def workflow_logging_context(job_id) -> None:
"""Initialize the workflow logging context.
Workflow executions are running as remote functions from
WorkflowManagementActor. Without logging redirection, workflow
inner execution logs will be pushed to the driver that initially
created WorkflowManagementActor rather than the driver that
actually submits the current workflow execution.
We use this conext manager to re-configure the log files to send
the logs to the correct driver, and to restore the log files once
the execution is done.
Args:
job_id: The ID of the job that submits the workflow execution.
"""
node = ray._private.worker._global_node
original_out_file, original_err_file = node.get_log_file_handles(
get_worker_log_file_name("WORKER"))
out_file, err_file = node.get_log_file_handles(
get_worker_log_file_name("WORKER", job_id))
try:
configure_log_file(out_file, err_file)
yield
finally:
configure_log_file(original_out_file, original_err_file)
ray.worker._global_node = node
ray.worker.connect(node, mode=mode, runtime_env_hash=args.runtime_env_hash)
# Add code search path to sys.path, set load_code_from_local.
core_worker = ray.worker.global_worker.core_worker
code_search_path = core_worker.get_job_config().code_search_path
load_code_from_local = False
if code_search_path:
load_code_from_local = True
for p in code_search_path:
if os.path.isfile(p):
p = os.path.dirname(p)
sys.path.insert(0, p)
ray.worker.global_worker.set_load_code_from_local(load_code_from_local)
# Setup log file.
out_file, err_file = node.get_log_file_handles(
get_worker_log_file_name(args.worker_type))
configure_log_file(out_file, err_file)
if mode == ray.WORKER_MODE:
ray.worker.global_worker.main_loop()
elif (mode == ray.RESTORE_WORKER_MODE or mode == ray.SPILL_WORKER_MODE
or mode == ray.UTIL_WORKER_MODE):
# It is handled by another thread in the C++ core worker.
# We just need to keep the worker alive.
while True:
time.sleep(100000)
else:
raise ValueError(f"Unexcepted worker mode: {mode}")