def wait_for_available_slots(self, min_np, min_hosts=1):
extra_message = ' An elastic job also requires that at least two hosts ' \
'are available to resolve compatible network interfaces. If you know which interfaces ' \
'are compatible in your network, set `--network-interface` to skip this check.' \
if min_hosts > 1 else ''
tmout = timeout.Timeout(
self._timeout,
message=
'Timed out waiting for {{activity}}. Please check that you have '
'enough resources to run at least {min_np} Horovod processes.{extra_message}'
.format(min_np=min_np, extra_message=extra_message))
self._wait_hosts_cond.acquire()
try:
while True:
current_hosts = self._host_manager.current_hosts
avail_slots = current_hosts.count_available_slots()
logging.debug(f"current available slots: {avail_slots}")
avail_hosts = len(current_hosts.available_hosts)
logging.debug(f"current available hosts: {avail_hosts}.")
if avail_slots >= min_np and avail_hosts >= min_hosts:
return current_hosts
if self._shutdown.is_set():
raise RuntimeError(
'Job has been shutdown, see above error messages for details.'
)
self._wait_hosts_cond.wait(tmout.remaining())
tmout.check_time_out_for(
'minimum number of slots to become available')
finally:
self._wait_hosts_cond.release()
def start_timeout(self):
return timeout.Timeout(
self.timeout_s,
message="Timed out waiting for {activity}. Please "
"check connectivity between servers. You "
"may need to increase the --start-timeout "
"parameter if you have too many servers.")
def _run_elastic(args):
# construct host discovery component
if args.host_discovery_script:
discover_hosts = discovery.HostDiscoveryScript(
args.host_discovery_script, args.slots)
elif args.hosts:
_, available_host_slots = hosts.parse_hosts_and_slots(args.hosts)
if len(available_host_slots) < 2:
raise ValueError(
'Cannot run in fault tolerance mode with fewer than 2 hosts.')
discover_hosts = discovery.FixedHosts(available_host_slots)
else:
raise ValueError(
'One of --host-discovery-script, --hosts, or --hostnames must be provided'
)
# horovodrun has to finish all the checks before this timeout runs out.
if args.start_timeout:
start_timeout = args.start_timeout
else:
# Lookup default timeout from the environment variable.
start_timeout = int(os.getenv('HOROVOD_START_TIMEOUT', '30'))
tmout = timeout.Timeout(start_timeout,
message='Timed out waiting for {activity}. Please '
'check connectivity between servers. You '
'may need to increase the --start-timeout '
'parameter if you have too many servers.')
settings = elastic_settings.ElasticSettings(
discovery=discover_hosts,
min_num_proc=args.min_num_proc or args.num_proc,
max_num_proc=args.max_num_proc,
elastic_timeout=args.elastic_timeout,
reset_limit=args.reset_limit,
cooldown_range=args.cooldown_range,
num_proc=args.num_proc,
verbose=2 if args.verbose else 0,
ssh_port=args.ssh_port,
ssh_identity_file=args.ssh_identity_file,
extra_mpi_args=args.mpi_args,
key=secret.make_secret_key(),
start_timeout=tmout,
output_filename=args.output_filename,
run_func_mode=args.run_func is not None,
nics=args.nics,
prefix_output_with_timestamp=args.prefix_output_with_timestamp)
if not gloo_built(verbose=(settings.verbose >= 2)):
raise ValueError(
'Gloo support is required to use elastic training, but has not been built. Ensure CMake is '
'installed and reinstall Horovod with HOROVOD_WITH_GLOO=1 to debug the build error.'
)
env = os.environ.copy()
config_parser.set_env_from_args(env, args)
executable = args.executable or sys.executable
return gloo_run_elastic(settings, env,
args.run_func if args.run_func else args.command,
executable)
def test_mpi_run_full(self):
if not mpi_available():
self.skipTest("MPI is not available")
cmd = ['cmd', 'arg1', 'arg2']
nics = ['eth0', 'eth1']
env = {'env1': 'val1', 'env2': 'val2'}
stdout = '<stdout>'
stderr = '<stderr>'
tmout = timeout.Timeout(5, message='Timed out waiting for something.')
settings = hvd_settings.Settings(
verbose=0,
ssh_port=1022,
extra_mpi_args='>mpi-extra args go here<',
binding_args='>binding args go here<',
key=secret.make_secret_key(),
start_timeout=tmout,
num_proc=1,
hosts='localhost:1',
output_filename='>output filename goes here<',
run_func_mode=True
)
def mpi_impl_flags(tcp, env=None):
return ["--mock-mpi-impl-flags"], []
with mock.patch("horovod.runner.mpi_run._get_mpi_implementation_flags", side_effect=mpi_impl_flags) as impl:
with mock.patch("horovod.runner.mpi_run.safe_shell_exec.execute", return_value=0) as execute:
mpi_run(settings, nics, env, cmd, stdout=stdout, stderr=stderr)
# assert call on _get_mpi_implementation_flags
impl.assert_called_once_with(None, env=env)
# call the mocked _get_mpi_implementation_flags method ourselves
mpi_flags, _ = horovod.runner.mpi_run._get_mpi_implementation_flags(False)
self.assertIsNotNone(mpi_flags)
expected_command = ('mpirun '
'--allow-run-as-root --tag-output '
'-np 1 -H {hosts} '
'>binding args go here< '
'{mpi_flags} '
'-mca plm_rsh_args "-p 1022" '
'-mca btl_tcp_if_include eth0,eth1 -x NCCL_SOCKET_IFNAME=eth0,eth1 '
'--output-filename >output filename goes here< '
'-x env1 -x env2 '
'>mpi-extra args go here< '
'cmd arg1 arg2').format(hosts=settings.hosts,
mpi_flags=' '.join(mpi_flags))
# remove PYTHONPATH from execute's env
# we cannot know the exact value of that env variable
# we test right handling of PYTHONPATH in test_mpi_run_*pythonpath* below
self.assertIn('env', execute.call_args.kwargs)
if 'PYTHONPATH' in execute.call_args.kwargs['env']:
execute.call_args.kwargs['env'].pop('PYTHONPATH')
expected_env = {'env1': 'val1', 'env2': 'val2', 'PATH': os.environ.get('PATH')}
execute.assert_called_once_with(expected_command, env=expected_env, stdout=stdout, stderr=stderr)
def _run_command(self, command, env, event,
stdout=None, stderr=None, index=None,
prefix_output_with_timestamp=False):
super(SparkTaskService, self)._run_command(command, env, event,
stdout, stderr, index,
prefix_output_with_timestamp)
if self._minimum_command_lifetime_s is not None:
self._minimum_command_lifetime = timeout.Timeout(self._minimum_command_lifetime_s,
message='Just measuring runtime')
def create_settings(min_np: int = 1,
max_np: int = None,
reset_limit: int = None,
elastic_timeout: int = 600,
timeout_s: int = 30,
ssh_identity_file: str = None,
nics: str = None,
**kwargs):
"""Returns a Settings object for ElasticRayExecutor.
Note that the `discovery` property will be set at runtime.
Args:
min_np (int): Minimum number of processes running for
training to continue. If number of available processes dips
below this threshold, then training will wait for
more instances to become available.
max_np (int): Maximum number of training processes,
beyond which no additional processes will be created.
If not specified, then will be unbounded.
reset_limit (int): Maximum number of times that the training
job can scale up or down the number of workers after
which the job is terminated.
elastic_timeout (int): Timeout for elastic initialisation after
re-scaling the cluster. The default value is 600 seconds.
Alternatively, the environment variable
HOROVOD_ELASTIC_TIMEOUT can also be used.'
timeout_s (int): Horovod performs all the checks and starts the
processes before the specified timeout.
The default value is 30 seconds.
ssh_identity_file (str): File on the driver from which
the identity (private key) is read.
nics (set): Network interfaces that can be used for communication.
"""
start_timeout = timeout.Timeout(
timeout_s,
message="Timed out waiting for {activity}. Please "
"check connectivity between servers. You "
"may need to increase the --start-timeout "
"parameter if you have too many servers.")
ssh_identity_file = ssh_identity_file or os.path.expanduser(
"~/ray_bootstrap_key.pem")
settings = ElasticSettings(
discovery=None,
min_np=min_np,
max_np=max_np,
elastic_timeout=elastic_timeout,
reset_limit=reset_limit,
num_proc=min_np,
ssh_identity_file=ssh_identity_file,
nics=nics,
start_timeout=start_timeout,
key=secret.make_secret_key() if secret else None,
**kwargs)
return settings
def _run_static(args):
nics_set = set(args.nics.split(',')) if args.nics else None
# horovodrun has to finish all the checks before this timeout runs out.
if args.start_timeout:
start_timeout = args.start_timeout
else:
# Lookup default timeout from the environment variable.
start_timeout = int(os.getenv('HOROVOD_START_TIMEOUT', '30'))
tmout = timeout.Timeout(start_timeout,
message='Timed out waiting for {activity}. Please '
'check connectivity between servers. You '
'may need to increase the --start-timeout '
'parameter if you have too many servers.')
settings = hvd_settings.Settings(verbose=2 if args.verbose else 0,
ssh_port=args.ssh_port,
ssh_identity_file=args.ssh_identity_file,
extra_mpi_args=args.mpi_args,
tcp_flag=args.tcp_flag,
binding_args=args.binding_args,
key=secret.make_secret_key(),
start_timeout=tmout,
num_proc=args.np,
hosts=args.hosts,
output_filename=args.output_filename,
run_func_mode=args.run_func is not None,
nics=nics_set)
# This cache stores the results of checks performed by horovod
# during the initialization step. It can be disabled by setting
# --disable-cache flag.
fn_cache = None
if not args.disable_cache:
params = ''
if args.np:
params += str(args.np) + ' '
if args.hosts:
params += str(args.hosts) + ' '
if args.ssh_port:
params += str(args.ssh_port)
if args.ssh_identity_file:
params += args.ssh_identity_file
parameters_hash = hashlib.md5(params.encode('utf-8')).hexdigest()
fn_cache = cache.Cache(CACHE_FOLDER, CACHE_STALENESS_THRESHOLD_MINUTES,
parameters_hash)
all_host_names, _ = hosts.parse_hosts_and_slots(args.hosts)
if settings.verbose >= 2:
print('Filtering local host names.')
remote_host_names = network.filter_local_addresses(all_host_names)
if settings.verbose >= 2:
print('Remote host found: ' + ' '.join(remote_host_names))
if len(remote_host_names) > 0:
if settings.verbose >= 2:
print('Checking ssh on all remote hosts.')
# Check if we can ssh into all remote hosts successfully.
if not _check_all_hosts_ssh_successful(remote_host_names,
args.ssh_port,
args.ssh_identity_file,
fn_cache=fn_cache):
raise RuntimeError('could not connect to some hosts via ssh')
if settings.verbose >= 2:
print('SSH was successful into all the remote hosts.')
nics = driver_service.get_common_interfaces(settings, all_host_names,
remote_host_names, fn_cache)
if args.run_func:
# get the driver IPv4 address
driver_ip = network.get_driver_ip(nics)
run_func_server = KVStoreServer(verbose=settings.verbose)
run_func_server_port = run_func_server.start_server()
put_data_into_kvstore(driver_ip, run_func_server_port, 'runfunc',
'func', args.run_func)
command = [
sys.executable, '-m', 'horovod.runner.run_task',
str(driver_ip),
str(run_func_server_port)
]
try:
_launch_job(args, settings, nics, command)
results = [None] * args.np
# TODO: make it parallel to improve performance
for i in range(args.np):
results[i] = read_data_from_kvstore(driver_ip,
run_func_server_port,
'runfunc_result', str(i))
return results
finally:
run_func_server.shutdown_server()
else:
command = args.command
_launch_job(args, settings, nics, command)
return None
def run_elastic(
fn,
args=(),
kwargs={},
num_proc=None,
min_num_proc=None,
max_num_proc=None,
start_timeout=None,
elastic_timeout=None,
reset_limit=None,
env=None,
stdout=None,
stderr=None,
verbose=1,
nics=None,
prefix_output_with_timestamp=False,
# np is deprecated, use min_num_proc instead
min_np=None,
# max_num_proc is deprecated, use max_num_proc instead
max_np=None):
"""
Runs Elastic Horovod on Spark. Runs `num_proc` processes executing `fn` using the same amount of Spark tasks.
Args:
fn: Function to run.
args: Arguments to pass to `fn`.
kwargs: Keyword arguments to pass to `fn`.
num_proc: Number of Horovod processes. Defaults to `spark.default.parallelism`.
min_num_proc: Minimum number of processes running for training to continue.
If number of available processes dips below this threshold,
then training will wait for more instances to become available.
max_num_proc: Maximum number of training processes,
beyond which no additional processes will be created.
If not specified, then will be unbounded.
start_timeout: Timeout for Spark tasks to spawn, register and start running the code, in seconds.
If not set, falls back to `HOROVOD_SPARK_START_TIMEOUT` environment variable value.
If it is not set as well, defaults to 600 seconds.
elastic_timeout: Timeout for elastic initialisation after re-scaling the cluster.
If not set, falls back to `HOROVOD_ELASTIC_TIMEOUT` environment variable value.
If it is not set as well, defaults to 600 seconds.
reset_limit: Maximum number of resets after which the job is terminated.
env: Environment dictionary to use in Horovod run. Defaults to `os.environ`.
stdout: Horovod stdout is redirected to this stream.
stderr: Horovod stderr is redirected to this stream.
verbose: Debug output verbosity (0-2). Defaults to 1.
nics: List of NICs for tcp network communication.
prefix_output_with_timestamp: shows timestamp in stdout/stderr forwarding on the driver
Returns:
List of results returned by running `fn` on each rank.
"""
if min_np is not None:
min_num_proc = min_np
warnings.warn('min_np is deprecated, use min_num_proc instead',
DeprecationWarning)
if max_np is not None:
max_num_proc = max_np
warnings.warn('max_np is deprecated, use max_num_proc instead',
DeprecationWarning)
if not gloo_built(verbose=(verbose >= 2)):
raise ValueError(
'Gloo support is required to use elastic training, but has not been built. Ensure CMake is '
'installed and reinstall Horovod with HOROVOD_WITH_GLOO=1 to debug the build error.'
)
spark_context = pyspark.SparkContext._active_spark_context
if spark_context is None:
raise Exception('Could not find an active SparkContext, are you '
'running in a PySpark session?')
if start_timeout is None:
# Lookup default timeout from the environment variable.
start_timeout = int(os.getenv('HOROVOD_SPARK_START_TIMEOUT', '600'))
# nics needs to be a set
if nics and not isinstance(nics, set):
nics = set(nics)
if num_proc is None:
# TODO: #2023 try spark.dynamicAllocation.initialExecutors
num_proc = spark_context.defaultParallelism
if verbose >= 1:
logging.info(
'Running %d processes (inferred from spark.default.parallelism)...',
num_proc)
else:
if verbose >= 1:
logging.info('Running %d processes...', num_proc)
if min_num_proc is None:
# TODO: #2023 try spark.dynamicAllocation.minExecutors
min_num_proc = num_proc
if max_num_proc is None:
# TODO: #2023 try spark.dynamicAllocation.maxExecutors
max_num_proc = num_proc
# start Spark driver service and launch settings.num_proc Spark tasks
key = secret.make_secret_key()
spark_job_group = 'horovod.spark.run.%d' % job_id.next_job_id()
driver = driver_service.SparkDriverService(num_proc, max_num_proc, fn,
args, kwargs, key, nics)
discovery = host_discovery.SparkDriverHostDiscovery(driver)
tmout = timeout.Timeout(
start_timeout,
message='Timed out waiting for {activity}. Please check that you have '
'enough resources to run all Horovod processes. Each Horovod '
'process runs in a Spark task. You may need to increase the '
'start_timeout parameter to a larger value if your Spark resources '
'are allocated on-demand.')
settings = hvd_elastic_settings.ElasticSettings(
discovery=discovery,
min_num_proc=min_num_proc,
max_num_proc=max_num_proc,
elastic_timeout=elastic_timeout,
reset_limit=reset_limit,
num_proc=num_proc,
verbose=verbose,
key=key,
start_timeout=tmout,
nics=nics,
run_func_mode=True,
prefix_output_with_timestamp=prefix_output_with_timestamp)
result_queue = queue.Queue(1)
# launch settings.num_proc / settings.max_num_proc Spark tasks
spark_thread = _make_spark_thread(spark_context,
spark_job_group,
driver,
result_queue,
settings,
use_gloo=True,
is_elastic=True)
try:
# Register task addresses of initial num_proc tasks
_register_task_addresses(driver, settings)
# Run the job
gloo_run_elastic(settings, driver, env, stdout, stderr)
except:
# Terminate Spark job.
spark_context.cancelJobGroup(spark_job_group)
# Re-raise exception.
raise
finally:
spark_thread.join()
driver.shutdown()
# Make sure Spark Job did not fail.
driver.check_for_spark_job_failure()
# get ranks from driver
indices_in_rank_order = _get_indices_in_rank_order(driver)
# If there's no exception, execution results are in this queue.
results = result_queue.get_nowait()
return [results[index] for index in indices_in_rank_order]
def run(fn,
args=(),
kwargs={},
num_proc=None,
start_timeout=None,
use_mpi=None,
use_gloo=None,
extra_mpi_args=None,
env=None,
stdout=None,
stderr=None,
verbose=1,
nics=None,
prefix_output_with_timestamp=False,
executable=None):
"""
Runs Horovod on Spark. Runs `num_proc` processes executing `fn` using the same amount of Spark tasks.
Args:
fn: Function to run.
args: Arguments to pass to `fn`.
kwargs: Keyword arguments to pass to `fn`.
num_proc: Number of Horovod processes. Defaults to `spark.default.parallelism`.
start_timeout: Timeout for Spark tasks to spawn, register and start running the code, in seconds.
If not set, falls back to `HOROVOD_SPARK_START_TIMEOUT` environment variable value.
If it is not set as well, defaults to 600 seconds.
extra_mpi_args: Extra arguments for mpi_run. Defaults to no extra args.
env: Environment dictionary to use in Horovod run.
stdout: Horovod stdout is redirected to this stream. Defaults to sys.stdout when used with MPI.
stderr: Horovod stderr is redirected to this stream. Defaults to sys.stderr when used with MPI.
verbose: Debug output verbosity (0-2). Defaults to 1.
nics: List of NICs for tcp network communication.
prefix_output_with_timestamp: shows timestamp in stdout/stderr forwarding on the driver
executable: Optional executable to run when launching the workers. Defaults to `sys.executable`.
Returns:
List of results returned by running `fn` on each rank.
"""
if start_timeout is None:
# Lookup default timeout from the environment variable.
start_timeout = int(os.getenv('HOROVOD_SPARK_START_TIMEOUT', '600'))
# nics needs to be a set
if nics and not isinstance(nics, set):
nics = set(nics)
tmout = timeout.Timeout(
start_timeout,
message='Timed out waiting for {activity}. Please check that you have '
'enough resources to run all Horovod processes. Each Horovod '
'process runs in a Spark task. You may need to increase the '
'start_timeout parameter to a larger value if your Spark resources '
'are allocated on-demand.')
settings = hvd_settings.Settings(
verbose=verbose,
extra_mpi_args=extra_mpi_args,
key=secret.make_secret_key(),
start_timeout=tmout,
nics=nics,
run_func_mode=True,
prefix_output_with_timestamp=prefix_output_with_timestamp)
spark_context = pyspark.SparkContext._active_spark_context
if spark_context is None:
raise Exception('Could not find an active SparkContext, are you '
'running in a PySpark session?')
if num_proc is None:
num_proc = spark_context.defaultParallelism
if settings.verbose >= 1:
logging.info(
'Running %d processes (inferred from spark.default.parallelism)...',
num_proc)
else:
if settings.verbose >= 1:
logging.info('Running %d processes...', num_proc)
settings.num_proc = num_proc
result_queue = queue.Queue(1)
# start Spark driver service and launch settings.num_proc Spark tasks
spark_job_group = 'horovod.spark.run.%d' % job_id.next_job_id()
driver = driver_service.SparkDriverService(settings.num_proc,
settings.num_proc, fn, args,
kwargs, settings.key,
settings.nics)
gloo_is_used = is_gloo_used(use_gloo=use_gloo,
use_mpi=use_mpi,
use_jsrun=False)
spark_thread = _make_spark_thread(spark_context,
spark_job_group,
driver,
result_queue,
settings,
use_gloo=gloo_is_used,
is_elastic=False)
try:
# wait for all tasks to register, notify them and initiate task-to-task address registration
_notify_and_register_task_addresses(driver, settings)
# Determine the index grouping based on host hashes.
# Barrel shift until index 0 is in the first host.
host_hashes = list(driver.task_host_hash_indices().keys())
host_hashes.sort()
while 0 not in driver.task_host_hash_indices()[host_hashes[0]]:
host_hashes = host_hashes[1:] + host_hashes[:1]
settings.hosts = ','.join(
'%s:%d' %
(host_hash, len(driver.task_host_hash_indices()[host_hash]))
for host_hash in host_hashes)
# Run the job
_launch_job(use_mpi, use_gloo, settings, driver, env, stdout, stderr,
executable)
except:
# Terminate Spark job.
spark_context.cancelJobGroup(spark_job_group)
# Re-raise exception.
raise
finally:
spark_thread.join()
driver.shutdown()
# Make sure Spark Job did not fail.
driver.check_for_spark_job_failure()
# get ranks from driver
indices_in_rank_order = _get_indices_in_rank_order(driver)
# If there's no exception, execution results are in this queue.
results = result_queue.get_nowait()
return [results[index] for index in indices_in_rank_order]
def _run_command(self, command, env, event):
super(SparkTaskService, self)._run_command(command, env, event)
if self._minimum_command_lifetime_s is not None:
self._minimum_command_lifetime = timeout.Timeout(self._minimum_command_lifetime_s,
message='Just measuring runtime')
def _handle(self, req, client_address):
if isinstance(req, RegisterDispatcherRequest):
self._wait_cond.acquire()
try:
if not 0 <= req.dispatcher_id <= self._max_dispatcher_id:
return IndexError(
f'Dispatcher id must be within [0..{self._max_dispatcher_id}]: '
f'{req.dispatcher_id}')
if self._dispatcher_addresses[req.dispatcher_id] is not None and \
self._dispatcher_addresses[req.dispatcher_id] != req.dispatcher_address:
return ValueError(
f'Dispatcher with id {req.dispatcher_id} has already been registered under '
f'different address {self._dispatcher_addresses[req.dispatcher_id]}: '
f'{req.dispatcher_address}')
self._dispatcher_addresses[
req.dispatcher_id] = req.dispatcher_address
self._wait_cond.notify_all()
finally:
self._wait_cond.release()
return network.AckResponse()
if isinstance(req, WaitForDispatcherRegistrationRequest):
self._wait_cond.acquire()
try:
if not 0 <= req.dispatcher_id <= self._max_dispatcher_id:
return IndexError(
f'Dispatcher id must be within [0..{self._max_dispatcher_id}]: '
f'{req.dispatcher_id}')
tmout = timeout.Timeout(
timeout=req.timeout,
message=
'Timed out waiting for {activity}. Try to find out what takes '
'the dispatcher so long to register or increase timeout.')
while self._dispatcher_addresses[req.dispatcher_id] is None:
self._wait_cond.wait(tmout.remaining())
tmout.check_time_out_for(
f'dispatcher {req.dispatcher_id} to register')
except TimeoutException as e:
return e
finally:
self._wait_cond.release()
return WaitForDispatcherRegistrationResponse(
self._dispatcher_addresses[req.dispatcher_id])
if isinstance(req, RegisterDispatcherWorkerRequest):
self._wait_cond.acquire()
try:
if not 0 <= req.dispatcher_id <= self._max_dispatcher_id:
return IndexError(
f'Dispatcher id must be within [0..{self._max_dispatcher_id}]: '
f'{req.dispatcher_id}')
self._dispatcher_worker_ids[req.dispatcher_id].update(
{req.worker_id})
self._wait_cond.notify_all()
finally:
self._wait_cond.release()
return network.AckResponse()
if isinstance(req, WaitForDispatcherWorkerRegistrationRequest):
# if there is only a single dispatcher, wait for that one instead of the requested one
dispatcher_id = req.dispatcher_id if self._max_dispatcher_id > 0 else 0
self._wait_cond.acquire()
try:
if not 0 <= req.dispatcher_id <= self._max_dispatcher_id:
return IndexError(
f'Dispatcher id must be within [0..{self._max_dispatcher_id}]: '
f'{req.dispatcher_id}')
tmout = timeout.Timeout(
timeout=req.timeout,
message=
'Timed out waiting for {activity}. Try to find out what takes '
'the workers so long to register or increase timeout.')
while len(self._dispatcher_worker_ids[dispatcher_id]
) < self._workers_per_dispatcher:
self._wait_cond.wait(tmout.remaining())
tmout.check_time_out_for(
f'workers for dispatcher {dispatcher_id} to register')
except TimeoutException as e:
return e
finally:
self._wait_cond.release()
return network.AckResponse()
if isinstance(req, ShutdownRequest):
in_thread(self.shutdown)
return network.AckResponse()
if isinstance(req, WaitForShutdownRequest):
self._wait_cond.acquire()
try:
while not self._shutdown:
self._wait_cond.wait()
finally:
self._wait_cond.release()
return network.AckResponse()
return super()._handle(req, client_address)
