import arvados.config
from arvados.keep import KeepClient
from arvados.errors import ApiError
+import arvados.commands._util as arv_cmd
from .arvcontainer import ArvadosContainer, RunnerContainer
from .arvjob import ArvadosJob, RunnerJob, RunnerTemplate
-from. runner import Runner, upload_docker, upload_job_order, upload_workflow_deps, upload_dependencies
+from. runner import Runner, upload_docker, upload_job_order, upload_workflow_deps
from .arvtool import ArvadosCommandTool
from .arvworkflow import ArvadosWorkflow, upload_workflow
from .fsaccess import CollectionFsAccess, CollectionFetcher, collectionResolver, CollectionCache
"""
def __init__(self, api_client, work_api=None, keep_client=None,
- output_name=None, output_tags=None, num_retries=4,
- thread_count=4):
+ output_name=None, output_tags=None, default_storage_classes="default",
+ num_retries=4, thread_count=4):
self.api = api_client
self.processes = {}
self.workflow_eval_lock = threading.Condition(threading.RLock())
self.final_output = None
self.final_status = None
- self.uploaded = {}
self.num_retries = num_retries
self.uuid = None
self.stop_polling = threading.Event()
self.trash_intermediate = False
self.thread_count = thread_count
self.poll_interval = 12
+ self.default_storage_classes = default_storage_classes
if keep_client is not None:
self.keep_client = keep_client
self.collection_cache = CollectionCache(self.api, self.keep_client, self.num_retries)
+ self.fetcher_constructor = partial(CollectionFetcher,
+ api_client=self.api,
+ fs_access=CollectionFsAccess("", collection_cache=self.collection_cache),
+ num_retries=self.num_retries)
+
self.work_api = None
expected_api = ["jobs", "containers"]
for api in expected_api:
def arv_make_tool(self, toolpath_object, **kwargs):
kwargs["work_api"] = self.work_api
- kwargs["fetcher_constructor"] = partial(CollectionFetcher,
- api_client=self.api,
- fs_access=CollectionFsAccess("", collection_cache=self.collection_cache),
- num_retries=self.num_retries)
+ kwargs["fetcher_constructor"] = self.fetcher_constructor
kwargs["resolver"] = partial(collectionResolver, self.api, num_retries=self.num_retries)
if "class" in toolpath_object and toolpath_object["class"] == "CommandLineTool":
return ArvadosCommandTool(self, toolpath_object, **kwargs)
with self.workflow_eval_lock:
self.processes[container.uuid] = container
- def process_done(self, uuid):
+ def process_done(self, uuid, record):
with self.workflow_eval_lock:
- if uuid in self.processes:
- del self.processes[uuid]
+ j = self.processes[uuid]
+ logger.info("%s %s is %s", self.label(j), uuid, record["state"])
+ self.task_queue.add(partial(j.done, record))
+ del self.processes[uuid]
def wrapped_callback(self, cb, obj, st):
with self.workflow_eval_lock:
j.update_pipeline_component(event["properties"]["new_attributes"])
logger.info("%s %s is Running", self.label(j), uuid)
elif event["properties"]["new_attributes"]["state"] in ("Complete", "Failed", "Cancelled", "Final"):
- with self.workflow_eval_lock:
- j = self.processes[uuid]
- self.task_queue.add(partial(j.done, event["properties"]["new_attributes"]))
- logger.info("%s %s is %s", self.label(j), uuid, event["properties"]["new_attributes"]["state"])
+ self.process_done(uuid, event["properties"]["new_attributes"])
def label(self, obj):
return "[%s %s]" % (self.work_api[0:-1], obj.name)
finally:
self.stop_polling.set()
- def get_uploaded(self):
- return self.uploaded.copy()
-
- def add_uploaded(self, src, pair):
- self.uploaded[src] = pair
-
def add_intermediate_output(self, uuid):
if uuid:
self.intermediate_output_collections.append(uuid)
self.api.collections().delete(uuid=i).execute(num_retries=self.num_retries)
except:
logger.warn("Failed to delete intermediate output: %s", sys.exc_info()[1], exc_info=(sys.exc_info()[1] if self.debug else False))
- if sys.exc_info()[0] is KeyboardInterrupt:
+ if sys.exc_info()[0] is KeyboardInterrupt or sys.exc_info()[0] is SystemExit:
break
def check_features(self, obj):
with SourceLine(obj, i, UnsupportedRequirement, logger.isEnabledFor(logging.DEBUG)):
self.check_features(v)
- def make_output_collection(self, name, tagsString, outputObj):
+ def make_output_collection(self, name, storage_classes, tagsString, outputObj):
outputObj = copy.deepcopy(outputObj)
files = []
with final.open("cwl.output.json", "w") as f:
json.dump(outputObj, f, sort_keys=True, indent=4, separators=(',',': '))
- final.save_new(name=name, owner_uuid=self.project_uuid, ensure_unique_name=True)
+ final.save_new(name=name, owner_uuid=self.project_uuid, storage_classes=storage_classes, ensure_unique_name=True)
logger.info("Final output collection %s \"%s\" (%s)", final.portable_data_hash(),
final.api_response()["name"],
if self.intermediate_output_ttl < 0:
raise Exception("Invalid value %d for --intermediate-output-ttl, cannot be less than zero" % self.intermediate_output_ttl)
+ if kwargs.get("submit_request_uuid") and self.work_api != "containers":
+ raise Exception("--submit-request-uuid requires containers API, but using '{}' api".format(self.work_api))
+
if not kwargs.get("name"):
kwargs["name"] = self.name = tool.tool.get("label") or tool.metadata.get("label") or os.path.basename(tool.tool["id"])
submit_runner_image=kwargs.get("submit_runner_image"),
intermediate_output_ttl=kwargs.get("intermediate_output_ttl"),
merged_map=merged_map,
+ default_storage_classes=self.default_storage_classes,
priority=kwargs.get("priority"),
secret_store=self.secret_store)
elif self.work_api == "jobs":
logger.info("Pipeline instance %s", self.pipeline["uuid"])
if runnerjob and not kwargs.get("wait"):
- runnerjob.run(**kwargs)
+ submitargs = kwargs.copy()
+ submitargs['submit'] = False
+ runnerjob.run(**submitargs)
return (runnerjob.uuid, "success")
self.poll_api = arvados.api('v1')
except UnsupportedRequirement:
raise
except:
- if sys.exc_info()[0] is KeyboardInterrupt:
+ if sys.exc_info()[0] is KeyboardInterrupt or sys.exc_info()[0] is SystemExit:
logger.error("Interrupted, workflow will be cancelled")
else:
logger.error("Execution failed: %s", sys.exc_info()[1], exc_info=(sys.exc_info()[1] if self.debug else False))
if self.final_output is None:
raise WorkflowException("Workflow did not return a result.")
+
if kwargs.get("submit") and isinstance(runnerjob, Runner):
logger.info("Final output collection %s", runnerjob.final_output)
else:
self.output_name = "Output of %s" % (shortname(tool.tool["id"]))
if self.output_tags is None:
self.output_tags = ""
- self.final_output, self.final_output_collection = self.make_output_collection(self.output_name, self.output_tags, self.final_output)
+
+ storage_classes = kwargs.get("storage_classes").strip().split(",")
+ self.final_output, self.final_output_collection = self.make_output_collection(self.output_name, storage_classes, self.output_tags, self.final_output)
self.set_crunch_output()
if kwargs.get("compute_checksum"):
help="Docker image for workflow runner job, default arvados/jobs:%s" % __version__,
default=None)
+ parser.add_argument("--submit-request-uuid", type=str,
+ default=None,
+ help="Update and commit supplied container request instead of creating a new one (containers API only).")
+
parser.add_argument("--name", type=str,
help="Name to use for workflow execution instance.",
default=None)
parser.add_argument("--enable-dev", action="store_true",
help="Enable loading and running development versions "
"of CWL spec.", default=False)
+ parser.add_argument('--storage-classes', default="default", type=str,
+ help="Specify comma separated list of storage classes to be used when saving workflow output to Keep.")
parser.add_argument("--intermediate-output-ttl", type=int, metavar="N",
help="If N > 0, intermediate output collections will be trashed N seconds after creation. Default is 0 (don't trash).",
"http://arvados.org/cwl#ReuseRequirement"
])
+def exit_signal_handler(sigcode, frame):
+ logger.error("Caught signal {}, exiting.".format(sigcode))
+ sys.exit(-sigcode)
+
def main(args, stdout, stderr, api_client=None, keep_client=None,
install_sig_handlers=True):
parser = arg_parser()
job_order_object = None
arvargs = parser.parse_args(args)
+ if len(arvargs.storage_classes.strip().split(',')) > 1:
+ logger.error("Multiple storage classes are not supported currently.")
+ return 1
+
if install_sig_handlers:
- signal.signal(signal.SIGTERM, lambda x, y: thread.interrupt_main())
+ arv_cmd.install_signal_handlers()
if arvargs.update_workflow:
if arvargs.update_workflow.find('-7fd4e-') == 5:
keep_client = arvados.keep.KeepClient(api_client=api_client, num_retries=4)
runner = ArvCwlRunner(api_client, work_api=arvargs.work_api, keep_client=keep_client,
num_retries=4, output_name=arvargs.output_name,
- output_tags=arvargs.output_tags,
+ output_tags=arvargs.output_tags, default_storage_classes=parser.get_default("storage_classes"),
thread_count=arvargs.thread_count)
except Exception as e:
logger.error(e)
projects / arvados.git / blobdiff