Source code for snakemake.executors

__author__ = "Johannes Köster"
__copyright__ = "Copyright 2021, Johannes Köster"
__email__ = "johannes.koester@uni-due.de"
__license__ = "MIT"

import os
import sys
import contextlib
import time
import datetime
import json
import textwrap
import stat
import shutil
import shlex
import threading
import concurrent.futures
import subprocess
import signal
import tempfile
from functools import partial
from itertools import chain
from collections import namedtuple
from snakemake.io import _IOFile
import random
import base64
import uuid
import re
import math

from snakemake.jobs import Job
from snakemake.shell import shell
from snakemake.logging import logger
from snakemake.stats import Stats
from snakemake.utils import format, Unformattable, makedirs
from snakemake.io import get_wildcard_names, Wildcards
from snakemake.exceptions import print_exception, get_exception_origin
from snakemake.exceptions import format_error, RuleException, log_verbose_traceback
from snakemake.exceptions import (
    ProtectedOutputException,
    WorkflowError,
    ImproperShadowException,
    SpawnedJobError,
    CacheMissException,
)
from snakemake.common import Mode, __version__, get_container_image, get_uuid


# TODO move each executor into a separate submodule


[docs]def sleep(): # do not sleep on CI. In that case we just want to quickly test everything. if os.environ.get("CI") != "true": time.sleep(10)
[docs]class AbstractExecutor: def __init__( self, workflow, dag, printreason=False, quiet=False, printshellcmds=False, printthreads=True, latency_wait=3, keepincomplete=False, keepmetadata=True, ): self.workflow = workflow self.dag = dag self.quiet = quiet self.printreason = printreason self.printshellcmds = printshellcmds self.printthreads = printthreads self.latency_wait = latency_wait self.keepincomplete = keepincomplete self.keepmetadata = keepmetadata
[docs] def get_default_remote_provider_args(self): if self.workflow.default_remote_provider: return ( " --default-remote-provider {} " "--default-remote-prefix {} " ).format( self.workflow.default_remote_provider.__module__.split(".")[-1], self.workflow.default_remote_prefix, ) return ""
def _format_key_value_args(self, flag, kwargs): if kwargs: return " {} {} ".format( flag, " ".join("{}={}".format(key, value) for key, value in kwargs.items()), ) return ""
[docs] def get_set_threads_args(self): return self._format_key_value_args( "--set-threads", self.workflow.overwrite_threads )
[docs] def get_set_scatter_args(self): return self._format_key_value_args( "--set-scatter", self.workflow.overwrite_scatter )
[docs] def get_default_resources_args(self): if self.workflow.default_resources.args is not None: def fmt(res): if isinstance(res, str): res = res.replace('"', r"\"") return '"{}"'.format(res) args = " --default-resources {} ".format( " ".join(map(fmt, self.workflow.default_resources.args)) ) return args return ""
[docs] def get_behavior_args(self): if self.workflow.conda_not_block_search_path_envvars: return " --conda-not-block-search-path-envvars " return ""
[docs] def run_jobs(self, jobs, callback=None, submit_callback=None, error_callback=None): """Run a list of jobs that is ready at a given point in time. By default, this method just runs each job individually. This method can be overwritten to submit many jobs in a more efficient way than one-by-one. Note that in any case, for each job, the callback functions have to be called individually! """ for job in jobs: self.run( job, callback=callback, submit_callback=submit_callback, error_callback=error_callback, )
[docs] def run(self, job, callback=None, submit_callback=None, error_callback=None): """Run a specific job or group job.""" self._run(job) callback(job)
[docs] def shutdown(self): pass
[docs] def cancel(self): pass
def _run(self, job): job.check_protected_output() self.printjob(job)
[docs] def rule_prefix(self, job): return "local " if job.is_local else ""
[docs] def printjob(self, job): job.log_info(skip_dynamic=True)
[docs] def print_job_error(self, job, msg=None, **kwargs): job.log_error(msg, **kwargs)
[docs] def handle_job_success(self, job): pass
[docs] def handle_job_error(self, job): pass
[docs]class DryrunExecutor(AbstractExecutor):
[docs] def printjob(self, job): super().printjob(job) if job.is_group(): for j in job.jobs: self.printcache(j) else: self.printcache(job)
[docs] def printcache(self, job): if self.workflow.is_cached_rule(job.rule): if self.workflow.output_file_cache.exists(job): logger.info( "Output file {} will be obtained from global between-workflow cache.".format( job.output[0] ) ) else: logger.info( "Output file {} will be written to global between-workflow cache.".format( job.output[0] ) )
[docs]class RealExecutor(AbstractExecutor): def __init__( self, workflow, dag, printreason=False, quiet=False, printshellcmds=False, latency_wait=3, assume_shared_fs=True, keepincomplete=False, keepmetadata=False, ): super().__init__( workflow, dag, printreason=printreason, quiet=quiet, printshellcmds=printshellcmds, latency_wait=latency_wait, keepincomplete=keepincomplete, keepmetadata=keepmetadata, ) self.assume_shared_fs = assume_shared_fs self.stats = Stats() self.snakefile = workflow.snakefile
[docs] def register_job(self, job): job.register()
def _run(self, job, callback=None, error_callback=None): super()._run(job) self.stats.report_job_start(job) try: self.register_job(job) except IOError as e: logger.info( "Failed to set marker file for job started ({}). " "Snakemake will work, but cannot ensure that output files " "are complete in case of a kill signal or power loss. " "Please ensure write permissions for the " "directory {}".format(e, self.workflow.persistence.path) )
[docs] def handle_job_success( self, job, upload_remote=True, handle_log=True, handle_touch=True, ignore_missing_output=False, ): job.postprocess( upload_remote=upload_remote, handle_log=handle_log, handle_touch=handle_touch, ignore_missing_output=ignore_missing_output, latency_wait=self.latency_wait, assume_shared_fs=self.assume_shared_fs, keep_metadata=self.keepmetadata, ) self.stats.report_job_end(job)
[docs] def handle_job_error(self, job, upload_remote=True): job.postprocess( error=True, assume_shared_fs=self.assume_shared_fs, latency_wait=self.latency_wait, )
[docs] def get_additional_args(self): """Return a string to add to self.exec_job that includes additional arguments from the command line. This is currently used in the ClusterExecutor and CPUExecutor, as both were using the same code. Both have base class of the RealExecutor. """ additional = "" if not self.workflow.cleanup_scripts: additional += " --skip-script-cleanup " if self.workflow.shadow_prefix: additional += " --shadow-prefix {} ".format(self.workflow.shadow_prefix) if self.workflow.use_conda: additional += " --use-conda " if self.workflow.conda_prefix: additional += " --conda-prefix {} ".format(self.workflow.conda_prefix) if self.workflow.use_singularity: additional += " --use-singularity " if self.workflow.singularity_prefix: additional += " --singularity-prefix {} ".format( self.workflow.singularity_prefix ) if self.workflow.singularity_args: additional += ' --singularity-args "{}"'.format( self.workflow.singularity_args ) if not self.workflow.execute_subworkflows: additional += " --no-subworkflows" if self.workflow.use_env_modules: additional += " --use-envmodules" if not self.keepmetadata: additional += " --drop-metadata" return additional
[docs] def format_job_pattern(self, pattern, job=None, **kwargs): overwrite_workdir = [] if self.workflow.overwrite_workdir: overwrite_workdir.extend(("--directory", self.workflow.overwrite_workdir)) overwrite_config = [] if self.workflow.overwrite_configfiles: # add each of the overwriting configfiles in the original order if self.workflow.overwrite_configfiles: overwrite_config.append("--configfiles") overwrite_config.extend(self.workflow.overwrite_configfiles) if self.workflow.config_args: overwrite_config.append("--config") overwrite_config.extend(self.workflow.config_args) printshellcmds = "" if self.workflow.printshellcmds: printshellcmds = "-p" if not job.is_branched and not job.is_updated: # Restrict considered rules. This does not work for updated jobs # because they need to be updated in the spawned process as well. rules = ["--allowed-rules"] rules.extend(job.rules) else: rules = [] target = kwargs.get("target", job.get_targets()) snakefile = kwargs.get("snakefile", self.snakefile) cores = kwargs.get("cores", self.cores) if "target" in kwargs: del kwargs["target"] if "snakefile" in kwargs: del kwargs["snakefile"] if "cores" in kwargs: del kwargs["cores"] cmd = format( pattern, job=job, attempt=job.attempt, overwrite_workdir=overwrite_workdir, overwrite_config=overwrite_config, printshellcmds=printshellcmds, workflow=self.workflow, snakefile=snakefile, cores=cores, benchmark_repeats=job.benchmark_repeats if not job.is_group() else None, target=target, rules=rules, **kwargs, ) return cmd
[docs]class TouchExecutor(RealExecutor):
[docs] def run(self, job, callback=None, submit_callback=None, error_callback=None): super()._run(job) try: # Touching of output files will be done by handle_job_success time.sleep(0.1) callback(job) except OSError as ex: print_exception(ex, self.workflow.linemaps) error_callback(job)
[docs] def handle_job_success(self, job): super().handle_job_success(job, ignore_missing_output=True)
_ProcessPoolExceptions = (KeyboardInterrupt,) try: from concurrent.futures.process import BrokenProcessPool _ProcessPoolExceptions = (KeyboardInterrupt, BrokenProcessPool) except ImportError: pass
[docs]class CPUExecutor(RealExecutor): def __init__( self, workflow, dag, workers, printreason=False, quiet=False, printshellcmds=False, use_threads=False, latency_wait=3, cores=1, keepincomplete=False, keepmetadata=True, ): super().__init__( workflow, dag, printreason=printreason, quiet=quiet, printshellcmds=printshellcmds, latency_wait=latency_wait, keepincomplete=keepincomplete, keepmetadata=keepmetadata, ) self.exec_job = "\\\n".join( ( "cd {workflow.workdir_init} && ", "{sys.executable} -m snakemake {target} --snakefile {snakefile} ", "--force -j{cores} --keep-target-files --keep-remote ", "--attempt {attempt} --scheduler {workflow.scheduler_type} ", "--force-use-threads --wrapper-prefix {workflow.wrapper_prefix} ", "--max-inventory-time 0 --ignore-incomplete ", "--latency-wait {latency_wait} ", self.get_default_remote_provider_args(), self.get_default_resources_args(), self.get_behavior_args(), self.get_set_scatter_args(), self.get_set_threads_args(), "{overwrite_workdir} {overwrite_config} {printshellcmds} {rules} ", "--notemp --quiet --no-hooks --nolock --mode {} ".format( Mode.subprocess ), ) ) self.exec_job += self.get_additional_args() self.use_threads = use_threads self.cores = cores # Zero thread jobs do not need a thread, but they occupy additional workers. # Hence we need to reserve additional workers for them. self.workers = workers + 5 self.pool = concurrent.futures.ThreadPoolExecutor(max_workers=self.workers)
[docs] def run(self, job, callback=None, submit_callback=None, error_callback=None): super()._run(job) if job.is_group(): # if we still don't have enough workers for this group, create a new pool here missing_workers = max(len(job) - self.workers, 0) if missing_workers: self.workers += missing_workers self.pool = concurrent.futures.ThreadPoolExecutor( max_workers=self.workers ) # the future waits for the entire group job future = self.pool.submit(self.run_group_job, job) else: future = self.run_single_job(job) future.add_done_callback(partial(self._callback, job, callback, error_callback))
[docs] def job_args_and_prepare(self, job): job.prepare() conda_env = job.conda_env_path if self.workflow.use_conda else None container_img = ( job.container_img_path if self.workflow.use_singularity else None ) env_modules = job.env_modules if self.workflow.use_env_modules else None benchmark = None benchmark_repeats = job.benchmark_repeats or 1 if job.benchmark is not None: benchmark = str(job.benchmark) return ( job.rule, job.input._plainstrings(), job.output._plainstrings(), job.params, job.wildcards, job.threads, job.resources, job.log._plainstrings(), benchmark, benchmark_repeats, conda_env, container_img, self.workflow.singularity_args, env_modules, self.workflow.use_singularity, self.workflow.linemaps, self.workflow.debug, self.workflow.cleanup_scripts, job.shadow_dir, job.jobid, self.workflow.edit_notebook, )
[docs] def run_single_job(self, job): if self.use_threads or (not job.is_shadow and not job.is_run): future = self.pool.submit( self.cached_or_run, job, run_wrapper, *self.job_args_and_prepare(job) ) else: # run directive jobs are spawned into subprocesses future = self.pool.submit(self.cached_or_run, job, self.spawn_job, job) return future
[docs] def run_group_job(self, job): """Run a pipe group job. This lets all items run simultaneously.""" # we only have to consider pipe groups because in local running mode, # these are the only groups that will occur futures = [self.run_single_job(j) for j in job] while True: k = 0 for f in futures: if f.done(): ex = f.exception() if ex is not None: # kill all shell commands of the other group jobs # there can be only shell commands because the # run directive is not allowed for pipe jobs for j in job: shell.kill(j.jobid) raise ex else: k += 1 if k == len(futures): return time.sleep(1)
[docs] def spawn_job(self, job): exec_job = self.exec_job cmd = self.format_job_pattern( exec_job, job=job, _quote_all=True, latency_wait=self.latency_wait ) try: subprocess.check_call(cmd, shell=True) except subprocess.CalledProcessError as e: raise SpawnedJobError()
[docs] def cached_or_run(self, job, run_func, *args): """ Either retrieve result from cache, or run job with given function. """ to_cache = self.workflow.is_cached_rule(job.rule) try: if to_cache: self.workflow.output_file_cache.fetch(job) return except CacheMissException: pass run_func(*args) if to_cache: self.workflow.output_file_cache.store(job)
[docs] def shutdown(self): self.pool.shutdown()
[docs] def cancel(self): self.pool.shutdown()
def _callback(self, job, callback, error_callback, future): try: ex = future.exception() if ex is not None: raise ex callback(job) except _ProcessPoolExceptions: self.handle_job_error(job) # no error callback, just silently ignore the interrupt as the main scheduler is also killed except SpawnedJobError: # don't print error message, this is done by the spawned subprocess error_callback(job) except (Exception, BaseException) as ex: self.print_job_error(job) if not (job.is_group() or job.shellcmd) or self.workflow.verbose: print_exception(ex, self.workflow.linemaps) error_callback(job)
[docs] def handle_job_success(self, job): super().handle_job_success(job)
[docs] def handle_job_error(self, job): super().handle_job_error(job) if not self.keepincomplete: job.cleanup() self.workflow.persistence.cleanup(job)
[docs]class ClusterExecutor(RealExecutor): """Backend for distributed execution. The key idea is that a job is converted into a script that invokes Snakemake again, in whatever environment is targeted. The script is submitted to some job management platform (e.g. a cluster scheduler like slurm). This class can be specialized to generate more specific backends, also for the cloud. """ default_jobscript = "jobscript.sh" def __init__( self, workflow, dag, cores, jobname="snakejob.{name}.{jobid}.sh", printreason=False, quiet=False, printshellcmds=False, latency_wait=3, cluster_config=None, local_input=None, restart_times=None, exec_job=None, assume_shared_fs=True, max_status_checks_per_second=1, disable_default_remote_provider_args=False, disable_get_default_resources_args=False, keepincomplete=False, keepmetadata=True, ): from ratelimiter import RateLimiter local_input = local_input or [] super().__init__( workflow, dag, printreason=printreason, quiet=quiet, printshellcmds=printshellcmds, latency_wait=latency_wait, assume_shared_fs=assume_shared_fs, keepincomplete=keepincomplete, keepmetadata=keepmetadata, ) if not self.assume_shared_fs: # use relative path to Snakefile self.snakefile = os.path.relpath(workflow.snakefile) jobscript = workflow.jobscript if jobscript is None: jobscript = os.path.join(os.path.dirname(__file__), self.default_jobscript) try: with open(jobscript) as f: self.jobscript = f.read() except IOError as e: raise WorkflowError(e) if not "jobid" in get_wildcard_names(jobname): raise WorkflowError( 'Defined jobname ("{}") has to contain the wildcard {jobid}.' ) if exec_job is None: self.exec_job = "\\\n".join( ( "{envvars} " "cd {workflow.workdir_init} && " if assume_shared_fs else "", "{path:u} {sys.executable} " if assume_shared_fs else "python ", "-m snakemake {target} --snakefile {snakefile} ", "--force -j{cores} --keep-target-files --keep-remote --max-inventory-time 0 ", "--wait-for-files {wait_for_files} --latency-wait {latency_wait} ", " --attempt {attempt} {use_threads} --scheduler {workflow.scheduler_type} ", "--wrapper-prefix {workflow.wrapper_prefix} ", "{overwrite_workdir} {overwrite_config} {printshellcmds} {rules} " "--nocolor --notemp --no-hooks --nolock ", "--mode {} ".format(Mode.cluster), ) ) else: self.exec_job = exec_job self.exec_job += self.get_additional_args() if not disable_default_remote_provider_args: self.exec_job += self.get_default_remote_provider_args() if not disable_get_default_resources_args: self.exec_job += self.get_default_resources_args() self.exec_job += self.get_behavior_args() self.exec_job += self.get_set_scatter_args() self.exec_job += self.get_set_threads_args() self.jobname = jobname self._tmpdir = None self.cores = cores if cores else "" self.cluster_config = cluster_config if cluster_config else dict() self.restart_times = restart_times self.active_jobs = list() self.lock = threading.Lock() self.wait = True self.wait_thread = threading.Thread(target=self._wait_for_jobs) self.wait_thread.daemon = True self.wait_thread.start() self.max_status_checks_per_second = max_status_checks_per_second self.status_rate_limiter = RateLimiter( max_calls=self.max_status_checks_per_second, period=1 )
[docs] def shutdown(self): with self.lock: self.wait = False self.wait_thread.join() if not self.workflow.immediate_submit: # Only delete tmpdir (containing jobscripts) if not using # immediate_submit. With immediate_submit, jobs can be scheduled # after this method is completed. Hence we have to keep the # directory. shutil.rmtree(self.tmpdir)
[docs] def cancel(self): self.shutdown()
def _run(self, job, callback=None, error_callback=None): if self.assume_shared_fs: job.remove_existing_output() job.download_remote_input() super()._run(job, callback=callback, error_callback=error_callback) @property def tmpdir(self): if self._tmpdir is None: self._tmpdir = tempfile.mkdtemp(dir=".snakemake", prefix="tmp.") return os.path.abspath(self._tmpdir)
[docs] def get_jobscript(self, job): f = job.format_wildcards(self.jobname, cluster=self.cluster_wildcards(job)) if os.path.sep in f: raise WorkflowError( "Path separator ({}) found in job name {}. " "This is not supported.".format(os.path.sep, f) ) return os.path.join(self.tmpdir, f)
[docs] def format_job(self, pattern, job, **kwargs): wait_for_files = [] path = "" if self.assume_shared_fs: wait_for_files.append(self.tmpdir) wait_for_files.extend(job.get_wait_for_files()) # Prepend PATH of current python executable to PATH. # This way, we ensure that the snakemake process in the cluster node runs # in the same environment as the current process. # This is necessary in order to find the pulp solver backends (e.g. coincbc). path = "PATH='{}':$PATH".format(os.path.dirname(sys.executable)) format_p = partial( self.format_job_pattern, job=job, properties=job.properties(cluster=self.cluster_params(job)), latency_wait=self.latency_wait, wait_for_files=wait_for_files, path=path, **kwargs, ) try: return format_p(pattern) except KeyError as e: raise WorkflowError( "Error formatting jobscript: {} not found\n" "Make sure that your custom jobscript is up to date.".format(e) )
[docs] def write_jobscript(self, job, jobscript, **kwargs): # only force threads if this is not a group job # otherwise we want proper process handling use_threads = "--force-use-threads" if not job.is_group() else "" envvars = " ".join( "{}={}".format(var, os.environ[var]) for var in self.workflow.envvars ) exec_job = self.format_job( self.exec_job, job, _quote_all=True, use_threads=use_threads, envvars=envvars, **kwargs, ) content = self.format_job(self.jobscript, job, exec_job=exec_job, **kwargs) logger.debug("Jobscript:\n{}".format(content)) with open(jobscript, "w") as f: print(content, file=f) os.chmod(jobscript, os.stat(jobscript).st_mode | stat.S_IXUSR | stat.S_IRUSR)
[docs] def cluster_params(self, job): """Return wildcards object for job from cluster_config.""" cluster = self.cluster_config.get("__default__", dict()).copy() cluster.update(self.cluster_config.get(job.name, dict())) # Format values with available parameters from the job. for key, value in list(cluster.items()): if isinstance(value, str): try: cluster[key] = job.format_wildcards(value) except NameError as e: if job.is_group(): msg = ( "Failed to format cluster config for group job. " "You have to ensure that your default entry " "does not contain any items that group jobs " "cannot provide, like {rule}, {wildcards}." ) else: msg = ( "Failed to format cluster config " "entry for job {}.".format(job.rule.name) ) raise WorkflowError(msg, e) return cluster
[docs] def cluster_wildcards(self, job): return Wildcards(fromdict=self.cluster_params(job))
[docs] def handle_job_success(self, job): super().handle_job_success( job, upload_remote=False, handle_log=False, handle_touch=False )
[docs] def handle_job_error(self, job): # TODO what about removing empty remote dirs?? This cannot be decided # on the cluster node. super().handle_job_error(job, upload_remote=False) logger.debug("Cleanup job metadata.") # We have to remove metadata here as well. # It will be removed by the CPUExecutor in case of a shared FS, # but we might not see the removal due to filesystem latency. # By removing it again, we make sure that it is gone on the host FS. if not self.keepincomplete: self.workflow.persistence.cleanup(job) # Also cleanup the jobs output files, in case the remote job # was not able to, due to e.g. timeout. logger.debug("Cleanup failed jobs output files.") job.cleanup()
[docs] def print_cluster_job_error(self, job_info, jobid): job = job_info.job kind = ( "rule {}".format(job.rule.name) if not job.is_group() else "group job {}".format(job.groupid) ) logger.error( "Error executing {} on cluster (jobid: {}, external: " "{}, jobscript: {}). For error details see the cluster " "log and the log files of the involved rule(s).".format( kind, jobid, job_info.jobid, job_info.jobscript ) )
GenericClusterJob = namedtuple( "GenericClusterJob", "job jobid callback error_callback jobscript jobfinished jobfailed", )
[docs]class GenericClusterExecutor(ClusterExecutor): def __init__( self, workflow, dag, cores, submitcmd="qsub", statuscmd=None, cluster_config=None, jobname="snakejob.{rulename}.{jobid}.sh", printreason=False, quiet=False, printshellcmds=False, latency_wait=3, restart_times=0, assume_shared_fs=True, max_status_checks_per_second=1, keepincomplete=False, keepmetadata=True, ): self.submitcmd = submitcmd if not assume_shared_fs and statuscmd is None: raise WorkflowError( "When no shared filesystem can be assumed, a " "status command must be given." ) self.statuscmd = statuscmd self.external_jobid = dict() super().__init__( workflow, dag, cores, jobname=jobname, printreason=printreason, quiet=quiet, printshellcmds=printshellcmds, latency_wait=latency_wait, cluster_config=cluster_config, restart_times=restart_times, assume_shared_fs=assume_shared_fs, max_status_checks_per_second=max_status_checks_per_second, keepincomplete=keepincomplete, keepmetadata=keepmetadata, ) if statuscmd: self.exec_job += " && exit 0 || exit 1" elif assume_shared_fs: # TODO wrap with watch and touch {jobrunning} # check modification date of {jobrunning} in the wait_for_job method self.exec_job += " && touch {jobfinished} || (touch {jobfailed}; exit 1)" else: raise WorkflowError( "If no shared filesystem is used, you have to " "specify a cluster status command." )
[docs] def cancel(self): logger.info("Will exit after finishing currently running jobs.") self.shutdown()
[docs] def register_job(self, job): # Do not register job here. # Instead do it manually once the jobid is known. pass
[docs] def run(self, job, callback=None, submit_callback=None, error_callback=None): super()._run(job) workdir = os.getcwd() jobid = job.jobid jobscript = self.get_jobscript(job) jobfinished = os.path.join(self.tmpdir, "{}.jobfinished".format(jobid)) jobfailed = os.path.join(self.tmpdir, "{}.jobfailed".format(jobid)) self.write_jobscript( job, jobscript, jobfinished=jobfinished, jobfailed=jobfailed ) if self.statuscmd: ext_jobid = self.dag.incomplete_external_jobid(job) if ext_jobid: # Job is incomplete and still running. # We simply register it and wait for completion or failure. logger.info( "Resuming incomplete job {} with external jobid '{}'.".format( jobid, ext_jobid ) ) submit_callback(job) with self.lock: self.active_jobs.append( GenericClusterJob( job, ext_jobid, callback, error_callback, jobscript, jobfinished, jobfailed, ) ) return deps = " ".join( self.external_jobid[f] for f in job.input if f in self.external_jobid ) try: submitcmd = job.format_wildcards( self.submitcmd, dependencies=deps, cluster=self.cluster_wildcards(job) ) except AttributeError as e: raise WorkflowError(str(e), rule=job.rule if not job.is_group() else None) try: ext_jobid = ( subprocess.check_output( '{submitcmd} "{jobscript}"'.format( submitcmd=submitcmd, jobscript=jobscript ), shell=True, ) .decode() .split("\n") ) except subprocess.CalledProcessError as ex: logger.error( "Error submitting jobscript (exit code {}):\n{}".format( ex.returncode, ex.output.decode() ) ) error_callback(job) return if ext_jobid and ext_jobid[0]: ext_jobid = ext_jobid[0] self.external_jobid.update((f, ext_jobid) for f in job.output) logger.info( "Submitted {} {} with external jobid '{}'.".format( "group job" if job.is_group() else "job", jobid, ext_jobid ) ) self.workflow.persistence.started(job, external_jobid=ext_jobid) submit_callback(job) with self.lock: self.active_jobs.append( GenericClusterJob( job, ext_jobid, callback, error_callback, jobscript, jobfinished, jobfailed, ) )
def _wait_for_jobs(self): success = "success" failed = "failed" running = "running" if self.statuscmd is not None: def job_status(job): try: # this command shall return "success", "failed" or "running" return ( subprocess.check_output( "{statuscmd} {jobid}".format( jobid=job.jobid, statuscmd=self.statuscmd ), shell=True, ) .decode() .split("\n")[0] ) except subprocess.CalledProcessError as e: if e.returncode < 0: # Ignore SIGINT and all other issues due to signals # because it will be caused by hitting e.g. # Ctrl-C on the main process or sending killall to # snakemake. # Snakemake will handle the signal in # the master process. pass else: raise WorkflowError( "Failed to obtain job status. " "See above for error message." ) else: def job_status(job): if os.path.exists(active_job.jobfinished): os.remove(active_job.jobfinished) os.remove(active_job.jobscript) return success if os.path.exists(active_job.jobfailed): os.remove(active_job.jobfailed) os.remove(active_job.jobscript) return failed return running while True: with self.lock: if not self.wait: return active_jobs = self.active_jobs self.active_jobs = list() still_running = list() # logger.debug("Checking status of {} jobs.".format(len(active_jobs))) for active_job in active_jobs: with self.status_rate_limiter: status = job_status(active_job) if status == success: active_job.callback(active_job.job) elif status == failed: self.print_job_error( active_job.job, cluster_jobid=active_job.jobid if active_job.jobid else "unknown", ) self.print_cluster_job_error( active_job, self.dag.jobid(active_job.job) ) active_job.error_callback(active_job.job) else: still_running.append(active_job) with self.lock: self.active_jobs.extend(still_running) sleep()
SynchronousClusterJob = namedtuple( "SynchronousClusterJob", "job jobid callback error_callback jobscript process" )
[docs]class SynchronousClusterExecutor(ClusterExecutor): """ invocations like "qsub -sync y" (SGE) or "bsub -K" (LSF) are synchronous, blocking the foreground thread and returning the remote exit code at remote exit. """ def __init__( self, workflow, dag, cores, submitcmd="qsub", cluster_config=None, jobname="snakejob.{rulename}.{jobid}.sh", printreason=False, quiet=False, printshellcmds=False, latency_wait=3, restart_times=0, assume_shared_fs=True, keepincomplete=False, keepmetadata=True, ): super().__init__( workflow, dag, cores, jobname=jobname, printreason=printreason, quiet=quiet, printshellcmds=printshellcmds, latency_wait=latency_wait, cluster_config=cluster_config, restart_times=restart_times, assume_shared_fs=assume_shared_fs, max_status_checks_per_second=10, keepincomplete=keepincomplete, keepmetadata=keepmetadata, ) self.submitcmd = submitcmd self.external_jobid = dict()
[docs] def cancel(self): logger.info("Will exit after finishing currently running jobs.") self.shutdown()
[docs] def run(self, job, callback=None, submit_callback=None, error_callback=None): super()._run(job) workdir = os.getcwd() jobid = job.jobid jobscript = self.get_jobscript(job) self.write_jobscript(job, jobscript) deps = " ".join( self.external_jobid[f] for f in job.input if f in self.external_jobid ) try: submitcmd = job.format_wildcards( self.submitcmd, dependencies=deps, cluster=self.cluster_wildcards(job) ) except AttributeError as e: raise WorkflowError(str(e), rule=job.rule if not job.is_group() else None) process = subprocess.Popen( '{submitcmd} "{jobscript}"'.format( submitcmd=submitcmd, jobscript=jobscript ), shell=True, ) submit_callback(job) with self.lock: self.active_jobs.append( SynchronousClusterJob( job, process.pid, callback, error_callback, jobscript, process ) )
def _wait_for_jobs(self): while True: with self.lock: if not self.wait: return active_jobs = self.active_jobs self.active_jobs = list() still_running = list() for active_job in active_jobs: with self.status_rate_limiter: exitcode = active_job.process.poll() if exitcode is None: # job not yet finished still_running.append(active_job) elif exitcode == 0: # job finished successfully os.remove(active_job.jobscript) active_job.callback(active_job.job) else: # job failed os.remove(active_job.jobscript) self.print_job_error(active_job.job) self.print_cluster_job_error( active_job, self.dag.jobid(active_job.job) ) active_job.error_callback(active_job.job) with self.lock: self.active_jobs.extend(still_running) sleep()
DRMAAClusterJob = namedtuple( "DRMAAClusterJob", "job jobid callback error_callback jobscript" )
[docs]class DRMAAExecutor(ClusterExecutor): def __init__( self, workflow, dag, cores, jobname="snakejob.{rulename}.{jobid}.sh", printreason=False, quiet=False, printshellcmds=False, drmaa_args="", drmaa_log_dir=None, latency_wait=3, cluster_config=None, restart_times=0, assume_shared_fs=True, max_status_checks_per_second=1, keepincomplete=False, keepmetadata=True, ): super().__init__( workflow, dag, cores, jobname=jobname, printreason=printreason, quiet=quiet, printshellcmds=printshellcmds, latency_wait=latency_wait, cluster_config=cluster_config, restart_times=restart_times, assume_shared_fs=assume_shared_fs, max_status_checks_per_second=max_status_checks_per_second, keepincomplete=keepincomplete, keepmetadata=keepmetadata, ) try: import drmaa except ImportError: raise WorkflowError( "Python support for DRMAA is not installed. " "Please install it, e.g. with easy_install3 --user drmaa" ) except RuntimeError as e: raise WorkflowError("Error loading drmaa support:\n{}".format(e)) self.session = drmaa.Session() self.drmaa_args = drmaa_args self.drmaa_log_dir = drmaa_log_dir self.session.initialize() self.submitted = list()
[docs] def cancel(self): from drmaa.const import JobControlAction from drmaa.errors import InvalidJobException, InternalException for jobid in self.submitted: try: self.session.control(jobid, JobControlAction.TERMINATE) except (InvalidJobException, InternalException): # This is common - logging a warning would probably confuse the user. pass self.shutdown()
[docs] def run(self, job, callback=None, submit_callback=None, error_callback=None): super()._run(job) jobscript = self.get_jobscript(job) self.write_jobscript(job, jobscript) try: drmaa_args = job.format_wildcards( self.drmaa_args, cluster=self.cluster_wildcards(job) ) except AttributeError as e: raise WorkflowError(str(e), rule=job.rule) import drmaa if self.drmaa_log_dir: makedirs(self.drmaa_log_dir) try: jt = self.session.createJobTemplate() jt.remoteCommand = jobscript jt.nativeSpecification = drmaa_args if self.drmaa_log_dir: jt.outputPath = ":" + self.drmaa_log_dir jt.errorPath = ":" + self.drmaa_log_dir jt.jobName = os.path.basename(jobscript) jobid = self.session.runJob(jt) except ( drmaa.DeniedByDrmException, drmaa.InternalException, drmaa.InvalidAttributeValueException, ) as e: print_exception( WorkflowError("DRMAA Error: {}".format(e)), self.workflow.linemaps ) error_callback(job) return logger.info( "Submitted DRMAA job {} with external jobid {}.".format(job.jobid, jobid) ) self.submitted.append(jobid) self.session.deleteJobTemplate(jt) submit_callback(job) with self.lock: self.active_jobs.append( DRMAAClusterJob(job, jobid, callback, error_callback, jobscript) )
[docs] def shutdown(self): super().shutdown() self.session.exit()
def _wait_for_jobs(self): import drmaa while True: with self.lock: if not self.wait: return active_jobs = self.active_jobs self.active_jobs = list() still_running = list() for active_job in active_jobs: with self.status_rate_limiter: try: retval = self.session.wait( active_job.jobid, drmaa.Session.TIMEOUT_NO_WAIT ) except drmaa.ExitTimeoutException as e: # job still active still_running.append(active_job) continue except (drmaa.InternalException, Exception) as e: print_exception( WorkflowError("DRMAA Error: {}".format(e)), self.workflow.linemaps, ) os.remove(active_job.jobscript) active_job.error_callback(active_job.job) continue # job exited os.remove(active_job.jobscript) if ( not retval.wasAborted and retval.hasExited and retval.exitStatus == 0 ): active_job.callback(active_job.job) else: self.print_job_error(active_job.job) self.print_cluster_job_error( active_job, self.dag.jobid(active_job.job) ) active_job.error_callback(active_job.job) with self.lock: self.active_jobs.extend(still_running) sleep()
[docs]@contextlib.contextmanager def change_working_directory(directory=None): """ Change working directory in execution context if provided. """ if directory: try: saved_directory = os.getcwd() logger.info("Changing to shadow directory: {}".format(directory)) os.chdir(directory) yield finally: os.chdir(saved_directory) else: yield
KubernetesJob = namedtuple( "KubernetesJob", "job jobid callback error_callback kubejob jobscript" )
[docs]class KubernetesExecutor(ClusterExecutor): def __init__( self, workflow, dag, namespace, container_image=None, jobname="{rulename}.{jobid}", printreason=False, quiet=False, printshellcmds=False, latency_wait=3, cluster_config=None, local_input=None, restart_times=None, keepincomplete=False, keepmetadata=True, ): self.workflow = workflow exec_job = ( "cp -rf /source/. . && " "snakemake {target} --snakefile {snakefile} " "--force -j{cores} --keep-target-files --keep-remote " "--latency-wait {latency_wait} --scheduler {workflow.scheduler_type} " " --attempt {attempt} {use_threads} --max-inventory-time 0 " "--wrapper-prefix {workflow.wrapper_prefix} " "{overwrite_config} {printshellcmds} {rules} --nocolor " "--notemp --no-hooks --nolock " ) super().__init__( workflow, dag, None, jobname=jobname, printreason=printreason, quiet=quiet, printshellcmds=printshellcmds, latency_wait=latency_wait, cluster_config=cluster_config, local_input=local_input, restart_times=restart_times, exec_job=exec_job, assume_shared_fs=False, max_status_checks_per_second=10, ) # use relative path to Snakefile self.snakefile = os.path.relpath(workflow.snakefile) try: from kubernetes import config except ImportError: raise WorkflowError( "The Python 3 package 'kubernetes' " "must be installed to use Kubernetes" ) config.load_kube_config() import kubernetes.client self.kubeapi = kubernetes.client.CoreV1Api() self.batchapi = kubernetes.client.BatchV1Api() self.namespace = namespace self.envvars = workflow.envvars self.secret_files = {} self.run_namespace = str(uuid.uuid4()) self.secret_envvars = {} self.register_secret() self.container_image = container_image or get_container_image()
[docs] def register_secret(self): import kubernetes.client secret = kubernetes.client.V1Secret() secret.metadata = kubernetes.client.V1ObjectMeta() # create a random uuid secret.metadata.name = self.run_namespace secret.type = "Opaque" secret.data = {} for i, f in enumerate(self.workflow.get_sources()): if f.startswith(".."): logger.warning( "Ignoring source file {}. Only files relative " "to the working directory are allowed.".format(f) ) continue # The kubernetes API can't create secret files larger than 1MB. source_file_size = os.path.getsize(f) max_file_size = 1048576 if source_file_size > max_file_size: logger.warning( "Skipping the source file {f}. Its size {source_file_size} exceeds " "the maximum file size (1MB) that can be passed " "from host to kubernetes.".format( f=f, source_file_size=source_file_size ) ) continue with open(f, "br") as content: key = "f{}".format(i) # Some files are smaller than 1MB, but grows larger after being base64 encoded # We should exclude them as well, otherwise Kubernetes APIs will complain encoded_contents = base64.b64encode(content.read()).decode() encoded_size = len(encoded_contents) if encoded_size > 1048576: logger.warning( "Skipping the source file {f} for secret key {key}. " "Its base64 encoded size {encoded_size} exceeds " "the maximum file size (1MB) that can be passed " "from host to kubernetes.".format( f=f, source_file_size=source_file_size, key=key, encoded_size=encoded_size, ) ) continue self.secret_files[key] = f secret.data[key] = encoded_contents for e in self.envvars: try: key = e.lower() secret.data[key] = base64.b64encode(os.environ[e].encode()).decode() self.secret_envvars[key] = e except KeyError: continue # Test if the total size of the configMap exceeds 1MB config_map_size = sum( [len(base64.b64decode(v)) for k, v in secret.data.items()] ) if config_map_size > 1048576: logger.warning( "The total size of the included files and other Kubernetes secrets " "is {}, exceeding the 1MB limit.\n".format(config_map_size) ) logger.warning( "The following are the largest files. Consider removing some of them " "(you need remove at least {} bytes):".format(config_map_size - 1048576) ) entry_sizes = { self.secret_files[k]: len(base64.b64decode(v)) for k, v in secret.data.items() if k in self.secret_files } for k, v in sorted(entry_sizes.items(), key=lambda item: item[1])[:-6:-1]: logger.warning(" * File: {k}, original size: {v}".format(k=k, v=v)) raise WorkflowError("ConfigMap too large") self.kubeapi.create_namespaced_secret(self.namespace, secret)
[docs] def unregister_secret(self): import kubernetes.client safe_delete_secret = lambda: self.kubeapi.delete_namespaced_secret( self.run_namespace, self.namespace, body=kubernetes.client.V1DeleteOptions() ) self._kubernetes_retry(safe_delete_secret)
# In rare cases, deleting a pod may rais 404 NotFound error.
[docs] def safe_delete_pod(self, jobid, ignore_not_found=True): import kubernetes.client body = kubernetes.client.V1DeleteOptions() try: self.kubeapi.delete_namespaced_pod(jobid, self.namespace, body=body) except kubernetes.client.rest.ApiException as e: if e.status == 404 and ignore_not_found: # Can't find the pod. Maybe it's already been # destroyed. Proceed with a warning message. logger.warning( "[WARNING] 404 not found when trying to delete the pod: {jobid}\n" "[WARNING] Ignore this error\n".format(jobid=jobid) ) else: raise e
[docs] def shutdown(self): self.unregister_secret() super().shutdown()
[docs] def cancel(self): import kubernetes.client body = kubernetes.client.V1DeleteOptions() with self.lock: for j in self.active_jobs: func = lambda: self.safe_delete_pod(j.jobid, ignore_not_found=True) self._kubernetes_retry(func) self.shutdown()
[docs] def run(self, job, callback=None, submit_callback=None, error_callback=None): import kubernetes.client super()._run(job) exec_job = self.format_job( self.exec_job, job, _quote_all=True, use_threads="--force-use-threads" if not job.is_group() else "", ) # Kubernetes silently does not submit a job if the name is too long # therefore, we ensure that it is not longer than snakejob+uuid. jobid = "snakejob-{}".format( get_uuid("{}-{}-{}".format(self.run_namespace, job.jobid, job.attempt)) ) body = kubernetes.client.V1Pod() body.metadata = kubernetes.client.V1ObjectMeta(labels={"app": "snakemake"}) body.metadata.name = jobid # container container = kubernetes.client.V1Container(name=jobid) container.image = self.container_image container.command = shlex.split("/bin/sh") container.args = ["-c", exec_job] container.working_dir = "/workdir" container.volume_mounts = [ kubernetes.client.V1VolumeMount(name="workdir", mount_path="/workdir") ] container.volume_mounts = [ kubernetes.client.V1VolumeMount(name="source", mount_path="/source") ] body.spec = kubernetes.client.V1PodSpec(containers=[container]) # fail on first error body.spec.restart_policy = "Never" # source files as a secret volume # we copy these files to the workdir before executing Snakemake too_large = [ path for path in self.secret_files.values() if os.path.getsize(path) > 1000000 ] if too_large: raise WorkflowError( "The following source files exceed the maximum " "file size (1MB) that can be passed from host to " "kubernetes. These are likely not source code " "files. Consider adding them to your " "remote storage instead or (if software) use " "Conda packages or container images:\n{}".format("\n".join(too_large)) ) secret_volume = kubernetes.client.V1Volume(name="source") secret_volume.secret = kubernetes.client.V1SecretVolumeSource() secret_volume.secret.secret_name = self.run_namespace secret_volume.secret.items = [ kubernetes.client.V1KeyToPath(key=key, path=path) for key, path in self.secret_files.items() ] # workdir as an emptyDir volume of undefined size workdir_volume = kubernetes.client.V1Volume(name="workdir") workdir_volume.empty_dir = kubernetes.client.V1EmptyDirVolumeSource() body.spec.volumes = [secret_volume, workdir_volume] # env vars container.env = [] for key, e in self.secret_envvars.items(): envvar = kubernetes.client.V1EnvVar(name=e) envvar.value_from = kubernetes.client.V1EnvVarSource() envvar.value_from.secret_key_ref = kubernetes.client.V1SecretKeySelector( key=key, name=self.run_namespace ) container.env.append(envvar) # request resources container.resources = kubernetes.client.V1ResourceRequirements() container.resources.requests = {} container.resources.requests["cpu"] = job.resources["_cores"] if "mem_mb" in job.resources.keys(): container.resources.requests["memory"] = "{}M".format( job.resources["mem_mb"] ) # capabilities if job.needs_singularity and self.workflow.use_singularity: # TODO this should work, but it doesn't currently because of # missing loop devices # singularity inside docker requires SYS_ADMIN capabilities # see https://groups.google.com/a/lbl.gov/forum/#!topic/singularity/e9mlDuzKowc # container.capabilities = kubernetes.client.V1Capabilities() # container.capabilities.add = ["SYS_ADMIN", # "DAC_OVERRIDE", # "SETUID", # "SETGID", # "SYS_CHROOT"] # Running in priviledged mode always works container.security_context = kubernetes.client.V1SecurityContext( privileged=True ) pod = self._kubernetes_retry( lambda: self.kubeapi.create_namespaced_pod(self.namespace, body) ) logger.info( "Get status with:\n" "kubectl describe pod {jobid}\n" "kubectl logs {jobid}".format(jobid=jobid) ) self.active_jobs.append( KubernetesJob(job, jobid, callback, error_callback, pod, None) )
# Sometimes, certain k8s requests throw kubernetes.client.rest.ApiException # Solving this issue requires reauthentication, as _kubernetes_retry shows # However, reauthentication itself, under rare conditions, may also throw # errors such as: # kubernetes.client.exceptions.ApiException: (409), Reason: Conflict # # This error doesn't mean anything wrong with the k8s cluster, and users can safely # ignore it. def _reauthenticate_and_retry(self, func=None): import kubernetes # Unauthorized. # Reload config in order to ensure token is # refreshed. Then try again. logger.info("Trying to reauthenticate") kubernetes.config.load_kube_config() subprocess.run(["kubectl", "get", "nodes"]) self.kubeapi = kubernetes.client.CoreV1Api() self.batchapi = kubernetes.client.BatchV1Api() try: self.register_secret() except kubernetes.client.rest.ApiException as e: if e.status == 409 and e.reason == "Conflict": logger.warning("409 conflict ApiException when registering secrets") logger.warning(e) else: raise WorkflowError( e, "This is likely a bug in " "https://github.com/kubernetes-client/python.", ) if func: return func() def _kubernetes_retry(self, func): import kubernetes import urllib3 with self.lock: try: return func() except kubernetes.client.rest.ApiException as e: if e.status == 401: # Unauthorized. # Reload config in order to ensure token is # refreshed. Then try again. return self._reauthenticate_and_retry(func) # Handling timeout that may occur in case of GKE master upgrade except urllib3.exceptions.MaxRetryError as e: logger.info( "Request time out! " "check your connection to Kubernetes master" "Workflow will pause for 5 minutes to allow any update operations to complete" ) time.sleep(300) try: return func() except: # Still can't reach the server after 5 minutes raise WorkflowError( e, "Error 111 connection timeout, please check" " that the k8 cluster master is reachable!", ) def _wait_for_jobs(self): import kubernetes while True: with self.lock: if not self.wait: return active_jobs = self.active_jobs self.active_jobs = list() still_running = list() for j in active_jobs: with self.status_rate_limiter: logger.debug("Checking status for pod {}".format(j.jobid)) job_not_found = False try: res = self._kubernetes_retry( lambda: self.kubeapi.read_namespaced_pod_status( j.jobid, self.namespace ) ) except kubernetes.client.rest.ApiException as e: if e.status == 404: # Jobid not found # The job is likely already done and was deleted on # the server. j.callback(j.job) continue except WorkflowError as e: print_exception(e, self.workflow.linemaps) j.error_callback(j.job) continue if res is None: msg = ( "Unknown pod {jobid}. " "Has the pod been deleted " "manually?" ).format(jobid=j.jobid) self.print_job_error(j.job, msg=msg, jobid=j.jobid) j.error_callback(j.job) elif res.status.phase == "Failed": msg = ( "For details, please issue:\n" "kubectl describe pod {jobid}\n" "kubectl logs {jobid}" ).format(jobid=j.jobid) # failed self.print_job_error(j.job, msg=msg, jobid=j.jobid) j.error_callback(j.job) elif res.status.phase == "Succeeded": # finished j.callback(j.job) func = lambda: self.safe_delete_pod( j.jobid, ignore_not_found=True ) self._kubernetes_retry(func) else: # still active still_running.append(j) with self.lock: self.active_jobs.extend(still_running) sleep()
TibannaJob = namedtuple( "TibannaJob", "job jobname jobid exec_arn callback error_callback" )
[docs]class TibannaExecutor(ClusterExecutor): def __init__( self, workflow, dag, cores, tibanna_sfn, precommand="", tibanna_config=False, container_image=None, printreason=False, quiet=False, printshellcmds=False, latency_wait=3, local_input=None, restart_times=None, max_status_checks_per_second=1, keepincomplete=False, keepmetadata=True, ): self.workflow = workflow self.workflow_sources = [] for wfs in workflow.get_sources(): if os.path.isdir(wfs): for (dirpath, dirnames, filenames) in os.walk(wfs): self.workflow_sources.extend( [os.path.join(dirpath, f) for f in filenames] ) else: self.workflow_sources.append(os.path.abspath(wfs)) log = "sources=" for f in self.workflow_sources: log += f logger.debug(log) self.snakefile = workflow.snakefile self.envvars = {e: os.environ[e] for e in workflow.envvars} if self.envvars: logger.debug("envvars = %s" % str(self.envvars)) self.tibanna_sfn = tibanna_sfn if precommand: self.precommand = precommand else: self.precommand = "" self.s3_bucket = workflow.default_remote_prefix.split("/")[0] self.s3_subdir = re.sub( "^{}/".format(self.s3_bucket), "", workflow.default_remote_prefix ) logger.debug("precommand= " + self.precommand) logger.debug("bucket=" + self.s3_bucket) logger.debug("subdir=" + self.s3_subdir) self.quiet = quiet exec_job = ( "snakemake {target} --snakefile {snakefile} " "--force -j{cores} --keep-target-files --keep-remote " "--latency-wait 0 --scheduler {workflow.scheduler_type} " "--attempt 1 {use_threads} --max-inventory-time 0 " "{overwrite_config} {rules} --nocolor " "--notemp --no-hooks --nolock " ) super().__init__( workflow, dag, cores, printreason=printreason, quiet=quiet, printshellcmds=printshellcmds, latency_wait=latency_wait, local_input=local_input, restart_times=restart_times, exec_job=exec_job, assume_shared_fs=False, max_status_checks_per_second=max_status_checks_per_second, disable_default_remote_provider_args=True, disable_get_default_resources_args=True, ) self.container_image = container_image or get_container_image() self.tibanna_config = tibanna_config
[docs] def shutdown(self): # perform additional steps on shutdown if necessary logger.debug("shutting down Tibanna executor") super().shutdown()
[docs] def cancel(self): from tibanna.core import API for j in self.active_jobs: logger.info("killing job {}".format(j.jobname)) while True: try: res = API().kill(j.exec_arn) if not self.quiet: print(res) break except KeyboardInterrupt: pass self.shutdown()
[docs] def split_filename(self, filename, checkdir=None): f = os.path.abspath(filename) if checkdir: checkdir = checkdir.rstrip("/") if f.startswith(checkdir): fname = re.sub("^{}/".format(checkdir), "", f) fdir = checkdir else: direrrmsg = ( "All source files including Snakefile, " + "conda env files, and rule script files " + "must be in the same working directory: {} vs {}" ) raise WorkflowError(direrrmsg.format(checkdir, f)) else: fdir, fname = os.path.split(f) return fname, fdir
[docs] def remove_prefix(self, s): return re.sub("^{}/{}/".format(self.s3_bucket, self.s3_subdir), "", s)
[docs] def handle_remote(self, target): if isinstance(target, _IOFile) and target.remote_object.provider.is_default: return self.remove_prefix(target) else: return target
[docs] def add_command(self, job, tibanna_args, tibanna_config): # snakefile, with file name remapped snakefile_fname = tibanna_args.snakemake_main_filename # targets, with file name remapped targets = job.get_targets() if not isinstance(targets, list): targets = [targets] targets_default = " ".join([self.handle_remote(t) for t in targets]) # use_threads use_threads = "--force-use-threads" if not job.is_group() else "" # format command command = self.format_job_pattern( self.exec_job, job, target=targets_default, snakefile=snakefile_fname, use_threads=use_threads, cores=tibanna_config["cpu"], ) if self.precommand: command = self.precommand + "; " + command logger.debug("command = " + str(command)) tibanna_args.command = command
[docs] def add_workflow_files(self, job, tibanna_args): snakefile_fname, snakemake_dir = self.split_filename(self.snakefile) snakemake_child_fnames = [] for src in self.workflow_sources: src_fname, _ = self.split_filename(src, snakemake_dir) if src_fname != snakefile_fname: # redundant snakemake_child_fnames.append(src_fname) # change path for config files self.workflow.overwrite_configfiles = [ self.split_filename(cf, snakemake_dir)[0] for cf in self.workflow.overwrite_configfiles ] tibanna_args.snakemake_directory_local = snakemake_dir tibanna_args.snakemake_main_filename = snakefile_fname tibanna_args.snakemake_child_filenames = list(set(snakemake_child_fnames))
[docs] def adjust_filepath(self, f): if not hasattr(f, "remote_object"): rel = self.remove_prefix(f) # log/benchmark elif ( hasattr(f.remote_object, "provider") and f.remote_object.provider.is_default ): rel = self.remove_prefix(f) else: rel = f return rel
[docs] def make_tibanna_input(self, job): from tibanna import ec2_utils, core as tibanna_core # input & output # Local snakemake command here must be run with --default-remote-prefix # and --default-remote-provider (forced) but on VM these options will be removed. # The snakemake on the VM will consider these input and output as not remote. # They files are transferred to the container by Tibanna before running snakemake. # In short, the paths on VM must be consistent with what's in Snakefile. # but the actual location of the files is on the S3 bucket/prefix. # This mapping info must be passed to Tibanna. for i in job.input: logger.debug("job input " + str(i)) logger.debug("job input is remote= " + ("true" if i.is_remote else "false")) if hasattr(i.remote_object, "provider"): logger.debug( " is remote default= " + ("true" if i.remote_object.provider.is_default else "false") ) for o in job.expanded_output: logger.debug("job output " + str(o)) logger.debug( "job output is remote= " + ("true" if o.is_remote else "false") ) if hasattr(o.remote_object, "provider"): logger.debug( " is remote default= " + ("true" if o.remote_object.provider.is_default else "false") ) file_prefix = ( "file:///data1/snakemake" # working dir inside snakemake container on VM ) input_source = dict() for ip in job.input: ip_rel = self.adjust_filepath(ip) input_source[os.path.join(file_prefix, ip_rel)] = "s3://" + ip output_target = dict() output_all = [eo for eo in job.expanded_output] if job.log: if isinstance(job.log, list): output_all.extend([str(_) for _ in job.log]) else: output_all.append(str(job.log)) if hasattr(job, "benchmark") and job.benchmark: if isinstance(job.benchmark, list): output_all.extend([str(_) for _ in job.benchmark]) else: output_all.append(str(job.benchmark)) for op in output_all: op_rel = self.adjust_filepath(op) output_target[os.path.join(file_prefix, op_rel)] = "s3://" + op # mem & cpu mem = job.resources["mem_mb"] / 1024 if "mem_mb" in job.resources.keys() else 1 cpu = job.threads # jobid, grouping, run_name jobid = tibanna_core.create_jobid() if job.is_group(): run_name = "snakemake-job-%s-group-%s" % (str(jobid), str(job.groupid)) else: run_name = "snakemake-job-%s-rule-%s" % (str(jobid), str(job.rule)) # tibanna input tibanna_config = { "run_name": run_name, "mem": mem, "cpu": cpu, "ebs_size": math.ceil(job.resources["disk_mb"] / 1024), "log_bucket": self.s3_bucket, } logger.debug("additional tibanna config: " + str(self.tibanna_config)) if self.tibanna_config: tibanna_config.update(self.tibanna_config) tibanna_args = ec2_utils.Args( output_S3_bucket=self.s3_bucket, language="snakemake", container_image=self.container_image, input_files=input_source, output_target=output_target, input_env=self.envvars, ) self.add_workflow_files(job, tibanna_args) self.add_command(job, tibanna_args, tibanna_config) tibanna_input = { "jobid": jobid, "config": tibanna_config, "args": tibanna_args.as_dict(), } logger.debug(json.dumps(tibanna_input, indent=4)) return tibanna_input
[docs] def run(self, job, callback=None, submit_callback=None, error_callback=None): logger.info("running job using Tibanna...") from tibanna.core import API super()._run(job) # submit job here, and obtain job ids from the backend tibanna_input = self.make_tibanna_input(job) jobid = tibanna_input["jobid"] exec_info = API().run_workflow( tibanna_input, sfn=self.tibanna_sfn, verbose=not self.quiet, jobid=jobid, sleep=0, ) exec_arn = exec_info.get("_tibanna", {}).get("exec_arn", "") jobname = tibanna_input["config"]["run_name"] jobid = tibanna_input["jobid"] # register job as active, using your own namedtuple. # The namedtuple must at least contain the attributes # job, jobid, callback, error_callback. self.active_jobs.append( TibannaJob(job, jobname, jobid, exec_arn, callback, error_callback) )
def _wait_for_jobs(self): # busy wait on job completion # This is only needed if your backend does not allow to use callbacks # for obtaining job status. from tibanna.core import API while True: # always use self.lock to avoid race conditions with self.lock: if not self.wait: return active_jobs = self.active_jobs self.active_jobs = list() still_running = list() for j in active_jobs: # use self.status_rate_limiter to avoid too many API calls. with self.status_rate_limiter: if j.exec_arn: status = API().check_status(j.exec_arn) else: status = "FAILED_AT_SUBMISSION" if not self.quiet or status != "RUNNING": logger.debug("job %s: %s" % (j.jobname, status)) if status == "RUNNING": still_running.append(j) elif status == "SUCCEEDED": j.callback(j.job) else: j.error_callback(j.job) with self.lock: self.active_jobs.extend(still_running) sleep()
[docs]def run_wrapper( job_rule, input, output, params, wildcards, threads, resources, log, benchmark, benchmark_repeats, conda_env, container_img, singularity_args, env_modules, use_singularity, linemaps, debug, cleanup_scripts, shadow_dir, jobid, edit_notebook, ): """ Wrapper around the run method that handles exceptions and benchmarking. Arguments job_rule -- the ``job.rule`` member input -- list of input files output -- list of output files wildcards -- so far processed wildcards threads -- usable threads log -- list of log files shadow_dir -- optional shadow directory root """ # get shortcuts to job_rule members run = job_rule.run_func version = job_rule.version rule = job_rule.name is_shell = job_rule.shellcmd is not None if os.name == "posix" and debug: sys.stdin = open("/dev/stdin") if benchmark is not None: from snakemake.benchmark import ( BenchmarkRecord, benchmarked, write_benchmark_records, ) # Change workdir if shadow defined and not using singularity. # Otherwise, we do the change from inside the container. passed_shadow_dir = None if use_singularity and container_img: passed_shadow_dir = shadow_dir shadow_dir = None try: with change_working_directory(shadow_dir): if benchmark: bench_records = [] for bench_iteration in range(benchmark_repeats): # Determine whether to benchmark this process or do not # benchmarking at all. We benchmark this process unless the # execution is done through the ``shell:``, ``script:``, or # ``wrapper:`` stanza. is_sub = ( job_rule.shellcmd or job_rule.script or job_rule.wrapper or job_rule.cwl ) if is_sub: # The benchmarking through ``benchmarked()`` is started # in the execution of the shell fragment, script, wrapper # etc, as the child PID is available there. bench_record = BenchmarkRecord() run( input, output, params, wildcards, threads, resources, log, version, rule, conda_env, container_img, singularity_args, use_singularity, env_modules, bench_record, jobid, is_shell, bench_iteration, cleanup_scripts, passed_shadow_dir, edit_notebook, ) else: # The benchmarking is started here as we have a run section # and the generated Python function is executed in this # process' thread. with benchmarked() as bench_record: run( input, output, params, wildcards, threads, resources, log, version, rule, conda_env, container_img, singularity_args, use_singularity, env_modules, bench_record, jobid, is_shell, bench_iteration, cleanup_scripts, passed_shadow_dir, edit_notebook, ) # Store benchmark record for this iteration bench_records.append(bench_record) else: run( input, output, params, wildcards, threads, resources, log, version, rule, conda_env, container_img, singularity_args, use_singularity, env_modules, None, jobid, is_shell, None, cleanup_scripts, passed_shadow_dir, edit_notebook, ) except (KeyboardInterrupt, SystemExit) as e: # Re-raise the keyboard interrupt in order to record an error in the # scheduler but ignore it raise e except (Exception, BaseException) as ex: log_verbose_traceback(ex) # this ensures that exception can be re-raised in the parent thread lineno, file = get_exception_origin(ex, linemaps) raise RuleException( format_error( ex, lineno, linemaps=linemaps, snakefile=file, show_traceback=True ) ) if benchmark is not None: try: write_benchmark_records(bench_records, benchmark) except (Exception, BaseException) as ex: raise WorkflowError(ex)