# Copyright (C) 2015-2018 Regents of the University of California
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from future import standard_library
standard_library.install_aliases()
from future.utils import with_metaclass
from builtins import object
import enum
import os
import shutil
import logging
from abc import ABCMeta, abstractmethod
from collections import namedtuple
from contextlib import contextmanager
from toil.lib.objects import abstractclassmethod
from toil.batchSystems import registry
from toil.common import Toil, cacheDirName
from toil.fileStores.abstractFileStore import AbstractFileStore
from toil.deferred import DeferredFunctionManager
try:
from toil.cwl.cwltoil import CWL_INTERNAL_JOBS
except ImportError:
# CWL extra not installed
CWL_INTERNAL_JOBS = ()
logger = logging.getLogger(__name__)
UpdatedBatchJobInfo = namedtuple('UpdatedBatchJobInfo', (
'jobID',
# The exit status (integer value) of the job. 0 implies successful.
# EXIT_STATUS_UNAVAILABLE_VALUE is used when the exit status is not available (e.g. job is lost).
'exitStatus',
'exitReason', # The exit reason, if available. One of BatchJobExitReason enum.
'wallTime'))
class BatchJobExitReason(enum.Enum):
FINISHED = 1 # Successfully finished.
FAILED = 2 # Job finished, but failed.
LOST = 3 # Preemptable failure.
KILLED = 4 # Job killed before finishing.
ERROR = 5 # Internal error.
# Value to use as exitStatus in UpdatedBatchJobInfo.exitStatus when status is not available.
EXIT_STATUS_UNAVAILABLE_VALUE = 255
# A class containing the information required for worker cleanup on shutdown of the batch system.
WorkerCleanupInfo = namedtuple('WorkerCleanupInfo', (
# A path to the value of config.workDir (where the cache would go)
'workDir',
# The value of config.workflowID (used to identify files specific to this workflow)
'workflowID',
# The value of the cleanWorkDir flag
'cleanWorkDir'))
[docs]class AbstractBatchSystem(with_metaclass(ABCMeta, object)):
"""
An abstract (as far as Python currently allows) base class to represent the interface the batch
system must provide to Toil.
"""
# noinspection PyMethodParameters
[docs] @abstractclassmethod
def supportsAutoDeployment(cls):
"""
Whether this batch system supports auto-deployment of the user script itself. If it does,
the :meth:`.setUserScript` can be invoked to set the resource object representing the user
script.
Note to implementors: If your implementation returns True here, it should also override
:rtype: bool
"""
raise NotImplementedError()
# noinspection PyMethodParameters
[docs] @abstractclassmethod
def supportsWorkerCleanup(cls):
"""
Indicates whether this batch system invokes :meth:`workerCleanup` after the last job for
a particular workflow invocation finishes. Note that the term *worker* refers to an
entire node, not just a worker process. A worker process may run more than one job
sequentially, and more than one concurrent worker process may exist on a worker node,
for the same workflow. The batch system is said to *shut down* after the last worker
process terminates.
:rtype: bool
"""
raise NotImplementedError()
[docs] def setUserScript(self, userScript):
"""
Set the user script for this workflow. This method must be called before the first job is
issued to this batch system, and only if :meth:`.supportsAutoDeployment` returns True,
otherwise it will raise an exception.
:param toil.resource.Resource userScript: the resource object representing the user script
or module and the modules it depends on.
"""
raise NotImplementedError()
[docs] @abstractmethod
def issueBatchJob(self, jobNode):
"""
Issues a job with the specified command to the batch system and returns a unique jobID.
:param jobNode a toil.job.JobNode
:return: a unique jobID that can be used to reference the newly issued job
:rtype: int
"""
raise NotImplementedError()
[docs] @abstractmethod
def killBatchJobs(self, jobIDs):
"""
Kills the given job IDs. After returning, the killed jobs will not
appear in the results of getRunningBatchJobIDs.
:param jobIDs: list of IDs of jobs to kill
:type jobIDs: list[int]
"""
raise NotImplementedError()
# FIXME: Return value should be a set (then also fix the tests)
[docs] @abstractmethod
def getIssuedBatchJobIDs(self):
"""
Gets all currently issued jobs
:return: A list of jobs (as jobIDs) currently issued (may be running, or may be
waiting to be run). Despite the result being a list, the ordering should not
be depended upon.
:rtype: list[str]
"""
raise NotImplementedError()
[docs] @abstractmethod
def getRunningBatchJobIDs(self):
"""
Gets a map of jobs as jobIDs that are currently running (not just waiting)
and how long they have been running, in seconds.
:return: dictionary with currently running jobID keys and how many seconds they have
been running as the value
:rtype: dict[int,float]
"""
raise NotImplementedError()
[docs] @abstractmethod
def getUpdatedBatchJob(self, maxWait):
"""
Returns information about job that has updated its status (i.e. ceased
running, either successfully or with an error). Each such job will be
returned exactly once.
:param float maxWait: the number of seconds to block, waiting for a result
:rtype: UpdatedBatchJobInfo or None
:return: If a result is available, returns UpdatedBatchJobInfo.
Otherwise it returns None. wallTime is the number of seconds (a strictly
positive float) in wall-clock time the job ran for, or None if this
batch system does not support tracking wall time. Returns None for jobs
that were killed.
"""
raise NotImplementedError()
[docs] @abstractmethod
def shutdown(self):
"""
Called at the completion of a toil invocation.
Should cleanly terminate all worker threads.
"""
raise NotImplementedError()
[docs] def setEnv(self, name, value=None):
"""
Set an environment variable for the worker process before it is launched. The worker
process will typically inherit the environment of the machine it is running on but this
method makes it possible to override specific variables in that inherited environment
before the worker is launched. Note that this mechanism is different to the one used by
the worker internally to set up the environment of a job. A call to this method affects
all jobs issued after this method returns. Note to implementors: This means that you
would typically need to copy the variables before enqueuing a job.
If no value is provided it will be looked up from the current environment.
"""
raise NotImplementedError()
[docs] @classmethod
def setOptions(cls, setOption):
"""
Process command line or configuration options relevant to this batch system.
The
:param setOption: A function with signature setOption(varName, parsingFn=None, checkFn=None, default=None)
used to update run configuration
"""
pass
class BatchSystemSupport(AbstractBatchSystem):
"""
Partial implementation of AbstractBatchSystem, support methods.
"""
def __init__(self, config, maxCores, maxMemory, maxDisk):
"""
Initializes initial state of the object
:param toil.common.Config config: object is setup by the toilSetup script and
has configuration parameters for the jobtree. You can add code
to that script to get parameters for your batch system.
:param float maxCores: the maximum number of cores the batch system can
request for any one job
:param int maxMemory: the maximum amount of memory the batch system can
request for any one job, in bytes
:param int maxDisk: the maximum amount of disk space the batch system can
request for any one job, in bytes
"""
super(BatchSystemSupport, self).__init__()
self.config = config
self.maxCores = maxCores
self.maxMemory = maxMemory
self.maxDisk = maxDisk
self.environment = {}
"""
:type: dict[str,str]
"""
self.workerCleanupInfo = WorkerCleanupInfo(workDir=self.config.workDir,
workflowID=self.config.workflowID,
cleanWorkDir=self.config.cleanWorkDir)
def checkResourceRequest(self, memory, cores, disk):
"""
Check resource request is not greater than that available or allowed.
:param int memory: amount of memory being requested, in bytes
:param float cores: number of cores being requested
:param int disk: amount of disk space being requested, in bytes
:raise InsufficientSystemResources: raised when a resource is requested in an amount
greater than allowed
"""
assert memory is not None
assert disk is not None
assert cores is not None
if cores > self.maxCores:
raise InsufficientSystemResources('cores', cores, self.maxCores)
if memory > self.maxMemory:
raise InsufficientSystemResources('memory', memory, self.maxMemory)
if disk > self.maxDisk:
raise InsufficientSystemResources('disk', disk, self.maxDisk)
def setEnv(self, name, value=None):
"""
Set an environment variable for the worker process before it is launched. The worker
process will typically inherit the environment of the machine it is running on but this
method makes it possible to override specific variables in that inherited environment
before the worker is launched. Note that this mechanism is different to the one used by
the worker internally to set up the environment of a job. A call to this method affects
all jobs issued after this method returns. Note to implementors: This means that you
would typically need to copy the variables before enqueuing a job.
If no value is provided it will be looked up from the current environment.
:param str name: the environment variable to be set on the worker.
:param str value: if given, the environment variable given by name will be set to this value.
if None, the variable's current value will be used as the value on the worker
:raise RuntimeError: if value is None and the name cannot be found in the environment
"""
if value is None:
try:
value = os.environ[name]
except KeyError:
raise RuntimeError("%s does not exist in current environment", name)
self.environment[name] = value
def formatStdOutErrPath(self, jobID, batchSystem, batchJobIDfmt, fileDesc):
"""
Format path for batch system standard output/error and other files
generated by the batch system itself.
Files will be written to the Toil work directory (which may
be on a shared file system) with names containing both the Toil and
batch system job IDs, for ease of debugging job failures.
:param: string jobID : Toil job ID
:param: string batchSystem : Name of the batch system
:param: string batchJobIDfmt : A string which the particular batch system
will format into the batch job ID once it is submitted
:param: string fileDesc : File description, should be 'std_output' for standard
output, 'std_error' for standard error, and as appropriate for other files
:rtype: string : Formatted filename; however if self.config.noStdOutErr is true,
returns '/dev/null' or equivalent.
"""
if self.config.noStdOutErr:
return os.devnull
workflowID = self.config.workflowID
workDir = Toil.getToilWorkDir(self.config.workDir)
fileName = 'toil_workflow_{workflowID}_job_{jobID}_batch_{batchSystem}_{batchJobIDfmt}_{fileDesc}.log'.format(
workflowID=workflowID, jobID=jobID, batchSystem=batchSystem, batchJobIDfmt=batchJobIDfmt, fileDesc=fileDesc)
return os.path.join(workDir, fileName)
@staticmethod
def workerCleanup(info):
"""
Cleans up the worker node on batch system shutdown. Also see :meth:`supportsWorkerCleanup`.
:param WorkerCleanupInfo info: A named tuple consisting of all the relevant information
for cleaning up the worker.
"""
assert isinstance(info, WorkerCleanupInfo)
workflowDir = Toil.getLocalWorkflowDir(info.workflowID, info.workDir)
DeferredFunctionManager.cleanupWorker(workflowDir)
workflowDirContents = os.listdir(workflowDir)
AbstractFileStore.shutdownFileStore(workflowDir, info.workflowID)
if (info.cleanWorkDir == 'always'
or info.cleanWorkDir in ('onSuccess', 'onError')
and workflowDirContents in ([], [cacheDirName(info.workflowID)])):
shutil.rmtree(workflowDir, ignore_errors=True)
class BatchSystemLocalSupport(BatchSystemSupport):
"""
Adds a local queue for helper jobs, useful for CWL & others
"""
def __init__(self, config, maxCores, maxMemory, maxDisk):
super(BatchSystemLocalSupport, self).__init__(config, maxCores, maxMemory, maxDisk)
self.localBatch = registry.batchSystemFactoryFor(
registry.defaultBatchSystem())()(
config, config.maxLocalJobs, maxMemory, maxDisk)
def handleLocalJob(self, jobNode): # type: (JobNode) -> Optional[int]
"""
To be called by issueBatchJobs.
Returns the jobID if the jobNode has been submitted to the local queue,
otherwise returns None
"""
if (not self.config.runCwlInternalJobsOnWorkers
and jobNode.jobName.startswith(CWL_INTERNAL_JOBS)):
return self.localBatch.issueBatchJob(jobNode)
else:
return None
def killLocalJobs(self, jobIDs):
"""
To be called by killBatchJobs. Will kill all local jobs that match the
provided jobIDs.
"""
self.localBatch.killBatchJobs(jobIDs)
def getIssuedLocalJobIDs(self):
"""To be called by getIssuedBatchJobIDs"""
return self.localBatch.getIssuedBatchJobIDs()
def getRunningLocalJobIDs(self):
"""To be called by getRunningBatchJobIDs()."""
return self.localBatch.getRunningBatchJobIDs()
def getUpdatedLocalJob(self, maxWait):
# type: (int) -> Optional[Tuple[int, int, int]]
"""To be called by getUpdatedBatchJob()"""
return self.localBatch.getUpdatedBatchJob(maxWait)
def getNextJobID(self): # type: () -> int
"""
Must be used to get job IDs so that the local and batch jobs do not
conflict.
"""
with self.localBatch.jobIndexLock:
jobID = self.localBatch.jobIndex
self.localBatch.jobIndex += 1
return jobID
def shutdownLocal(self): # type: () -> None
"""To be called from shutdown()"""
self.localBatch.shutdown()
class NodeInfo(object):
"""
The coresUsed attribute is a floating point value between 0 (all cores idle) and 1 (all cores
busy), reflecting the CPU load of the node.
The memoryUsed attribute is a floating point value between 0 (no memory used) and 1 (all memory
used), reflecting the memory pressure on the node.
The coresTotal and memoryTotal attributes are the node's resources, not just the used resources
The requestedCores and requestedMemory attributes are all the resources that Toil Jobs have reserved on the
node, regardless of whether the resources are actually being used by the Jobs.
The workers attribute is an integer reflecting the number of workers currently active workers
on the node.
"""
def __init__(self, coresUsed, memoryUsed, coresTotal, memoryTotal,
requestedCores, requestedMemory, workers):
self.coresUsed = coresUsed
self.memoryUsed = memoryUsed
self.coresTotal = coresTotal
self.memoryTotal = memoryTotal
self.requestedCores = requestedCores
self.requestedMemory = requestedMemory
self.workers = workers
class AbstractScalableBatchSystem(AbstractBatchSystem):
"""
A batch system that supports a variable number of worker nodes. Used by :class:`toil.
provisioners.clusterScaler.ClusterScaler` to scale the number of worker nodes in the cluster
up or down depending on overall load.
"""
@abstractmethod
def getNodes(self, preemptable=None):
"""
Returns a dictionary mapping node identifiers of preemptable or non-preemptable nodes to
NodeInfo objects, one for each node.
:param bool preemptable: If True (False) only (non-)preemptable nodes will be returned.
If None, all nodes will be returned.
:rtype: dict[str,NodeInfo]
"""
raise NotImplementedError()
@abstractmethod
def nodeInUse(self, nodeIP):
"""
Can be used to determine if a worker node is running any tasks. If the node is doesn't
exist, this function should simply return False.
:param str nodeIP: The worker nodes private IP address
:return: True if the worker node has been issued any tasks, else False
:rtype: bool
"""
raise NotImplementedError()
@abstractmethod
@contextmanager
def nodeFiltering(self, filter):
"""
Used to prevent races in autoscaling where
1) nodes have reported to the autoscaler as having no jobs
2) scaler decides to terminate these nodes. In parallel the batch system assigns jobs to the same nodes
3) scaler terminates nodes, resulting in job failures for all jobs on that node.
Call this method prior to node termination to ensure that nodes being considered for termination are not
assigned new jobs. Call the method again passing None as the filter to disable the filtering
after node termination is done.
:param method: This will be used as a filter on nodes considered when assigning new jobs.
After this context manager exits the filter should be removed
:rtype: None
"""
raise NotImplementedError()
@abstractmethod
def ignoreNode(self, nodeAddress):
"""
Stop sending jobs to this node. Used in autoscaling
when the autoscaler is ready to terminate a node, but
jobs are still running. This allows the node to be terminated
after the current jobs have finished.
:param str: IP address of node to ignore.
:rtype: None
"""
raise NotImplementedError()
@abstractmethod
def unignoreNode(self, nodeAddress):
"""
Stop ignoring this address, presumably after
a node with this address has been terminated. This allows for the
possibility of a new node having the same address as a terminated one.
"""
raise NotImplementedError()
class InsufficientSystemResources(Exception):
"""
To be raised when a job requests more of a particular resource than is either currently allowed
or avaliable
"""
def __init__(self, resource, requested, available):
"""
Creates an instance of this exception that indicates which resource is insufficient for current
demands, as well as the amount requested and amount actually available.
:param str resource: string representing the resource type
:param int|float requested: the amount of the particular resource requested that resulted
in this exception
:param int|float available: amount of the particular resource actually available
"""
self.requested = requested
self.available = available
self.resource = resource
def __str__(self):
return 'Requesting more {} than either physically available, or enforced by --max{}. ' \
'Requested: {}, Available: {}'.format(self.resource, self.resource.capitalize(),
self.requested, self.available)
