handler = RuntimeStatusLoggingHandler(self.runtime_status_update)
root_logger.addHandler(handler)
- self.runtimeContext = ArvRuntimeContext(vars(arvargs))
- self.runtimeContext.make_fs_access = partial(CollectionFsAccess,
+ self.toplevel_runtimeContext = ArvRuntimeContext(vars(arvargs))
+ self.toplevel_runtimeContext.make_fs_access = partial(CollectionFsAccess,
collection_cache=self.collection_cache)
- validate_cluster_target(self, self.runtimeContext)
+ validate_cluster_target(self, self.toplevel_runtimeContext)
def arv_make_tool(self, toolpath_object, loadingContext):
updated_tool.visit(self.check_features)
- self.project_uuid = runtimeContext.project_uuid
self.pipeline = None
self.fs_access = runtimeContext.make_fs_access(runtimeContext.basedir)
self.secret_store = runtimeContext.secret_store
if runtimeContext.submit_request_uuid and self.work_api != "containers":
raise Exception("--submit-request-uuid requires containers API, but using '{}' api".format(self.work_api))
+ runtimeContext = runtimeContext.copy()
+
default_storage_classes = ",".join([k for k,v in self.api.config().get("StorageClasses", {"default": {"Default": True}}).items() if v.get("Default") is True])
if runtimeContext.storage_classes == "default":
runtimeContext.storage_classes = default_storage_classes
if not runtimeContext.name:
runtimeContext.name = self.name = updated_tool.tool.get("label") or updated_tool.metadata.get("label") or os.path.basename(updated_tool.tool["id"])
+ if runtimeContext.copy_deps is None and (runtimeContext.create_workflow or runtimeContext.update_workflow):
+ # When creating or updating workflow record, by default
+ # always copy dependencies and ensure Docker images are up
+ # to date.
+ runtimeContext.copy_deps = True
+ runtimeContext.match_local_docker = True
+
+ if runtimeContext.update_workflow and self.project_uuid is None:
+ # If we are updating a workflow, make sure anything that
+ # gets uploaded goes into the same parent project, unless
+ # an alternate --project-uuid was provided.
+ existing_wf = self.api.workflows().get(uuid=runtimeContext.update_workflow).execute()
+ runtimeContext.project_uuid = existing_wf["owner_uuid"]
+
+ self.project_uuid = runtimeContext.project_uuid
+
# Upload local file references in the job order.
job_order = upload_job_order(self, "%s input" % runtimeContext.name,
- updated_tool, job_order)
+ updated_tool, job_order, runtimeContext)
# the last clause means: if it is a command line tool, and we
# are going to wait for the result, and always_submit_runner
# Upload direct dependencies of workflow steps, get back mapping of files to keep references.
# Also uploads docker images.
- merged_map = upload_workflow_deps(self, tool)
+ merged_map = upload_workflow_deps(self, tool, runtimeContext)
# Recreate process object (ArvadosWorkflow or
# ArvadosCommandTool) because tool document may have been
loadingContext.metadata = tool.metadata
tool = load_tool(tool.tool, loadingContext)
- existing_uuid = runtimeContext.update_workflow
- if existing_uuid or runtimeContext.create_workflow:
+ if runtimeContext.update_workflow or runtimeContext.create_workflow:
# Create a pipeline template or workflow record and exit.
if self.work_api == "containers":
uuid = upload_workflow(self, tool, job_order,
- self.project_uuid,
- uuid=existing_uuid,
- submit_runner_ram=runtimeContext.submit_runner_ram,
- name=runtimeContext.name,
- merged_map=merged_map,
- submit_runner_image=runtimeContext.submit_runner_image)
+ runtimeContext.project_uuid,
+ runtimeContext,
+ uuid=runtimeContext.update_workflow,
+ submit_runner_ram=runtimeContext.submit_runner_ram,
+ name=runtimeContext.name,
+ merged_map=merged_map,
+ submit_runner_image=runtimeContext.submit_runner_image)
self.stdout.write(uuid + "\n")
return (None, "success")
self.ignore_docker_for_reuse = runtimeContext.ignore_docker_for_reuse
self.eval_timeout = runtimeContext.eval_timeout
- runtimeContext = runtimeContext.copy()
runtimeContext.use_container = True
runtimeContext.tmpdir_prefix = "tmp"
runtimeContext.work_api = self.work_api