# **************************************************************************
# *
# * Authors: J.M. De la Rosa Trevin (jmdelarosa@cnb.csic.es)
# *
# * Unidad de Bioinformatica of Centro Nacional de Biotecnologia, CSIC
# *
# * This program is free software; you can redistribute it and/or modify
# * it under the terms of the GNU General Public License as published by
# * the Free Software Foundation; either version 3 of the License, or
# * (at your option) any later version.
# *
# * This program is distributed in the hope that it will be useful,
# * but WITHOUT ANY WARRANTY; without even the implied warranty of
# * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# * GNU General Public License for more details.
# *
# * You should have received a copy of the GNU General Public License
# * along with this program; if not, write to the Free Software
# * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA
# * 02111-1307 USA
# *
# * All comments concerning this program package may be sent to the
# * e-mail address 'scipion@cnb.csic.es'
# *
# **************************************************************************
"""
This module have the classes for execution of protocol steps.
The basic one will run steps, one by one, after completion.
There is one based on threads to execute steps in parallel
using different threads and the last one with MPI processes.
"""
import logging
logger = logging.getLogger(__name__)
import time
import datetime
import threading
import os
import re
from subprocess import Popen, PIPE
import pyworkflow.utils.process as process
from pyworkflow.utils.path import getParentFolder, removeExt
from . import constants as cts
from .launch import _submit, UNKNOWN_JOBID
[docs]class StepExecutor:
""" Run a list of Protocol steps. """
def __init__(self, hostConfig, **kwargs):
self.hostConfig = hostConfig
self.gpuList = kwargs.get(cts.GPU_LIST, None)
self.protocol = None
[docs] def getGpuList(self):
""" Return the GPU list assigned to current thread. """
return self.gpuList
[docs] def setProtocol(self, protocol):
""" Set protocol to append active jobs to its jobIds. """
self.protocol = protocol
[docs] def runJob(self, log, programName, params,
numberOfMpi=1, numberOfThreads=1,
env=None, cwd=None):
""" This function is a wrapper around runJob,
providing the host configuration.
"""
process.runJob(log, programName, params,
numberOfMpi, numberOfThreads,
self.hostConfig,
env=env, cwd=cwd, gpuList=self.getGpuList())
def _getRunnable(self, steps, n=1):
""" Return the n steps that are 'new' and all its
dependencies have been finished, or None if none ready.
"""
rs = [] # return a list of runnable steps
for s in steps:
if (s.getStatus() == cts.STATUS_NEW and
all(steps[i-1].isFinished() for i in s._prerequisites)):
rs.append(s)
if len(rs) == n:
break
return rs
def _arePending(self, steps):
""" Return True if there are pending steps (either running or waiting)
that can be done and thus enable other steps to be executed.
"""
return any(s.isRunning() or s.isWaiting() for s in steps)
[docs] def runSteps(self, steps,
stepStartedCallback,
stepFinishedCallback,
stepsCheckCallback,
stepsCheckSecs=3):
# Even if this will run the steps in a single thread
# let's follow a similar approach than the parallel one
# In this way we can take into account the steps graph
# dependency and also the case when using streaming
delta = datetime.timedelta(seconds=stepsCheckSecs)
lastCheck = datetime.datetime.now()
while True:
# Get a step to run, if there is any
runnableSteps = self._getRunnable(steps)
if runnableSteps:
step = runnableSteps[0]
# We found a step to work in, so let's start a new
# thread to do the job and book it.
step.setRunning()
stepStartedCallback(step)
step.run()
doContinue = stepFinishedCallback(step)
if not doContinue:
break
elif self._arePending(steps):
# We have not found any runnable step, but still there
# there are some running or waiting for dependencies
# So, let's wait a bit to check if something changes
time.sleep(0.5)
else:
# No steps to run, neither running or waiting
# So, we are done, either failed or finished :)
break
now = datetime.datetime.now()
if now - lastCheck > delta:
stepsCheckCallback()
lastCheck = now
stepsCheckCallback() # one last check to finalize stuff
[docs]class StepThread(threading.Thread):
""" Thread to run Steps in parallel. """
def __init__(self, thId, step, lock):
threading.Thread.__init__(self)
self.thId = thId
self.step = step
self.lock = lock
[docs] def run(self):
error = None
try:
self.step._run() # not self.step.run() , to avoid race conditions
except Exception as e:
error = str(e)
logger.error("Couldn't run the code in a thread." , exc_info=e)
finally:
with self.lock:
if error is None:
self.step.setFinished()
else:
self.step.setFailed(error)
[docs]class ThreadStepExecutor(StepExecutor):
""" Run steps in parallel using threads. """
def __init__(self, hostConfig, nThreads, **kwargs):
StepExecutor.__init__(self, hostConfig, **kwargs)
self.numberOfProcs = nThreads
# If the gpuList was specified, we need to distribute GPUs among
# all the threads
self.gpuDict = {}
if self.gpuList:
nodes = range(nThreads)
nGpu = len(self.gpuList)
if nGpu > nThreads:
chunk = int(nGpu / nThreads)
for i, node in enumerate(nodes):
self.gpuDict[node] = list(self.gpuList[i*chunk:(i+1)*chunk])
else:
# Expand gpuList repeating until reach nThreads items
if nThreads > nGpu:
newList = self.gpuList * (int(nThreads/nGpu)+1)
self.gpuList = newList[:nThreads]
for node, gpu in zip(nodes, self.gpuList):
self.gpuDict[node] = [gpu]
[docs] def getGpuList(self):
""" Return the GPU list assigned to current thread
or empty list if not using GPUs. """
return self.gpuDict.get(threading.currentThread().thId, [])
[docs] def runSteps(self, steps,
stepStartedCallback,
stepFinishedCallback,
stepsCheckCallback,
stepsCheckSecs=5):
"""
Creates threads and synchronize the steps execution.
:param steps: list of steps to run
:param stepStartedCallback: callback to be called before starting any step
:param stepFinishedCallback: callback to be run after all steps are done
:param stepsCheckCallback: callback to check if there are new steps to add (streaming)
:param stepsCheckSecs: seconds between stepsCheckCallback calls
"""
delta = datetime.timedelta(seconds=stepsCheckSecs)
lastCheck = datetime.datetime.now()
sharedLock = threading.Lock()
runningSteps = {} # currently running step in each node ({node: step})
freeNodes = list(range(self.numberOfProcs)) # available nodes to send jobs
while True:
# See which of the runningSteps are not really running anymore.
# Update them and freeNodes, and call final callback for step.
with sharedLock:
nodesFinished = [node for node, step in runningSteps.items()
if not step.isRunning()]
doContinue = True
for node in nodesFinished:
step = runningSteps.pop(node) # remove entry from runningSteps
freeNodes.append(node) # the node is available now
# Notify steps termination and check if we should continue
doContinue = stepFinishedCallback(step)
if not doContinue:
break
if not doContinue:
break
anyLaunched = False
# If there are available nodes, send next runnable step.
with sharedLock:
if freeNodes:
runnableSteps = self._getRunnable(steps, len(freeNodes))
for step in runnableSteps:
# We found a step to work in, so let's start a new
# thread to do the job and book it.
anyLaunched = True
step.setRunning()
stepStartedCallback(step)
node = freeNodes.pop() # take an available node
runningSteps[node] = step
t = StepThread(node, step, sharedLock)
# won't keep process up if main thread ends
t.daemon = True
t.start()
anyPending = self._arePending(steps)
if not anyLaunched:
if anyPending: # nothing running
time.sleep(0.5)
else:
break # yeah, we are done, either failed or finished :)
now = datetime.datetime.now()
if now - lastCheck > delta:
stepsCheckCallback()
lastCheck = now
stepsCheckCallback()
# Wait for all threads now.
for t in threading.enumerate():
if t is not threading.current_thread():
t.join()
[docs]class QueueStepExecutor(ThreadStepExecutor):
def __init__(self, hostConfig, submitDict, nThreads, **kwargs):
ThreadStepExecutor.__init__(self, hostConfig, nThreads, **kwargs)
self.submitDict = submitDict
# Command counter per thread
self.threadCommands = {}
for threadId in range(nThreads):
self.threadCommands[threadId] = 0
if nThreads > 1:
self.runJobs = ThreadStepExecutor.runSteps
else:
self.runJobs = StepExecutor.runSteps
self.renameGpuIds()
[docs] def renameGpuIds(self):
""" Reorganize the gpus ids starting from 0 since the queue engine is the one assigning them.
https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars """
for threadId, gpuList in self.gpuDict.items():
for i in range(len(gpuList)):
self.gpuDict[threadId][i] = i
logger.debug("Updated gpus ids rebase starting from 0: %s per thread" %self.gpuDict)
[docs] def runJob(self, log, programName, params, numberOfMpi=1, numberOfThreads=1, env=None, cwd=None):
threadId = threading.current_thread().thId
submitDict = dict(self.hostConfig.getQueuesDefault())
submitDict.update(self.submitDict)
submitDict['JOB_COMMAND'] = process.buildRunCommand(programName, params, numberOfMpi,
self.hostConfig, env,
gpuList=self.getGpuList())
self.threadCommands[threadId] += 1
subthreadId = '-%s-%s' % (threadId, self.threadCommands[threadId])
submitDict['JOB_NAME'] = submitDict['JOB_NAME'] + subthreadId
submitDict['JOB_SCRIPT'] = os.path.abspath(removeExt(submitDict['JOB_SCRIPT']) + subthreadId + ".job")
submitDict['JOB_LOGS'] = os.path.join(getParentFolder(submitDict['JOB_SCRIPT']), submitDict['JOB_NAME'])
jobid = _submit(self.hostConfig, submitDict, cwd, env)
# if self.protocol._lock: # Just in case we detect concurrency problem
self.protocol.appendJobId(jobid) # append active jobs
self.protocol._store(self.protocol._jobId)
if (jobid is None) or (jobid == UNKNOWN_JOBID):
logger.info("jobId is none therefore we set it to fail")
raise Exception("Failed to submit to queue.")
status = cts.STATUS_RUNNING
wait = 3
# Check status while job running
# REVIEW this to minimize the overhead in time put by this delay check
while self._checkJobStatus(self.hostConfig, jobid) == cts.STATUS_RUNNING:
time.sleep(wait)
if wait < 300:
wait += 3
# if self.protocol._lock: # Just in case we detect concurrency problem
self.protocol.removeJobId(jobid) # After completion, remove inactive jobs.
self.protocol._store(self.protocol._jobId)
return status
def _checkJobStatus(self, hostConfig, jobid):
command = hostConfig.getCheckCommand() % {"JOB_ID": jobid}
logger.debug("checking job status for %s: %s" %(jobid, command))
p = Popen(command, shell=True, stdout=PIPE, preexec_fn=os.setsid)
out = p.communicate()[0].decode(errors='backslashreplace')
jobDoneRegex = hostConfig.getJobDoneRegex()
logger.debug("Queue engine replied %s, variable JOB_DONE_REGEX has %s" %(out, jobDoneRegex))
# If nothing is returned we assume job is no longer in queue and thus finished
if out == "":
logger.warning("Empty response from queue system to job (%s)" %jobid)
return cts.STATUS_FINISHED
# If some string is returned we use the JOB_DONE_REGEX variable (if present) to infer the status
elif jobDoneRegex is not None:
s = re.search(jobDoneRegex, out)
if s:
logger.debug("Job (%s) finished" %jobid)
return cts.STATUS_FINISHED
else:
logger.debug("Job (%s) still running" %jobid)
return cts.STATUS_RUNNING
# If JOB_DONE_REGEX is not defined and queue has returned something we assume that job is still running
else:
return cts.STATUS_RUNNING