if runtime_req:
if "keep_cache" in runtime_req:
runtime_constraints["keep_cache_mb_per_task"] = runtime_req["keep_cache"]
+ runtime_constraints["min_ram_mb_per_node"] += runtime_req["keep_cache"]
if "outputDirType" in runtime_req:
if runtime_req["outputDirType"] == "local_output_dir":
script_parameters["task.keepTmpOutput"] = False
find_or_create=self.enable_reuse
).execute(num_retries=self.arvrunner.num_retries)
+ if self.enable_reuse:
+ # When reusing jobs, copy its output/log collection to the desired project
+ reused_collections = [('Output', job.get('output', None)),
+ ('Log', job.get('log', None))]
+ for col_type, pdh in [(n, p) for n, p in reused_collections if p]:
+ c = arvados.collection.Collection(pdh,
+ api_client=self.arvrunner.api,
+ keep_client=self.arvrunner.keep_client,
+ num_retries=self.arvrunner.num_retries)
+ c.save_new(name="{} of {}".format(col_type, self.name),
+ owner_uuid=self.arvrunner.project_uuid,
+ ensure_unique_name=True,
+ num_retries=self.arvrunner.num_retries)
+ logger.info("Copied reused job's %s to collection %s",
+ col_type.lower(),
+ c.manifest_locator())
+ # Give read permission to the desired project on reused jobs
+ for job_name, job_uuid in job.get('components', {}).items():
+ self.arvrunner.api.links().create(body={
+ 'link_class': 'can_read',
+ 'tail_uuid': self.arvrunner.project_uuid,
+ 'head_uuid': job_uuid,
+ }).execute(num_retries=self.arvrunner.num_retries)
+
for k,v in job_spec["script_parameters"].items():
if v is False or v is None or isinstance(v, dict):
job_spec["script_parameters"][k] = {"value": v}