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Source code for galaxy.jobs

Support for running a tool in Galaxy via an internal job management system
import copy
import datetime
import errno
import json
import logging
import os
import pwd
import shlex
import shutil
import string
import sys
import time
import traceback
from abc import (
from json import loads

import packaging.version
import six
import yaml
from pulsar.client.staging import COMMAND_VERSION_FILENAME

import galaxy
from galaxy import (
from galaxy.datatypes import sniff
from galaxy.exceptions import (
from galaxy.job_execution.datasets import (
from galaxy.job_execution.output_collect import collect_extra_files
from galaxy.job_execution.setup import ensure_configs_directory
from galaxy.jobs.actions.post import ActionBox
from galaxy.jobs.mapper import (
from galaxy.jobs.runners import BaseJobRunner, JobState
from galaxy.metadata import get_metadata_compute_strategy
from galaxy.model import store
from galaxy.objectstore import ObjectStorePopulator
from galaxy.tool_util.deps import requirements
from galaxy.tool_util.output_checker import (
from galaxy.util import (
from galaxy.util.bunch import Bunch
from galaxy.util.expressions import ExpressionContext
from galaxy.util.xml_macros import load
from galaxy.web_stack.handlers import ConfiguresHandlers

log = logging.getLogger(__name__)

# Legacy definition - this is read by certain misbehaving tool wrappers
# that import Galaxy internals - but it shouldn't be used in Galaxy's code
# itself.
TOOL_PROVIDED_JOB_METADATA_KEYS = ['name', 'info', 'dbkey', 'created_from_basename']

# Override with config.default_job_shell.
DEFAULT_JOB_SHELL = '/bin/bash'


[docs]class JobDestination(Bunch): """ Provides details about where a job runs """
[docs] def __init__(self, **kwds): self['id'] = None self['url'] = None self['tags'] = None self['runner'] = None self['legacy'] = False self['converted'] = False self['shell'] = None self['env'] = [] self['resubmit'] = [] # dict is appropriate (rather than a bunch) since keys may not be valid as attributes self['params'] = dict() # Use the values persisted in an existing job if 'from_job' in kwds and kwds['from_job'].destination_id is not None: self['id'] = kwds['from_job'].destination_id self['params'] = kwds['from_job'].destination_params super(JobDestination, self).__init__(**kwds)
[docs]class JobToolConfiguration(Bunch): """ Provides details on what handler and destination a tool should use A JobToolConfiguration will have the required attribute 'id' and optional attributes 'handler', 'destination', and 'params' """
[docs] def __init__(self, **kwds): self['handler'] = None self['destination'] = None self['params'] = dict() super(JobToolConfiguration, self).__init__(**kwds)
[docs] def get_resource_group(self): return self.get("resources", None)
[docs]def config_exception(e, file): abs_path = os.path.abspath(file) message = 'Problem parsing the XML in file %s, ' % abs_path message += 'please correct the indicated portion of the file and restart Galaxy. ' message += unicodify(e) log.exception(message) return Exception(message)
[docs]def job_config_xml_to_dict(config, root): config_dict = {} runners = {} config_dict["runners"] = runners # Parser plugins section populate 'runners' and 'dynamic' in config_dict. plugins = root.find('plugins') if plugins is not None: for plugin in ConfiguresHandlers._findall_with_required(plugins, 'plugin', ('id', 'type', 'load')): if plugin.get('type') == 'runner': workers = plugin.get('workers', plugins.get('workers', JobConfiguration.DEFAULT_NWORKERS)) runner_kwds = JobConfiguration.get_params(config, plugin) plugin_id = plugin.get('id') runner_info = dict(id=plugin_id, load=plugin.get('load'), workers=int(workers), kwds=runner_kwds) runners[plugin_id] = runner_info else: log.error('Unknown plugin type: %s' % plugin.get('type')) for plugin in ConfiguresHandlers._findall_with_required(plugins, 'plugin', ('id', 'type')): if plugin.get('id') == 'dynamic' and plugin.get('type') == 'runner': config_dict["dynamic"] = JobConfiguration.get_params(config, plugin) handling_config_dict = ConfiguresHandlers.xml_to_dict(config, root.find("handlers")) config_dict["handling"] = handling_config_dict # Parse destinations environments = [] destinations = root.find('destinations') for destination in ConfiguresHandlers._findall_with_required(destinations, 'destination', ('id', 'runner')): destination_id = destination.get('id') destination_metrics = destination.get("metrics", None) environment = {"id": destination_id} metrics_to_dict = {"src": "default"} if destination_metrics: if not util.asbool(destination_metrics): metrics_to_dict = {"src": "disabled"} else: metrics_to_dict = {"src": "path", "path": destination_metrics} else: metrics_elements = ConfiguresHandlers._findall_with_required(destination, 'job_metrics', ()) if metrics_elements: metrics_to_dict = {"src": "xml_element", 'xml_element': metrics_elements[0]} environment["metrics"] = metrics_to_dict params = JobConfiguration.get_params(config, destination) # Handle legacy XML enabling sudo when using docker by default. if "docker_sudo" not in params: params["docker_sudo"] = "true" # TODO: handle enabled/disabled in configure_from environment['params'] = params environment['env'] = JobConfiguration.get_envs(destination) destination_resubmits = JobConfiguration.get_resubmits(destination) if destination_resubmits: environment['resubmit'] = destination_resubmits # TODO: handle empty resubmits defaults in configure_from runner = destination.get('runner') if runner: environment['runner'] = runner tags = destination.get('tags') # Store tags as a list if tags is not None: tags = [x.strip() for x in tags.split(',')] environment['tags'] = tags environments.append(environment) config_dict['execution'] = { 'environments': environments, } default_destination = ConfiguresHandlers.get_xml_default(config, destinations) if default_destination: config_dict['execution']['default'] = default_destination resources_config_dict = {} resource_groups = {} # Parse resources... resources = root.find('resources') if resources is not None: default_resource_group = resources.get("default", None) if default_resource_group: resources_config_dict["default"] = default_resource_group for group in ConfiguresHandlers._findall_with_required(resources, 'group'): group_id = group.get('id') fields_str = group.get('fields', None) or group.text or '' fields = [f for f in fields_str.split(",") if f] resource_groups[group_id] = fields resources_config_dict["groups"] = resource_groups config_dict["resources"] = resources_config_dict # Parse tool mappings tools = root.find('tools') config_dict['tools'] = [] if tools is not None: for tool in ConfiguresHandlers._findall_with_required(tools, 'tool'): # There can be multiple definitions with identical ids, but different params tool_mapping_conf = {} for key in ['handler', 'destination', 'id', 'resources']: value = tool.get(key) if value: if key == "destination": key = "environment" tool_mapping_conf[key] = value tool_mapping_conf["params"] = JobConfiguration.get_params(config, tool) config_dict['tools'].append(tool_mapping_conf) limits_config = [] limits = root.find('limits') if limits is not None: for limit in JobConfiguration._findall_with_required(limits, 'limit', ('type',)): limit_dict = {} for key in ['type', 'tag', 'id', 'window']: if key == 'type' and key.startswith('destination_'): key = 'environment_%s' % key[len("destination_"):] value = limit.get(key) if value: limit_dict[key] = value limit_dict['value'] = limit.text limits_config.append(limit_dict) config_dict['limits'] = limits_config return config_dict
[docs]class JobConfiguration(ConfiguresHandlers): """A parser and interface to advanced job management features. These features are configured in the job configuration, by default, ``job_conf.xml`` """ DEFAULT_BASE_HANDLER_POOLS = ('job-handlers',) DEFAULT_NWORKERS = 4 JOB_RESOURCE_CONDITIONAL_XML = """<conditional name="__job_resource"> <param name="__job_resource__select" type="select" label="Job Resource Parameters"> <option value="no">Use default job resource parameters</option> <option value="yes">Specify job resource parameters</option> </param> <when value="no"/> <when value="yes"/> </conditional>"""
[docs] def __init__(self, app): """Parse the job configuration XML. """ self.app = app self.runner_plugins = [] self.dynamic_params = None self.handlers = {} self.handler_runner_plugins = {} self.default_handler_id = None self.handler_assignment_methods = None self.handler_assignment_methods_configured = False self.handler_max_grab = None self.destinations = {} self.destination_tags = {} self.default_destination_id = None self.tools = {} self.resource_groups = {} self.default_resource_group = None self.resource_parameters = {} self.limits = Bunch(registered_user_concurrent_jobs=None, anonymous_user_concurrent_jobs=None, walltime=None, walltime_delta=None, total_walltime={}, output_size=None, destination_user_concurrent_jobs={}, destination_total_concurrent_jobs={}) default_resubmits = [] default_resubmit_condition = self.app.config.default_job_resubmission_condition if default_resubmit_condition: default_resubmits.append(dict( environment=None, condition=default_resubmit_condition, handler=None, delay=None, )) self.default_resubmits = default_resubmits self.__parse_resource_parameters() # Initialize the config try: if 'job_config' in self.app.config.config_dict: job_config_dict = self.app.config.config_dict["job_config"] else: job_config_file = self.app.config.job_config_file if '.xml' in job_config_file: tree = load(job_config_file) job_config_dict = self.__parse_job_conf_xml(tree) else: with open(job_config_file, "r") as f: job_config_dict = yaml.safe_load(f) # Load tasks if configured if self.app.config.use_tasked_jobs: job_config_dict["runners"]["tasks"] = dict(id='tasks', load='tasks', workers=self.app.config.local_task_queue_workers, kwds={}) self._configure_from_dict(job_config_dict) log.debug('Done loading job configuration') except IOError: log.warning('Job configuration "%s" does not exist, using default job configuration', self.app.config.job_config_file) self.__set_default_job_conf() except Exception as e: raise config_exception(e, job_config_file)
def _configure_from_dict(self, job_config_dict): for runner_id, runner_info in job_config_dict["runners"].items(): if "kwds" not in runner_info: # convert all 'extra' parameters into kwds, allows defining a runner # with a flat dictionary. kwds = {} for key, value in runner_info.items(): if key in ['id', 'load', 'workers']: continue kwds[key] = value runner_info["kwds"] = kwds if not self.__is_enabled(runner_info.get("kwds")): continue runner_info["id"] = runner_id if runner_id == "dynamic": log.warning('Deprecated treatment of dynamic running configuration as an actual job runner.') self.dynamic_params = runner_info["kwds"] continue self.runner_plugins.append(runner_info) if "dynamic" in job_config_dict: self.dynamic_params = job_config_dict.get("dynamic", None) # Parse handlers handling_config_dict = job_config_dict.get("handling", {}) self._init_handler_assignment_methods(handling_config_dict) self._init_handlers(handling_config_dict) if not self.handler_assignment_methods_configured: self._set_default_handler_assignment_methods() else: self.app.application_stack.init_job_handling(self) log.info("Job handler assignment methods set to: %s", ', '.join(self.handler_assignment_methods)) for tag, handlers in [(t, h) for t, h in self.handlers.items() if isinstance(h, list)]: log.info("Tag [%s] handlers: %s", tag, ', '.join(handlers)) # Parse environments job_metrics = self.app.job_metrics execution_dict = job_config_dict.get('execution', {}) environments = execution_dict.get("environments", []) enviroment_iter = map(lambda e: (e["id"], e), environments) if isinstance(environments, list) else environments.items() for environment_id, environment_dict in enviroment_iter: metrics = environment_dict.get("metrics") if metrics is None: metrics = {"src": "default"} if isinstance(metrics, list): job_metrics.set_destination_conf_dicts(environment_id, metrics) else: metrics_src = metrics.get("src") or "default" if metrics_src != "default": # customized metrics for this environment. if metrics_src == "disabled": job_metrics.set_destination_instrumenter(environment_id, None) elif metrics_src == "xml_element": metrics_element = metrics.get("xml_element") job_metrics.set_destination_conf_element(environment_id, metrics_element) elif metrics_src == "path": metrics_conf_path = self.app.config.resolve_path(metrics.get("path")) job_metrics.set_destination_conf_file(environment_id, metrics_conf_path) destination_kwds = {} params = environment_dict.get("params") if params is None: # Treat the excess keys in the environment as the destination parameters # allowing a flat configuration of these things. params = {} for key, value in environment_dict.items(): if key in ['id', 'tags', 'runner', 'shell', 'env', 'resubmit']: continue params[key] = value environment_dict["params"] = params for key in ['tags', 'runner', 'shell', 'env', 'resubmit', 'params']: if key in environment_dict: destination_kwds[key] = environment_dict[key] destination_kwds["id"] = environment_id job_destination = JobDestination(**destination_kwds) if not self.__is_enabled(job_destination.params): continue if not job_destination.resubmit: resubmits = self.default_resubmits job_destination.resubmit = resubmits self.destinations[environment_id] = (job_destination,) if job_destination.tags is not None: for tag in job_destination.tags: if tag not in self.destinations: self.destinations[tag] = [] self.destinations[tag].append(job_destination) # Determine the default destination self.default_destination_id = self._ensure_default_set(execution_dict.get("default"), list(self.destinations.keys()), auto=True) # Read in resources resources = job_config_dict.get("resources", {}) self.default_resource_group = resources.get("default", None) for group_id, fields in resources.get("groups", {}).items(): self.resource_groups[group_id] = fields tools = job_config_dict.get('tools', []) for tool in tools: tool_id = tool.get('id').lower().rstrip('/') if tool_id not in self.tools: self.tools[tool_id] = list() params = tool.get("params") if params is None: params = {} for key, value in tool.items(): if key in ["environment", "handler", "id"]: continue params[key] = value tool["params"] = params if "environment" in tool: tool["destination"] = tool.pop("environment") self.tools[tool_id].append(JobToolConfiguration(**dict(tool.items()))) types = dict(registered_user_concurrent_jobs=int, anonymous_user_concurrent_jobs=int, walltime=str, total_walltime=str, output_size=util.size_to_bytes) # Parse job limits for limit_dict in job_config_dict.get("limits", []): limit_type = limit_dict.get('type') if limit_type.startswith("environment_"): limit_type = 'destination_%s' % limit_type[len("environment_"):] limit_value = limit_dict.get("value") # concurrent_jobs renamed to destination_user_concurrent_jobs in job_conf.xml if limit_type in ('destination_user_concurrent_jobs', 'concurrent_jobs', 'destination_total_concurrent_jobs'): id = limit_dict.get('tag', None) or limit_dict.get('id') if limit_type == 'destination_total_concurrent_jobs': self.limits.destination_total_concurrent_jobs[id] = int(limit_value) else: self.limits.destination_user_concurrent_jobs[id] = int(limit_value) elif limit_type == 'total_walltime': self.limits.total_walltime["window"] = ( int(limit_dict.get('window')) or 30 ) self.limits.total_walltime["raw"] = ( types.get(limit_type, str)(limit_value) ) elif limit_value: self.limits.__dict__[limit_type] = types.get(limit_type, str)(limit_value) if self.limits.walltime is not None: h, m, s = [int(v) for v in self.limits.walltime.split(':')] self.limits.walltime_delta = datetime.timedelta(0, s, 0, 0, m, h) if "raw" in self.limits.total_walltime: h, m, s = [int(v) for v in self.limits.total_walltime["raw"].split(':')] self.limits.total_walltime["delta"] = datetime.timedelta( 0, s, 0, 0, m, h ) def __parse_job_conf_xml(self, tree): """Loads the new-style job configuration from options in the job config file (by default, job_conf.xml). :param tree: Object representing the root ``<job_conf>`` object in the job config file. :type tree: ``lxml.etree._Element`` """ root = tree.getroot() log.debug('Loading job configuration from %s' % self.app.config.job_config_file) job_config_dict = job_config_xml_to_dict(self.app.config, root) return job_config_dict def _parse_handler(self, handler_id, process_dict): for plugin_id in process_dict.get("plugins") or []: if handler_id not in self.handler_runner_plugins: self.handler_runner_plugins[handler_id] = [] self.handler_runner_plugins[handler_id].append(plugin_id) def __set_default_job_conf(self): # Run jobs locally self.runner_plugins = [dict(id='local', load='local', workers=DEFAULT_LOCAL_WORKERS)] # Load tasks if configured if self.app.config.use_tasked_jobs: self.runner_plugins.append(dict(id='tasks', load='tasks', workers=DEFAULT_LOCAL_WORKERS)) # Set the handlers self._init_handler_assignment_methods() if not self.handler_assignment_methods_configured: self._set_default_handler_assignment_methods() else: self.app.application_stack.init_job_handling(self) # Set the destination self.default_destination_id = 'local' self.destinations['local'] = [JobDestination(id='local', runner='local')] log.debug('Done loading job configuration')
[docs] def get_tool_resource_xml(self, tool_id, tool_type): """ Given a tool id, return XML elements describing parameters to insert into job resources. :tool id: A tool ID (a string) :tool type: A tool type (a string) :returns: List of parameter elements. """ if tool_id and tool_type in ('default', 'manage_data'): # TODO: Only works with exact matches, should handle different kinds of ids # the way destination lookup does. resource_group = None if tool_id in self.tools: resource_group = self.tools[tool_id][0].get_resource_group() resource_group = resource_group or self.default_resource_group if resource_group and resource_group in self.resource_groups: fields_names = self.resource_groups[resource_group] fields = [self.resource_parameters[n] for n in fields_names] if fields: conditional_element = parse_xml_string(self.JOB_RESOURCE_CONDITIONAL_XML) when_yes_elem = conditional_element.findall('when')[1] for parameter in fields: when_yes_elem.append(parameter) return conditional_element
def __parse_resource_parameters(self): self.resource_parameters = util.parse_resource_parameters(self.app.config.job_resource_params_file)
[docs] @staticmethod def get_params(config, parent): rval = {} for param in parent.findall('param'): key = param.get('id') if key in ["container", "container_override"]: containers = map(requirements.container_from_element, param.findall('container')) param_value = list(map(lambda c: c.to_dict(), containers)) else: param_value = param.text if 'from_environ' in param.attrib: environ_var = param.attrib['from_environ'] param_value = os.environ.get(environ_var, param_value) elif 'from_config' in param.attrib: config_val = param.attrib['from_config'] param_value = config.config_dict.get(config_val, param_value) rval[key] = param_value return rval
def __get_params(self, parent): """Parses any child <param> tags in to a dictionary suitable for persistence. :param parent: Parent element in which to find child <param> tags. :type parent: ``lxml.etree._Element`` :returns: dict """ return JobConfiguration.get_params(self.app.config, parent)
[docs] @staticmethod def get_envs(parent): """Parses any child <env> tags in to a dictionary suitable for persistence. :param parent: Parent element in which to find child <env> tags. :type parent: ``lxml.etree._Element`` :returns: dict """ rval = [] for param in parent.findall('env'): rval.append(dict( name=param.get('id'), file=param.get('file'), execute=param.get('exec'), value=param.text, raw=util.asbool(param.get('raw', 'false')) )) return rval
[docs] @staticmethod def get_resubmits(parent): """Parses any child <resubmit> tags in to a dictionary suitable for persistence. :param parent: Parent element in which to find child <resubmit> tags. :type parent: ``lxml.etree._Element`` :returns: dict """ rval = [] for resubmit in parent.findall('resubmit'): rval.append(dict( condition=resubmit.get('condition'), environment=resubmit.get('destination'), handler=resubmit.get('handler'), delay=resubmit.get('delay'), )) return rval
def __is_enabled(self, params): """Check for an enabled parameter - pop it out - and return as boolean.""" enabled = True if "enabled" in (params or {}): raw_enabled = params.pop("enabled") enabled = util.asbool(raw_enabled) return enabled @property def default_job_tool_configuration(self): """ The default JobToolConfiguration, used if a tool does not have an explicit defintion in the configuration. It consists of a reference to the default handler and default destination. :returns: JobToolConfiguration -- a representation of a <tool> element that uses the default handler and destination """ return JobToolConfiguration(id='_default_', handler=self.default_handler_id, destination=self.default_destination_id) # Called upon instantiation of a Tool object
[docs] def get_job_tool_configurations(self, ids): """ Get all configured JobToolConfigurations for a tool ID, or, if given a list of IDs, the JobToolConfigurations for the first id in ``ids`` matching a tool definition. .. note:: You should not mix tool shed tool IDs, versionless tool shed IDs, and tool config tool IDs that refer to the same tool. :param ids: Tool ID or IDs to fetch the JobToolConfiguration of. :type ids: list or str. :returns: list -- JobToolConfiguration Bunches representing <tool> elements matching the specified ID(s). Example tool ID strings include: * Full tool shed id: ``toolshed.example.org/repos/nate/filter_tool_repo/filter_tool/1.0.0`` * Tool shed id less version: ``toolshed.example.org/repos/nate/filter_tool_repo/filter_tool`` * Tool config tool id: ``filter_tool`` """ rval = [] # listify if ids is a single (string) id ids = util.listify(ids) for id in ids: if id in self.tools: # If a tool has definitions that include job params but not a # definition for jobs without params, include the default # config for job_tool_configuration in self.tools[id]: if not job_tool_configuration.params: break else: rval.append(self.default_job_tool_configuration) rval.extend(self.tools[id]) break else: rval.append(self.default_job_tool_configuration) return rval
[docs] def get_destination(self, id_or_tag): """Given a destination ID or tag, return the JobDestination matching the provided ID or tag :param id_or_tag: A destination ID or tag. :type id_or_tag: str :returns: JobDestination -- A valid destination Destinations are deepcopied as they are expected to be passed in to job runners, which will modify them for persisting params set at runtime. """ if id_or_tag is None: id_or_tag = self.default_destination_id return copy.deepcopy(self._get_single_item(self.destinations[id_or_tag]))
[docs] def get_destinations(self, id_or_tag): """Given a destination ID or tag, return all JobDestinations matching the provided ID or tag :param id_or_tag: A destination ID or tag. :type id_or_tag: str :returns: list or tuple of JobDestinations Destinations are not deepcopied, so they should not be passed to anything which might modify them. """ return self.destinations.get(id_or_tag, None)
[docs] def get_job_runner_plugins(self, handler_id): """Load all configured job runner plugins :returns: list of job runner plugins """ rval = {} if handler_id in self.handler_runner_plugins: plugins_to_load = [rp for rp in self.runner_plugins if rp['id'] in self.handler_runner_plugins[handler_id]] log.info("Handler '%s' will load specified runner plugins: %s", handler_id, ', '.join(rp['id'] for rp in plugins_to_load)) else: plugins_to_load = self.runner_plugins log.info("Handler '%s' will load all configured runner plugins", handler_id) for runner in plugins_to_load: class_names = [] module = None id = runner['id'] load = runner['load'] if ':' in load: # Name to load was specified as '<module>:<class>' module_name, class_name = load.rsplit(':', 1) class_names = [class_name] module = __import__(module_name) else: # Name to load was specified as '<module>' if '.' not in load: # For legacy reasons, try from galaxy.jobs.runners first if there's no '.' in the name module_name = 'galaxy.jobs.runners.' + load try: module = __import__(module_name) except ImportError: # No such module, we'll retry without prepending galaxy.jobs.runners. # All other exceptions (e.g. something wrong with the module code) will raise pass if module is None: # If the name included a '.' or loading from the static runners path failed, try the original name module = __import__(load) module_name = load if module is None: # Module couldn't be loaded, error should have already been displayed continue for comp in module_name.split(".")[1:]: module = getattr(module, comp) if not class_names: # If there's not a ':', we check <module>.__all__ for class names try: assert module.__all__ class_names = module.__all__ except AssertionError: log.error('Runner "%s" does not contain a list of exported classes in __all__' % load) continue for class_name in class_names: runner_class = getattr(module, class_name) try: assert issubclass(runner_class, BaseJobRunner) except TypeError: log.warning("A non-class name was found in __all__, ignoring: %s" % id) continue except AssertionError: log.warning("Job runner classes must be subclassed from BaseJobRunner, %s has bases: %s" % (id, runner_class.__bases__)) continue try: rval[id] = runner_class(self.app, runner.get('workers', JobConfiguration.DEFAULT_NWORKERS), **runner.get('kwds', {})) except TypeError: log.exception("Job runner '%s:%s' has not been converted to a new-style runner or encountered TypeError on load", module_name, class_name) rval[id] = runner_class(self.app) log.debug("Loaded job runner '%s:%s' as '%s'" % (module_name, class_name, id)) return rval
[docs] def is_id(self, collection): """Given a collection of handlers or destinations, indicate whether the collection represents a tag or a real ID :param collection: A representation of a destination or handler :type collection: tuple or list :returns: bool """ return type(collection) == tuple
[docs] def is_tag(self, collection): """Given a collection of handlers or destinations, indicate whether the collection represents a tag or a real ID :param collection: A representation of a destination or handler :type collection: tuple or list :returns: bool """ return type(collection) == list
[docs] def convert_legacy_destinations(self, job_runners): """Converts legacy (from a URL) destinations to contain the appropriate runner params defined in the URL. :param job_runners: All loaded job runner plugins. :type job_runners: list of job runner plugins """ for id, destination in [(id, destinations[0]) for id, destinations in self.destinations.items() if self.is_id(destinations)]: # Only need to deal with real destinations, not members of tags if destination.legacy and not destination.converted: if destination.runner in job_runners: destination.params = job_runners[destination.runner].url_to_destination(destination.url).params destination.converted = True if destination.params: log.debug("Legacy destination with id '%s', url '%s' converted, got params:" % (id, destination.url)) for k, v in destination.params.items(): log.debug(" %s: %s" % (k, v)) else: log.debug("Legacy destination with id '%s', url '%s' converted, got params:" % (id, destination.url)) else: log.warning("Legacy destination with id '%s' could not be converted: Unknown runner plugin: %s" % (id, destination.runner))
[docs]class HasResourceParameters(object):
[docs] def get_resource_parameters(self, job=None): # Find the dymically inserted resource parameters and give them # to rule. if job is None: job = self.get_job() app = self.app param_values = job.get_param_values(app, ignore_errors=True) resource_params = {} try: resource_params_raw = param_values["__job_resource"] if resource_params_raw["__job_resource__select"].lower() in ["1", "yes", "true"]: for key, value in resource_params_raw.items(): resource_params[key] = value except KeyError: pass return resource_params
[docs]class JobWrapper(HasResourceParameters): """ Wraps a 'model.Job' with convenience methods for running processes and state management. """
[docs] def __init__(self, job, queue, use_persisted_destination=False): self.job_id = job.id self.session_id = job.session_id self.user_id = job.user_id self.tool = queue.app.toolbox.get_tool(job.tool_id, job.tool_version, exact=True) self.queue = queue self.app = queue.app self.sa_session = self.app.model.context self.extra_filenames = [] self.command_line = None self.dependencies = [] self._dependency_shell_commands = None # Tool versioning variables self.write_version_cmd = None self.version_string = "" self.__galaxy_lib_dir = None # If the job has an object_store_id ensure working directory is setup, otherwise # wait for that to be assigned before configuring it. Either way the working # directory does not to be configured on this object before prepare() is called. if job.object_store_id: self._setup_working_directory(job=job) # the path rewriter needs destination params, so it cannot be set up until after the destination has been # resolved self._dataset_path_rewriter = None self.output_paths = None self.output_hdas_and_paths = None self.tool_provided_job_metadata = None self.job_runner_mapper = JobRunnerMapper(self, queue.dispatcher.url_to_destination, self.app.job_config) self.params = None if job.params: self.params = loads(job.params) if use_persisted_destination: self.job_runner_mapper.cached_job_destination = JobDestination(from_job=job) # Wrapper holding the info required to restore and clean up from files used for setting metadata externally try: metadata_strategy_override = self.get_destination_configuration('metadata_strategy', None) except JobMappingException: metadata_strategy_override = None if job.tasks: metadata_strategy_override = "directory" self.external_output_metadata = get_metadata_compute_strategy(self.app.config, job.id, metadata_strategy_override=metadata_strategy_override, tool_id=job.tool_id) self.__commands_in_new_shell = True self.__user_system_pwent = None self.__galaxy_system_pwent = None self.__working_directory = None
@property def _job_dataset_path_rewriter(self): if self._dataset_path_rewriter is None: outputs_to_working_directory = util.asbool(self.get_destination_configuration("outputs_to_working_directory", False)) if outputs_to_working_directory: output_directory = self.outputs_directory self._dataset_path_rewriter = OutputsToWorkingDirectoryPathRewriter(self.working_directory, output_directory) else: self._dataset_path_rewriter = NullDatasetPathRewriter() return self._dataset_path_rewriter @property def dataset_path_rewriter(self): return self._job_dataset_path_rewriter @property def outputs_directory(self): """Default location of ``outputs_to_working_directory``. """ return None if self.created_with_galaxy_version < packaging.version.parse("20.01") else "outputs" @property def created_with_galaxy_version(self): galaxy_version = self.get_job().galaxy_version or "19.05" return packaging.version.parse(galaxy_version) @property def dependency_shell_commands(self): """Shell fragment to inject dependencies.""" if self._dependency_shell_commands is None: self._dependency_shell_commands = self.tool.build_dependency_shell_commands( job_directory=self.working_directory ) return self._dependency_shell_commands @property def cleanup_job(self): """ Remove the job after it is complete, should return "always", "onsuccess", or "never". """ return self.get_destination_configuration("cleanup_job", DEFAULT_CLEANUP_JOB) @property def requires_containerization(self): return util.asbool(self.get_destination_configuration("require_container", "False")) @property def use_metadata_binary(self): return util.asbool(self.get_destination_configuration('use_metadata_binary', "False"))
[docs] def can_split(self): # Should the job handler split this job up? return self.app.config.use_tasked_jobs and self.tool.parallelism
[docs] def get_job_runner_url(self): log.warning('(%s) Job runner URLs are deprecated, use destinations instead.' % self.job_id) return self.job_destination.url
[docs] def get_parallelism(self): return self.tool.parallelism
@property def shell(self): return self.job_destination.shell or getattr(self.app.config, 'default_job_shell', DEFAULT_JOB_SHELL)
[docs] def disable_commands_in_new_shell(self): """Provide an extension point to disable this isolation, Pulsar builds its own job script so this is not needed for remote jobs.""" self.__commands_in_new_shell = False
@property def strict_shell(self): return self.tool.strict_shell @property def commands_in_new_shell(self): return self.__commands_in_new_shell @property def galaxy_lib_dir(self): if self.__galaxy_lib_dir is None: self.__galaxy_lib_dir = os.path.abspath("lib") # cwd = galaxy root return self.__galaxy_lib_dir @property def galaxy_virtual_env(self): return os.environ.get('VIRTUAL_ENV', None) # legacy naming get_job_runner = get_job_runner_url @property def job_destination(self): """Return the JobDestination that this job will use to run. This will either be a configured destination, a randomly selected destination if the configured destination was a tag, or a dynamically generated destination from the dynamic runner. Calling this method for the first time causes the dynamic runner to do its calculation, if any. :returns: ``JobDestination`` """ return self.job_runner_mapper.get_job_destination(self.params)
[docs] def get_job(self): return self.sa_session.query(model.Job).get(self.job_id)
[docs] def get_id_tag(self): # For compatibility with drmaa, which uses job_id right now, and TaskWrapper return self.get_job().get_id_tag()
[docs] def get_param_dict(self, _job=None): """ Restore the dictionary of parameters from the database. """ job = _job or self.get_job() param_dict = dict([(p.name, p.value) for p in job.parameters]) param_dict = self.tool.params_from_strings(param_dict, self.app) return param_dict
@property def validate_outputs(self): job = self.get_job() for p in job.parameters: if p.name == "__validate_outputs__": log.info("validate... %s" % p.value) return loads(p.value) return False
[docs] def get_version_string_path(self): return os.path.abspath(os.path.join(self.working_directory, "outputs", COMMAND_VERSION_FILENAME))
# TODO: Remove in Galaxy 21.XX, for running jobs at GX upgrade
[docs] def get_version_string_path_legacy(self): return os.path.abspath(os.path.join(self.working_directory, COMMAND_VERSION_FILENAME))
def __prepare_upload_paramfile(self, tool_evaluator): """Special case paramfile handling for the upload tool. Moves the paramfile to the working directory """ new = os.path.join(self.working_directory, 'upload_params.json') try: shutil.move(tool_evaluator.param_dict['paramfile'], new) except (OSError, IOError) as exc: # It won't exist at the old path if setup was interrupted and tried again later if exc.errno != errno.ENOENT or not os.path.exists(new): raise tool_evaluator.param_dict['paramfile'] = new
[docs] def prepare(self, compute_environment=None): """ Prepare the job to run by creating the working directory and the config files. """ self.sa_session.expunge_all() # this prevents the metadata reverting that has been seen in conjunction with the PBS job runner if not os.path.exists(self.working_directory): os.mkdir(self.working_directory) job = self._load_job() def get_special(): special = self.sa_session.query(model.JobExportHistoryArchive).filter_by(job=job).first() if not special: special = self.sa_session.query(model.GenomeIndexToolData).filter_by(job=job).first() return special tool_evaluator = self._get_tool_evaluator(job) compute_environment = compute_environment or self.default_compute_environment(job) self.galaxy_url = compute_environment.galaxy_url tool_evaluator.set_compute_environment(compute_environment, get_special=get_special) self.sa_session.flush() # TODO: The upload tool actions that create the paramfile can probably be turned in to a configfile to remove this special casing if job.tool_id == 'upload1': self.__prepare_upload_paramfile(tool_evaluator) self.command_line, self.extra_filenames, self.environment_variables = tool_evaluator.build() # Ensure galaxy_lib_dir is set in case there are any later chdirs self.galaxy_lib_dir if self.tool.requires_galaxy_python_environment: # These tools (upload, metadata, data_source) may need access to the datatypes registry. self.app.datatypes_registry.to_xml_file(os.path.join(self.working_directory, 'registry.xml')) # We need command_line persisted to the db in order for Galaxy to re-queue the job # if the server was stopped and restarted before the job finished job.command_line = unicodify(self.command_line) job.dependencies = self.tool.dependencies self.interactivetools = getattr(tool_evaluator, 'interactivetools', None) self.sa_session.add(job) self.sa_session.flush() # Return list of all extra files self.param_dict = tool_evaluator.param_dict version_string_cmd_raw = self.tool.version_string_cmd if version_string_cmd_raw: version_command_template = string.Template(version_string_cmd_raw) version_string_cmd = version_command_template.safe_substitute({"__tool_directory__": compute_environment.tool_directory()}) self.write_version_cmd = "%s > %s 2>&1" % (version_string_cmd, compute_environment.version_path()) else: self.write_version_cmd = None return self.extra_filenames
def _setup_working_directory(self, job=None): if job is None: job = self.get_job() try: working_directory = self._create_working_directory(job) self.__working_directory = working_directory # The tool execution is given a working directory beneath the # "job" working directory. safe_makedirs(self.tool_working_directory) safe_makedirs(os.path.join(working_directory, 'outputs')) log.debug('(%s) Working directory for job is: %s', self.job_id, self.working_directory) except ObjectInvalid: raise Exception('(%s) Unable to create job working directory', job.id) @property def guest_ports(self): if hasattr(self, "interactivetools"): guest_ports = [ep.get('port') for ep in self.interactivetools] return guest_ports else: return [] @property def working_directory(self): if self.__working_directory is None: job = self.get_job() # object_store_id needs to be set before get_filename can be called, this # will also create the directory on the worker. # It is possible these next two lines are not needed - if a job a cannot be recovered # before enqueue is called (seems likely) - this shouldn't be needed. if job.object_store_id: self._set_object_store_ids(job) self.__working_directory = self.app.object_store.get_filename( job, base_dir='job_work', dir_only=True, obj_dir=True) return self.__working_directory
[docs] def working_directory_exists(self): job = self.get_job() return self.app.object_store.exists(job, base_dir='job_work', dir_only=True, obj_dir=True)
@property def tool_working_directory(self): return os.path.join(self.working_directory, "working") def _create_working_directory(self, job): self.object_store.create( job, base_dir='job_work', dir_only=True, obj_dir=True) working_directory = self.object_store.get_filename( job, base_dir='job_work', dir_only=True, obj_dir=True) return working_directory
[docs] def clear_working_directory(self): job = self.get_job() if not os.path.exists(self.working_directory): log.warning('(%s): Working directory clear requested but %s does ' 'not exist', self.job_id, self.working_directory) return self.object_store.create( job, base_dir='job_work', dir_only=True, obj_dir=True, extra_dir='_cleared_contents', extra_dir_at_root=True) base = self.object_store.get_filename( job, base_dir='job_work', dir_only=True, obj_dir=True, extra_dir='_cleared_contents', extra_dir_at_root=True) date_str = datetime.datetime.now().strftime('%Y%m%d-%H%M%S') arc_dir = os.path.join(base, date_str) shutil.move(self.working_directory, arc_dir) self._setup_working_directory(job=job) log.debug('(%s) Previous working directory moved to %s', self.job_id, arc_dir)
[docs] def default_compute_environment(self, job=None): if not job: job = self.get_job() return SharedComputeEnvironment(self, job)
def _load_job(self): # Load job from database and verify it has user or session. # Restore parameters from the database job = self.get_job() if job.user is None and job.galaxy_session is None: raise Exception('Job %s has no user and no session.' % job.id) return job def _get_tool_evaluator(self, job): # Hacky way to avoid cirular import for now. # Placing ToolEvaluator in either jobs or tools # result in ciruclar dependency. from galaxy.tools.evaluation import ToolEvaluator tool_evaluator = ToolEvaluator( app=self.app, job=job, tool=self.tool, local_working_directory=self.working_directory, ) return tool_evaluator def _fix_output_permissions(self): for path in [dp.real_path for dp in self.get_mutable_output_fnames()]: if os.path.exists(path): util.umask_fix_perms(path, self.app.config.umask, 0o666, self.app.config.gid)
[docs] def fail(self, message, exception=False, tool_stdout="", tool_stderr="", exit_code=None, job_stdout=None, job_stderr=None): """ Indicate job failure by setting state and message on all output datasets. """ job = self.get_job() self.sa_session.refresh(job) # If this fail method is being called because a dynamic rule raised JobMappingException, the call to # self.get_destination_configuration() below accesses self.job_destination and will just cause # JobMappingException to be raised again. try: self.job_destination except JobMappingException as exc: log.debug("(%s) fail(): Job destination raised JobMappingException('%s'), caching fake '__fail__' " "destination for completion of fail method", self.get_id_tag(), unicodify(exc.failure_message)) self.job_runner_mapper.cached_job_destination = JobDestination(id='__fail__') # Might be AssertionError or other exception message = str(message) working_directory_exists = self.working_directory_exists() # if the job was deleted, don't fail it if not job.state == job.states.DELETED: # Check if the failure is due to an exception if exception: # Save the traceback immediately in case we generate another # below job.traceback = unicodify(traceback.format_exc(), strip_null=True) # Get the exception and let the tool attempt to generate # a better message etype, evalue, tb = sys.exc_info() outputs_to_working_directory = util.asbool(self.get_destination_configuration("outputs_to_working_directory", False)) if outputs_to_working_directory and not self.__link_file_check() and working_directory_exists: for dataset_path in self.get_output_fnames(): try: shutil.move(dataset_path.false_path, dataset_path.real_path) log.debug("fail(): Moved %s to %s", dataset_path.false_path, dataset_path.real_path) except (IOError, OSError) as e: log.error("fail(): Missing output file in working directory: %s", unicodify(e)) for dataset_assoc in job.output_datasets + job.output_library_datasets: dataset = dataset_assoc.dataset self.sa_session.refresh(dataset) dataset.state = dataset.states.ERROR dataset.blurb = 'tool error' dataset.info = message dataset.set_size() dataset.dataset.set_total_size() dataset.mark_unhidden() if dataset.ext == 'auto': dataset.extension = 'data' self.__update_output(job, dataset) # Pause any dependent jobs (and those jobs' outputs) for dep_job_assoc in dataset.dependent_jobs: self.pause(dep_job_assoc.job, "Execution of this dataset's job is paused because its input datasets are in an error state.") self.sa_session.add(dataset) self.sa_session.flush() job.set_final_state(job.states.ERROR) job.command_line = unicodify(self.command_line) job.info = message # TODO: Put setting the stdout, stderr, and exit code in one place # (not duplicated with the finish method). job.set_streams(tool_stdout, tool_stderr, job_stdout=job_stdout, job_stderr=job_stderr) # Let the exit code be Null if one is not provided: if (exit_code is not None): job.exit_code = exit_code self.sa_session.add(job) self.sa_session.flush() else: for dataset_assoc in job.output_datasets: dataset = dataset_assoc.dataset # Any reason for clean_only here? We should probably be more consistent and transfer # the partial files to the object store regardless of whether job.state == DELETED self.__update_output(job, dataset, clean_only=True) if working_directory_exists: self._fix_output_permissions() self._report_error() # Perform email action even on failure. for pja in [pjaa.post_job_action for pjaa in job.post_job_actions if pjaa.post_job_action.action_type == "EmailAction"]: ActionBox.execute(self.app, self.sa_session, pja, job) # If the job was deleted, call tool specific fail actions (used for e.g. external metadata) and clean up if self.tool: self.tool.job_failed(self, message, exception) cleanup_job = self.cleanup_job delete_files = cleanup_job == 'always' or (cleanup_job == 'onsuccess' and job.state == job.states.DELETED) self.cleanup(delete_files=delete_files)
[docs] def pause(self, job=None, message=None): if job is None: job = self.get_job() if message is None: message = "Execution of this dataset's job is paused" if job.state == job.states.NEW: for dataset_assoc in job.output_datasets + job.output_library_datasets: dataset_assoc.dataset.dataset.state = dataset_assoc.dataset.dataset.states.PAUSED dataset_assoc.dataset.info = message self.sa_session.add(dataset_assoc.dataset) log.debug("Pausing Job '%d', %s", job.id, message) job.set_state(job.states.PAUSED) self.sa_session.add(job)
[docs] def is_ready_for_resubmission(self, job=None): if job is None: job = self.get_job() destination_params = job.destination_params if "__resubmit_delay_seconds" in destination_params: delay = float(destination_params["__resubmit_delay_seconds"]) if job.seconds_since_updated < delay: return False return True
[docs] def mark_as_resubmitted(self, info=None): job = self.get_job() self.sa_session.refresh(job) if info is not None: job.info = info job.set_state(model.Job.states.RESUBMITTED) self.sa_session.add(job) self.sa_session.flush()
[docs] def change_state(self, state, info=False, flush=True, job=None): job_supplied = job is not None if not job_supplied: job = self.get_job() self.sa_session.refresh(job) # Else: # If this is a new job (e.g. initially queued) - we are in the same # thread and no other threads are working on the job yet - so don't refresh. if job.state in model.Job.terminal_states: log.warning("(%s) Ignoring state change from '%s' to '%s' for job " "that is already terminal", job.id, job.state, state) return for dataset_assoc in job.output_datasets + job.output_library_datasets: dataset = dataset_assoc.dataset if not job_supplied: self.sa_session.refresh(dataset) state_changed = dataset.raw_set_dataset_state(state) if state_changed: # Arguably a hack to get state changes to appear in the history panel because # the history panel polls on hda.update_time and ignores hda.dataset.update_time. # For those less pragmatic needing a more theoretically sound reason for the update, # perhaps arguments can be made that the entity that is the HDA # really should be described as updated since its effective state did change and its # RESTful representation in the API does change as a result of the above dataset update. dataset.update() if info: dataset.info = info self.sa_session.add(dataset) if info: job.info = info job.set_state(state) self.sa_session.add(job) if flush: self.sa_session.flush()
[docs] def get_state(self): job = self.get_job() self.sa_session.refresh(job) return job.state
[docs] def set_runner(self, runner_url, external_id): log.warning('set_runner() is deprecated, use set_job_destination()') self.set_job_destination(self.job_destination, external_id)
[docs] def set_job_destination(self, job_destination, external_id=None, flush=True, job=None): """ Persist job destination params in the database for recovery. self.job_destination is not used because a runner may choose to rewrite parts of the destination (e.g. the params). """ if job is None: job = self.get_job() log.debug('(%s) Persisting job destination (destination id: %s)' % (job.id, job_destination.id)) job.destination_id = job_destination.id job.destination_params = job_destination.params job.job_runner_name = job_destination.runner job.job_runner_external_id = external_id self.sa_session.add(job) if flush: self.sa_session.flush()
[docs] def set_external_id(self, external_id, job=None, flush=True): if job is None: job = self.get_job() job.job_runner_external_id = external_id self.sa_session.add(job) if flush: self.sa_session.flush()
@property def home_target(self): home_target = self.tool.home_target return home_target @property def tmp_target(self): return self.tool.tmp_target
[docs] def get_destination_configuration(self, key, default=None): """ Get a destination parameter that can be defaulted back in app.config if it needs to be applied globally. """ dest_params = self.job_destination.params return self.get_job().get_destination_configuration( dest_params, self.app.config, key, default )
[docs] def enqueue(self): job = self.get_job() # Change to queued state before handing to worker thread so the runner won't pick it up again self.change_state(model.Job.states.QUEUED, flush=False, job=job) # Persist the destination so that the job will be included in counts if using concurrency limits self.set_job_destination(self.job_destination, None, flush=False, job=job) # Set object store after job destination so can leverage parameters... self._set_object_store_ids(job) self.sa_session.flush()
def _set_object_store_ids(self, job): if job.object_store_id: # We aren't setting this during job creation anymore, but some existing # jobs may have this set. Skip this following code if that is the case. return object_store_populator = ObjectStorePopulator(self.app) object_store_id = self.get_destination_configuration("object_store_id", None) if object_store_id: object_store_populator.object_store_id = object_store_id # Ideally we would do this without loading the actual job association # objects but change_state isn't yet optimized to do that so we need to # do that anyway. In the future though - this should be done with a # custom query that just loads Dataset.ids, passes them through object # store code, and sets object_store_id on those ids with a multi-update # afterward. State below needs to happen the same way. for dataset_assoc in job.output_datasets + job.output_library_datasets: dataset = dataset_assoc.dataset object_store_populator.set_object_store_id(dataset) job.object_store_id = object_store_populator.object_store_id self._setup_working_directory(job=job) def _finish_dataset(self, output_name, dataset, job, context, final_job_state, remote_metadata_directory): implicit_collection_jobs = job.implicit_collection_jobs_association purged = dataset.dataset.purged if not purged and dataset.dataset.external_filename is None: trynum = 0 while trynum < self.app.config.retry_job_output_collection: try: # Attempt to short circuit NFS attribute caching os.stat(dataset.dataset.file_name) os.chown(dataset.dataset.file_name, os.getuid(), -1) trynum = self.app.config.retry_job_output_collection except (OSError, ObjectNotFound) as e: trynum += 1 log.warning('Error accessing dataset with ID %i, will retry: %s', dataset.dataset.id, unicodify(e)) time.sleep(2) if getattr(dataset, "hidden_beneath_collection_instance", None): dataset.visible = False dataset.blurb = 'done' dataset.peek = 'no peek' dataset.info = (dataset.info or '') if context['stdout'].strip(): # Ensure white space between entries dataset.info = dataset.info.rstrip() + "\n" + context['stdout'].strip() if context['stderr'].strip(): # Ensure white space between entries dataset.info = dataset.info.rstrip() + "\n" + context['stderr'].strip() dataset.tool_version = self.version_string dataset.set_size() if 'uuid' in context: dataset.dataset.uuid = context['uuid'] self.__update_output(job, dataset) if not purged: collect_extra_files(self.object_store, dataset, self.working_directory) if job.states.ERROR == final_job_state: dataset.blurb = "error" if not implicit_collection_jobs: # Only unhide dataset outputs that are not part of a implicit collection dataset.mark_unhidden() elif not purged: # If the tool was expected to set the extension, attempt to retrieve it if dataset.ext == 'auto': dataset.extension = context.get('ext', 'data') dataset.init_meta(copy_from=dataset) # if a dataset was copied, it won't appear in our dictionary: # either use the metadata from originating output dataset, or call set_meta on the copies # it would be quicker to just copy the metadata from the originating output dataset, # but somewhat trickier (need to recurse up the copied_from tree), for now we'll call set_meta() retry_internally = util.asbool(self.get_destination_configuration("retry_metadata_internally", True)) metadata_set_successfully = self.external_output_metadata.external_metadata_set_successfully(dataset, output_name, self.sa_session, working_directory=self.working_directory) if retry_internally and not metadata_set_successfully: # If Galaxy was expected to sniff type and didn't - do so. if dataset.ext == "_sniff_": extension = sniff.handle_uploaded_dataset_file(dataset.dataset.file_name, self.app.datatypes_registry) dataset.extension = extension # call datatype.set_meta directly for the initial set_meta call during dataset creation dataset.datatype.set_meta(dataset, overwrite=False) elif (job.states.ERROR != final_job_state and not metadata_set_successfully): dataset._state = model.Dataset.states.FAILED_METADATA else: self.external_output_metadata.load_metadata(dataset, output_name, self.sa_session, working_directory=self.working_directory, remote_metadata_directory=remote_metadata_directory) line_count = context.get('line_count', None) try: # Certain datatype's set_peek methods contain a line_count argument dataset.set_peek(line_count=line_count) except TypeError: # ... and others don't dataset.set_peek() else: # Handle purged datasets. dataset.blurb = "empty" if dataset.ext == 'auto': dataset.extension = context.get('ext', 'txt') for context_key in TOOL_PROVIDED_JOB_METADATA_KEYS: if context_key in context: context_value = context[context_key] setattr(dataset, context_key, context_value) self.sa_session.add(dataset)
[docs] def finish( self, tool_stdout, tool_stderr, tool_exit_code=None, job_stdout=None, job_stderr=None, check_output_detected_state=None, remote_metadata_directory=None, job_metrics_directory=None, ): """ Called to indicate that the associated command has been run. Updates the output datasets based on stderr and stdout from the command, and the contents of the output files. """ finish_timer = self.app.execution_timer_factory.get_timer( 'internals.galaxy.jobs.job_wrapper_finish', 'job_wrapper.finish for job ${job_id} executed' ) # default post job setup self.sa_session.expunge_all() job = self.get_job() def fail(): return self.fail(job.info, tool_stdout=tool_stdout, tool_stderr=tool_stderr, exit_code=tool_exit_code, job_stdout=job_stdout, job_stderr=job_stderr) # TODO: After failing here, consider returning from the function. try: self.reclaim_ownership() except Exception: log.exception('(%s) Failed to change ownership of %s, failing' % (job.id, self.working_directory)) return fail() # if the job was deleted, don't finish it if job.state == job.states.DELETED or job.state == job.states.ERROR: # SM: Note that, at this point, the exit code must be saved in case # there was an error. Errors caught here could mean that the job # was deleted by an administrator (based on old comments), but it # could also mean that a job was broken up into tasks and one of # the tasks failed. So include the stderr, stdout, and exit code: return fail() extended_metadata = self.external_output_metadata.extended # We collect the stderr from tools that write their stderr to galaxy.json tool_provided_metadata = self.get_tool_provided_job_metadata() # Check the tool's stdout, stderr, and exit code for errors, but only # if the job has not already been marked as having an error. # The job's stdout and stderr will be set accordingly. # We set final_job_state to use for dataset management, but *don't* set # job.state until after dataset discovery to prevent history issues if check_output_detected_state is None: check_output_detected_state = self.check_tool_output(tool_stdout, tool_stderr, tool_exit_code=tool_exit_code, job=job, job_stdout=job_stdout, job_stderr=job_stderr) if check_output_detected_state == DETECTED_JOB_STATE.OK and not tool_provided_metadata.has_failed_outputs(): final_job_state = job.states.OK else: final_job_state = job.states.ERROR if self.tool.version_string_cmd: version_filename = self.get_version_string_path() # TODO: Remove in Galaxy 20.XX, for running jobs at GX upgrade if not os.path.exists(version_filename): version_filename = self.get_version_string_path_legacy() if os.path.exists(version_filename): with open(version_filename, 'rb') as fh: self.version_string = galaxy.util.shrink_and_unicodify(fh.read()) os.unlink(version_filename) outputs_to_working_directory = util.asbool(self.get_destination_configuration("outputs_to_working_directory", False)) if not extended_metadata and outputs_to_working_directory and not self.__link_file_check(): # output will be moved by job if metadata_strategy is extended_metadata, so skip moving here for dataset_path in self.get_output_fnames(): try: shutil.move(dataset_path.false_path, dataset_path.real_path) log.debug("finish(): Moved %s to %s" % (dataset_path.false_path, dataset_path.real_path)) except (IOError, OSError): # this can happen if Galaxy is restarted during the job's # finish method - the false_path file has already moved, # and when the job is recovered, it won't be found. if os.path.exists(dataset_path.real_path) and os.stat(dataset_path.real_path).st_size > 0: log.warning("finish(): %s not found, but %s is not empty, so it will be used instead" % (dataset_path.false_path, dataset_path.real_path)) else: # Prior to fail we need to set job.state job.set_state(final_job_state) return self.fail("Job %s's output dataset(s) could not be read" % job.id) job_context = ExpressionContext(dict(stdout=job.stdout, stderr=job.stderr)) if extended_metadata: try: import_options = store.ImportOptions(allow_dataset_object_edit=True, allow_edit=True) import_model_store = store.get_import_model_store_for_directory(os.path.join(self.working_directory, 'metadata', 'outputs_populated'), app=self.app, import_options=import_options) import_model_store.perform_import(history=job.history) except Exception: log.exception("problem importing job outputs. stdout [%s] stderr [%s]" % (job.stdout, job.stderr)) raise output_dataset_associations = job.output_datasets + job.output_library_datasets for dataset_assoc in output_dataset_associations: context = self.get_dataset_finish_context(job_context, dataset_assoc) # should this also be checking library associations? - can a library item be added from a history before the job has ended? - # lets not allow this to occur # need to update all associated output hdas, i.e. history was shared with job running for dataset in dataset_assoc.dataset.dataset.history_associations + dataset_assoc.dataset.dataset.library_associations: output_name = dataset_assoc.name standard_job_finish = not extended_metadata if extended_metadata: if job.states.ERROR == final_job_state: dataset.blurb = "error" dataset.mark_unhidden() if standard_job_finish: # Handles retry internally on error for instance... self._finish_dataset( output_name, dataset, job, context, final_job_state, remote_metadata_directory ) for dataset_assoc in output_dataset_associations: if job.states.ERROR == final_job_state: log.debug("(%s) setting dataset %s state to ERROR", job.id, dataset_assoc.dataset.dataset.id) # TODO: This is where the state is being set to error. Change it! dataset_assoc.dataset.dataset.state = model.Dataset.states.ERROR # Pause any dependent jobs (and those jobs' outputs) for dep_job_assoc in dataset_assoc.dataset.dependent_jobs: self.pause(dep_job_assoc.job, "Execution of this dataset's job is paused because its input datasets are in an error state.") else: dataset_assoc.dataset.dataset.state = model.Dataset.states.OK # If any of the rest of the finish method below raises an # exception, the fail method will run and set the datasets to # ERROR. The user will never see that the datasets are in error if # they were flushed as OK here, since upon doing so, the history # panel stops checking for updates. So allow the # self.sa_session.flush() at the bottom of this method set # the state instead. for pja in job.post_job_actions: ActionBox.execute(self.app, self.sa_session, pja.post_job_action, job) # Flush all the dataset and job changes above. Dataset state changes # will now be seen by the user. self.sa_session.flush() # The exit code will be null if there is no exit code to be set. # This is so that we don't assign an exit code, such as 0, that # is either incorrect or has the wrong semantics. if tool_exit_code is not None: job.exit_code = tool_exit_code # custom post process setup inp_data, out_data, out_collections = job.io_dicts() if not extended_metadata: # importing metadata will discover outputs if extended metadata # is enabled. self.discover_outputs(job, inp_data, out_data, out_collections, final_job_state=final_job_state) # Certain tools require tasks to be completed after job execution # ( this used to be performed in the "exec_after_process" hook, but hooks are deprecated ). param_dict = self.get_param_dict(job) self.tool.exec_after_process(self.app, inp_data, out_data, param_dict, job=job, final_job_state=final_job_state) # Call 'exec_after_process' hook self.tool.call_hook('exec_after_process', self.app, inp_data=inp_data, out_data=out_data, param_dict=param_dict, tool=self.tool, stdout=job.stdout, stderr=job.stderr) job.command_line = unicodify(self.command_line) collected_bytes = 0 # Once datasets are collected, set the total dataset size (includes extra files) for dataset_assoc in job.output_datasets: if not dataset_assoc.dataset.dataset.purged: dataset_assoc.dataset.dataset.set_total_size() collected_bytes += dataset_assoc.dataset.dataset.get_total_size() if job.user: job.user.adjust_total_disk_usage(collected_bytes) # Empirically, we need to update job.user and # job.workflow_invocation_step.workflow_invocation in separate # transactions. Best guess as to why is that the workflow_invocation # may or may not exist when the job is first loaded by the handler - # and depending on whether it is or not sqlalchemy orders the updates # differently and deadlocks can occur (one thread updates user and # waits on invocation and the other updates invocation and waits on # user). self.sa_session.flush() self._fix_output_permissions() # Finally set the job state. This should only happen *after* all # dataset creation, and will allow us to eliminate force_history_refresh. job.set_final_state(final_job_state) if not job.tasks: # If job was composed of tasks, don't attempt to recollect statisitcs self._collect_metrics(job, job_metrics_directory) self.sa_session.flush() if job.state == job.states.ERROR: self._report_error() cleanup_job = self.cleanup_job delete_files = cleanup_job == 'always' or (job.state == job.states.OK and cleanup_job == 'onsuccess') self.cleanup(delete_files=delete_files) log.debug(finish_timer.to_str(job_id=self.job_id, tool_id=job.tool_id))
[docs] def discover_outputs(self, job, inp_data, out_data, out_collections, final_job_state): # Try to just recover input_ext and dbkey from job parameters (used and set in # galaxy.tools.actions). Old jobs may have not set these in the job parameters # before persisting them. input_params = job.raw_param_dict() input_ext = input_params.get("__input_ext") input_dbkey = input_params.get("dbkey") if input_ext is not None: input_ext = loads(input_ext) input_dbkey = loads(input_dbkey) else: # Legacy jobs without __input_ext. input_ext = 'data' input_dbkey = '?' for _, data in inp_data.items(): # For loop odd, but sort simulating behavior in galaxy.tools.actions if not data: continue input_ext = data.ext input_dbkey = data.dbkey or '?' # Create generated output children and primary datasets. tool_working_directory = self.tool_working_directory tool_provided_job_metadata = self.get_tool_provided_job_metadata() self.tool.discover_outputs( out_data, out_collections, tool_provided_job_metadata, job=job, tool_working_directory=tool_working_directory, inp_data=inp_data, input_ext=input_ext, input_dbkey=input_dbkey, final_job_state=final_job_state, )
[docs] def check_tool_output(self, tool_stdout, tool_stderr, tool_exit_code, job, job_stdout=None, job_stderr=None): job_id_tag = "<unknown job id>" if job is not None: job_id_tag = job.get_id_tag() state, tool_stdout, tool_stderr, job_messages = check_output(self.tool.stdio_regexes, self.tool.stdio_exit_codes, tool_stdout, tool_stderr, tool_exit_code, job_id_tag) # Store the modified stdout and stderr in the job: if job is not None: job.set_streams(tool_stdout, tool_stderr, job_messages=job_messages, job_stdout=job_stdout, job_stderr=job_stderr) return state
[docs] def cleanup(self, delete_files=True): # At least one of these tool cleanup actions (job import), is needed # for the tool to work properly, that is why one might want to run # cleanup but not delete files. try: if delete_files: for fname in self.extra_filenames: try: os.remove(fname) except EnvironmentError as e: if e.errno != errno.ENOENT: raise self.external_output_metadata.cleanup_external_metadata(self.sa_session) galaxy.tools.imp_exp.JobExportHistoryArchiveWrapper(self.app, self.job_id).cleanup_after_job() galaxy.tools.imp_exp.JobImportHistoryArchiveWrapper(self.app, self.job_id).cleanup_after_job() if delete_files: self.object_store.delete(self.get_job(), base_dir='job_work', entire_dir=True, dir_only=True, obj_dir=True) except Exception: log.exception("Unable to cleanup job %d", self.job_id)
def _collect_metrics(self, has_metrics, job_metrics_directory=None): job = has_metrics.get_job() job_metrics_directory = job_metrics_directory or self.working_directory per_plugin_properties = self.app.job_metrics.collect_properties(job.destination_id, self.job_id, job_metrics_directory) if per_plugin_properties: log.info("Collecting metrics for %s %s in %s" % (type(has_metrics).__name__, getattr(has_metrics, 'id', None), job_metrics_directory)) for plugin, properties in per_plugin_properties.items(): for metric_name, metric_value in properties.items(): if metric_value is not None: has_metrics.add_metric(plugin, metric_name, metric_value)
[docs] def get_output_sizes(self): sizes = [] output_paths = self.get_output_fnames() for outfile in [unicodify(o) for o in output_paths]: if os.path.exists(outfile): sizes.append((outfile, os.stat(outfile).st_size)) else: sizes.append((outfile, 0)) return sizes
[docs] def check_limits(self, runtime=None): if self.app.job_config.limits.output_size and self.app.job_config.limits.output_size > 0: for outfile, size in self.get_output_sizes(): if size > self.app.job_config.limits.output_size: log.warning('(%s) Job output size %s has exceeded the global output size limit', self.get_id_tag(), os.path.basename(outfile)) return (JobState.runner_states.OUTPUT_SIZE_LIMIT, 'Job output file grew too large (greater than %s), please try different inputs or parameters' % util.nice_size(self.app.job_config.limits.output_size)) if self.app.job_config.limits.walltime_delta is not None and runtime is not None: if runtime > self.app.job_config.limits.walltime_delta: log.warning('(%s) Job runtime %s has exceeded the global walltime, it will be terminated', self.get_id_tag(), runtime) return (JobState.runner_states.GLOBAL_WALLTIME_REACHED, 'Job ran longer than the maximum allowed execution time (runtime: %s, limit: %s), please try different inputs or parameters' % (str(runtime).split('.')[0], self.app.job_config.limits.walltime)) return None
[docs] def has_limits(self): has_output_limit = self.app.job_config.limits.output_size and self.app.job_config.limits.output_size > 0 has_walltime_limit = self.app.job_config.limits.walltime_delta is not None return has_output_limit or has_walltime_limit
[docs] def get_command_line(self): return self.command_line
[docs] def get_session_id(self): return self.session_id
[docs] def get_env_setup_clause(self): if self.app.config.environment_setup_file is None: return '' return '[ -f "%s" ] && . %s' % (self.app.config.environment_setup_file, self.app.config.environment_setup_file)
[docs] def get_input_dataset_fnames(self, ds): filenames = [] filenames = [ds.file_name] # we will need to stage in metadata file names also # TODO: would be better to only stage in metadata files that are actually needed (found in command line, referenced in config files, etc.) for key, value in ds.metadata.items(): if isinstance(value, model.MetadataFile): filenames.append(value.file_name) return filenames
[docs] def get_input_fnames(self): job = self.get_job() filenames = [] for da in job.input_datasets + job.input_library_datasets: # da is JobToInputDatasetAssociation object if da.dataset: filenames.extend(self.get_input_dataset_fnames(da.dataset)) return filenames
[docs] def get_input_paths(self, job=None): if job is None: job = self.get_job() paths = [] for da in job.input_datasets + job.input_library_datasets: # da is JobToInputDatasetAssociation object if da.dataset: paths.append(self.get_input_path(da.dataset)) return paths
[docs] def get_input_path(self, dataset): real_path = dataset.file_name false_path = self.dataset_path_rewriter.rewrite_dataset_path(dataset, 'input') return DatasetPath( dataset.dataset.id, real_path=real_path, false_path=false_path, mutable=False, dataset_uuid=dataset.dataset.uuid, object_store_id=dataset.dataset.object_store_id, )
[docs] def get_output_basenames(self): return [os.path.basename(str(fname)) for fname in self.get_output_fnames()]
[docs] def get_output_fnames(self): if self.output_paths is None: self.compute_outputs() return self.output_paths
[docs] def get_output_path(self, dataset): if getattr(dataset, "fake_dataset_association", False): return dataset.file_name assert dataset.id is not None, "{} needs to be flushed to find output path".format(dataset) if self.output_paths is None: self.compute_outputs() for (hda, dataset_path) in self.output_hdas_and_paths.values(): if hda.id == dataset.id: return dataset_path raise KeyError("Couldn't find job output for [%s] in [%s]" % (dataset, self.output_hdas_and_paths.values()))
[docs] def get_mutable_output_fnames(self): if self.output_paths is None: self.compute_outputs() return [dsp for dsp in self.output_paths if dsp.mutable]
[docs] def get_output_hdas_and_fnames(self): if self.output_hdas_and_paths is None: self.compute_outputs() return self.output_hdas_and_paths
[docs] def compute_outputs(self): dataset_path_rewriter = self.dataset_path_rewriter job = self.get_job() # Job output datasets are combination of history, library, and jeha datasets. special = self.sa_session.query(model.JobExportHistoryArchive).filter_by(job=job).first() false_path = None results = [] for da in job.output_datasets + job.output_library_datasets: da_false_path = dataset_path_rewriter.rewrite_dataset_path(da.dataset, 'output') mutable = da.dataset.dataset.external_filename is None dataset_path = DatasetPath(da.dataset.dataset.id, da.dataset.file_name, false_path=da_false_path, mutable=mutable) results.append((da.name, da.dataset, dataset_path)) self.output_paths = [t[2] for t in results] self.output_hdas_and_paths = dict([(t[0], t[1:]) for t in results]) if special: false_path = dataset_path_rewriter.rewrite_dataset_path(special, 'output') dsp = DatasetPath(special.dataset.id, special.dataset.file_name, false_path) self.output_paths.append(dsp) return self.output_paths
[docs] def get_output_file_id(self, file): if self.output_paths is None: self.get_output_fnames() for dp in self.output_paths: outputs_to_working_directory = util.asbool(self.get_destination_configuration("outputs_to_working_directory", False)) if outputs_to_working_directory and os.path.basename(dp.false_path) == file: return dp.dataset_id elif os.path.basename(dp.real_path) == file: return dp.dataset_id return None
@property def object_store(self): return self.app.object_store @property def tmp_dir_creation_statement(self): tmp_dir = self.get_destination_configuration("tmp_dir", None) if not tmp_dir or tmp_dir.lower() == "true": working_directory = self.working_directory return '''$([ ! -e '{0}/tmp' ] || mv '{0}/tmp' '{0}'/tmp.$(date +%Y%m%d-%H%M%S) ; mkdir '{0}/tmp'; echo '{0}/tmp')'''.format(working_directory) else: return tmp_dir
[docs] def home_directory(self): home_target = self.home_target return self._target_to_directory(home_target)
[docs] def tmp_directory(self): tmp_target = self.tmp_target return self._target_to_directory(tmp_target)
def _target_to_directory(self, target): working_directory = self.working_directory tmp_dir = self.get_destination_configuration("tmp_dir", None) if target is None or (target == "job_tmp_if_explicit" and tmp_dir is None): return None elif target in ["job_tmp", "job_tmp_if_explicit"]: return "$_GALAXY_JOB_TMP_DIR" elif target == "shared_home": return self.get_destination_configuration("shared_home_dir", None) elif target == "job_home": return "$_GALAXY_JOB_HOME_DIR" elif target == "pwd": return os.path.join(working_directory, "working") else: raise Exception("Unknown target type [%s]" % target)
[docs] def get_tool_provided_job_metadata(self): if self.tool_provided_job_metadata is not None: return self.tool_provided_job_metadata self.tool_provided_job_metadata = self.tool.tool_provided_metadata(self) return self.tool_provided_job_metadata
[docs] def get_dataset_finish_context(self, job_context, output_dataset_assoc): meta = {} tool_provided_metadata = self.get_tool_provided_job_metadata() dataset = output_dataset_assoc.dataset.dataset meta = tool_provided_metadata.get_dataset_meta(output_dataset_assoc.name, dataset.id, dataset.uuid) if meta: return ExpressionContext(meta, job_context) return job_context
[docs] def invalidate_external_metadata(self): job = self.get_job() self.external_output_metadata.invalidate_external_metadata([output_dataset_assoc.dataset for output_dataset_assoc in job.output_datasets + job.output_library_datasets], self.sa_session)
[docs] def setup_external_metadata(self, exec_dir=None, tmp_dir=None, dataset_files_path=None, config_root=None, config_file=None, datatypes_config=None, resolve_metadata_dependencies=False, set_extension=True, **kwds): # extension could still be 'auto' if this is the upload tool. job = self.get_job() if set_extension: for output_dataset_assoc in job.output_datasets: if output_dataset_assoc.dataset.ext == 'auto': context = self.get_dataset_finish_context(dict(), output_dataset_assoc) output_dataset_assoc.dataset.extension = context.get('ext', 'data') self.sa_session.flush() if tmp_dir is None: # this dir should should relative to the exec_dir tmp_dir = self.app.config.new_file_path if dataset_files_path is None: dataset_files_path = self.app.model.Dataset.file_path if config_root is None: config_root = self.app.config.root if config_file is None: config_file = self.app.config.config_file if datatypes_config is None: datatypes_config = os.path.join(self.working_directory, 'metadata', 'registry.xml') safe_makedirs(os.path.join(self.working_directory, 'metadata')) self.app.datatypes_registry.to_xml_file(path=datatypes_config) inp_data, out_data, out_collections = job.io_dicts(exclude_implicit_outputs=True) job_metadata = os.path.join(self.tool_working_directory, self.tool.provided_metadata_file) object_store_conf = self.object_store.to_dict() command = self.external_output_metadata.setup_external_metadata(out_data, out_collections, self.sa_session, exec_dir=exec_dir, tmp_dir=tmp_dir, dataset_files_path=dataset_files_path, config_root=config_root, config_file=config_file, datatypes_config=datatypes_config, job_metadata=job_metadata, provided_metadata_style=self.tool.provided_metadata_style, object_store_conf=object_store_conf, tool=self.tool, job=job, max_metadata_value_size=self.app.config.max_metadata_value_size, validate_outputs=self.validate_outputs, **kwds) if resolve_metadata_dependencies: metadata_tool = self.app.toolbox.get_tool("__SET_METADATA__") if metadata_tool is not None: # Due to tool shed hacks for migrate and installed tool tests... # see (``setup_shed_tools_for_test`` in test/base/driver_util.py). dependency_shell_commands = metadata_tool.build_dependency_shell_commands(job_directory=self.working_directory, metadata=True) if dependency_shell_commands: dependency_shell_commands = "; ".join(dependency_shell_commands) command = "%s; %s" % (dependency_shell_commands, command) return command
[docs] def check_for_entry_points(self, check_already_configured=True): if not self.tool.produces_entry_points: return True job = self.get_job() if check_already_configured and job.all_entry_points_configured: return True working_directory = self.working_directory container_runtime_path = os.path.join(working_directory, "container_runtime.json") if os.path.exists(container_runtime_path): with open(container_runtime_path, "r") as f: try: container_runtime = json.load(f) except ValueError: # File exists, but is not fully populated yet return False log.debug("found container runtime %s" % container_runtime) self.app.interactivetool_manager.configure_entry_points(job, container_runtime) return True
[docs] def container_monitor_command(self, container, **kwds): if not container or not self.tool.produces_entry_points: return None exec_dir = kwds.get('exec_dir', os.path.abspath(os.getcwd())) work_dir = self.working_directory configs_dir = ensure_configs_directory(work_dir) container_config = os.path.join(configs_dir, "container_config.json") self.extra_filenames.append(container_config) # What should be done with the result... 'file' or 'callback' result = self.get_destination_configuration("container_monitor_result", "file") galaxy_url = self.galaxy_url() container_config_dict = { "container_name": container.container_name, "container_type": container.container_type, "connection_configuration": container.connection_configuration, } if result == "callback": job_id = self.job_id encoded_job_id = self.app.security.encode_id(job_id) job_key = self.app.security.encode_id(job_id, kind="jobs_files") endpoint_base = "%s/api/jobs/%s/ports?job_key=%s" callback_url = endpoint_base % ( galaxy_url, encoded_job_id, job_key ) container_config_dict["callback_url"] = callback_url with open(container_config, "w") as f: json.dump(container_config_dict, f) return "(python '%s'/lib/galaxy_ext/container_monitor/monitor.py &); sleep 1 " % exec_dir
@property def user(self): job = self.get_job() if job.user is not None: return job.user.email elif job.galaxy_session is not None and job.galaxy_session.user is not None: return job.galaxy_session.user.email elif job.history is not None and job.history.user is not None: return job.history.user.email elif job.galaxy_session is not None: return 'anonymous@' + job.galaxy_session.remote_addr.split()[-1] else: return 'anonymous@unknown' def __update_output(self, job, hda, clean_only=False): """Handle writing outputs to the object store. This should be called regardless of whether the job was failed or not so that writing of partial results happens and so that the object store is cleaned up if the dataset has been purged. """ dataset = hda.dataset if dataset not in job.output_library_datasets: purged = dataset.purged if not purged and not clean_only: self.object_store.update_from_file(dataset, create=True) else: # If the dataset is purged and Galaxy is configured to write directly # to the object store from jobs - be sure that file is cleaned up. This # is a bit of hack - our object store abstractions would be stronger # and more consistent if tools weren't writing there directly. try: dataset.full_delete() except ObjectNotFound: pass def __link_file_check(self): """ outputs_to_working_directory breaks library uploads where data is linked. This method is a hack that solves that problem, but is specific to the upload tool and relies on an injected job param. This method should be removed ASAP and replaced with some properly generic and stateful way of determining link-only datasets. -nate """ if self.tool: job = self.get_job() param_dict = job.get_param_values(self.app) return self.tool.id == 'upload1' and param_dict.get('link_data_only', None) == 'link_to_files' else: # The tool is unavailable, we try to move the outputs. return False def _change_ownership(self, username, gid): job = self.get_job() external_chown_script = self.get_destination_configuration("external_chown_script", None) if external_chown_script is not None: cmd = shlex.split(external_chown_script) cmd.extend([self.working_directory, username, str(gid)]) log.debug('(%s) Changing ownership of working directory with: %s' % (job.id, ' '.join(cmd))) try: commands.execute(cmd) except commands.CommandLineException as e: log.error('external script failed: %s' % unicodify(e))
[docs] def change_ownership_for_run(self): job = self.get_job() external_chown_script = self.get_destination_configuration("external_chown_script", None) if external_chown_script and job.user is not None: try: self._change_ownership(self.user_system_pwent[0], str(self.user_system_pwent[3])) except Exception: log.exception('(%s) Failed to change ownership of %s, making world-writable instead' % (job.id, self.working_directory)) os.chmod(self.working_directory, RWXRWXRWX)
[docs] def reclaim_ownership(self): job = self.get_job() external_chown_script = self.get_destination_configuration("external_chown_script", None) if external_chown_script and job.user is not None: self._change_ownership(self.galaxy_system_pwent[0], str(self.galaxy_system_pwent[3]))
@property def user_system_pwent(self): if self.__user_system_pwent is None: job = self.get_job() self.__user_system_pwent = job.user.system_user_pwent(self.app.config.real_system_username) return self.__user_system_pwent @property def galaxy_system_pwent(self): if self.__galaxy_system_pwent is None: self.__galaxy_system_pwent = pwd.getpwuid(os.getuid()) return self.__galaxy_system_pwent
[docs] def get_output_destination(self, output_path): """ Destination for outputs marked as from_work_dir. This is the normal case, just copy these files directly to the ultimate destination. """ return output_path
@property def requires_setting_metadata(self): if self.tool: return self.tool.requires_setting_metadata return False def _report_error(self): job = self.get_job() tool = self.app.toolbox.get_tool(job.tool_id, tool_version=job.tool_version) or None for dataset in job.output_datasets: self.app.error_reports.default_error_plugin.submit_report(dataset, job, tool, user_submission=False)
[docs] def set_container(self, container): if container: cont = model.JobContainerAssociation(job=self.get_job(), container_type=container.container_type, container_name=container.container_name, container_info=container.container_info) self.sa_session.add(cont) self.sa_session.flush()
[docs]class TaskWrapper(JobWrapper): """ Extension of JobWrapper intended for running tasks. Should be refactored into a generalized executable unit wrapper parent, then jobs and tasks. """ # Abstract this to be more useful for running tasks that *don't* necessarily compose a job.
[docs] def __init__(self, task, queue): self.task_id = task.id super(TaskWrapper, self).__init__(task.job, queue) if task.prepare_input_files_cmd is not None: self.prepare_input_files_cmds = [task.prepare_input_files_cmd] else: self.prepare_input_files_cmds = None self.status = task.states.NEW
@property def dataset_path_rewriter(self): if self._dataset_path_rewriter is None: self._dataset_path_rewriter = TaskPathRewriter(self.working_directory, self._job_dataset_path_rewriter) return self._dataset_path_rewriter
[docs] def can_split(self): # Should the job handler split this job up? TaskWrapper should # always return False as the job has already been split. return False
[docs] def get_job(self): if self.job_id: return self.sa_session.query(model.Job).get(self.job_id) else: return None
[docs] def get_task(self): return self.sa_session.query(model.Task).get(self.task_id)
[docs] def get_id_tag(self): # For compatibility with drmaa job runner and TaskWrapper, instead of using job_id directly return self.get_task().get_id_tag()
[docs] def prepare(self, compute_environment=None): """ Prepare the job to run by creating the working directory and the config files. """ # Restore parameters from the database job = self._load_job() task = self.get_task() # DBTODO New method for generating command line for a task? tool_evaluator = self._get_tool_evaluator(job) compute_environment = compute_environment or self.default_compute_environment(job) tool_evaluator.set_compute_environment(compute_environment) self.sa_session.flush() self.command_line, extra_filenames, self.environment_variables = tool_evaluator.build() self.extra_filenames.extend(extra_filenames) # Ensure galaxy_lib_dir is set in case there are any later chdirs self.galaxy_lib_dir # We need command_line persisted to the db in order for Galaxy to re-queue the job # if the server was stopped and restarted before the job finished task.command_line = self.command_line self.sa_session.add(task) self.sa_session.flush() self.param_dict = tool_evaluator.param_dict self.status = 'prepared' return self.extra_filenames
[docs] def fail(self, message, exception=False): log.error("TaskWrapper Failure %s" % message) self.status = 'error'
# How do we want to handle task failure? Fail the job and let it clean up?
[docs] def change_state(self, state, info=False, flush=True, job=None): task = self.get_task() self.sa_session.refresh(task) if info: task.info = info task.state = state self.sa_session.add(task) self.sa_session.flush()
[docs] def get_state(self): task = self.get_task() self.sa_session.refresh(task) return task.state
[docs] def get_exit_code(self): task = self.get_task() self.sa_session.refresh(task) return task.exit_code
[docs] def set_runner(self, runner_url, external_id): task = self.get_task() self.sa_session.refresh(task) task.task_runner_name = runner_url task.task_runner_external_id = external_id # DBTODO Check task job_runner_stuff self.sa_session.add(task) self.sa_session.flush()
[docs] def finish(self, stdout, stderr, tool_exit_code=None, **kwds): # DBTODO integrate previous finish logic. # Simple finish for tasks. Just set the flag OK. """ Called to indicate that the associated command has been run. Updates the output datasets based on stderr and stdout from the command, and the contents of the output files. """ # This may have ended too soon log.debug('task %s for job %d ended; exit code: %d' % (self.task_id, self.job_id, tool_exit_code if tool_exit_code is not None else -256)) # default post job setup_external_metadata self.sa_session.expunge_all() task = self.get_task() # if the job was deleted, don't finish it if task.state == task.states.DELETED: # Job was deleted by an administrator delete_files = self.cleanup_job in ('always', 'onsuccess') self.cleanup(delete_files=delete_files) return elif task.state == task.states.ERROR: self.fail(task.info) return # Check what the tool returned. If the stdout or stderr matched # regular expressions that indicate errors, then set an error. # The same goes if the tool's exit code was in a given range. if self.check_tool_output(stdout, stderr, tool_exit_code=tool_exit_code, job=task) == DETECTED_JOB_STATE.OK: task.state = task.states.OK else: task.state = task.states.ERROR # Save stdout and stderr task.set_streams(stdout, stderr) self._collect_metrics(task) task.exit_code = tool_exit_code task.command_line = self.command_line self.sa_session.flush()
[docs] def cleanup(self, delete_files=True): # There is no task cleanup. The job cleans up for all tasks. pass
[docs] def get_command_line(self): return self.command_line
[docs] def get_session_id(self): return self.session_id
[docs] def get_output_file_id(self, file): # There is no permanent output file for tasks. return None
[docs] def get_tool_provided_job_metadata(self): # DBTODO Handle this as applicable for tasks. return None
[docs] def get_dataset_finish_context(self, job_context, dataset): # Handled at the parent job level. Do nothing here. pass
[docs] def setup_external_metadata(self, exec_dir=None, tmp_dir=None, dataset_files_path=None, config_root=None, config_file=None, datatypes_config=None, set_extension=True, **kwds): # There is no metadata setting for tasks. This is handled after the merge, at the job level. return ""
[docs] def get_output_destination(self, output_path): """ Destination for outputs marked as from_work_dir. These must be copied with the same basenme as the path for the ultimate output destination. This is required in the task case so they can be merged. """ return os.path.join(self.working_directory, os.path.basename(output_path))
def _create_working_directory(self, job): task = self.get_task() working_directory = task.working_directory safe_makedirs(working_directory) return working_directory
[docs]@six.add_metaclass(ABCMeta) class ComputeEnvironment(object): """ Definition of the job as it will be run on the (potentially) remote compute server. """
[docs] @abstractmethod def output_names(self): """ Output unqualified filenames defined by job. """
[docs] @abstractmethod def input_path_rewrite(self, dataset): """Input path for specified dataset."""
[docs] @abstractmethod def output_path_rewrite(self, dataset): """Output path for specified dataset."""
[docs] @abstractmethod def input_extra_files_rewrite(self, dataset): """Input extra files path rewrite for specified dataset."""
[docs] @abstractmethod def output_extra_files_rewrite(self, dataset): """Output extra files path rewrite for specified dataset."""
[docs] @abstractmethod def input_metadata_rewrite(self, dataset, metadata_value): """Input metadata path rewrite for specified dataset."""
[docs] @abstractmethod def unstructured_path_rewrite(self, path): """Rewrite loc file paths, etc.."""
[docs] @abstractmethod def working_directory(self): """ Job working directory (potentially remote) """
[docs] @abstractmethod def config_directory(self): """ Directory containing config files (potentially remote) """
[docs] @abstractmethod def sep(self): """ os.path.sep for the platform this job will execute in. """
[docs] @abstractmethod def new_file_path(self): """ Absolute path to dump new files for this job on compute server. """
[docs] @abstractmethod def tool_directory(self): """ Absolute path to tool files for this job on compute server. """
[docs] @abstractmethod def version_path(self): """ Location of the version file for the underlying tool. """
[docs] @abstractmethod def home_directory(self): """Home directory of target job - none if HOME should not be set."""
[docs] @abstractmethod def tmp_directory(self): """Temp directory of target job - none if HOME should not be set."""
[docs] @abstractmethod def galaxy_url(self): """URL to access Galaxy API from for this compute environment."""
[docs]class SimpleComputeEnvironment(object):
[docs] def config_directory(self): return os.path.join(self.working_directory(), "configs")
[docs] def sep(self): return os.path.sep
[docs]class SharedComputeEnvironment(SimpleComputeEnvironment): """ Default ComputeEnviornment for job and task wrapper to pass to ToolEvaluator - valid when Galaxy and compute share all the relevant file systems. """
[docs] def __init__(self, job_wrapper, job): self.app = job_wrapper.app self.job_wrapper = job_wrapper self.job = job
[docs] def output_names(self): return self.job_wrapper.get_output_basenames()
[docs] def output_paths(self): return self.job_wrapper.get_output_fnames()
[docs] def input_path_rewrite(self, dataset): return self.job_wrapper.get_input_path(dataset).false_path
[docs] def output_path_rewrite(self, dataset): dataset_path = self.job_wrapper.get_output_path(dataset) if hasattr(dataset_path, "false_path"): return dataset_path.false_path else: return dataset_path
[docs] def input_extra_files_rewrite(self, dataset): return None
[docs] def output_extra_files_rewrite(self, dataset): return None
[docs] def input_metadata_rewrite(self, dataset, metadata_value): return None
[docs] def unstructured_path_rewrite(self, path): return None
[docs] def working_directory(self): return self.job_wrapper.working_directory
[docs] def new_file_path(self): return os.path.abspath(self.app.config.new_file_path)
[docs] def version_path(self): return self.job_wrapper.get_version_string_path()
[docs] def tool_directory(self): tool_dir = self.job_wrapper.tool.tool_dir if tool_dir is not None: tool_dir = os.path.abspath(tool_dir) return tool_dir
[docs] def home_directory(self): return self.job_wrapper.home_directory()
[docs] def tmp_directory(self): return self.job_wrapper.tmp_directory()
[docs] def galaxy_url(self): return self.job_wrapper.get_destination_configuration("galaxy_infrastructure_url")
[docs]class NoopQueue(object): """ Implements the JobQueue / JobStopQueue interface but does nothing """
[docs] def put(self, *args, **kwargs): return
[docs] def put_stop(self, *args): return
[docs] def shutdown(self): return