def run_indri(args, output, overwrite_threads=False):
from subprocess import Popen, PIPE
import os
cancel = Variable('cancel', get_client())
if cancel.get():
return ('canceled', get_worker().address, 0, get_loadinfo())
start = time.time()
if overwrite_threads:
processes = len(os.sched_getaffinity(0)) - 1
args = (args[0], '-threads={}'.format(processes), *args[1:])
with Popen(args, stdout=PIPE, stderr=PIPE) as proc:
content = []
for l in proc.stdout:
content.append(l)
if len(content) % 1000 != 0:
continue
if cancel.get():
proc.kill()
return ('killed', get_worker().address, time.time() - start,
get_loadinfo())
with open(output, 'wb') as f:
f.writelines(content)
return ('completed', get_worker().address, time.time() - start,
get_loadinfo())
def _func_init_nccl(sessionId, uniqueId):
"""
Initialize ncclComm_t on worker
Parameters
----------
sessionId : str
session identifier from a comms instance
uniqueId : array[byte]
The NCCL unique Id generated from the
client.
"""
worker = get_worker()
raft_comm_state = get_raft_comm_state(
sessionId=sessionId, state_object=get_worker()
)
wid = raft_comm_state["wid"]
nWorkers = raft_comm_state["nworkers"]
try:
n = nccl()
n.init(nWorkers, uniqueId, wid)
raft_comm_state["nccl"] = n
except Exception as e:
worker.log_event(
topic="error", msg=f"An error occurred initializing NCCL: {e}."
)
raise
def get_endpoints(addr_ports):
# Create endpoints to all other workers
ucx = get_worker()._ucx
for address, port in addr_ports:
if address != get_worker().address:
host, p = parse_host_port(address)
ucx.get_endpoint(host, port)
def build_ucx():
# Create listener and cache on worker
get_worker()._callback_invoked = False
def mock_callback(ep):
get_worker()._callback_invoked = True
ucx = UCX.get(mock_callback)
get_worker()._ucx = ucx
return get_worker().address, ucx.listener_port()
def xgboost_postprocess(pkl_path):
with open(pkl_path, 'rb') as rf:
obj = pickle.load(rf)
try:
worker = get_worker()
dp = None
for plg in worker.plugins:
if 'process' in plg:
dp = worker.plugins[plg]
break
if dp is None:
raise ValueError('No process plugin registered')
except Exception as e:
logger.error(str(e), exc_info=True)
raise e
objects = obj['content']
objects = postprocess(dp.postprocess_model, dp.classes, objects)
# remove empty strings returned from postprocess
objects = [i for i in objects if i != '']
obj['xgboost_content'] = objects
with open(pkl_path, 'wb') as wf:
pickle.dump(obj, wf)
return pkl_path
def _wrapped_function(function, *args, **kwargs):
available_resources = kwargs['available_resources']
per_worker_logging = kwargs.pop('per_worker_logging')
gpu_assignments = kwargs.pop('gpu_assignments')
# Set up the logging per worker if the flag is set to True.
if per_worker_logging:
# Each worker should have its own log file.
kwargs['logger_path'] = '{}.log'.format(get_worker().id)
if available_resources.number_of_gpus > 0:
worker_id = distributed.get_worker().id
available_resources._gpu_device_indices = (
'0' if worker_id not in gpu_assignments else
gpu_assignments[worker_id])
logging.info(
f'Launching a job with access to GPUs {available_resources._gpu_device_indices}'
)
return_value = _Multiprocessor.run(function, *args, **kwargs)
return return_value
def _func_set_worker_as_nccl_root(sessionId, verbose):
"""
Creates a persistent nccl uniqueId on the scheduler node.
Parameters
----------
sessionId : Associated session to attach the unique ID to.
verbose : Indicates whether or not to emit additional information
Return
------
uniqueId : byte str
NCCL uniqueId, associating this DASK worker as its root node.
"""
worker = get_worker()
if verbose:
worker.log_event(
topic="info",
msg=f"Setting worker as NCCL root for session, '{sessionId}'",
)
nccl_uid = set_nccl_root(sessionId=sessionId, state_object=worker)
if verbose:
worker.log_event(
topic="info", msg="Done setting scheduler as NCCL root."
)
return nccl_uid
def _wrapped_function(function, *args, **kwargs):
available_resources = kwargs["available_resources"]
per_worker_logging = kwargs.pop("per_worker_logging")
gpu_assignments = kwargs.pop("gpu_assignments")
# Set up the logging per worker if the flag is set to True.
if per_worker_logging:
# Each worker should have its own log file.
os.makedirs("worker-logs", exist_ok=True)
kwargs["logger_path"] = os.path.join("worker-logs",
f"{get_worker().id}.log")
if available_resources.number_of_gpus > 0:
worker_id = distributed.get_worker().id
available_resources._gpu_device_indices = (
"0" if worker_id not in gpu_assignments else
gpu_assignments[worker_id])
logger.info(f"Launching a job with access to GPUs "
f"{available_resources._gpu_device_indices}")
return_value = _Multiprocessor.run(function, *args, **kwargs)
return return_value
def evalOneMax(individual, lmbd=3):
#lmbd = 3
edge = '18_1'
start_time = 57600
end_time = 86400
try:
#rank = mp.current_process()._identity[0]
#rank = scoop.worker.decode("utf-8")
#rank = rank.replace(".", "")
#rank = rank.replace(":","")
#rank = random.randint(0, 100)
get_worker().id
except:
rank = 0
return run_sim(lmbd, edge, start_time, end_time, rank, individual)
def dask_to_fst(*args, **kwargs):
import sys
import logging
from dask.distributed import get_worker
#logger name is the same for all workers
logger = logging.getLogger(logging_basename)
#add handlers if none are present for this worker
if not len(logger.handlers):
command_line_args = args[2]
logger.setLevel(command_line_args.log_level)
logging.captureWarnings(True)
#handlers
worker_id = str(get_worker().id).lower()
stream_handler = logging.StreamHandler(sys.stdout)
file_handler = logging.FileHandler('logs/' + worker_id, 'w')
#levels
stream_handler.setLevel(command_line_args.log_level)
file_handler.setLevel(command_line_args.log_level)
#format
formatter_stream = logging.Formatter(worker_id + ' %(message)s')
stream_handler.setFormatter(formatter_stream)
formatter_file = logging.Formatter(
'%(asctime)s - %(name)s in %(funcName)s - %(levelname)s - %(message)s'
)
file_handler.setFormatter(formatter_file)
#add handlers
logger.addHandler(stream_handler)
logger.addHandler(file_handler)
return to_fst(*args, **kwargs)
def _train_part(params, model_factory, list_of_parts, worker_address_to_port, return_model,
time_out=120, **kwargs):
local_worker_address = get_worker().address
machine_list = ','.join([
'%s:%d' % (urlparse(worker_address).hostname, port)
for worker_address, port
in worker_address_to_port.items()
])
network_params = {
'machines': machine_list,
'local_listen_port': worker_address_to_port[local_worker_address],
'time_out': time_out,
'num_machines': len(worker_address_to_port)
}
params.update(network_params)
# Concatenate many parts into one
parts = tuple(zip(*list_of_parts))
data = _concat(parts[0])
label = _concat(parts[1])
weight = _concat(parts[2]) if len(parts) == 3 else None
try:
model = model_factory(**params)
model.fit(data, label, sample_weight=weight, **kwargs)
finally:
_safe_call(_LIB.LGBM_NetworkFree())
return model if return_model else None
def __setstate__(self, state):
# When we are running on a dask worker, functions
# are executed in a different thread from the worker
# itself, even if there is only one thread. To prevent
# problems with SQLite, we check if this is a worker and
# if there is only one thread, in which case we can
# safely ignore the fact that the database is accessed
# from a different thread than where it is created.
from dask.distributed import get_worker
try:
worker = get_worker()
except ValueError:
n_threads = -1
else:
n_threads = worker.nthreads
database_path = state.pop('_sqlitedb_path_', None)
database_readonly = state.pop('_sqlitedb_readonly_', False)
self.__dict__ = state
if database_path and not database_readonly:
from ...database import SQLiteDB
if os.path.exists(database_path):
self.db = SQLiteDB(
database_path,
initialize='skip',
readonly=database_readonly,
check_same_thread=(n_threads != 1),
)
def construct_linked_kb(eids):
try:
worker = get_worker()
dp = None
for plg in worker.plugins:
if 'Linking' in plg:
dp = worker.plugins[plg]
break
if dp is None:
raise Exception('No linking plugin registered')
if eids is None:
return None
results = []
for eid in eids:
entity = dp.linker.kb.cui_to_entity[eid]
result = {
'id': entity.concept_id,
'name': entity.canonical_name,
'aliases': tuple(entity.aliases),
'types': tuple(entity.types),
'description': entity.definition
}
results.append(result)
result_df = pd.DataFrame(results)
return result_df
except Exception as e:
logger.error(str(e), exc_info=True)
return None
def link(content, score_threshold=0.8):
try:
worker = get_worker()
dp = None
for plg in worker.plugins:
if 'Linking' in plg:
dp = worker.plugins[plg]
break
if dp is None:
raise Exception('No linking plugin registered')
linking_result = dp.nlp(content)
ent_set = set()
nonlinked_list = set() # We'll only add one copy of the entity mention per paragraph.
for ent in linking_result.ents:
linked = False
for ent_id, score in ent._.kb_ents:
if score > score_threshold:
linked = True
if ent_id in ent_set:
continue
ent_set.add(ent_id)
break
if not linked:
nonlinked_list.add(ent.text)
ent_set = list(ent_set)
nonlinked_list = list(nonlinked_list)
return nonlinked_list, ent_set
except Exception as e:
logger.error(str(e), exc_info=True)
return (None, None)
def func_chunk(chunk_ds):
worker = get_worker()
# 'memory_limit': '2GB',
worker.memory_target_fraction = 0.95
worker.memory_spill_fraction = 0.95
worker.memory_pause_fraction = 0.95
worker.memory_terminate_fraction = 0.95 # False
# print('chunk started:', chunk_ds.lat[0].data, chunk_ds.lon[0].data, flush=True)
res_ds = nested_groupby_apply(chunk_ds, ['lat', 'lon', 'prob'],
func_pwc_mr_fd)
# group_by removes the dimensions mentioned, so the resulting ds is
# lower dimensional, unfortunatley, map_blocks does not do that and so
# putting the sub result datasets back together becomes technically difficult
# chunk_fake_ds = make_fake_ds(chunk_ds).chunk(sub_chunk_dict)
# sub_chunk_ds = chunk_ds.chunk(sub_chunk_dict)
# res_ds = xr.map_blocks(func_pwc_mr_fd, sub_chunk_ds, template=chunk_fake_ds)
# print(
# 'chunk finished:',
# chunk_ds.lat[0].data, chunk_ds.lon[0].data, chunk_ds.prob[0].data,
# flush=True
# )
# write_to_logfile(
# 'chunk finished,',
# "lat:", chunk_ds.lat[0].data,
# "lon:", chunk_ds.lon[0].data,
# "prob:", chunk_ds.prob[0].data
# )
return res_ds
def fake_remote_fct(
docker_auth: DockerBasicAuth,
service_key: str,
service_version: str,
input_data: TaskInputData,
output_data_keys: TaskOutputDataSchema,
log_file_url: AnyUrl,
command: List[str],
) -> TaskOutputData:
# get the task data
worker = get_worker()
task = worker.tasks.get(worker.get_current_task())
assert task is not None
print(f"--> task {task=} started")
cancel_event = Event(TaskCancelEventName.format(task.key))
# tell the client we are started
start_event = Event(_DASK_EVENT_NAME)
start_event.set()
# sleep a bit in case someone is aborting us
print("--> waiting for task to be aborted...")
cancel_event.wait(timeout=10)
if cancel_event.is_set():
# NOTE: asyncio.CancelledError is not propagated back to the client...
print("--> raising cancellation error now")
raise TaskCancelledError
return TaskOutputData.parse_obj({"some_output_key": 123})
def dask_mapper(current_range):
"""
Gets the paths to the file(s) in the current executor, then
declares the headers found.
Args:
current_range (tuple): The current range of the dataset being
processed on the executor.
Returns:
function: The map function to be executed on each executor,
complete with all headers needed for the analysis.
"""
# Retrieve the current worker local directory
localdir = get_worker().local_directory
# Get and declare headers on each worker
headers_on_executor = [
os.path.join(localdir, os.path.basename(filepath))
for filepath in headers
]
Utils.declare_headers(headers_on_executor)
# Get and declare shared libraries on each worker
shared_libs_on_ex = [
os.path.join(localdir, os.path.basename(filepath))
for filepath in shared_libraries
]
Utils.declare_shared_libraries(shared_libs_on_ex)
return mapper(current_range)
async def _func_ucp_create_endpoints(sessionId, worker_info):
"""
Runs on each worker to create ucp endpoints to all other workers
:param sessionId: uuid unique id for this instance
:param worker_info: dict Maps worker address to rank & UCX port
:param r: float a random number to stop the function from being cached
"""
dask_worker = get_worker()
local_address = dask_worker.address
eps = [None] * len(worker_info)
count = 1
for k in worker_info:
if str(k) != str(local_address):
ip, port = parse_host_port(k)
ep = await ucp.create_endpoint(ip,
worker_info[k]["p"])
eps[worker_info[k]["r"]] = ep
count += 1
worker_state(sessionId)["ucp_eps"] = eps
def worker_load(h5pyFileName, localNames, svNames, elements, allSums):
from dask.distributed import get_worker
worker = get_worker()
worker._structures = {}
worker._true_forces = {}
with h5py.File(h5pyFileName, 'r') as h5pyFile:
for struct in localNames:
worker._structures[struct] = {}
for sv in svNames:
worker._structures[struct][sv] = {}
for elem in elements:
worker._structures[struct][sv][elem] = {}
group = h5pyFile[struct][sv][elem]
energyData = np.array(group['energy'][()],
dtype=np.float32)
forcesData = np.array(group['forces'][()],
dtype=np.float32)
if (allSums) and (len(forcesData.shape) == 4):
forcesData = forcesData.sum(axis=0)
worker._structures[struct][sv][elem]['energy'] = energyData
worker._structures[struct][sv][elem]['forces'] = forcesData
tvF = h5pyFile[struct].attrs['forces']
worker._true_forces[struct] = np.array(tvF, dtype=np.float32)
def get_raft_comm_state(sessionId, state_object=None):
"""
Retrieves cuML comms state on the scheduler node, for the given sessionId,
creating a new session if it does not exist. If no session id is given,
returns the state dict for all sessions.
Parameters
----------
sessionId : SessionId value to retrieve from the dask_scheduler instances
state_object : Object (either Worker, or Scheduler) on which the raft
comm state will retrieved (or created)
Returns
-------
session state : str
session state associated with sessionId
"""
state_object = state_object if state_object is not None else get_worker()
if not hasattr(state_object, "_raft_comm_state"):
state_object._raft_comm_state = {}
if (
sessionId is not None
and sessionId not in state_object._raft_comm_state
):
state_object._raft_comm_state[sessionId] = {"ts": time.time()}
if sessionId is not None:
return state_object._raft_comm_state[sessionId]
return state_object._raft_comm_state
def _train_part(params,
model_factory,
list_of_parts,
worker_addresses,
return_model,
local_listen_port=12400,
time_out=120,
**kwargs):
network_params = build_network_params(worker_addresses,
get_worker().address,
local_listen_port, time_out)
params.update(network_params)
# Concatenate many parts into one
parts = tuple(zip(*list_of_parts))
data = concat(parts[0])
label = concat(parts[1])
weight = concat(parts[2]) if len(parts) == 3 else None
try:
model = model_factory(**params)
model.fit(data, label, sample_weight=weight)
finally:
_safe_call(_LIB.LGBM_NetworkFree())
return model if return_model else None
def call_anneal_method(remote_worker,
sampler_state,
lambdas,
noneq_trajectory_filename=None,
num_integration_steps=1,
return_timer=False,
return_sampler_state=False,
rethermalize=False,
compute_incremental_work=True):
"""
this function calls LocallyOptimalAnnealing.anneal;
since we can only map functions with parallelisms (no actors), we need to submit a function that calls
the LocallyOptimalAnnealing.anneal method.
"""
if remote_worker == 'remote':
_class = distributed.get_worker()
else:
_class = remote_worker
incremental_work, new_sampler_state, timer, _pass, endstate_corrections = _class.annealing_class.anneal(
sampler_state=sampler_state,
lambdas=lambdas,
noneq_trajectory_filename=noneq_trajectory_filename,
num_integration_steps=num_integration_steps,
return_timer=return_timer,
return_sampler_state=return_sampler_state,
rethermalize=rethermalize,
compute_incremental_work=compute_incremental_work)
return incremental_work, new_sampler_state, timer, _pass, endstate_corrections
def fake_sidecar_fct(
docker_auth: DockerBasicAuth,
service_key: str,
service_version: str,
input_data: TaskInputData,
output_data_keys: TaskOutputDataSchema,
log_file_url: AnyUrl,
command: List[str],
expected_annotations: Dict[str, Any],
) -> TaskOutputData:
sub = Sub(TaskCancelEvent.topic_name())
# get the task data
worker = get_worker()
task = worker.tasks.get(worker.get_current_task())
assert task is not None
print(f"--> task {task=} started")
assert task.annotations == expected_annotations
# sleep a bit in case someone is aborting us
print("--> waiting for task to be aborted...")
for msg in sub:
assert msg
print(f"--> received cancellation msg: {msg=}")
cancel_event = TaskCancelEvent.parse_raw(msg) # type: ignore
assert cancel_event
if cancel_event.job_id == task.key:
print("--> raising cancellation error now")
raise asyncio.CancelledError("task cancelled")
return TaskOutputData.parse_obj({"some_output_key": 123})
def _fit_local(params,
model_factory,
list_of_parts,
worker_addresses,
return_model,
local_listen_port=12400,
listen_time_out=120,
**kwargs):
network_params = build_network_params(worker_addresses,
get_worker().address,
local_listen_port, listen_time_out)
params = {**params, **network_params}
# Prepare data
if len(list_of_parts[0]) == 3:
data, labels, weight = zip(*list_of_parts)
weight = concat(weight)
else:
data, labels = zip(*list_of_parts)
weight = None
data = concat(data) # Concatenate many parts into one
labels = concat(labels)
try:
classifier = model_factory(**params)
classifier.fit(data, labels, sample_weight=weight)
finally:
_safe_call(_LIB.LGBM_NetworkFree())
if return_model:
return classifier
else:
return None
def process(self, inputs):
'''
'''
import cudf
worker = None
try:
from dask.distributed import get_worker
worker = get_worker()
except (ValueError, ImportError):
pass
logname = convert(self.__class__.__name__)
logmgr = MortgagePluginsLoggerMgr(worker, logname)
logger = logmgr.get_logger()
worker_name = ''
if worker is not None:
worker_name = 'WORKER {} '.format(worker.name)
performance_path = self.conf['csvfile_perfdata']
logger.info(worker_name + 'LOADING: {}'.format(performance_path))
cols = list(self.addition.keys())
dtypes = list(self.addition.values())
mortgage_gdf = cudf.read_csv(performance_path,
names=cols,
dtype=dtypes,
delimiter='|',
skiprows=1)
logmgr.cleanup()
return mortgage_gdf
async def _func_ucp_create_endpoints(sessionId, worker_info):
"""
Runs on each worker to create ucp endpoints to all other workers
Parameters
----------
sessionId : str
uuid unique id for this instance
worker_info : dict
Maps worker addresses to NCCL ranks & UCX ports
"""
eps = [None] * len(worker_info)
count = 1
for k in worker_info:
ip, port = parse_host_port(k)
ep = await get_ucx().get_endpoint(ip, worker_info[k]["port"])
eps[worker_info[k]["rank"]] = ep
count += 1
raft_comm_state = get_raft_comm_state(
sessionId=sessionId, state_object=get_worker()
)
raft_comm_state["ucp_eps"] = eps
def _func_store_initial_state(nworkers, sessionId, uniqueId, wid):
raft_comm_state = get_raft_comm_state(
sessionId=sessionId, state_object=get_worker()
)
raft_comm_state["nccl_uid"] = uniqueId
raft_comm_state["wid"] = wid
raft_comm_state["nworkers"] = nworkers
def _func_build_handle(sessionId, streams_per_handle, verbose):
"""
Builds a handle_t on the current worker given the initialized comms
Parameters
----------
sessionId : str id to reference state for current comms instance.
streams_per_handle : int number of internal streams to create
verbose : bool print verbose logging output
"""
worker = get_worker()
if verbose:
worker.log_event(
topic="info", msg="Finished injecting comms on handle."
)
handle = Handle(streams_per_handle)
raft_comm_state = get_raft_comm_state(
sessionId=sessionId, state_object=worker
)
workerId = raft_comm_state["wid"]
nWorkers = raft_comm_state["nworkers"]
nccl_comm = raft_comm_state["nccl"]
inject_comms_on_handle_coll_only(
handle, nccl_comm, nWorkers, workerId, verbose
)
raft_comm_state["handle"] = handle
def wrapped(doc: str, *args, **kwargs):
worker = get_worker()
try:
nlp = worker.nlp
except AttributeError:
nlp = spacy.load(model)
worker.nlp = nlp
return func(nlp(doc), *args, **kwargs)
async def _func_init_all(
sessionId, uniqueId, comms_p2p, worker_info, verbose, streams_per_handle
):
worker = get_worker()
raft_comm_state = get_raft_comm_state(
sessionId=sessionId, state_object=worker
)
raft_comm_state["nccl_uid"] = uniqueId
raft_comm_state["wid"] = worker_info[get_worker().address]["rank"]
raft_comm_state["nworkers"] = len(worker_info)
if verbose:
worker.log_event(topic="info", msg="Initializing NCCL.")
start = time.time()
_func_init_nccl(sessionId, uniqueId)
if verbose:
elapsed = time.time() - start
worker.log_event(
topic="info", msg=f"NCCL Initialization took: {elapsed} seconds."
)
if comms_p2p:
if verbose:
worker.log_event(topic="info", msg="Initializing UCX Endpoints")
if verbose:
start = time.time()
await _func_ucp_create_endpoints(sessionId, worker_info)
if verbose:
elapsed = time.time() - start
msg = (
f"Done initializing UCX endpoints."
f"Took: {elapsed} seconds.\nBuilding handle."
)
worker.log_event(topic="info", msg=msg)
_func_build_handle_p2p(sessionId, streams_per_handle, verbose)
if verbose:
worker.log_event(topic="info", msg="Done building handle.")
else:
_func_build_handle(sessionId, streams_per_handle, verbose)
