- tool = RunnerContainer(self, tool, loadingContext, runtimeContext.enable_reuse,
- self.output_name,
- self.output_tags,
- submit_runner_ram=runtimeContext.submit_runner_ram,
- name=runtimeContext.name,
- on_error=runtimeContext.on_error,
- submit_runner_image=runtimeContext.submit_runner_image,
- intermediate_output_ttl=runtimeContext.intermediate_output_ttl,
- merged_map=merged_map,
- priority=runtimeContext.priority,
- secret_store=self.secret_store,
- collection_cache_size=runtimeContext.collection_cache_size,
- collection_cache_is_default=self.should_estimate_cache_size)
- elif self.work_api == "jobs":
- tool = RunnerJob(self, tool, loadingContext, runtimeContext.enable_reuse,
- self.output_name,
- self.output_tags,
- submit_runner_ram=runtimeContext.submit_runner_ram,
- name=runtimeContext.name,
- on_error=runtimeContext.on_error,
- submit_runner_image=runtimeContext.submit_runner_image,
- merged_map=merged_map)
- elif runtimeContext.cwl_runner_job is None and self.work_api == "jobs":
- # Create pipeline for local run
- self.pipeline = self.api.pipeline_instances().create(
- body={
- "owner_uuid": self.project_uuid,
- "name": runtimeContext.name if runtimeContext.name else shortname(tool.tool["id"]),
- "components": {},
- "state": "RunningOnClient"}).execute(num_retries=self.num_retries)
- logger.info("Pipeline instance %s", self.pipeline["uuid"])