def test_pause_executor(c, s, a):
memory = psutil.Process().memory_info().rss
a.memory_limit = memory / 0.5 + 200e6
np = pytest.importorskip("numpy")
def f():
x = np.ones(int(400e6), dtype="u1")
sleep(1)
with captured_logger(logging.getLogger("distributed.worker")) as logger:
future = c.submit(f)
futures = c.map(slowinc, range(30), delay=0.1)
start = time()
while not a.paused:
yield gen.sleep(0.01)
assert time() < start + 4, (
format_bytes(psutil.Process().memory_info().rss),
format_bytes(a.memory_limit),
len(a.data),
)
out = logger.getvalue()
assert "memory" in out.lower()
assert "pausing" in out.lower()
assert sum(f.status == "finished" for f in futures) < 4
yield wait(futures)
def print_ds_info(ds, var):
"""Function for printing chunking information"""
dt = ds[var].dtype
itemsize = dt.itemsize
chunk_size = ds[var].data.chunksize
size = format_bytes(ds.nbytes)
_bytes = reduce(mul, chunk_size) * itemsize
chunk_size_bytes = format_bytes(_bytes)
print(f'Variable name: {var}')
print(f'Dataset dimensions: {ds[var].dims}')
print(f'Chunk shape: {chunk_size}')
print(f'Dataset shape: {ds[var].shape}')
print(f'Chunk size: {chunk_size_bytes}')
print(f'Dataset size: {size}')
def main(args=None):
args = parse_args(args)
if args.protocol == 'ucx':
sched_str = "ucx://" + args.server + ":" + args.port
client = Client(sched_str)
else:
kwargs = {'n_workers': 2, 'threads_per_worker': 40}
kwargs['processes'] = args.protocol == 'tcp'
cluster = LocalCluster(**kwargs)
client = Client(cluster)
print(f"Connected to {client}")
N = 1_000_000
P = 1_000
X = da.random.uniform(size=(N, P), chunks=(N // 100, P))
print(format_bytes(X.nbytes))
result = X.T.dot(X)
start = clock()
result.compute()
stop = clock()
print(result)
print(f"\tTook {stop - start:0.2f}s")
time.sleep(10)
def _widget_status(self):
workers = len(self.scheduler.workers)
cores = sum(ws.ncores for ws in self.scheduler.workers.values())
memory = sum(ws.memory_limit for ws in self.scheduler.workers.values())
memory = format_bytes(memory)
text = """
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table style="text-align: right;">
<tr><th>Workers</th> <td>%d</td></tr>
<tr><th>Cores</th> <td>%d</td></tr>
<tr><th>Memory</th> <td>%s</td></tr>
</table>
</div>
""" % (
workers,
cores,
memory,
)
return text
def _widget_status(self):
client = self._dask_client()
workers = client.scheduler_info()['workers']
n_workers = len(workers)
cores = sum(w['nthreads'] for w in workers.values())
memory = sum(w['memory_limit'] for w in workers.values())
text = """
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table style="text-align: right;">
<tr><th>Workers</th> <td>%d</td></tr>
<tr><th>Cores</th> <td>%d</td></tr>
<tr><th>Memory</th> <td>%s</td></tr>
</table>
</div>
""" % (n_workers, cores, format_bytes(memory))
return text
def main(args=None):
args = parse_args(args)
if args.protocol == 'ucx':
address = dask.config.get("distributed.comm.ucxaddress")
if address is None:
raise ValueError("Set distributed.comm.ucxaddress")
client = Client(address)
else:
kwargs = {'n_workers': 2, 'threads_per_worker': 40}
kwargs['processes'] = args.protocol == 'tcp'
cluster = LocalCluster(**kwargs)
client = Client(cluster)
print(f"Connected to {client}")
N = 1_000_000
P = 1_000
X = da.random.uniform(size=(N, P), chunks=(N // 100, P))
print(format_bytes(X.nbytes))
result = X.T.dot(X)
start = clock()
result.compute()
stop = clock()
print(result)
print(f"\tTook {stop - start:0.2f}s")
time.sleep(10)
def _widget_status(self):
### reporting proper number of nodes vs workers in a multi-GPU worker scenario
nodes = len(self.scheduler_info["workers"])
if self.use_gpu:
nodes = int(nodes / self.n_gpus_per_node)
if hasattr(self, "worker_spec"):
requested = sum(
1 if "group" not in each else len(each["group"])
for each in self.worker_spec.values()
)
elif hasattr(self, "nodes"):
requested = len(self.nodes)
else:
requested = nodes
nodes = self._format_nodes(nodes, requested, self.use_gpu, self.n_gpus_per_node)
cores = sum(v["nthreads"] for v in self.scheduler_info["workers"].values())
cores_or_gpus = "Workers (GPUs)" if self.use_gpu else "Workers (vCPUs)"
memory = (
sum(
v["gpu"]["memory-total"][0]
for v in self.scheduler_info["workers"].values()
)
if self.use_gpu
else sum(v["memory_limit"] for v in self.scheduler_info["workers"].values())
)
memory = format_bytes(memory)
text = """
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table style="text-align: right;">
<tr> <th>Nodes</th> <td>%s</td></tr>
<tr> <th>%s</th> <td>%s</td></tr>
<tr> <th>Memory</th> <td>%s</td></tr>
</table>
</div>
""" % (
nodes,
cores_or_gpus,
cores,
memory,
)
return text
def make_cluster_model(
cluster_id: str,
cluster_name: str,
cluster: Cluster,
adaptive: Union[Adaptive, None],
) -> ClusterModel:
"""
Make a cluster model. This is a JSON-serializable representation
of the information about a cluster that can be sent over the wire.
Parameters
----------
cluster_id: string
A unique string for the cluster.
cluster_name: string
A display name for the cluster.
cluster: Cluster
The cluster out of which to make the cluster model.
adaptive: Adaptive
The adaptive controller for the number of workers for the cluster, or
none if the cluster is not scaled adaptively.
"""
# This would be a great target for a dataclass
# once python 3.7 is in wider use.
try:
info = cluster.scheduler_info
except AttributeError:
info = cluster.scheduler.identity()
try:
cores = sum(d["nthreads"] for d in info["workers"].values())
except KeyError: # dask.__version__ < 2.0
cores = sum(d["ncores"] for d in info["workers"].values())
assert isinstance(info, dict)
model = dict(
id=cluster_id,
name=cluster_name,
scheduler_address=cluster.scheduler_address,
dashboard_link=cluster.dashboard_link or "",
workers=len(info["workers"]),
memory=utils.format_bytes(
sum(d["memory_limit"] for d in info["workers"].values())
),
cores=cores,
)
if hasattr(cluster, "_supports_scaling"):
model["supports_scaling"] = cluster._supports_scaling
else:
model["supports_scaling"] = True
if adaptive:
model["adapt"] = {"minimum": adaptive.minimum, "maximum": adaptive.maximum}
return model
def __repr__(self):
running_workers = self._count_active_workers()
running_cores = running_workers * self.worker_process_threads
total_jobs = len(self.pending_jobs) + len(self.running_jobs)
total_workers = total_jobs * self.worker_processes
running_memory = running_workers * self.worker_memory / self.worker_processes
return (self.__class__.__name__ +
'(cores=%d, memory=%s, workers=%d/%d, jobs=%d/%d)' %
(running_cores, format_bytes(running_memory), running_workers,
total_workers, len(self.running_jobs), total_jobs))
def _widget_status(self):
try:
workers = self.scheduler_info["workers"]
except KeyError:
return None
else:
n_workers = len(workers)
cores = sum(w["nthreads"] for w in workers.values())
memory = sum(w["memory_limit"] for w in workers.values())
return _widget_status_template % (n_workers, cores, format_bytes(memory))
def test_pause_executor(c, s, a):
memory = psutil.Process().memory_info().rss
a.memory_limit = memory / 0.8 + 200e6
np = pytest.importorskip('numpy')
def f():
x = np.ones(int(300e6), dtype='u1')
sleep(1)
with captured_logger(logging.getLogger('distributed.worker')) as logger:
future = c.submit(f)
futures = c.map(slowinc, range(10), delay=0.1)
yield gen.sleep(0.3)
assert a.paused, (format_bytes(psutil.Process().memory_info().rss),
format_bytes(a.memory_limit))
out = logger.getvalue()
assert 'memory' in out.lower()
assert 'pausing' in out.lower()
assert sum(f.status == 'finished' for f in futures) < 4
yield wait(futures)
def test_pause_executor(c, s, a):
memory = psutil.Process().memory_info().rss
a.memory_limit = memory / 0.8 + 200e6
np = pytest.importorskip('numpy')
def f():
x = np.ones(int(300e6), dtype='u1')
sleep(1)
with captured_logger(logging.getLogger('distributed.worker')) as logger:
future = c.submit(f)
futures = c.map(slowinc, range(10), delay=0.1)
yield gen.sleep(0.3)
assert a.paused, (format_bytes(psutil.Process().memory_info().rss),
format_bytes(a.memory_limit))
out = logger.getvalue()
assert 'memory' in out.lower()
assert 'pausing' in out.lower()
assert sum(f.status == 'finished' for f in futures) < 4
yield wait(futures)
def _widget_status(self):
if self._internal_client is None:
return None
try:
workers = self._internal_client._scheduler_identity["workers"]
except KeyError:
if self._internal_client.status in ("closing", "closed"):
return None
else:
n_workers = len(workers)
cores = sum(w["nthreads"] for w in workers.values())
memory = sum(w["memory_limit"] for w in workers.values())
return _widget_status_template % (n_workers, cores,
format_bytes(memory))
def main(args=None):
args = parse_args(args)
if args.protocol == 'ucx':
sched_str = "ucx://"+ args.server + ":13337"
client = Client(sched_str)
elif args.protocol == 'tcp':
sched_str = "tcp://"+ args.server + ":13337"
client = Client(sched_str)
else:
kwargs = {'n_workers': 2, 'threads_per_worker': 40}
kwargs['processes'] = args.protocol == 'tcp'
cluster = LocalCluster(**kwargs)
client = Client(cluster)
print(f"Connected to {client}")
N = int(args.length)
P = int(args.length)
RS = da.random.RandomState(RandomState=cupy.random.RandomState)
#RS = da.random.RandomState(123)
X = RS.normal(10, 1, size=(N, P))
#X = da.random.uniform(size=(N, P), chunks=(N/100, P/100))
X.persist()
print(format_bytes(X.nbytes))
result = (X + X.T).sum() #(x + x.T).sum().compute()
start = clock()
result.compute()
#with get_task_stream() as ts:
# result.compute()
stop = clock()
#print(ts.data)
print(result)
print(format_bytes(X.nbytes))
print(f"\tTook {stop - start:0.2f}s")
time.sleep(1)
def main(args=None):
args = parse_args(args)
client = Client(args.scheduler_address) # noqa
X = da.random.random(size=(100_000, 10_000), chunks=1_000)
protocol = client.scheduler_info()['address'].split(":")[0]
ctx = base.maybe_setup_profile(args.profile, 'bench-array-ops', protocol)
x = X[:10].dot(X.T).sum(1)
print("Array size:", format_bytes(X.nbytes))
print("Client :", client)
print("Profile? :", "yes" if args.profile else "no")
print("-" * 80)
with ctx:
start = clock()
dask.compute(x.sum(), x.mean(), x.std())
stop = clock()
print(f"\t Took {stop - start:0.2f}s")
def main(args=None):
args = parse_args(args)
client = Client(address=args.scheduler_address)
protocol = client.scheduler_info()['address'].split(":")[0]
ctx = base.maybe_setup_profile(args.profile, 'dot-product', protocol)
print(f"Connected to {client}")
N = 1_000_000
P = 1_000
X = da.random.uniform(size=(N, P), chunks=(N // 100, P))
print(format_bytes(X.nbytes))
result = X.T.dot(X)
with ctx:
start = clock()
result.compute()
stop = clock()
print(f"\tTook {stop - start:0.2f}s")
async def connect(host, port, n_bytes, n_iter, recv, np, verbose,
increment):
ep = ucp.get_endpoint(host.encode(), port)
arr = np.zeros(n_bytes, dtype='u1')
start = clock()
for i in range(n_iter):
await ep.send_obj(arr)
if recv == 'recv_into':
await ep.recv_into(arr, arr.nbytes)
else:
# This is failing right now
msg = await ep.recv_obj(arr.nbytes, cuda=np.__name__ == 'cupy')
arr = np.asarray(msg.get_obj())
stop = clock()
expected = np.ones(n_bytes, dtype='u1')
# 0 or n_iter
expected *= (int(increment) * n_iter)
np.testing.assert_array_equal(arr, expected)
took = stop - start
# 2 for round-trip, n_iter for number of trips.
print("Roundtrip benchmark")
print("-------------------")
print(f"n_iter | {n_iter}")
print(f"n_bytes | {format_bytes(n_bytes)}")
print(f"recv | {recv}")
print(f"object | {np.__name__}")
print(f"inc | {increment}")
print("\n===================")
print(format_bytes(2 * n_iter * arr.nbytes / took), '/ s')
print("===================")
await ep.recv_future()
await ep.send_obj(np.ones(1))
ep.close()
def main():
args = parse_args()
q1 = mp.Queue()
p1 = mp.Process(target=server, args=(q1, args))
p1.start()
port = q1.get()
q2 = mp.Queue()
p2 = mp.Process(target=client, args=(q2, port, args))
p2.start()
times = q2.get()
p1.join()
p2.join()
assert not p1.exitcode
assert not p2.exitcode
assert len(times) == args.n_iter
print("Roundtrip benchmark")
print("--------------------------")
print(f"n_iter | {args.n_iter}")
print(f"n_bytes | {format_bytes(args.n_bytes)}")
print(f"object | {args.object_type}")
print(f"reuse alloc | {args.reuse_alloc}")
print("==========================")
if args.object_type == "numpy":
print(f"Device(s) | Single CPU")
else:
print(f"Device(s) | {args.server_dev}, {args.client_dev}")
print(
f"Average | {format_bytes(2 * args.n_iter * args.n_bytes / sum(times))}/s"
)
print("--------------------------")
print("Iterations")
print("--------------------------")
for i, t in enumerate(times):
ts = format_bytes(2 * args.n_bytes / t)
ts = (" " * (9 - len(ts))) + ts
print("%03d |%s/s" % (i, ts))
async def connect(host, port, n_bytes, n_iter, recv, np, verbose, increment):
"""
connect to server and write data
"""
ep = await ucp.create_endpoint(host, port)
msg = np.zeros(n_bytes, dtype="u1")
msg_size = numpy.array([msg.nbytes], dtype=np.uint64)
start = clock()
for i in range(n_iter):
# send first message
await ep.send(msg, msg_size) # send the real message
resp = np.empty_like(msg)
await ep.recv(resp, msg_size) # receive the echo
stop = clock()
expected = np.ones(n_bytes, dtype="u1")
expected *= int(increment) * n_iter
np.testing.assert_array_equal(msg, expected)
took = stop - start
# 2 for round-trip, n_iter for number of trips.
print("Roundtrip benchmark")
print("-------------------")
print(f"n_iter | {n_iter}")
print(f"n_bytes | {format_bytes(n_bytes)}")
print(f"recv | {recv}")
print(f"object | {np.__name__}")
print(f"inc | {increment}")
print("\n===================")
print(format_bytes(2 * n_iter * msg.nbytes / took), "/ s")
print("===================")
from dask.distributed import Client
import time
#client = Client(processes=False, threads_per_worker=4, n_workers=1, memory_limit='2GB')
client = Client('localhost:8786')
print(client)
import dask
from distributed.utils import format_bytes
import dask_ml.cluster
import dask_ml.datasets
X, y = dask_ml.datasets.make_blobs(
n_samples=100000,
n_features=50,
centers=3,
chunks=10000,
)
format_bytes(X.nbytes)
X = X.persist()
km = dask_ml.cluster.KMeans(n_clusters=3, init_max_iter=2, oversampling_factor=10, random_state=0)
t = time.time()
km.fit(X)
print('Time kmeans distributed:' time.time()-t)
def __init__(self,
name=None,
cores=None,
memory=None,
processes=None,
interface=None,
death_timeout=None,
local_directory=None,
extra=None,
env_extra=None,
log_directory=None,
walltime=None,
threads=None,
python=sys.executable,
**kwargs):
""" """
# """
# This initializer should be considered as Abstract, and never used directly.
# """
if threads is not None:
raise ValueError(threads_deprecation_message)
if not self.scheduler_name:
raise NotImplementedError(
'JobQueueCluster is an abstract class that should not be instanciated.'
)
if name is None:
name = dask.config.get('jobqueue.%s.name' % self.scheduler_name)
if cores is None:
cores = dask.config.get('jobqueue.%s.cores' % self.scheduler_name)
if memory is None:
memory = dask.config.get('jobqueue.%s.memory' %
self.scheduler_name)
if processes is None:
processes = dask.config.get('jobqueue.%s.processes' %
self.scheduler_name)
if interface is None:
interface = dask.config.get('jobqueue.%s.interface' %
self.scheduler_name)
if death_timeout is None:
death_timeout = dask.config.get('jobqueue.%s.death-timeout' %
self.scheduler_name)
if local_directory is None:
local_directory = dask.config.get('jobqueue.%s.local-directory' %
self.scheduler_name)
if extra is None:
extra = dask.config.get('jobqueue.%s.extra' % self.scheduler_name)
if env_extra is None:
env_extra = dask.config.get('jobqueue.%s.env-extra' %
self.scheduler_name)
if log_directory is None:
log_directory = dask.config.get('jobqueue.%s.log-directory' %
self.scheduler_name)
if dask.config.get('jobqueue.%s.threads', None):
warnings.warn(threads_deprecation_message)
if cores is None:
raise ValueError(
"You must specify how many cores to use per job like ``cores=8``"
)
if memory is None:
raise ValueError(
"You must specify how much memory to use per job like ``memory='24 GB'``"
)
# This attribute should be overridden
self.job_header = None
if interface:
extra += ['--interface', interface]
kwargs.setdefault('ip', get_ip_interface(interface))
else:
kwargs.setdefault('ip', '')
# Bokeh diagnostics server should listen on all interfaces
diagnostics_ip_and_port = ('', 8787)
self.local_cluster = LocalCluster(
n_workers=0, diagnostics_port=diagnostics_ip_and_port, **kwargs)
# Keep information on process, cores, and memory, for use in subclasses
self.worker_memory = parse_bytes(
memory) if memory is not None else None
self.worker_processes = processes
self.worker_cores = cores
self.name = name
# plugin for tracking job status
self._scheduler_plugin = JobQueuePlugin()
self.local_cluster.scheduler.add_plugin(self._scheduler_plugin)
self._adaptive = None
self._env_header = '\n'.join(env_extra)
# dask-worker command line build
dask_worker_command = '%(python)s -m distributed.cli.dask_worker' % dict(
python=python)
command_args = [dask_worker_command, self.scheduler.address]
command_args += ['--nthreads', self.worker_threads]
if processes is not None and processes > 1:
command_args += ['--nprocs', processes]
mem = format_bytes(self.worker_memory / self.worker_processes)
command_args += ['--memory-limit', mem.replace(' ', '')]
command_args += ['--name', '%s--${JOB_ID}--' % name]
if death_timeout is not None:
command_args += ['--death-timeout', death_timeout]
if local_directory is not None:
command_args += ['--local-directory', local_directory]
if extra is not None:
command_args += extra
self._command_template = ' '.join(map(str, command_args))
self._target_scale = 0
self.log_directory = log_directory
if self.log_directory is not None:
if not os.path.exists(self.log_directory):
os.makedirs(self.log_directory)
def worker_process_memory(self):
mem = format_bytes(self.worker_memory / self.worker_processes)
mem = mem.replace(" ", "")
return mem
broadcast=True)
cl_bin_utils = scatter_dict(kappa0.cl_bin_utils, broadcast=True)
xi_bin_utils = scatter_dict(kappa0.xi_bin_utils, broadcast=True)
##`fix_cosmo` sets only one cosmology. If `False`, then it recalculates the power spectrum every time.
if fix_cosmo:
kappa0.Ang_PS.angular_power_z()
else:
kappa0.Ang_PS.reset()
print('kappa0 pk', kappa0.Ang_PS.PS.pk_func)
kappa0 = client.scatter(kappa0, broadcast=True)
proc = psutil.Process()
print('starting mcmc ', 'mem, peak mem: ',
format_bytes(proc.memory_info().rss),
int(getrusage(RUSAGE_SELF).ru_maxrss / 1024. / 1024.))
##define functions
def get_priors(params): #assume flat priors for now
x = np.logical_or(np.any(params > priors_max, axis=1),
np.any(params < priors_min, axis=1))
p = np.zeros(len(params))
p[x] = -np.inf
return p
def assign_zparams(zbins={}, p_name='', p_value=None):
pp = p_name.split('_')
p_n = pp[0]
bin_indx = np.int(pp[1])
zbins[bin_indx][p_n] = p_value
def client(queue, port, server_address, args):
if args.client_cpu_affinity >= 0:
os.sched_setaffinity(0, [args.client_cpu_affinity])
ucp.init()
if args.object_type == "numpy":
import numpy as np
elif args.object_type == "cupy":
import cupy as np
np.cuda.runtime.setDevice(args.client_dev)
else:
import cupy as np
import rmm
rmm.reinitialize(
pool_allocator=True,
managed_memory=False,
initial_pool_size=args.rmm_init_pool_size,
devices=[args.client_dev],
)
np.cuda.runtime.setDevice(args.client_dev)
np.cuda.set_allocator(rmm.rmm_cupy_allocator)
async def run():
ep = await ucp.create_endpoint(server_address, port)
msg_send_list = []
msg_recv_list = []
if not args.reuse_alloc:
for i in range(args.n_iter):
msg_send_list.append(np.arange(args.n_bytes, dtype="u1"))
msg_recv_list.append(np.zeros(args.n_bytes, dtype="u1"))
else:
t1 = np.arange(args.n_bytes, dtype="u1")
t2 = np.zeros(args.n_bytes, dtype="u1")
for i in range(args.n_iter):
msg_send_list.append(t1)
msg_recv_list.append(t2)
assert msg_send_list[0].nbytes == args.n_bytes
assert msg_recv_list[0].nbytes == args.n_bytes
if args.cuda_profile:
np.cuda.profiler.start()
times = []
for i in range(args.n_iter):
start = clock()
await ep.send(msg_send_list[i], args.n_bytes)
await ep.recv(msg_recv_list[i], args.n_bytes)
stop = clock()
times.append(stop - start)
if args.cuda_profile:
np.cuda.profiler.stop()
queue.put(times)
loop = asyncio.get_event_loop()
loop.run_until_complete(run())
loop.close()
times = queue.get()
assert len(times) == args.n_iter
print("Roundtrip benchmark")
print("--------------------------")
print(f"n_iter | {args.n_iter}")
print(f"n_bytes | {format_bytes(args.n_bytes)}")
print(f"object | {args.object_type}")
print(f"reuse alloc | {args.reuse_alloc}")
print("==========================")
if args.object_type == "numpy":
print("Device(s) | CPU-only")
s_aff = (args.server_cpu_affinity
if args.server_cpu_affinity >= 0 else "affinity not set")
c_aff = (args.client_cpu_affinity
if args.client_cpu_affinity >= 0 else "affinity not set")
print(f"Server CPU | {s_aff}")
print(f"Client CPU | {c_aff}")
else:
print(f"Device(s) | {args.server_dev}, {args.client_dev}")
print(
f"Average | {format_bytes(2 * args.n_iter * args.n_bytes / sum(times))}/s"
)
print("--------------------------")
print("Iterations")
print("--------------------------")
for i, t in enumerate(times):
ts = format_bytes(2 * args.n_bytes / t)
ts = (" " * (9 - len(ts))) + ts
print("%03d |%s/s" % (i, ts))
def run(self):
logger.warning('Reading configuration YAML config file')
operation_choice = self.params['operation_choice']
machine = self.params['machine']
job_scheduler = self.params['job_scheduler']
queue = self.params['queue']
walltime = self.params['walltime']
maxmemory_per_node = self.params['maxmemory_per_node']
maxcore_per_node = self.params['maxcore_per_node']
chunk_per_worker = self.params['chunk_per_worker']
freq = self.params['freq']
spil = self.params['spil']
output_dir = self.params.get('output_dir', results_dir)
now = datetime.datetime.now()
output_dir = os.path.join(output_dir, f'{machine}/{str(now.date())}')
os.makedirs(output_dir, exist_ok=True)
parameters = self.params['parameters']
num_workers = parameters['number_of_workers_per_nodes']
num_threads = parameters.get('number_of_threads_per_workers', 1)
num_nodes = parameters['number_of_nodes']
chunking_schemes = parameters['chunking_scheme']
io_formats = parameters['io_format']
filesystems = parameters['filesystem']
fixed_totalsize = parameters['fixed_totalsize']
chsz = parameters['chunk_size']
writefile_dir = parameters['writefile_dir']
for wpn in num_workers:
self.create_cluster(
job_scheduler=job_scheduler,
maxcore=maxcore_per_node,
walltime=walltime,
memory=maxmemory_per_node,
queue=queue,
wpn=wpn,
)
for num in num_nodes:
self.client.cluster.scale(num * wpn)
cluster_wait(self.client, num * wpn)
timer = DiagnosticTimer()
# dfs = []
logger.warning(
'#####################################################################\n'
f'Dask cluster:\n'
f'\t{self.client.cluster}\n')
now = datetime.datetime.now()
csv_filename = f"{output_dir}/compute_study_{now.strftime('%Y-%m-%d_%H-%M-%S')}.csv"
for chunk_size in chsz:
for io_format in io_formats:
for filesystem in filesystems:
if filesystem == 's3':
profile = parameters['profile']
bucket = parameters['bucket']
endpoint_url = parameters['endpoint_url']
fs = fsspec.filesystem(
's3',
profile=profile,
anon=False,
client_kwargs={
'endpoint_url': endpoint_url
},
)
root = f'{bucket}/test1'
elif filesystem == 'posix':
fs = LocalFileSystem()
root = writefile_dir
if not os.path.isdir(f'{root}'):
os.makedirs(f'{root}')
for chunking_scheme in chunking_schemes:
logger.warning(
f'Benchmark starting with: \n\tworker_per_node = {wpn},'
f'\n\tnum_nodes = {num}, \n\tchunk_size = {chunk_size},'
f'\n\tchunking_scheme = {chunking_scheme},'
f'\n\tchunk per worker = {chunk_per_worker}'
f'\n\tio_format = {io_format}'
f'\n\tfilesystem = {filesystem}')
ds, chunks = timeseries(
fixed_totalsize=fixed_totalsize,
chunk_per_worker=chunk_per_worker,
chunk_size=chunk_size,
chunking_scheme=chunking_scheme,
io_format=io_format,
num_nodes=num,
freq=freq,
worker_per_node=wpn,
)
# wait(ds)
dataset_size = format_bytes(ds.nbytes)
logger.warning(ds)
logger.warning(
f'Dataset total size: {dataset_size}')
for op in self.operations[operation_choice]:
with timer.time(
'runtime',
operation=op.__name__,
fixed_totalsize=fixed_totalsize,
chunk_size=chunk_size,
chunk_per_worker=chunk_per_worker,
dataset_size=dataset_size,
worker_per_node=wpn,
threads_per_worker=num_threads,
num_nodes=num,
chunking_scheme=chunking_scheme,
io_format=io_format,
filesystem=filesystem,
root=root,
machine=machine,
maxmemory_per_node=
maxmemory_per_node,
maxcore_per_node=maxcore_per_node,
spil=spil,
):
fname = f'{chunk_size}{chunking_scheme}{filesystem}{num}'
if op.__name__ == 'writefile':
print(ds.sst.data.chunksize)
filename = op(
ds, fs, io_format, root, fname)
elif op.__name__ == 'openfile':
ds = op(fs, io_format, root,
chunks, chunk_size)
elif op.__name__ == 'deletefile':
ds = op(fs, io_format, root,
filename)
else:
op(ds)
# kills ds, and every other dependent computation
logger.warning('Computation done')
self.client.cancel(ds)
temp_df = timer.dataframe()
temp_df.to_csv(csv_filename, index=False)
# dfs.append(temp_df)
# now = datetime.datetime.now()
# filename = f"{output_dir}/compute_study_{now.strftime('%Y-%m-%d_%H-%M-%S')}.csv"
# df = pd.concat(dfs)
# df.to_csv(filename, index=False)
logger.warning(
f'Persisted benchmark result file: {csv_filename}')
logger.warning(
'Shutting down the client and cluster before changing number of workers per nodes'
)
self.client.cluster.close()
logger.warning('Cluster shutdown finished')
self.client.close()
logger.warning('Client shutdown finished')
logger.warning('=====> The End <=========')
def generate_maps(self, ):
client = client_get(scheduler_info=self.scheduler_info)
SJ = client.scatter(self, broadcast=True)
step = self.nworkers * self.njobs_submit_per_worker # min(nsim,len(client.scheduler_info()['workers']))
i = 0
j = 0
futures = [delayed(get_clsim)(SJ, i) for i in np.arange(self.nsim)]
futures_done = []
while j < self.nsim:
futures_j = client.compute(futures[j:j + step])
wait_futures(futures_j)
futures_done += futures_j
j += step
del futures
if self.kappa_class.do_pseudo_cl:
self.cl_b = {
im: {
'full': np.zeros(self.sim_clb_shape, dtype='float32')
}
for im in self.Master_algs
}
for im in self.Master_algs:
self.cl_b[im].update({jks: {} for jks in self.jk_stat_keys})
self.pcl_b = {
'full': np.zeros(self.sim_clb_shape, dtype='float32')
}
self.pcl_b.update({jks: {} for jks in self.jk_stat_keys})
if self.do_xi:
self.xi_b = {
'full': np.zeros(self.sim_xib_shape, dtype='float32')
} # {im:np.zeros(sim_clb_shape,dtype='float32') for im in Master_algs}}
self.xi_b.update({
jks: {}
for jks in self.jk_stat_keys
}) #{im:{} for im in Master_algs} for jks in jk_stat_keys})
im = 'xi_imaster'
self.cl_b[im] = {
'full': np.zeros(self.sim_clb_shape, dtype='float32')
}
self.cl_b[im].update({jks: {} for jks in self.jk_stat_keys})
for i in np.arange(self.nsim):
tt = futures_done[i].result()
if self.kappa_class.do_pseudo_cl:
self.pcl_b[i] = tt[0]
for k in self.Master_algs:
self.cl_b[k][i] = tt[1][k]
if self.do_xi:
self.xi_b[i] = tt[2]
k = 'xi_imaster'
self.cl_b[k][i] = tt[1][k]
client.cancel(futures_done[i])
proc = psutil.Process()
print('done map ', i, thread_count(), 'mem, peak mem: ',
format_bytes(proc.memory_info().rss),
int(getrusage(RUSAGE_SELF).ru_maxrss / 1024. / 1024.))
# del futures_done
print('done map ', i, thread_count(), 'mem, peak mem: ',
format_bytes(proc.memory_info().rss),
int(getrusage(RUSAGE_SELF).ru_maxrss / 1024. / 1024.))
j += step
def __init__(self,
name=None,
cores=None,
memory=None,
processes=None,
interface=None,
death_timeout=None,
local_directory=None,
extra=None,
env_extra=None,
walltime=None,
threads=None,
**kwargs
):
""" """
# """
# This initializer should be considered as Abstract, and never used
# directly.
# """
if threads is not None:
raise ValueError(threads_deprecation_message)
if not self.scheduler_name:
raise NotImplementedError('JobQueueCluster is an abstract class '
'that should not be instanciated.')
if name is None:
name = dask.config.get('jobqueue.%s.name' % self.scheduler_name)
if cores is None:
cores = dask.config.get('jobqueue.%s.cores' % self.scheduler_name)
if memory is None:
memory = dask.config.get('jobqueue.%s.memory' % self.scheduler_name)
if processes is None:
processes = dask.config.get('jobqueue.%s.processes' % self.scheduler_name)
if interface is None:
interface = dask.config.get('jobqueue.%s.interface' % self.scheduler_name)
if death_timeout is None:
death_timeout = dask.config.get('jobqueue.%s.death-timeout' % self.scheduler_name)
if local_directory is None:
local_directory = dask.config.get('jobqueue.%s.local-directory' % self.scheduler_name)
if extra is None:
extra = dask.config.get('jobqueue.%s.extra' % self.scheduler_name)
if env_extra is None:
env_extra = dask.config.get('jobqueue.%s.env-extra' % self.scheduler_name)
if dask.config.get('jobqueue.%s.threads', None):
warnings.warn(threads_deprecation_message)
if cores is None:
raise ValueError("You must specify how many cores to use per job "
"like ``cores=8``")
if memory is None:
raise ValueError("You must specify how much memory to use per job "
"like ``memory='24 GB'``")
#This attribute should be overriden
self.job_header = None
if interface:
host = get_ip_interface(interface)
extra += ' --interface %s ' % interface
else:
host = socket.gethostname()
self.local_cluster = LocalCluster(n_workers=0, ip=host, **kwargs)
# Keep information on process, cores, and memory, for use in subclasses
self.worker_memory = parse_bytes(memory)
self.worker_processes = processes
self.worker_cores = cores
self.name = name
self.jobs = dict()
self.n = 0
self._adaptive = None
self._env_header = '\n'.join(env_extra)
# dask-worker command line build
dask_worker_command = (
'%(python)s -m distributed.cli.dask_worker' % dict(python=sys.executable))
self._command_template = ' '.join([dask_worker_command, self.scheduler.address])
self._command_template += " --nthreads %d" % self.worker_threads
if processes is not None and processes > 1:
self._command_template += " --nprocs %d" % processes
mem = format_bytes(self.worker_memory / self.worker_processes)
mem = mem.replace(' ', '')
self._command_template += " --memory-limit %s" % mem
if name is not None:
self._command_template += " --name %s" % name
self._command_template += "-%(n)d" # Keep %(n) to be replaced later
if death_timeout is not None:
self._command_template += " --death-timeout %s" % death_timeout
if local_directory is not None:
self._command_template += " --local-directory %s" % local_directory
if extra is not None:
self._command_template += extra