None
self._submit_callback = self._noop
self._finish_callback = partial(
self._proceed,
update_dynamic=not self.dryrun,
print_progress=not self.quiet and not self.dryrun,
)
self._local_executor = None
if dryrun:
self._executor = DryrunExecutor(
workflow,
dag,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
)
elif touch:
self._executor = TouchExecutor(
workflow,
dag,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
)
elif cluster or cluster_sync or (drmaa is not None):
if not workflow.immediate_submit:
# No local jobs when using immediate submit!
# Otherwise, they will fail due to missing input
self._local_executor = CPUExecutor(
workflow,
dag,
local_cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
cores=local_cores,
keepincomplete=keepincomplete,
)
if cluster or cluster_sync:
if cluster_sync:
constructor = SynchronousClusterExecutor
else:
constructor = partial(
GenericClusterExecutor,
statuscmd=cluster_status,
max_status_checks_per_second=
max_status_checks_per_second,
)
self._executor = constructor(
workflow,
dag,
None,
submitcmd=(cluster or cluster_sync),
cluster_config=cluster_config,
jobname=jobname,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
assume_shared_fs=assume_shared_fs,
keepincomplete=keepincomplete,
)
if workflow.immediate_submit:
self._submit_callback = partial(
self._proceed,
update_dynamic=False,
print_progress=False,
update_resources=False,
handle_job_success=False,
)
else:
self._executor = DRMAAExecutor(
workflow,
dag,
None,
drmaa_args=drmaa,
drmaa_log_dir=drmaa_log_dir,
jobname=jobname,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
cluster_config=cluster_config,
assume_shared_fs=assume_shared_fs,
max_status_checks_per_second=max_status_checks_per_second,
keepincomplete=keepincomplete,
)
elif kubernetes:
self._local_executor = CPUExecutor(
workflow,
dag,
local_cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
cores=local_cores,
keepincomplete=keepincomplete,
)
self._executor = KubernetesExecutor(
workflow,
dag,
kubernetes,
container_image=container_image,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
cluster_config=cluster_config,
keepincomplete=keepincomplete,
)
elif tibanna:
self._local_executor = CPUExecutor(
workflow,
dag,
local_cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
use_threads=use_threads,
latency_wait=latency_wait,
cores=local_cores,
keepincomplete=keepincomplete,
)
self._executor = TibannaExecutor(
workflow,
dag,
cores,
tibanna_sfn,
precommand=precommand,
tibanna_config=tibanna_config,
container_image=container_image,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
keepincomplete=keepincomplete,
)
else:
self._executor = CPUExecutor(
workflow,
dag,
cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
use_threads=use_threads,
latency_wait=latency_wait,
cores=cores,
keepincomplete=keepincomplete,
)
if self.max_jobs_per_second and not self.dryrun:
max_jobs_frac = Fraction(
self.max_jobs_per_second).limit_denominator()
self.rate_limiter = RateLimiter(max_calls=max_jobs_frac.numerator,
period=max_jobs_frac.denominator)
else:
# essentially no rate limit
self.rate_limiter = DummyRateLimiter()
self._user_kill = None
signal.signal(signal.SIGTERM, self.exit_gracefully)
self._open_jobs.release()
class JobScheduler:
def __init__(self,
workflow,
dag,
cores,
dryrun=False,
touch=False,
cluster=None,
immediate_submit=False,
quiet=False,
printreason=False,
printshellcmds=False,
keepgoing=False,
output_wait=3):
""" Create a new instance of KnapsackJobScheduler. """
self.cluster = cluster
self.dag = dag
self.workflow = workflow
self.dryrun = dryrun
self.quiet = quiet
self.keepgoing = keepgoing
self.running = set()
self.failed = set()
self.finished_jobs = 0
self.resources = dict(self.workflow.global_resources)
use_threads = os.name != "posix" or cluster
if not use_threads:
self._open_jobs = multiprocessing.Event()
self._lock = multiprocessing.Lock()
else:
self._open_jobs = threading.Event()
self._lock = threading.Lock()
self._errors = False
self._finished = False
self._job_queue = None
self._submit_callback = self._noop
self._finish_callback = partial(self._proceed,
update_dynamic=not self.dryrun,
print_progress=not self.quiet
and not self.dryrun)
if dryrun:
self._executor = DryrunExecutor(workflow,
dag,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
output_wait=output_wait)
self.rule_reward = self.dryrun_rule_reward
elif touch:
self._executor = TouchExecutor(workflow,
dag,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
output_wait=output_wait)
elif cluster:
# TODO properly set cores
self._local_executor = CPUExecutor(workflow,
dag,
cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
threads=use_threads,
output_wait=output_wait)
self._executor = ClusterExecutor(workflow,
dag,
None,
submitcmd=cluster,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
output_wait=output_wait)
self.rule_weight = partial(self.rule_weight, maxcores=1)
if immediate_submit:
self.rule_reward = self.dryrun_rule_reward
self._submit_callback = partial(self._proceed,
update_dynamic=False,
print_progress=False,
update_resources=False)
else:
self.run = self.run_cluster_or_local
else:
self._executor = CPUExecutor(workflow,
dag,
cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
threads=use_threads,
output_wait=output_wait)
self._open_jobs.set()
@property
def stats(self):
try:
return self._executor.stats
except AttributeError:
raise TypeError("Executor does not support stats")
def candidate(self, job):
""" Return whether a job is a candidate to be executed. """
return (job not in self.running and job not in self.failed
and (self.dryrun or
(not job.dynamic_input and not self.dag.dynamic(job))))
@property
def open_jobs(self):
""" Return open jobs. """
return filter(self.candidate, list(self.dag.ready_jobs))
def schedule(self):
""" Schedule jobs that are ready, maximizing cpu usage. """
while True:
try:
self._open_jobs.wait()
except:
# this will be caused because of SIGTERM or SIGINT
self._executor.shutdown()
return False
self._open_jobs.clear()
if not self.keepgoing and self._errors:
logger.warning("Will exit after finishing "
"currently running jobs.")
self._executor.shutdown()
return False
if not any(self.open_jobs):
self._executor.shutdown()
return not self._errors
needrun = list(self.open_jobs)
assert needrun
logger.debug("Ready jobs:\n\t" + "\n\t".join(map(str, needrun)))
run = self.job_selector(needrun)
logger.debug("Selected jobs:\n\t" + "\n\t".join(map(str, run)))
self.running.update(run)
for job in run:
self.run(job)
def run(self, job):
self._executor.run(job,
callback=self._finish_callback,
submit_callback=self._submit_callback,
error_callback=self._error)
def run_cluster_or_local(self, job):
executor = self._local_executor if self.workflow.is_local(
job.rule) else self._executor
executor.run(job,
callback=self._finish_callback,
submit_callback=self._submit_callback,
error_callback=self._error)
def _noop(self, job):
pass
def _proceed(self,
job,
update_dynamic=True,
print_progress=False,
update_resources=True):
""" Do stuff after job is finished. """
with self._lock:
if update_resources:
self.finished_jobs += 1
self.running.remove(job)
for name, value in job.resources.items():
if name in self.resources:
self.resources[name] += value
self.dag.finish(job, update_dynamic=update_dynamic)
if print_progress:
self.progress()
if any(self.open_jobs) or not self.running:
# go on scheduling if open jobs are ready or no job is running
self._open_jobs.set()
def _error(self, job):
""" Clear jobs and stop the workflow. """
with self._lock:
self._errors = True
self.running.remove(job)
self.failed.add(job)
if self.keepgoing:
logger.warning("Job failed, going on with independent jobs.")
else:
self._open_jobs.set()
def _job_selector(self, jobs):
""" Solve 0-1 knapsack to maximize cpu utilization. """
dimi, dimj = len(jobs) + 1, self._cores + 1
K = [[(0, 0) for c in range(dimj)] for i in range(dimi)]
for i in range(1, dimi):
for j in range(1, dimj):
job = jobs[i - 1]
w = self.job_weight(job)
v = job.priority, job.inputsize if not self.dryrun else 0
if w > j:
K[i][j] = K[i - 1][j]
else:
K[i][j] = max(K[i - 1][j],
tuple(map(operator.add, v, K[i - 1][j - w])))
solution = set()
i = dimi - 1
j = dimj - 1
while i > 0:
if K[i][j] != K[i - 1][j]:
job = jobs[i - 1]
solution.add(job)
j = j - job.threads
i -= 1
return solution
def job_selector(self, jobs):
"""
Using the greedy heuristic from
"A Greedy Algorithm for the General Multidimensional Knapsack
Problem", Akcay, Li, Xu, Annals of Operations Research, 2012
"""
# solve over the rules instead of jobs (much less)
_jobs = defaultdict(list)
for job in jobs:
_jobs[job.rule].append(job)
jobs = _jobs
# sort the jobs by priority
for _jobs in jobs.values():
_jobs.sort(key=self.dag.priority, reverse=True)
rules = list(jobs)
# greedyness (1 means take all possible jobs for a selected rule
alpha = 1
# Step 1: initialization
n = len(rules)
x = [0] * n # selected jobs of each rule
E = set(range(n)) # rules free to select
u = [len(jobs[rule]) for rule in rules] # number of jobs left
b = list(self.resources.values()) # resource capacities
a = list(map(self.rule_weight, rules)) # resource usage of rules
c = list(map(partial(self.rule_reward, jobs=jobs),
rules)) # matrix of cumulative rewards over jobs
while True:
# Step 2: compute effective capacities
y = [(min((min(u[j], b_i // a_j_i) if a_j_i > 0 else u[j])
for b_i, a_j_i in zip(b, a[j])
if a_j_i) if j in E else 0) for j in range(n)]
if not any(y):
break
# Step 3: compute rewards on cumulative sums and normalize by y
# in order to not prefer rules with small weights
reward = [([((crit[x_j + y_j] - crit[x_j]) / y_j if y_j else 0)
for crit in c_j] if j in E else [0] * len(c_j))
for j, (c_j, y_j, x_j) in enumerate(zip(c, y, x))]
j_sel = max(E, key=reward.__getitem__) # argmax
# Step 4: batch increment
y_sel = min(u[j_sel], max(1, alpha * y[j_sel]))
# Step 5: update information
x[j_sel] += y_sel
b = [b_i - (a_j_i * y_sel) for b_i, a_j_i in zip(b, a[j_sel])]
u[j_sel] -= y_sel
if not u[j_sel] or alpha == 1:
E.remove(j_sel)
if not E:
break
# Solution is the list of jobs that was selected from the selected rules
solution = list(chain(*[jobs[rules[j]][:x_]
for j, x_ in enumerate(x)]))
# update resources
for name, b_i in zip(self.resources, b):
self.resources[name] = b_i
return solution
def rule_weight(self, rule, maxcores=None):
res = rule.resources
if maxcores is None:
maxcores = self.workflow.global_resources["_cores"]
def calc_res(item):
name, value = item
if name == "_cores":
return min(maxcores, res["_cores"])
return min(res.get(name, 0), value)
return list(map(calc_res, self.workflow.global_resources.items()))
def rule_reward(self, rule, jobs=None):
jobs = jobs[rule]
return (self.priority_reward(jobs), self.downstream_reward(jobs),
cumsum(map(operator.attrgetter("inputsize"), jobs)))
def dryrun_rule_reward(self, rule, jobs=None):
jobs = jobs[rule]
return (self.priority_reward(jobs), self.downstream_reward(jobs),
[0] * (len(jobs) + 1))
def priority_reward(self, jobs):
return cumsum(map(self.dag.priority, jobs))
def downstream_reward(self, jobs):
return cumsum(map(self.dag.downstream_size, jobs))
def job_weight(self, job):
""" Job weight that uses threads. """
return job.threads
def simple_job_weight(self, job):
""" Job weight that ignores threads. """
return 1
def progress(self):
""" Display the progress. """
logger.info("{} of {} steps ({:.0%}) done".format(
self.finished_jobs, len(self.dag),
self.finished_jobs / len(self.dag)))
def __init__(self,
workflow,
dag,
cores,
dryrun=False,
touch=False,
cluster=None,
immediate_submit=False,
quiet=False,
printreason=False,
printshellcmds=False,
keepgoing=False,
output_wait=3):
""" Create a new instance of KnapsackJobScheduler. """
self.cluster = cluster
self.dag = dag
self.workflow = workflow
self.dryrun = dryrun
self.quiet = quiet
self.keepgoing = keepgoing
self.running = set()
self.failed = set()
self.finished_jobs = 0
self.resources = dict(self.workflow.global_resources)
use_threads = os.name != "posix" or cluster
if not use_threads:
self._open_jobs = multiprocessing.Event()
self._lock = multiprocessing.Lock()
else:
self._open_jobs = threading.Event()
self._lock = threading.Lock()
self._errors = False
self._finished = False
self._job_queue = None
self._submit_callback = self._noop
self._finish_callback = partial(self._proceed,
update_dynamic=not self.dryrun,
print_progress=not self.quiet
and not self.dryrun)
if dryrun:
self._executor = DryrunExecutor(workflow,
dag,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
output_wait=output_wait)
self.rule_reward = self.dryrun_rule_reward
elif touch:
self._executor = TouchExecutor(workflow,
dag,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
output_wait=output_wait)
elif cluster:
# TODO properly set cores
self._local_executor = CPUExecutor(workflow,
dag,
cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
threads=use_threads,
output_wait=output_wait)
self._executor = ClusterExecutor(workflow,
dag,
None,
submitcmd=cluster,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
output_wait=output_wait)
self.rule_weight = partial(self.rule_weight, maxcores=1)
if immediate_submit:
self.rule_reward = self.dryrun_rule_reward
self._submit_callback = partial(self._proceed,
update_dynamic=False,
print_progress=False,
update_resources=False)
else:
self.run = self.run_cluster_or_local
else:
self._executor = CPUExecutor(workflow,
dag,
cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
threads=use_threads,
output_wait=output_wait)
self._open_jobs.set()
class JobScheduler:
def __init__(
self,
workflow,
dag,
local_cores=1,
dryrun=False,
touch=False,
cluster=None,
cluster_status=None,
cluster_config=None,
cluster_sync=None,
drmaa=None,
drmaa_log_dir=None,
kubernetes=None,
container_image=None,
tibanna=None,
tibanna_sfn=None,
google_lifesciences=None,
google_lifesciences_regions=None,
google_lifesciences_location=None,
google_lifesciences_cache=False,
tes=None,
precommand="",
preemption_default=None,
preemptible_rules=None,
tibanna_config=False,
jobname=None,
quiet=False,
printreason=False,
printshellcmds=False,
keepgoing=False,
max_jobs_per_second=None,
max_status_checks_per_second=100,
latency_wait=3,
greediness=1.0,
force_use_threads=False,
assume_shared_fs=True,
keepincomplete=False,
keepmetadata=True,
scheduler_type=None,
scheduler_ilp_solver=None,
):
"""Create a new instance of KnapsackJobScheduler."""
from ratelimiter import RateLimiter
cores = workflow.global_resources["_cores"]
self.cluster = cluster
self.cluster_config = cluster_config
self.cluster_sync = cluster_sync
self.dag = dag
self.workflow = workflow
self.dryrun = dryrun
self.touch = touch
self.quiet = quiet
self.keepgoing = keepgoing
self.running = set()
self.failed = set()
self.finished_jobs = 0
self.greediness = 1
self.max_jobs_per_second = max_jobs_per_second
self.keepincomplete = keepincomplete
self.keepmetadata = keepmetadata
self.scheduler_type = scheduler_type
self.scheduler_ilp_solver = scheduler_ilp_solver
self._tofinish = []
self.global_resources = {
name: (sys.maxsize if res is None else res)
for name, res in workflow.global_resources.items()
}
if workflow.global_resources["_nodes"] is not None:
# Do not restrict cores locally if nodes are used (i.e. in case of cluster/cloud submission).
self.global_resources["_cores"] = sys.maxsize
self.resources = dict(self.global_resources)
use_threads = (
force_use_threads
or (os.name != "posix")
or cluster
or cluster_sync
or drmaa
)
self._open_jobs = threading.Semaphore(0)
self._lock = threading.Lock()
self._errors = False
self._finished = False
self._job_queue = None
self._submit_callback = self._noop
self._finish_callback = partial(
self._proceed,
update_dynamic=not self.dryrun,
print_progress=not self.quiet and not self.dryrun,
)
self._local_executor = None
if dryrun:
self._executor = DryrunExecutor(
workflow,
dag,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
)
elif touch:
self._executor = TouchExecutor(
workflow,
dag,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
)
elif cluster or cluster_sync or (drmaa is not None):
if not workflow.immediate_submit:
# No local jobs when using immediate submit!
# Otherwise, they will fail due to missing input
self._local_executor = CPUExecutor(
workflow,
dag,
local_cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
cores=local_cores,
keepincomplete=keepincomplete,
keepmetadata=keepmetadata,
)
if cluster or cluster_sync:
if cluster_sync:
constructor = SynchronousClusterExecutor
else:
constructor = partial(
GenericClusterExecutor,
statuscmd=cluster_status,
max_status_checks_per_second=max_status_checks_per_second,
)
self._executor = constructor(
workflow,
dag,
None,
submitcmd=(cluster or cluster_sync),
cluster_config=cluster_config,
jobname=jobname,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
assume_shared_fs=assume_shared_fs,
keepincomplete=keepincomplete,
keepmetadata=keepmetadata,
)
if workflow.immediate_submit:
self._submit_callback = partial(
self._proceed,
update_dynamic=False,
print_progress=False,
update_resources=False,
handle_job_success=False,
)
else:
self._executor = DRMAAExecutor(
workflow,
dag,
None,
drmaa_args=drmaa,
drmaa_log_dir=drmaa_log_dir,
jobname=jobname,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
cluster_config=cluster_config,
assume_shared_fs=assume_shared_fs,
max_status_checks_per_second=max_status_checks_per_second,
keepincomplete=keepincomplete,
keepmetadata=keepmetadata,
)
elif kubernetes:
self._local_executor = CPUExecutor(
workflow,
dag,
local_cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
cores=local_cores,
keepincomplete=keepincomplete,
keepmetadata=keepmetadata,
)
self._executor = KubernetesExecutor(
workflow,
dag,
kubernetes,
container_image=container_image,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
cluster_config=cluster_config,
keepincomplete=keepincomplete,
keepmetadata=keepmetadata,
)
elif tibanna:
self._local_executor = CPUExecutor(
workflow,
dag,
local_cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
use_threads=use_threads,
latency_wait=latency_wait,
cores=local_cores,
keepincomplete=keepincomplete,
keepmetadata=keepmetadata,
)
self._executor = TibannaExecutor(
workflow,
dag,
cores,
tibanna_sfn,
precommand=precommand,
tibanna_config=tibanna_config,
container_image=container_image,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
keepincomplete=keepincomplete,
keepmetadata=keepmetadata,
)
elif google_lifesciences:
self._local_executor = CPUExecutor(
workflow,
dag,
local_cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
cores=local_cores,
)
self._executor = GoogleLifeSciencesExecutor(
workflow,
dag,
cores,
container_image=container_image,
regions=google_lifesciences_regions,
location=google_lifesciences_location,
cache=google_lifesciences_cache,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
preemption_default=preemption_default,
preemptible_rules=preemptible_rules,
)
elif tes:
self._local_executor = CPUExecutor(
workflow,
dag,
local_cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
cores=local_cores,
keepincomplete=keepincomplete,
)
self._executor = TaskExecutionServiceExecutor(
workflow,
dag,
cores=local_cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
tes_url=tes,
container_image=container_image,
)
else:
self._executor = CPUExecutor(
workflow,
dag,
cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
use_threads=use_threads,
latency_wait=latency_wait,
cores=cores,
keepincomplete=keepincomplete,
keepmetadata=keepmetadata,
)
if self.max_jobs_per_second and not self.dryrun:
max_jobs_frac = Fraction(self.max_jobs_per_second).limit_denominator()
self.rate_limiter = RateLimiter(
max_calls=max_jobs_frac.numerator, period=max_jobs_frac.denominator
)
else:
# essentially no rate limit
self.rate_limiter = DummyRateLimiter()
# Choose job selector (greedy or ILP)
self.job_selector = self.job_selector_greedy
if scheduler_type == "ilp":
import pulp
if pulp.apis.LpSolverDefault is None:
logger.warning(
"Falling back to greedy scheduler because no default "
"solver is found for pulp (you have to install either "
"coincbc or glpk)."
)
else:
self.job_selector = self.job_selector_ilp
self._user_kill = None
try:
signal.signal(signal.SIGTERM, self.exit_gracefully)
except ValueError:
# If this fails, it is due to scheduler not being invoked in the main thread.
# This can only happen with --gui, in which case it is fine for now.
pass
self._open_jobs.release()
@property
def stats(self):
try:
return self._executor.stats
except AttributeError:
raise TypeError("Executor does not support stats")
@property
def open_jobs(self):
"""Return open jobs."""
jobs = self.dag.ready_jobs
if not self.dryrun:
jobs = [
job
for job in jobs
if not job.dynamic_input and not self.dag.dynamic(job)
]
return jobs
@property
def remaining_jobs(self):
"""Return jobs to be scheduled including not yet ready ones."""
return [
job
for job in self.dag.needrun_jobs
if job not in self.running and not self.dag.finished(job)
]
def schedule(self):
"""Schedule jobs that are ready, maximizing cpu usage."""
try:
while True:
# work around so that the wait does not prevent keyboard interrupts
# while not self._open_jobs.acquire(False):
# time.sleep(1)
self._open_jobs.acquire()
# obtain needrun and running jobs in a thread-safe way
with self._lock:
self._finish_jobs()
needrun = set(self.open_jobs)
running = list(self.running)
errors = self._errors
user_kill = self._user_kill
# handle errors
if user_kill or (not self.keepgoing and errors):
if user_kill == "graceful":
logger.info(
"Will exit after finishing " "currently running jobs."
)
if not running:
logger.info("Shutting down, this might take some time.")
self._executor.shutdown()
if not user_kill:
logger.error(_ERROR_MSG_FINAL)
return False
continue
# normal shutdown because all jobs have been finished
if not needrun and (not running or self.workflow.immediate_submit):
self._executor.shutdown()
if errors:
logger.error(_ERROR_MSG_FINAL)
return not errors
# continue if no new job needs to be executed
if not needrun:
continue
# select jobs by solving knapsack problem (omit with dryrun)
if self.dryrun:
run = needrun
else:
logger.debug(
"Resources before job selection: {}".format(self.resources)
)
logger.debug(
"Ready jobs ({}):\n\t".format(len(needrun))
+ "\n\t".join(map(str, needrun))
)
run = self.job_selector(needrun)
logger.debug(
"Selected jobs ({}):\n\t".format(len(run))
+ "\n\t".join(map(str, run))
)
logger.debug(
"Resources after job selection: {}".format(self.resources)
)
# update running jobs
with self._lock:
self.running.update(run)
# remove from ready_jobs
self.dag.register_running(run)
# reset params and resources because they might contain TBDs
if not self.dryrun:
for job in run:
job.reset_params_and_resources()
# actually run jobs
local_runjobs = [job for job in run if job.is_local]
runjobs = [job for job in run if not job.is_local]
self.run(local_runjobs, executor=self._local_executor or self._executor)
self.run(runjobs)
except (KeyboardInterrupt, SystemExit):
logger.info(
"Terminating processes on user request, this might take some time."
)
self._executor.cancel()
return False
def _finish_jobs(self):
# must be called from within lock
for job, update_dynamic in self._tofinish:
self.dag.finish(job, update_dynamic=update_dynamic)
self._tofinish.clear()
def run(self, jobs, executor=None):
if executor is None:
executor = self._executor
executor.run_jobs(
jobs,
callback=self._finish_callback,
submit_callback=self._submit_callback,
error_callback=self._error,
)
def get_executor(self, job):
if job.is_local and self._local_executor is not None:
return self._local_executor
return self._executor
def _noop(self, job):
pass
def _free_resources(self, job):
for name, value in job.resources.items():
if name in self.resources:
value = self.calc_resource(name, value)
self.resources[name] += value
def _proceed(
self,
job,
update_dynamic=True,
print_progress=False,
update_resources=True,
handle_job_success=True,
):
"""Do stuff after job is finished."""
with self._lock:
if handle_job_success:
# by calling this behind the lock, we avoid race conditions
try:
self.get_executor(job).handle_job_success(job)
except (RuleException, WorkflowError) as e:
# if an error occurs while processing job output,
# we do the same as in case of errors during execution
print_exception(e, self.workflow.linemaps)
self._handle_error(job)
return
self._tofinish.append((job, update_dynamic))
if update_resources:
# normal jobs have len=1, group jobs have len>1
self.finished_jobs += len(job)
self.running.remove(job)
self._free_resources(job)
if print_progress:
if job.is_group():
for j in job:
logger.job_finished(jobid=j.jobid)
else:
logger.job_finished(jobid=job.jobid)
self.progress()
if self.dryrun:
if not self.running:
# During dryrun, only release when all running jobs are done.
# This saves a lot of time, as self.open_jobs has to be
# evaluated less frequently.
self._open_jobs.release()
else:
# go on scheduling if there is any free core
self._open_jobs.release()
def _error(self, job):
with self._lock:
self._handle_error(job)
def _handle_error(self, job):
"""Clear jobs and stop the workflow.
If Snakemake is configured to restart jobs then the job might have
"restart_times" left and we just decrement and let the scheduler
try to run the job again.
"""
self.get_executor(job).handle_job_error(job)
self.running.remove(job)
self._free_resources(job)
# attempt starts counting from 1, but the first attempt is not
# a restart, hence we subtract 1.
if job.restart_times > job.attempt - 1:
logger.info("Trying to restart job {}.".format(self.dag.jobid(job)))
job.attempt += 1
# add job to those being ready again
self.dag._ready_jobs.add(job)
else:
self._errors = True
self.failed.add(job)
if self.keepgoing:
logger.info("Job failed, going on with independent jobs.")
self._open_jobs.release()
def exit_gracefully(self, *args):
with self._lock:
self._user_kill = "graceful"
self._open_jobs.release()
def job_selector_ilp(self, jobs):
"""
Job scheduling by optimization of resource usage by solving ILP using pulp
"""
import pulp
from pulp import lpSum
from stopit import ThreadingTimeout as Timeout, TimeoutException
logger.info("Select jobs to execute...")
with self._lock:
if not self.resources["_cores"]:
return set()
# assert self.resources["_cores"] > 0
scheduled_jobs = {
job: pulp.LpVariable(
"job_{}".format(idx),
lowBound=0,
upBound=1,
cat=pulp.LpInteger,
)
for idx, job in enumerate(jobs)
}
def size_gb(f):
if self.touch:
# In case of touch mode, there is no need to prioritize based on size.
# We cannot access it anyway, because the files might be temporary and
# not present.
return 0
else:
return f.size / 1e9
temp_files = {
temp_file for job in jobs for temp_file in self.dag.temp_input(job)
}
temp_job_improvement = {
temp_file: pulp.LpVariable(
"temp_file_{}".format(idx), lowBound=0, upBound=1, cat="Continuous"
)
for idx, temp_file in enumerate(temp_files)
}
temp_file_deletable = {
temp_file: pulp.LpVariable(
"deletable_{}".format(idx),
lowBound=0,
upBound=1,
cat=pulp.LpInteger,
)
for idx, temp_file in enumerate(temp_files)
}
prob = pulp.LpProblem("JobScheduler", pulp.LpMaximize)
total_temp_size = max(
sum([size_gb(temp_file) for temp_file in temp_files]), 1
)
total_core_requirement = sum(
[max(job.resources.get("_cores", 1), 1) for job in jobs]
)
# Objective function
# Job priority > Core load
# Core load > temp file removal
# Instant removal > temp size
prob += (
2
* total_core_requirement
* 2
* total_temp_size
* lpSum([job.priority * scheduled_jobs[job] for job in jobs])
+ 2
* total_temp_size
* lpSum(
[
max(job.resources.get("_cores", 1), 1) * scheduled_jobs[job]
for job in jobs
]
)
+ total_temp_size
* lpSum(
[
temp_file_deletable[temp_file] * size_gb(temp_file)
for temp_file in temp_files
]
)
+ lpSum(
[
temp_job_improvement[temp_file] * size_gb(temp_file)
for temp_file in temp_files
]
)
)
# Constraints:
for name in self.workflow.global_resources:
prob += (
lpSum(
[
scheduled_jobs[job] * job.resources.get(name, 0)
for job in jobs
]
)
<= self.resources[name]
)
# Choose jobs that lead to "fastest" (minimum steps) removal of existing temp file
remaining_jobs = self.remaining_jobs
for temp_file in temp_files:
prob += temp_job_improvement[temp_file] <= lpSum(
[
scheduled_jobs[job] * self.required_by_job(temp_file, job)
for job in jobs
]
) / lpSum(
[self.required_by_job(temp_file, job) for job in remaining_jobs]
)
prob += (
temp_file_deletable[temp_file] <= temp_job_improvement[temp_file]
)
try:
with Timeout(10, swallow_exc=False):
self._solve_ilp(prob)
except TimeoutException as e:
logger.warning(
"Failed to solve scheduling problem with ILP solver in time (10s). "
"Falling back to greedy solver."
)
return self.job_selector_greedy(jobs)
except pulp.apis.core.PulpSolverError as e:
logger.warning(
"Failed to solve scheduling problem with ILP solver. Falling back to greedy solver. "
"Run Snakemake with --verbose to see the full solver output for debugging the problem."
)
return self.job_selector_greedy(jobs)
selected_jobs = set(
job for job, variable in scheduled_jobs.items() if variable.value() == 1.0
)
if not selected_jobs:
# No selected jobs. This could be due to insufficient resources or a failure in the ILP solver
# Hence, we silently fall back to the greedy solver to make sure that we don't miss anything.
return self.job_selector_greedy(jobs)
for name in self.workflow.global_resources:
self.resources[name] -= sum(
[job.resources.get(name, 0) for job in selected_jobs]
)
return selected_jobs
def _solve_ilp(self, prob):
import pulp
old_path = os.environ["PATH"]
if self.workflow.scheduler_solver_path is None:
# Temporarily prepend the given snakemake env to the path, such that the solver can be found in any case.
# This is needed for cluster envs, where the cluster job might have a different environment but
# still needs access to the solver binary.
os.environ["PATH"] = "{}:{}".format(
self.workflow.scheduler_solver_path, os.environ["PATH"]
)
try:
solver = (
pulp.get_solver(self.scheduler_ilp_solver)
if self.scheduler_ilp_solver
else pulp.apis.LpSolverDefault
)
finally:
os.environ["PATH"] = old_path
solver.msg = self.workflow.verbose
prob.solve(solver)
def required_by_job(self, temp_file, job):
return 1 if temp_file in self.dag.temp_input(job) else 0
def job_selector_greedy(self, jobs):
"""
Using the greedy heuristic from
"A Greedy Algorithm for the General Multidimensional Knapsack
Problem", Akcay, Li, Xu, Annals of Operations Research, 2012
Args:
jobs (list): list of jobs
"""
with self._lock:
if not self.resources["_cores"]:
return set()
# each job is an item with one copy (0-1 MDKP)
n = len(jobs)
x = [0] * n # selected jobs
E = set(range(n)) # jobs still free to select
u = [1] * n
a = list(map(self.job_weight, jobs)) # resource usage of jobs
c = list(map(self.job_reward, jobs)) # job rewards
def calc_reward():
return [c_j * y_j for c_j, y_j in zip(c, y)]
b = [
self.resources[name] for name in self.global_resources
] # resource capacities
while True:
# Step 2: compute effective capacities
y = [
(
min(
(min(u[j], b_i // a_j_i) if a_j_i > 0 else u[j])
for b_i, a_j_i in zip(b, a[j])
if a_j_i
)
if j in E
else 0
)
for j in range(n)
]
if not any(y):
break
y = [
(max(1, int(self.greediness * y_j)) if y_j > 0 else 0) for y_j in y
]
# Step 3: compute rewards on cumulative sums
reward = calc_reward()
j_sel = max(E, key=reward.__getitem__) # argmax
# Step 4: batch increment
y_sel = y[j_sel]
# Step 5: update information
x[j_sel] += y_sel
b = [b_i - (a_j_i * y_sel) for b_i, a_j_i in zip(b, a[j_sel])]
u[j_sel] -= y_sel
if not u[j_sel] or self.greediness == 1:
E.remove(j_sel)
if not E:
break
solution = set(job for job, sel in zip(jobs, x) if sel)
# update resources
for name, b_i in zip(self.global_resources, b):
self.resources[name] = b_i
return solution
def calc_resource(self, name, value):
gres = self.global_resources[name]
if value > gres:
if name == "_cores":
name = "threads"
raise WorkflowError(
"Job needs {name}={res} but only {name}={gres} "
"are available. This is likely because two "
"jobs are connected via a pipe and have to run "
"simultaneously. Consider providing more "
"resources (e.g. via --cores).".format(name=name, res=value, gres=gres)
)
return value
def rule_weight(self, rule):
res = rule.resources
return [
self.calc_resource(name, res.get(name, 0)) for name in self.global_resources
]
def job_weight(self, job):
res = job.resources
return [
self.calc_resource(name, res.get(name, 0)) for name in self.global_resources
]
def job_reward(self, job):
if self.touch or self.dryrun or self.workflow.immediate_submit:
temp_size = 0
input_size = 0
else:
temp_size = self.dag.temp_size(job)
input_size = job.inputsize
# Usually, this should guide the scheduler to first schedule all jobs
# that remove the largest temp file, then the second largest and so on.
# Since the weight is summed up, it can in theory be that it sometimes
# prefers a set of many jobs that all depend on smaller temp files though.
# A real solution to the problem is therefore to use dummy jobs that
# ensure selection of groups of jobs that together delete the same temp
# file.
return (job.priority, temp_size, input_size)
def progress(self):
"""Display the progress."""
logger.progress(done=self.finished_jobs, total=len(self.dag))
def __init__(
self,
workflow,
dag,
local_cores=1,
dryrun=False,
touch=False,
cluster=None,
cluster_status=None,
cluster_config=None,
cluster_sync=None,
drmaa=None,
drmaa_log_dir=None,
kubernetes=None,
container_image=None,
tibanna=None,
tibanna_sfn=None,
google_lifesciences=None,
google_lifesciences_regions=None,
google_lifesciences_location=None,
google_lifesciences_cache=False,
tes=None,
precommand="",
preemption_default=None,
preemptible_rules=None,
tibanna_config=False,
jobname=None,
quiet=False,
printreason=False,
printshellcmds=False,
keepgoing=False,
max_jobs_per_second=None,
max_status_checks_per_second=100,
latency_wait=3,
greediness=1.0,
force_use_threads=False,
assume_shared_fs=True,
keepincomplete=False,
keepmetadata=True,
scheduler_type=None,
scheduler_ilp_solver=None,
):
"""Create a new instance of KnapsackJobScheduler."""
from ratelimiter import RateLimiter
cores = workflow.global_resources["_cores"]
self.cluster = cluster
self.cluster_config = cluster_config
self.cluster_sync = cluster_sync
self.dag = dag
self.workflow = workflow
self.dryrun = dryrun
self.touch = touch
self.quiet = quiet
self.keepgoing = keepgoing
self.running = set()
self.failed = set()
self.finished_jobs = 0
self.greediness = 1
self.max_jobs_per_second = max_jobs_per_second
self.keepincomplete = keepincomplete
self.keepmetadata = keepmetadata
self.scheduler_type = scheduler_type
self.scheduler_ilp_solver = scheduler_ilp_solver
self._tofinish = []
self.global_resources = {
name: (sys.maxsize if res is None else res)
for name, res in workflow.global_resources.items()
}
if workflow.global_resources["_nodes"] is not None:
# Do not restrict cores locally if nodes are used (i.e. in case of cluster/cloud submission).
self.global_resources["_cores"] = sys.maxsize
self.resources = dict(self.global_resources)
use_threads = (
force_use_threads
or (os.name != "posix")
or cluster
or cluster_sync
or drmaa
)
self._open_jobs = threading.Semaphore(0)
self._lock = threading.Lock()
self._errors = False
self._finished = False
self._job_queue = None
self._submit_callback = self._noop
self._finish_callback = partial(
self._proceed,
update_dynamic=not self.dryrun,
print_progress=not self.quiet and not self.dryrun,
)
self._local_executor = None
if dryrun:
self._executor = DryrunExecutor(
workflow,
dag,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
)
elif touch:
self._executor = TouchExecutor(
workflow,
dag,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
)
elif cluster or cluster_sync or (drmaa is not None):
if not workflow.immediate_submit:
# No local jobs when using immediate submit!
# Otherwise, they will fail due to missing input
self._local_executor = CPUExecutor(
workflow,
dag,
local_cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
cores=local_cores,
keepincomplete=keepincomplete,
keepmetadata=keepmetadata,
)
if cluster or cluster_sync:
if cluster_sync:
constructor = SynchronousClusterExecutor
else:
constructor = partial(
GenericClusterExecutor,
statuscmd=cluster_status,
max_status_checks_per_second=max_status_checks_per_second,
)
self._executor = constructor(
workflow,
dag,
None,
submitcmd=(cluster or cluster_sync),
cluster_config=cluster_config,
jobname=jobname,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
assume_shared_fs=assume_shared_fs,
keepincomplete=keepincomplete,
keepmetadata=keepmetadata,
)
if workflow.immediate_submit:
self._submit_callback = partial(
self._proceed,
update_dynamic=False,
print_progress=False,
update_resources=False,
handle_job_success=False,
)
else:
self._executor = DRMAAExecutor(
workflow,
dag,
None,
drmaa_args=drmaa,
drmaa_log_dir=drmaa_log_dir,
jobname=jobname,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
cluster_config=cluster_config,
assume_shared_fs=assume_shared_fs,
max_status_checks_per_second=max_status_checks_per_second,
keepincomplete=keepincomplete,
keepmetadata=keepmetadata,
)
elif kubernetes:
self._local_executor = CPUExecutor(
workflow,
dag,
local_cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
cores=local_cores,
keepincomplete=keepincomplete,
keepmetadata=keepmetadata,
)
self._executor = KubernetesExecutor(
workflow,
dag,
kubernetes,
container_image=container_image,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
cluster_config=cluster_config,
keepincomplete=keepincomplete,
keepmetadata=keepmetadata,
)
elif tibanna:
self._local_executor = CPUExecutor(
workflow,
dag,
local_cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
use_threads=use_threads,
latency_wait=latency_wait,
cores=local_cores,
keepincomplete=keepincomplete,
keepmetadata=keepmetadata,
)
self._executor = TibannaExecutor(
workflow,
dag,
cores,
tibanna_sfn,
precommand=precommand,
tibanna_config=tibanna_config,
container_image=container_image,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
keepincomplete=keepincomplete,
keepmetadata=keepmetadata,
)
elif google_lifesciences:
self._local_executor = CPUExecutor(
workflow,
dag,
local_cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
cores=local_cores,
)
self._executor = GoogleLifeSciencesExecutor(
workflow,
dag,
cores,
container_image=container_image,
regions=google_lifesciences_regions,
location=google_lifesciences_location,
cache=google_lifesciences_cache,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
preemption_default=preemption_default,
preemptible_rules=preemptible_rules,
)
elif tes:
self._local_executor = CPUExecutor(
workflow,
dag,
local_cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
cores=local_cores,
keepincomplete=keepincomplete,
)
self._executor = TaskExecutionServiceExecutor(
workflow,
dag,
cores=local_cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
tes_url=tes,
container_image=container_image,
)
else:
self._executor = CPUExecutor(
workflow,
dag,
cores,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
use_threads=use_threads,
latency_wait=latency_wait,
cores=cores,
keepincomplete=keepincomplete,
keepmetadata=keepmetadata,
)
if self.max_jobs_per_second and not self.dryrun:
max_jobs_frac = Fraction(self.max_jobs_per_second).limit_denominator()
self.rate_limiter = RateLimiter(
max_calls=max_jobs_frac.numerator, period=max_jobs_frac.denominator
)
else:
# essentially no rate limit
self.rate_limiter = DummyRateLimiter()
# Choose job selector (greedy or ILP)
self.job_selector = self.job_selector_greedy
if scheduler_type == "ilp":
import pulp
if pulp.apis.LpSolverDefault is None:
logger.warning(
"Falling back to greedy scheduler because no default "
"solver is found for pulp (you have to install either "
"coincbc or glpk)."
)
else:
self.job_selector = self.job_selector_ilp
self._user_kill = None
try:
signal.signal(signal.SIGTERM, self.exit_gracefully)
except ValueError:
# If this fails, it is due to scheduler not being invoked in the main thread.
# This can only happen with --gui, in which case it is fine for now.
pass
self._open_jobs.release()
def __init__(self, workflow, dag, cores,
local_cores=1,
dryrun=False,
touch=False,
cluster=None,
cluster_config=None,
cluster_sync=None,
drmaa=None,
jobname=None,
immediate_submit=False,
quiet=False,
printreason=False,
printshellcmds=False,
keepgoing=False,
max_jobs_per_second=None,
latency_wait=3,
benchmark_repeats=1,
greediness=1.0):
""" Create a new instance of KnapsackJobScheduler. """
self.cluster = cluster
self.cluster_config = cluster_config
self.cluster_sync = cluster_sync
self.dag = dag
self.workflow = workflow
self.dryrun = dryrun
self.quiet = quiet
self.keepgoing = keepgoing
self.running = set()
self.failed = set()
self.finished_jobs = 0
self.greediness = 1
self.resources = dict(self.workflow.global_resources)
use_threads = os.name != "posix"
if not use_threads:
self._open_jobs = multiprocessing.Event()
self._lock = multiprocessing.Lock()
else:
self._open_jobs = threading.Event()
self._lock = threading.Lock()
self._errors = False
self._finished = False
self._job_queue = None
self._submit_callback = self._noop
self._finish_callback = partial(
self._proceed,
update_dynamic=not self.dryrun,
print_progress=not self.quiet and not self.dryrun)
if dryrun:
self._executor = DryrunExecutor(workflow, dag,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait)
self.job_reward = self.dryrun_job_reward
elif touch:
self._executor = TouchExecutor(workflow, dag,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait)
elif cluster or cluster_sync or (drmaa is not None):
workers = min(max(1, sum(1 for _ in dag.local_needrun_jobs)), local_cores)
self._local_executor = CPUExecutor(
workflow, dag, workers,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
threads=use_threads,
latency_wait=latency_wait,
benchmark_repeats=benchmark_repeats)
self.run = self.run_cluster_or_local
if cluster or cluster_sync:
constructor = SynchronousClusterExecutor if cluster_sync \
else GenericClusterExecutor
self._executor = constructor(
workflow, dag, None,
submitcmd=(cluster or cluster_sync),
cluster_config=cluster_config,
jobname=jobname,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
benchmark_repeats=benchmark_repeats,
max_jobs_per_second=max_jobs_per_second)
if immediate_submit:
self.job_reward = self.dryrun_job_reward
self._submit_callback = partial(self._proceed,
update_dynamic=False,
print_progress=False,
update_resources=False, )
else:
self._executor = DRMAAExecutor(
workflow, dag, None,
drmaa_args=drmaa,
jobname=jobname,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
benchmark_repeats=benchmark_repeats,
cluster_config=cluster_config,
max_jobs_per_second=max_jobs_per_second)
else:
# local execution or execution of cluster job
# calculate how many parallel workers the executor shall spawn
# each job has at least one thread, hence we need to have
# the minimum of given cores and number of jobs
workers = min(cores, max(1, len(dag)))
self._executor = CPUExecutor(workflow, dag, workers,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
threads=use_threads,
latency_wait=latency_wait,
benchmark_repeats=benchmark_repeats, )
self._open_jobs.set()
class JobScheduler:
def __init__(self, workflow, dag, cores,
local_cores=1,
dryrun=False,
touch=False,
cluster=None,
cluster_config=None,
cluster_sync=None,
drmaa=None,
jobname=None,
immediate_submit=False,
quiet=False,
printreason=False,
printshellcmds=False,
keepgoing=False,
max_jobs_per_second=None,
latency_wait=3,
benchmark_repeats=1,
greediness=1.0):
""" Create a new instance of KnapsackJobScheduler. """
self.cluster = cluster
self.cluster_config = cluster_config
self.cluster_sync = cluster_sync
self.dag = dag
self.workflow = workflow
self.dryrun = dryrun
self.quiet = quiet
self.keepgoing = keepgoing
self.running = set()
self.failed = set()
self.finished_jobs = 0
self.greediness = 1
self.resources = dict(self.workflow.global_resources)
use_threads = os.name != "posix"
if not use_threads:
self._open_jobs = multiprocessing.Event()
self._lock = multiprocessing.Lock()
else:
self._open_jobs = threading.Event()
self._lock = threading.Lock()
self._errors = False
self._finished = False
self._job_queue = None
self._submit_callback = self._noop
self._finish_callback = partial(
self._proceed,
update_dynamic=not self.dryrun,
print_progress=not self.quiet and not self.dryrun)
if dryrun:
self._executor = DryrunExecutor(workflow, dag,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait)
self.job_reward = self.dryrun_job_reward
elif touch:
self._executor = TouchExecutor(workflow, dag,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait)
elif cluster or cluster_sync or (drmaa is not None):
workers = min(max(1, sum(1 for _ in dag.local_needrun_jobs)), local_cores)
self._local_executor = CPUExecutor(
workflow, dag, workers,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
threads=use_threads,
latency_wait=latency_wait,
benchmark_repeats=benchmark_repeats)
self.run = self.run_cluster_or_local
if cluster or cluster_sync:
constructor = SynchronousClusterExecutor if cluster_sync \
else GenericClusterExecutor
self._executor = constructor(
workflow, dag, None,
submitcmd=(cluster or cluster_sync),
cluster_config=cluster_config,
jobname=jobname,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
benchmark_repeats=benchmark_repeats,
max_jobs_per_second=max_jobs_per_second)
if immediate_submit:
self.job_reward = self.dryrun_job_reward
self._submit_callback = partial(self._proceed,
update_dynamic=False,
print_progress=False,
update_resources=False, )
else:
self._executor = DRMAAExecutor(
workflow, dag, None,
drmaa_args=drmaa,
jobname=jobname,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
benchmark_repeats=benchmark_repeats,
cluster_config=cluster_config,
max_jobs_per_second=max_jobs_per_second)
else:
# local execution or execution of cluster job
# calculate how many parallel workers the executor shall spawn
# each job has at least one thread, hence we need to have
# the minimum of given cores and number of jobs
workers = min(cores, max(1, len(dag)))
self._executor = CPUExecutor(workflow, dag, workers,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
threads=use_threads,
latency_wait=latency_wait,
benchmark_repeats=benchmark_repeats, )
self._open_jobs.set()
@property
def stats(self):
try:
return self._executor.stats
except AttributeError:
raise TypeError("Executor does not support stats")
def candidate(self, job):
""" Return whether a job is a candidate to be executed. """
return (job not in self.running and job not in self.failed and
(self.dryrun or
(not job.dynamic_input and not self.dag.dynamic(job))))
@property
def open_jobs(self):
""" Return open jobs. """
return filter(self.candidate, list(self.dag.ready_jobs))
def schedule(self):
""" Schedule jobs that are ready, maximizing cpu usage. """
try:
while True:
# work around so that the wait does not prevent keyboard interrupts
while not self._open_jobs.wait(1):
pass
# obtain needrun and running jobs in a thread-safe way
with self._lock:
needrun = list(self.open_jobs)
running = list(self.running)
# free the event
self._open_jobs.clear()
# handle errors
if not self.keepgoing and self._errors:
logger.info("Will exit after finishing "
"currently running jobs.")
if not running:
self._executor.shutdown()
logger.error(_ERROR_MSG_FINAL)
return False
continue
# normal shutdown because all jobs have been finished
if not needrun and not running:
self._executor.shutdown()
if self._errors:
logger.error(_ERROR_MSG_FINAL)
return not self._errors
# continue if no new job needs to be executed
if not needrun:
continue
logger.debug("Resources before job selection: {}".format(
self.resources))
logger.debug("Ready jobs ({}):\n\t".format(len(needrun)) +
"\n\t".join(map(str, needrun)))
# select jobs by solving knapsack problem
run = self.job_selector(needrun)
logger.debug("Selected jobs ({}):\n\t".format(len(run)) +
"\n\t".join(map(str, run)))
# update running jobs
with self._lock:
self.running.update(run)
logger.debug(
"Resources after job selection: {}".format(self.resources))
# actually run jobs
for job in run:
self.run(job)
except (KeyboardInterrupt, SystemExit):
logger.info("Terminating processes on user request.")
self._executor.cancel()
with self._lock:
running = list(self.running)
for job in running:
job.cleanup()
return False
def run(self, job):
self._executor.run(job,
callback=self._finish_callback,
submit_callback=self._submit_callback,
error_callback=self._error)
def run_cluster_or_local(self, job):
executor = self._local_executor if self.workflow.is_local(
job.rule) else self._executor
executor.run(job,
callback=self._finish_callback,
submit_callback=self._submit_callback,
error_callback=self._error)
def _noop(self, job):
pass
def _free_resources(self, job):
for name, value in job.resources.items():
if name in self.resources:
value = self.calc_resource(name, value)
self.resources[name] += value
logger.debug("Releasing {} {} (now {}).".format(
value, name, self.resources[name]))
def _proceed(self, job,
update_dynamic=True,
print_progress=False,
update_resources=True):
""" Do stuff after job is finished. """
with self._lock:
if update_resources:
self.finished_jobs += 1
self.running.remove(job)
self._free_resources(job)
self.dag.finish(job, update_dynamic=update_dynamic)
logger.job_finished(jobid=self.dag.jobid(job))
if print_progress:
self.progress()
if any(self.open_jobs) or not self.running:
# go on scheduling if open jobs are ready or no job is running
self._open_jobs.set()
def _error(self, job):
""" Clear jobs and stop the workflow. """
with self._lock:
self._errors = True
self.running.remove(job)
self.failed.add(job)
self._free_resources(job)
if self.keepgoing:
logger.info("Job failed, going on with independent jobs.")
self._open_jobs.set()
def job_selector(self, jobs):
"""
Using the greedy heuristic from
"A Greedy Algorithm for the General Multidimensional Knapsack
Problem", Akcay, Li, Xu, Annals of Operations Research, 2012
Args:
jobs (list): list of jobs
"""
with self._lock:
# each job is an item with one copy (0-1 MDKP)
n = len(jobs)
x = [0] * n # selected jobs
E = set(range(n)) # jobs still free to select
u = [1] * n
a = list(map(self.job_weight, jobs)) # resource usage of jobs
c = list(map(self.job_reward, jobs)) # job rewards
def calc_reward():
return [c_j * y_j for c_j, y_j in zip(c, y)]
b = [self.resources[name]
for name in self.workflow.global_resources
] # resource capacities
while True:
# Step 2: compute effective capacities
y = [(min((min(u[j], b_i // a_j_i) if a_j_i > 0 else u[j])
for b_i, a_j_i in zip(b, a[j]) if a_j_i) if j in E
else 0) for j in range(n)]
if not any(y):
break
y = [(max(1, int(self.greediness * y_j)) if y_j > 0 else 0)
for y_j in y]
# Step 3: compute rewards on cumulative sums
reward = calc_reward()
j_sel = max(E, key=reward.__getitem__) # argmax
# Step 4: batch increment
y_sel = y[j_sel]
# Step 5: update information
x[j_sel] += y_sel
b = [b_i - (a_j_i * y_sel) for b_i, a_j_i in zip(b, a[j_sel])]
u[j_sel] -= y_sel
if not u[j_sel] or self.greediness == 1:
E.remove(j_sel)
if not E:
break
solution = [job for job, sel in zip(jobs, x) if sel]
# update resources
for name, b_i in zip(self.workflow.global_resources, b):
self.resources[name] = b_i
return solution
def calc_resource(self, name, value):
return min(value, self.workflow.global_resources[name])
def rule_weight(self, rule):
res = rule.resources
return [self.calc_resource(name, res.get(name, 0))
for name in self.workflow.global_resources]
def job_weight(self, job):
res = job.resources_dict
return [self.calc_resource(name, res.get(name, 0))
for name in self.workflow.global_resources]
def job_reward(self, job):
return (self.dag.priority(job), self.dag.temp_input_count(job), self.dag.downstream_size(job),
job.inputsize)
def dryrun_job_reward(self, job):
return (self.dag.priority(job), self.dag.temp_input_count(job), self.dag.downstream_size(job))
def progress(self):
""" Display the progress. """
logger.progress(done=self.finished_jobs, total=len(self.dag))
def __init__(self,
workflow,
dag,
cores,
local_cores=1,
dryrun=False,
touch=False,
cluster=None,
cluster_status=None,
cluster_config=None,
cluster_sync=None,
drmaa=None,
drmaa_log_dir=None,
kubernetes=None,
kubernetes_envvars=None,
container_image=None,
jobname=None,
quiet=False,
printreason=False,
printshellcmds=False,
keepgoing=False,
max_jobs_per_second=None,
max_status_checks_per_second=100,
latency_wait=3,
greediness=1.0,
force_use_threads=False,
assume_shared_fs=True):
""" Create a new instance of KnapsackJobScheduler. """
from ratelimiter import RateLimiter
self.cluster = cluster
self.cluster_config = cluster_config
self.cluster_sync = cluster_sync
self.dag = dag
self.workflow = workflow
self.dryrun = dryrun
self.touch = touch
self.quiet = quiet
self.keepgoing = keepgoing
self.running = set()
self.failed = set()
self.finished_jobs = 0
self.greediness = 1
self.max_jobs_per_second = max_jobs_per_second
self.resources = dict(self.workflow.global_resources)
use_threads = force_use_threads or (
os.name != "posix") or cluster or cluster_sync or drmaa
self._open_jobs = threading.Semaphore(0)
self._lock = threading.Lock()
self._errors = False
self._finished = False
self._job_queue = None
self._submit_callback = self._noop
self._finish_callback = partial(self._proceed,
update_dynamic=not self.dryrun,
print_progress=not self.quiet
and not self.dryrun)
self._local_executor = None
if dryrun:
self._executor = DryrunExecutor(workflow,
dag,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait)
elif touch:
self._executor = TouchExecutor(workflow,
dag,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait)
elif cluster or cluster_sync or (drmaa is not None):
workers = min(max(1, sum(1 for _ in dag.local_needrun_jobs)),
local_cores)
if not workflow.immediate_submit:
# No local jobs when using immediate submit!
# Otherwise, they will fail due to missing input
self._local_executor = CPUExecutor(
workflow,
dag,
workers,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
use_threads=use_threads,
latency_wait=latency_wait,
cores=local_cores)
if cluster or cluster_sync:
if cluster_sync:
constructor = SynchronousClusterExecutor
else:
constructor = partial(GenericClusterExecutor,
statuscmd=cluster_status,
max_status_checks_per_second=
max_status_checks_per_second)
self._executor = constructor(workflow,
dag,
None,
submitcmd=(cluster
or cluster_sync),
cluster_config=cluster_config,
jobname=jobname,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
assume_shared_fs=assume_shared_fs)
if workflow.immediate_submit:
self._submit_callback = partial(self._proceed,
update_dynamic=False,
print_progress=False,
update_resources=False,
handle_job_success=False)
else:
self._executor = DRMAAExecutor(
workflow,
dag,
None,
drmaa_args=drmaa,
drmaa_log_dir=drmaa_log_dir,
jobname=jobname,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
cluster_config=cluster_config,
assume_shared_fs=assume_shared_fs,
max_status_checks_per_second=max_status_checks_per_second)
elif kubernetes:
workers = min(max(1, sum(1 for _ in dag.local_needrun_jobs)),
local_cores)
self._local_executor = CPUExecutor(workflow,
dag,
workers,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
use_threads=use_threads,
latency_wait=latency_wait,
cores=local_cores)
self._executor = KubernetesExecutor(
workflow,
dag,
kubernetes,
kubernetes_envvars,
container_image=container_image,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
latency_wait=latency_wait,
cluster_config=cluster_config)
else:
# local execution or execution of cluster job
# calculate how many parallel workers the executor shall spawn
# each job has at least one thread, hence we need to have
# the minimum of given cores and number of jobs
workers = min(cores, max(1, len(dag)))
self._executor = CPUExecutor(workflow,
dag,
workers,
printreason=printreason,
quiet=quiet,
printshellcmds=printshellcmds,
use_threads=use_threads,
latency_wait=latency_wait,
cores=cores)
if self.max_jobs_per_second and not self.dryrun:
self.rate_limiter = RateLimiter(max_calls=self.max_jobs_per_second,
period=1)
else:
# essentially no rate limit
self.rate_limiter = DummyRateLimiter()
self._user_kill = None
signal.signal(signal.SIGTERM, self.exit_gracefully)
self._open_jobs.release()
def __init__(
self,
workflow,
dag,
cores,
dryrun=False,
touch=False,
cluster=None,
immediate_submit=False,
quiet=False,
printreason=False,
printshellcmds=False,
keepgoing=False,
output_wait=3):
""" Create a new instance of KnapsackJobScheduler. """
self.cluster = cluster
self.dag = dag
self.workflow = workflow
self.dryrun = dryrun
self.quiet = quiet
self.keepgoing = keepgoing
self.running = set()
self.failed = set()
self.finished_jobs = 0
self.resources = dict(self.workflow.global_resources)
use_threads = os.name != "posix" or cluster
if not use_threads:
self._open_jobs = multiprocessing.Event()
self._lock = multiprocessing.Lock()
else:
self._open_jobs = threading.Event()
self._lock = threading.Lock()
self._errors = False
self._finished = False
self._job_queue = None
self._submit_callback = self._noop
self._finish_callback = partial(
self._proceed,
update_dynamic=not self.dryrun,
print_progress=not self.quiet and not self.dryrun)
if dryrun:
self._executor = DryrunExecutor(
workflow, dag, printreason=printreason,
quiet=quiet, printshellcmds=printshellcmds,
output_wait=output_wait)
self.rule_reward = self.dryrun_rule_reward
elif touch:
self._executor = TouchExecutor(
workflow, dag, printreason=printreason,
quiet=quiet, printshellcmds=printshellcmds,
output_wait=output_wait)
elif cluster:
# TODO properly set cores
self._local_executor = CPUExecutor(
workflow, dag, cores, printreason=printreason,
quiet=quiet, printshellcmds=printshellcmds,
threads=use_threads,
output_wait=output_wait)
self._executor = ClusterExecutor(
workflow, dag, None, submitcmd=cluster,
printreason=printreason, quiet=quiet,
printshellcmds=printshellcmds, output_wait=output_wait)
self.rule_weight = partial(
self.rule_weight,
maxcores=1)
if immediate_submit:
self.rule_reward = self.dryrun_rule_reward
self._submit_callback = partial(
self._proceed,
update_dynamic=False,
print_progress=False,
update_resources=False)
else:
self.run = self.run_cluster_or_local
else:
self._executor = CPUExecutor(
workflow, dag, cores, printreason=printreason,
quiet=quiet, printshellcmds=printshellcmds,
threads=use_threads,
output_wait=output_wait)
self._open_jobs.set()
class JobScheduler:
def __init__(
self,
workflow,
dag,
cores,
dryrun=False,
touch=False,
cluster=None,
immediate_submit=False,
quiet=False,
printreason=False,
printshellcmds=False,
keepgoing=False,
output_wait=3):
""" Create a new instance of KnapsackJobScheduler. """
self.cluster = cluster
self.dag = dag
self.workflow = workflow
self.dryrun = dryrun
self.quiet = quiet
self.keepgoing = keepgoing
self.running = set()
self.failed = set()
self.finished_jobs = 0
self.resources = dict(self.workflow.global_resources)
use_threads = os.name != "posix" or cluster
if not use_threads:
self._open_jobs = multiprocessing.Event()
self._lock = multiprocessing.Lock()
else:
self._open_jobs = threading.Event()
self._lock = threading.Lock()
self._errors = False
self._finished = False
self._job_queue = None
self._submit_callback = self._noop
self._finish_callback = partial(
self._proceed,
update_dynamic=not self.dryrun,
print_progress=not self.quiet and not self.dryrun)
if dryrun:
self._executor = DryrunExecutor(
workflow, dag, printreason=printreason,
quiet=quiet, printshellcmds=printshellcmds,
output_wait=output_wait)
self.rule_reward = self.dryrun_rule_reward
elif touch:
self._executor = TouchExecutor(
workflow, dag, printreason=printreason,
quiet=quiet, printshellcmds=printshellcmds,
output_wait=output_wait)
elif cluster:
# TODO properly set cores
self._local_executor = CPUExecutor(
workflow, dag, cores, printreason=printreason,
quiet=quiet, printshellcmds=printshellcmds,
threads=use_threads,
output_wait=output_wait)
self._executor = ClusterExecutor(
workflow, dag, None, submitcmd=cluster,
printreason=printreason, quiet=quiet,
printshellcmds=printshellcmds, output_wait=output_wait)
self.rule_weight = partial(
self.rule_weight,
maxcores=1)
if immediate_submit:
self.rule_reward = self.dryrun_rule_reward
self._submit_callback = partial(
self._proceed,
update_dynamic=False,
print_progress=False,
update_resources=False)
else:
self.run = self.run_cluster_or_local
else:
self._executor = CPUExecutor(
workflow, dag, cores, printreason=printreason,
quiet=quiet, printshellcmds=printshellcmds,
threads=use_threads,
output_wait=output_wait)
self._open_jobs.set()
@property
def stats(self):
try:
return self._executor.stats
except AttributeError:
raise TypeError("Executor does not support stats")
def candidate(self, job):
""" Return whether a job is a candidate to be executed. """
return (job not in self.running and job not in self.failed and (self.dryrun or
(not job.dynamic_input and not self.dag.dynamic(job))))
@property
def open_jobs(self):
""" Return open jobs. """
return filter(self.candidate, list(self.dag.ready_jobs))
def schedule(self):
""" Schedule jobs that are ready, maximizing cpu usage. """
while True:
try:
self._open_jobs.wait()
except:
# this will be caused because of SIGTERM or SIGINT
self._executor.shutdown()
return False
self._open_jobs.clear()
if not self.keepgoing and self._errors:
logger.warning("Will exit after finishing "
"currently running jobs.")
self._executor.shutdown()
return False
if not any(self.open_jobs):
self._executor.shutdown()
return not self._errors
needrun = list(self.open_jobs)
assert needrun
logger.debug("Ready jobs:\n\t" + "\n\t".join(map(str, needrun)))
run = self.job_selector(needrun)
logger.debug("Selected jobs:\n\t" + "\n\t".join(map(str, run)))
self.running.update(run)
for job in run:
self.run(job)
def run(self, job):
self._executor.run(
job, callback=self._finish_callback,
submit_callback=self._submit_callback,
error_callback=self._error)
def run_cluster_or_local(self, job):
executor = self._local_executor if self.workflow.is_local(job.rule) else self._executor
executor.run(
job, callback=self._finish_callback,
submit_callback=self._submit_callback,
error_callback=self._error)
def _noop(self, job):
pass
def _proceed(
self, job, update_dynamic=True, print_progress=False,
update_resources=True):
""" Do stuff after job is finished. """
with self._lock:
if update_resources:
self.finished_jobs += 1
self.running.remove(job)
for name, value in job.resources.items():
if name in self.resources:
self.resources[name] += value
self.dag.finish(job, update_dynamic=update_dynamic)
if print_progress:
self.progress()
if any(self.open_jobs) or not self.running:
# go on scheduling if open jobs are ready or no job is running
self._open_jobs.set()
def _error(self, job):
""" Clear jobs and stop the workflow. """
with self._lock:
self._errors = True
self.running.remove(job)
self.failed.add(job)
if self.keepgoing:
logger.warning("Job failed, going on with independent jobs.")
else:
self._open_jobs.set()
def _job_selector(self, jobs):
""" Solve 0-1 knapsack to maximize cpu utilization. """
dimi, dimj = len(jobs) + 1, self._cores + 1
K = [[(0, 0) for c in range(dimj)] for i in range(dimi)]
for i in range(1, dimi):
for j in range(1, dimj):
job = jobs[i - 1]
w = self.job_weight(job)
v = job.priority, job.inputsize if not self.dryrun else 0
if w > j:
K[i][j] = K[i - 1][j]
else:
K[i][j] = max(K[i - 1][j],
tuple(map(operator.add, v, K[i - 1][j - w])))
solution = set()
i = dimi - 1
j = dimj - 1
while i > 0:
if K[i][j] != K[i - 1][j]:
job = jobs[i - 1]
solution.add(job)
j = j - job.threads
i -= 1
return solution
def job_selector(self, jobs):
"""
Using the greedy heuristic from
"A Greedy Algorithm for the General Multidimensional Knapsack
Problem", Akcay, Li, Xu, Annals of Operations Research, 2012
"""
# solve over the rules instead of jobs (much less)
_jobs = defaultdict(list)
for job in jobs:
_jobs[job.rule].append(job)
jobs = _jobs
# sort the jobs by priority
for _jobs in jobs.values():
_jobs.sort(key=self.dag.priority, reverse=True)
rules = list(jobs)
# greedyness (1 means take all possible jobs for a selected rule
alpha = 1
# Step 1: initialization
n = len(rules)
x = [0] * n # selected jobs of each rule
E = set(range(n)) # rules free to select
u = [len(jobs[rule]) for rule in rules] # number of jobs left
b = list(self.resources.values()) # resource capacities
a = list(map(self.rule_weight, rules)) # resource usage of rules
c = list(map(partial(self.rule_reward, jobs=jobs), rules)) # matrix of cumulative rewards over jobs
while True:
# Step 2: compute effective capacities
y = [
(
min(
(min(u[j], b_i // a_j_i) if a_j_i > 0 else u[j])
for b_i, a_j_i in zip(b, a[j]) if a_j_i)
if j in E else 0)
for j in range(n)]
if not any(y):
break
# Step 3: compute rewards on cumulative sums and normalize by y
# in order to not prefer rules with small weights
reward = [(
[((crit[x_j + y_j] - crit[x_j]) / y_j if y_j else 0) for crit in c_j]
if j in E else [0] * len(c_j))
for j, (c_j, y_j, x_j) in enumerate(zip(c, y, x))]
j_sel = max(E, key=reward.__getitem__) # argmax
# Step 4: batch increment
y_sel = min(u[j_sel], max(1, alpha * y[j_sel]))
# Step 5: update information
x[j_sel] += y_sel
b = [b_i - (a_j_i * y_sel) for b_i, a_j_i in zip(b, a[j_sel])]
u[j_sel] -= y_sel
if not u[j_sel] or alpha == 1:
E.remove(j_sel)
if not E:
break
# Solution is the list of jobs that was selected from the selected rules
solution = list(chain(
*[jobs[rules[j]][:x_] for j, x_ in enumerate(x)]))
# update resources
for name, b_i in zip(self.resources, b):
self.resources[name] = b_i
return solution
def rule_weight(self, rule, maxcores=None):
res = rule.resources
if maxcores is None:
maxcores = self.workflow.global_resources["_cores"]
def calc_res(item):
name, value = item
if name == "_cores":
return min(maxcores, res["_cores"])
return min(res.get(name, 0), value)
return list(map(calc_res, self.workflow.global_resources.items()))
def rule_reward(self, rule, jobs=None):
jobs = jobs[rule]
return (
self.priority_reward(jobs),
self.downstream_reward(jobs),
cumsum(map(operator.attrgetter("inputsize"), jobs)))
def dryrun_rule_reward(self, rule, jobs=None):
jobs = jobs[rule]
return (
self.priority_reward(jobs),
self.downstream_reward(jobs),
[0] * (len(jobs) + 1))
def priority_reward(self, jobs):
return cumsum(map(self.dag.priority, jobs))
def downstream_reward(self, jobs):
return cumsum(map(self.dag.downstream_size, jobs))
def job_weight(self, job):
""" Job weight that uses threads. """
return job.threads
def simple_job_weight(self, job):
""" Job weight that ignores threads. """
return 1
def progress(self):
""" Display the progress. """
logger.info("{} of {} steps ({:.0%}) done".format(self.finished_jobs,
len(self.dag), self.finished_jobs / len(self.dag)))
