def _maybe_pause_or_unpause(self, worker: Worker, memory: int) -> None:
if self.memory_pause_fraction is False:
return
assert self.memory_limit
frac = memory / self.memory_limit
# Pause worker threads if above 80% memory use
if frac > self.memory_pause_fraction:
# Try to free some memory while in paused state
self._throttled_gc.collect()
if worker.status == Status.running:
logger.warning(
"Worker is at %d%% memory usage. Pausing worker. "
"Process memory: %s -- Worker memory limit: %s",
int(frac * 100),
format_bytes(memory),
format_bytes(self.memory_limit)
if self.memory_limit is not None
else "None",
)
worker.status = Status.paused
elif worker.status == Status.paused:
logger.warning(
"Worker is at %d%% memory usage. Resuming worker. "
"Process memory: %s -- Worker memory limit: %s",
int(frac * 100),
format_bytes(memory),
format_bytes(self.memory_limit)
if self.memory_limit is not None
else "None",
)
worker.status = Status.running
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 test_ucx_config_w_env_var(cleanup, loop, monkeypatch):
size = "1000.00 MB"
monkeypatch.setenv("DASK_RMM__POOL_SIZE", size)
dask.config.refresh()
port = "13339"
sched_addr = "ucx://%s:%s" % (HOST, port)
with popen([
"dask-scheduler", "--no-dashboard", "--protocol", "ucx", "--port",
port
]) as sched:
with popen([
"dask-worker",
sched_addr,
"--no-dashboard",
"--protocol",
"ucx",
"--no-nanny",
]) as w:
with Client(sched_addr, loop=loop, timeout=10) as c:
while not c.scheduler_info()["workers"]:
sleep(0.1)
# configured with 1G pool
rmm_usage = c.run_on_scheduler(rmm.get_info)
assert size == format_bytes(rmm_usage.free)
# configured with 1G pool
worker_addr = list(c.scheduler_info()["workers"])[0]
worker_rmm_usage = c.run(rmm.get_info)
rmm_usage = worker_rmm_usage[worker_addr]
assert size == format_bytes(rmm_usage.free)
def main(args):
# Set up workers on the local machine
cluster = LocalCUDACluster(protocol=args.protocol,
n_workers=args.n_workers,
CUDA_VISIBLE_DEVICES=args.devs)
client = Client(cluster)
if args.no_pool_allocator:
client.run(cudf.set_allocator, "default", pool=False)
else:
client.run(cudf.set_allocator, "default", pool=True)
took_list = []
for _ in range(args.runs - 1):
took_list.append(run(args, write_profile=None))
took_list.append(run(
args, write_profile=args.profile)) # Only profiling the last run
# Collect, aggregate, and print peer-to-peer bandwidths
incoming_logs = client.run(
lambda dask_worker: dask_worker.incoming_transfer_log)
bandwidths = defaultdict(list)
total_nbytes = defaultdict(list)
for k, L in incoming_logs.items():
for d in L:
if d["total"] >= args.ignore_size:
bandwidths[k, d["who"]].append(d["bandwidth"])
total_nbytes[k, d["who"]].append(d["total"])
bandwidths = {(cluster.scheduler.workers[w1].name,
cluster.scheduler.workers[w2].name): [
"%s/s" % format_bytes(x)
for x in numpy.quantile(v, [0.25, 0.50, 0.75])
]
for (w1, w2), v in bandwidths.items()}
total_nbytes = {(
cluster.scheduler.workers[w1].name,
cluster.scheduler.workers[w2].name,
): format_bytes(sum(nb))
for (w1, w2), nb in total_nbytes.items()}
print("Merge benchmark")
print("--------------------------")
print(f"Chunk-size | {args.chunk_size}")
print(f"Frac-match | {args.frac_match}")
print(f"Ignore-size | {format_bytes(args.ignore_size)}")
print(f"Protocol | {args.protocol}")
print(f"Device(s) | {args.devs}")
print("==========================")
for took in took_list:
print(f"Total time | {format_time(took)}")
print("==========================")
print("(w1,w2) | 25% 50% 75% (total nbytes)")
print("--------------------------")
for (d1, d2), bw in sorted(bandwidths.items()):
print("(%02d,%02d) | %s %s %s (%s)" %
(d1, d2, bw[0], bw[1], bw[2], total_nbytes[(d1, d2)]))
def update(self):
with log_errors():
workers = list(self.scheduler.workers.values())
utilization = []
memory = []
gpu_index = []
y = []
memory_total = 0
memory_max = 0
worker = []
i = 0
for ws in workers:
info = ws.extra["gpu"]
metrics = ws.metrics["gpu"]
for j, (u, mem_used, mem_total) in enumerate(
zip(
metrics["utilization"],
metrics["memory-used"],
info["memory-total"],
)):
memory_max = max(memory_max, mem_total)
memory_total += mem_total
utilization.append(int(u))
memory.append(mem_used)
worker.append(ws.address)
gpu_index.append(j)
y.append(i)
i += 1
memory_text = [format_bytes(m) for m in memory]
result = {
"memory": memory,
"memory-half": [m / 2 for m in memory],
"memory_text": memory_text,
"utilization": utilization,
"utilization-half": [u / 2 for u in utilization],
"worker": worker,
"gpu-index": gpu_index,
"y": y,
"escaped_worker": [escape.url_escape(w) for w in worker],
}
self.memory_figure.title.text = "GPU Memory: %s / %s" % (
format_bytes(sum(memory)),
format_bytes(memory_total),
)
self.memory_figure.x_range.end = memory_max
update(self.source, result)
def update(self):
with log_errors():
workers = list(self.scheduler.workers.values())
utilization = []
memory = []
gpu_index = []
y = []
memory_total = 0
memory_max = 0
worker = []
for idx, ws in enumerate(workers):
try:
info = ws.extra["gpu"]
except KeyError:
continue
metrics = ws.metrics["gpu"]
u = metrics["utilization"]
mem_used = metrics["memory-used"]
mem_total = info["memory-total"]
memory_max = max(memory_max, mem_total)
memory_total += mem_total
utilization.append(int(u))
memory.append(mem_used)
worker.append(ws.address)
gpu_index.append(idx)
y.append(idx)
memory_text = [format_bytes(m) for m in memory]
result = {
"memory": memory,
"memory-half": [m / 2 for m in memory],
"memory_text": memory_text,
"utilization": utilization,
"utilization-half": [u / 2 for u in utilization],
"worker": worker,
"gpu-index": gpu_index,
"y": y,
"escaped_worker": [escape.url_escape(w) for w in worker],
}
self.memory_figure.title.text = "GPU Memory: {} / {}".format(
format_bytes(sum(memory)),
format_bytes(memory_total),
)
self.memory_figure.x_range.end = memory_max
update(self.source, result)
def update(self):
with log_errors():
outgoing = self.worker.outgoing_transfer_log
n = self.worker.outgoing_count - self.last_outgoing
outgoing = [outgoing[-i].copy() for i in range(1, n + 1)]
self.last_outgoing = self.worker.outgoing_count
incoming = self.worker.incoming_transfer_log
n = self.worker.incoming_count - self.last_incoming
incoming = [incoming[-i].copy() for i in range(1, n + 1)]
self.last_incoming = self.worker.incoming_count
for [msgs, source] in [
[incoming, self.incoming],
[outgoing, self.outgoing],
]:
for msg in msgs:
if "compressed" in msg:
del msg["compressed"]
del msg["keys"]
bandwidth = msg["total"] / (msg["duration"] or 0.5)
bw = max(min(bandwidth / 500e6, 1), 0.3)
msg["alpha"] = bw
try:
msg["y"] = self.who[msg["who"]]
except KeyError:
self.who[msg["who"]] = len(self.who)
msg["y"] = self.who[msg["who"]]
msg["hover"] = "%s / %s = %s/s" % (
format_bytes(msg["total"]),
format_time(msg["duration"]),
format_bytes(msg["total"] / msg["duration"]),
)
for k in ["middle", "duration", "start", "stop"]:
msg[k] = msg[k] * 1000
if msgs:
msgs = transpose(msgs)
if (
len(source.data["stop"])
and min(msgs["start"]) > source.data["stop"][-1] + 10000
):
source.data.update(msgs)
else:
source.stream(msgs, rollover=10000)
def test_filters():
template = get_template("bytes.html.j2")
assert format_bytes in FILTERS.values()
assert format_bytes(2e9) in template.render(foo=2e9)
template = get_template("custom_filter.html.j2")
assert "baz" in template.render(foo=None)
def test_job_script(tmpdir):
log_directory = tmpdir.strpath
with SGECluster(
cores=6,
processes=2,
memory="12GB",
queue="my-queue",
project="my-project",
walltime="02:00:00",
env_extra=["export MY_VAR=my_var"],
job_extra=["-w e", "-m e"],
log_directory=log_directory,
resource_spec="h_vmem=12G,mem_req=12G",
) as cluster:
job_script = cluster.job_script()
formatted_bytes = format_bytes(parse_bytes("6GB")).replace(" ", "")
for each in [
"--nprocs 2",
"--nthreads 3",
f"--memory-limit {formatted_bytes}",
"-q my-queue",
"-P my-project",
"-l h_rt=02:00:00",
"export MY_VAR=my_var",
"#$ -w e",
"#$ -m e",
"#$ -e {}".format(log_directory),
"#$ -o {}".format(log_directory),
"-l h_vmem=12G,mem_req=12G",
"#$ -cwd",
"#$ -j y",
]:
assert each in job_script
def _widget_status(self):
workers = len(self.scheduler.workers)
cores = sum(ws.nthreads 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 oom(nbytes: int) -> bool:
"""Try to handle an out-of-memory error by spilling"""
memory_freed = self.manager.evict(
nbytes=nbytes,
proxies_access=self.manager._dev.buffer_info,
serializer=lambda p: p._pxy_serialize(serializers=(
"dask", "pickle")),
)
gc.collect()
if memory_freed > 0:
return True # Ask RMM to retry the allocation
else:
with io.StringIO() as f:
traceback.print_stack(file=f)
f.seek(0)
tb = f.read()
self.logger.warning(
"RMM allocation of %s failed, spill-on-demand couldn't "
"find any device memory to spill:\n%s\ntraceback:\n%s\n",
format_bytes(nbytes),
self.manager.pprint(),
tb,
)
# Since we didn't find anything to spill, we give up.
return False
def test_job_script():
with OARCluster(walltime="00:02:00", processes=4, cores=8,
memory="28GB") as cluster:
job_script = cluster.job_script()
assert "#OAR" in job_script
assert "#OAR -n dask-worker" in job_script
formatted_bytes = format_bytes(parse_bytes("7GB")).replace(" ", "")
assert f"--memory-limit {formatted_bytes}" in job_script
assert "#OAR -l /nodes=1/core=8,walltime=00:02:00" in job_script
assert "#OAR --project" not in job_script
assert "#OAR -q" not in job_script
assert "export " not in job_script
assert ("{} -m distributed.cli.dask_worker tcp://".format(
sys.executable) in job_script)
formatted_bytes = format_bytes(parse_bytes("7GB")).replace(" ", "")
assert f"--nthreads 2 --nprocs 4 --memory-limit {formatted_bytes}" in job_script
with OARCluster(
walltime="00:02:00",
processes=4,
cores=8,
memory="28GB",
env_extra=[
'export LANG="en_US.utf8"',
'export LANGUAGE="en_US.utf8"',
'export LC_ALL="en_US.utf8"',
],
) as cluster:
job_script = cluster.job_script()
assert "#OAR" in job_script
assert "#OAR -n dask-worker" in job_script
formatted_bytes = format_bytes(parse_bytes("7GB")).replace(" ", "")
assert f"--memory-limit {formatted_bytes}" in job_script
assert "#OAR -l /nodes=1/core=8,walltime=00:02:00" in job_script
assert "#OAR --project" not in job_script
assert "#OAR -q" not in job_script
assert 'export LANG="en_US.utf8"' in job_script
assert 'export LANGUAGE="en_US.utf8"' in job_script
assert 'export LC_ALL="en_US.utf8"' in job_script
assert ("{} -m distributed.cli.dask_worker tcp://".format(
sys.executable) in job_script)
formatted_bytes = format_bytes(parse_bytes("7GB")).replace(" ", "")
assert f"--nthreads 2 --nprocs 4 --memory-limit {formatted_bytes}" in job_script
def cache_all_catalog_items(cat, cache_storage=None):
"""Cache all catalog items from catalog `cat` that contain `cache::` in url to
folder optionally specified by `cache_storage`.
Example:
>>> cat = intake.open_catalog('master.yaml')
>>> cache_all_catalog_items(cat, 'test_cache_folder')
>>> os.path.exists('test_cache_folder/HadCRUT.4.6.0.0.median.nc')
"""
fsspec.config.conf["simplecache"] = {"same_names": True}
if cache_storage:
fsspec.config.conf["simplecache"] = {
"cache_storage": cache_storage,
"same_names": True,
}
print("fsspec.config.conf", fsspec.config.conf, "\n")
for item_str in cat.walk(depth=2):
if "CRU_TS" not in item_str:
item = getattr(cat, item_str)
if not isinstance(cat[item_str],
intake.catalog.local.YAMLFileCatalog):
if "cache::" in item.urlpath:
filename = item.urlpath.split("/")[-1]
print(f"try to cache {item_str} from {item.urlpath} to"
f"{cache_storage}/{filename}")
try:
if isinstance(
item,
(
intake_geopandas.RegionmaskSource,
intake_geopandas.GeoPandasFileSource,
),
):
ds = item.read()
print(
item_str,
"\n",
type(item),
"\n",
ds.dims,
"\n",
ds.coords,
"\nsize = ",
format_bytes(ds.nbytes),
)
else:
ds = item.to_dask()
print(item_str, "\n", type(item))
if cache_storage:
assert filename in os.listdir(
f"{cache_storage}"), print(
item_str, "caching failed:",
os.listdir(cache_storage))
print("successful", item_str, "\n")
except Exception as e:
print(
f"{item_str} failed, error {type(e).__name__}: {e}\n"
)
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 _gc_callback(self, phase, info):
# Young generations are small and collected very often,
# don't waste time measuring them
if info["generation"] != 2:
return
if self._proc is not None:
rss = self._proc.memory_info().rss
else:
rss = 0
if phase == "start":
self._fractional_timer.start_timing()
self._gc_rss_before = rss
return
assert phase == "stop"
self._fractional_timer.stop_timing()
frac = self._fractional_timer.running_fraction
if frac is not None and frac >= self._warn_over_frac:
logger.warning(
"full garbage collections took %d%% CPU time "
"recently (threshold: %d%%)",
100 * frac,
100 * self._warn_over_frac,
)
rss_saved = self._gc_rss_before - rss
if rss_saved >= self._info_over_rss_win:
logger.info(
"full garbage collection released %s "
"from %d reference cycles (threshold: %s)",
format_bytes(rss_saved),
info["collected"],
format_bytes(self._info_over_rss_win),
)
if info["uncollectable"] > 0:
# This should ideally never happen on Python 3, but who knows?
logger.warning(
"garbage collector couldn't collect %d objects, "
"please look in gc.garbage",
info["uncollectable"],
)
def test_job_script(Cluster):
with Cluster(walltime="00:02:00", processes=4, cores=8,
memory="28GB") as cluster:
job_script = cluster.job_script()
assert "#PBS" in job_script
assert "#PBS -N dask-worker" in job_script
assert "#PBS -l select=1:ncpus=8:mem=27GB" in job_script
assert "#PBS -l walltime=00:02:00" in job_script
assert "#PBS -q" not in job_script
assert "#PBS -A" not in job_script
assert ("{} -m distributed.cli.dask_worker tcp://".format(
sys.executable) in job_script)
formatted_bytes = format_bytes(parse_bytes("7GB")).replace(" ", "")
assert f"--nthreads 2 --nprocs 4 --memory-limit {formatted_bytes}" in job_script
with Cluster(
queue="regular",
project="DaskOnPBS",
processes=4,
cores=8,
resource_spec="select=1:ncpus=24:mem=100GB",
memory="28GB",
) as cluster:
job_script = cluster.job_script()
assert "#PBS -q regular" in job_script
assert "#PBS -N dask-worker" in job_script
assert "#PBS -l select=1:ncpus=24:mem=100GB" in job_script
assert "#PBS -l select=1:ncpus=8:mem=27GB" not in job_script
assert "#PBS -l walltime=" in job_script
assert "#PBS -A DaskOnPBS" in job_script
assert ("{} -m distributed.cli.dask_worker tcp://".format(
sys.executable) in job_script)
formatted_bytes = format_bytes(parse_bytes("7GB")).replace(" ", "")
assert f"--nthreads 2 --nprocs 4 --memory-limit {formatted_bytes}" in job_script
def __repr__(self):
text = "%s(%r, workers=%d, threads=%d" % (
self._cluster_class_name,
self.scheduler_address,
len(self.scheduler_info["workers"]),
sum(w["nthreads"] for w in self.scheduler_info["workers"].values()),
)
memory = [w["memory_limit"] for w in self.scheduler_info["workers"].values()]
if all(memory):
text += ", memory=" + format_bytes(sum(memory))
text += ")"
return text
def __repr__(self):
text = "%s(%r, workers=%d, threads=%d" % (
getattr(self, "_name",
type(self).__name__),
self.scheduler_address,
len(self.workers),
sum(w["nthreads"]
for w in self.scheduler_info["workers"].values()),
)
memory = [
w["memory_limit"] for w in self.scheduler_info["workers"].values()
]
if all(memory):
text += ", memory=" + format_bytes(sum(memory))
text += ")"
return text
def _widget_status(self):
workers = len(self.scheduler_info["workers"])
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, "workers"):
requested = len(self.workers)
else:
requested = workers
cores = sum(v["nthreads"]
for v in self.scheduler_info["workers"].values())
memory = 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>Workers</th> <td>%s</td></tr>
<tr> <th>Cores</th> <td>%d</td></tr>
<tr> <th>Memory</th> <td>%s</td></tr>
</table>
</div>
""" % (
workers if workers == requested else "%d / %d" %
(workers, requested),
cores,
memory,
)
return text
def test_job_script():
with HTCondorCluster(
cores=4,
processes=2,
memory="100MB",
disk="100MB",
env_extra=[
'export LANG="en_US.utf8"', 'export LC_ALL="en_US.utf8"'
],
job_extra={"+Extra": "True"},
submit_command_extra=["-verbose"],
cancel_command_extra=["-forcex"],
) as cluster:
job_script = cluster.job_script()
assert "RequestCpus = MY.DaskWorkerCores" in job_script
assert "RequestDisk = floor(MY.DaskWorkerDisk / 1024)" in job_script
assert "RequestMemory = floor(MY.DaskWorkerMemory / 1048576)" in job_script
assert "MY.DaskWorkerCores = 4" in job_script
assert "MY.DaskWorkerDisk = 100000000" in job_script
assert "MY.DaskWorkerMemory = 100000000" in job_script
assert 'MY.JobId = "$(ClusterId).$(ProcId)"' in job_script
assert "LANG=en_US.utf8" in job_script
assert "LC_ALL=en_US.utf8" in job_script
assert "export" not in job_script
assert "+Extra = True" in job_script
assert re.search(r"condor_submit\s.*-verbose",
cluster._dummy_job.submit_command)
assert re.search(r"condor_rm\s.*-forcex",
cluster._dummy_job.cancel_command)
assert ("{} -m distributed.cli.dask_worker tcp://".format(
sys.executable) in job_script)
formatted_bytes = format_bytes(parse_bytes("50MB")).replace(" ", "")
assert f"--memory-limit {formatted_bytes}" in job_script
assert "--nthreads 2" in job_script
assert "--nprocs 2" in job_script
def test_format_bytes(n, expect):
assert format_bytes(int(n)) == expect
async def run(args):
# Set up workers on the local machine
async with LocalCUDACluster(
protocol=args.protocol,
n_workers=len(args.devs.split(",")),
CUDA_VISIBLE_DEVICES=args.devs,
ucx_net_devices="auto",
enable_infiniband=True,
enable_nvlink=True,
asynchronous=True,
) as cluster:
async with Client(cluster, asynchronous=True) as client:
def _worker_setup(size=None):
import rmm
rmm.reinitialize(
pool_allocator=not args.no_rmm_pool,
devices=0,
initial_pool_size=size,
)
cupy.cuda.set_allocator(rmm.rmm_cupy_allocator)
await client.run(_worker_setup)
# Create an RMM pool on the scheduler due to occasional deserialization
# of CUDA objects. May cause issues with InfiniBand otherwise.
await client.run_on_scheduler(_worker_setup, 1e9)
# Create a simple random array
rs = da.random.RandomState(RandomState=cupy.random.RandomState)
x = rs.random((args.size, args.size),
chunks=args.chunk_size).persist()
await wait(x)
# Execute the operations to benchmark
if args.profile is not None:
async with performance_report(filename=args.profile):
t1 = clock()
await client.compute((x + x.T).sum())
took = clock() - t1
else:
t1 = clock()
await client.compute((x + x.T).sum())
took = clock() - t1
# Collect, aggregate, and print peer-to-peer bandwidths
incoming_logs = await client.run(
lambda dask_worker: dask_worker.incoming_transfer_log)
bandwidths = defaultdict(list)
total_nbytes = defaultdict(list)
for k, L in incoming_logs.items():
for d in L:
if d["total"] >= args.ignore_size:
bandwidths[k, d["who"]].append(d["bandwidth"])
total_nbytes[k, d["who"]].append(d["total"])
bandwidths = {(
cluster.scheduler.workers[w1].name,
cluster.scheduler.workers[w2].name,
): [
"%s/s" % format_bytes(x)
for x in np.quantile(v, [0.25, 0.50, 0.75])
]
for (w1, w2), v in bandwidths.items()}
total_nbytes = {(
cluster.scheduler.workers[w1].name,
cluster.scheduler.workers[w2].name,
): format_bytes(sum(nb))
for (w1, w2), nb in total_nbytes.items()}
print("Roundtrip benchmark")
print("--------------------------")
print(f"Size | {args.size}*{args.size}")
print(f"Chunk-size | {args.chunk_size}")
print(f"Ignore-size | {format_bytes(args.ignore_size)}")
print(f"Protocol | {args.protocol}")
print(f"Device(s) | {args.devs}")
print(f"npartitions | {x.npartitions}")
print("==========================")
print(f"Total time | {format_time(took)}")
print("==========================")
print("(w1,w2) | 25% 50% 75% (total nbytes)")
print("--------------------------")
for (d1, d2), bw in sorted(bandwidths.items()):
print("(%02d,%02d) | %s %s %s (%s)" %
(d1, d2, bw[0], bw[1], bw[2], total_nbytes[(d1, d2)]))
# # The workers
nb_workers=4
from dask.distributed import Client, LocalCluster
cluster = LocalCluster(n_workers=nb_workers)
c = Client(cluster)
c
from dask.utils import ensure_dict, format_bytes
wk = c.scheduler_info()["workers"]
text="Workers= " + str(len(wk))
memory = [w["memory_limit"] for w in wk.values()]
cores = sum(w["nthreads"] for w in wk.values())
text += ", Cores=" + str(cores)
if all(memory):
text += ", Memory=" + format_bytes(sum(memory))
print(text)
# # The data
%time ds=xr.open_zarr('/mnt/alberta/equipes/IGE/meom/workdir/albert/eNATL60/zarr/eNATL60-BLBT02-SSH-1h')
#3,6s
mean=ds.sossheig.mean(dim='time_counter')
%time mean.load()
#1h51
cluster.close()
def worker_process_memory(self):
mem = format_bytes(self.worker_memory / self.worker_processes)
mem = mem.replace(" ", "")
return mem
def main(args):
cluster_options = get_cluster_options(args)
Cluster = cluster_options["class"]
cluster_args = cluster_options["args"]
cluster_kwargs = cluster_options["kwargs"]
scheduler_addr = cluster_options["scheduler_addr"]
if args.sched_addr:
client = Client(args.sched_addr)
else:
filterwarnings("ignore",
message=".*NVLink.*rmm_pool_size.*",
category=UserWarning)
cluster = Cluster(*cluster_args, **cluster_kwargs)
if args.multi_node:
import time
# Allow some time for workers to start and connect to scheduler
# TODO: make this a command-line argument?
time.sleep(15)
client = Client(scheduler_addr if args.multi_node else cluster)
if args.type == "gpu":
client.run(
setup_memory_pool,
pool_size=args.rmm_pool_size,
disable_pool=args.disable_rmm_pool,
log_directory=args.rmm_log_directory,
)
# Create an RMM pool on the scheduler due to occasional deserialization
# of CUDA objects. May cause issues with InfiniBand otherwise.
client.run_on_scheduler(
setup_memory_pool,
pool_size=1e9,
disable_pool=args.disable_rmm_pool,
log_directory=args.rmm_log_directory,
)
scheduler_workers = client.run_on_scheduler(get_scheduler_workers)
n_workers = len(scheduler_workers)
client.wait_for_workers(n_workers)
if args.all_to_all:
all_to_all(client)
took_list = []
for _ in range(args.runs - 1):
took_list.append(run(client, args, n_workers, write_profile=None))
took_list.append(
run(client, args, n_workers,
write_profile=args.profile)) # Only profiling the last run
# Collect, aggregate, and print peer-to-peer bandwidths
incoming_logs = client.run(
lambda dask_worker: dask_worker.incoming_transfer_log)
bandwidths = defaultdict(list)
total_nbytes = defaultdict(list)
for k, L in incoming_logs.items():
for d in L:
if d["total"] >= args.ignore_size:
bandwidths[k, d["who"]].append(d["bandwidth"])
total_nbytes[k, d["who"]].append(d["total"])
bandwidths = {(scheduler_workers[w1].name, scheduler_workers[w2].name): [
"%s/s" % format_bytes(x)
for x in numpy.quantile(v, [0.25, 0.50, 0.75])
]
for (w1, w2), v in bandwidths.items()}
total_nbytes = {(
scheduler_workers[w1].name,
scheduler_workers[w2].name,
): format_bytes(sum(nb))
for (w1, w2), nb in total_nbytes.items()}
t_runs = numpy.empty(len(took_list))
if args.markdown:
print("```")
print("Shuffle benchmark")
print("-------------------------------")
print(f"backend | {args.backend}")
print(f"partition-size | {format_bytes(args.partition_size)}")
print(f"in-parts | {args.in_parts}")
print(f"protocol | {args.protocol}")
print(f"device(s) | {args.devs}")
if args.device_memory_limit:
print(f"memory-limit | {format_bytes(args.device_memory_limit)}")
print(f"rmm-pool | {(not args.disable_rmm_pool)}")
if args.protocol == "ucx":
print(f"tcp | {args.enable_tcp_over_ucx}")
print(f"ib | {args.enable_infiniband}")
print(f"nvlink | {args.enable_nvlink}")
print(f"data-processed | {format_bytes(took_list[0][0])}")
print("===============================")
print("Wall-clock | Throughput")
print("-------------------------------")
for idx, (data_processed, took) in enumerate(took_list):
throughput = int(data_processed / took)
m = format_time(took)
m += " " * (15 - len(m))
print(f"{m}| {format_bytes(throughput)}/s")
t_runs[idx] = float(format_bytes(throughput).split(" ")[0])
print("===============================")
if args.markdown:
print("\n```")
if args.plot is not None:
plot_benchmark(t_runs, args.plot, historical=True)
if args.backend == "dask":
if args.markdown:
print(
"<details>\n<summary>Worker-Worker Transfer Rates</summary>\n\n```"
)
print("(w1,w2) | 25% 50% 75% (total nbytes)")
print("-------------------------------")
for (d1, d2), bw in sorted(bandwidths.items()):
fmt = ("(%s,%s) | %s %s %s (%s)" if args.multi_node or
args.sched_addr else "(%02d,%02d) | %s %s %s (%s)")
print(fmt % (d1, d2, bw[0], bw[1], bw[2], total_nbytes[(d1, d2)]))
if args.markdown:
print("```\n</details>\n")
if args.benchmark_json:
bandwidths_json = {
"bandwidth_({d1},{d2})_{i}" if args.multi_node or args.sched_addr
else "(%02d,%02d)_%s" % (d1, d2, i): parse_bytes(v.rstrip("/s"))
for (d1, d2), bw in sorted(bandwidths.items()) for i, v in zip(
["25%", "50%", "75%", "total_nbytes"],
[bw[0], bw[1], bw[2], total_nbytes[(d1, d2)]],
)
}
with open(args.benchmark_json, "a") as fp:
for data_processed, took in took_list:
fp.write(
dumps(
dict(
{
"backend": args.backend,
"partition_size": args.partition_size,
"in_parts": args.in_parts,
"protocol": args.protocol,
"devs": args.devs,
"device_memory_limit":
args.device_memory_limit,
"rmm_pool": not args.disable_rmm_pool,
"tcp": args.enable_tcp_over_ucx,
"ib": args.enable_infiniband,
"nvlink": args.enable_nvlink,
"data_processed": data_processed,
"wall_clock": took,
"throughput": data_processed / took,
},
**bandwidths_json,
)) + "\n")
if args.multi_node:
client.shutdown()
client.close()
async def _maybe_spill(self, worker: Worker, memory: int) -> None:
if self.memory_spill_fraction is False:
return
# SpillBuffer or a duct-type compatible MutableMapping which offers the
# fast property and evict() methods. Dask-CUDA uses this.
if not hasattr(self.data, "fast") or not hasattr(self.data, "evict"):
return
data = cast(ManualEvictProto, self.data)
assert self.memory_limit
frac = memory / self.memory_limit
if frac <= self.memory_spill_fraction:
return
total_spilled = 0
logger.debug(
"Worker is at %.0f%% memory usage. Start spilling data to disk.",
frac * 100,
)
# Implement hysteresis cycle where spilling starts at the spill threshold and
# stops at the target threshold. Normally that here the target threshold defines
# process memory, whereas normally it defines reported managed memory (e.g.
# output of sizeof() ). If target=False, disable hysteresis.
target = self.memory_limit * (
self.memory_target_fraction or self.memory_spill_fraction
)
count = 0
need = memory - target
while memory > target:
if not data.fast:
logger.warning(
"Unmanaged memory use is high. This may indicate a memory leak "
"or the memory may not be released to the OS; see "
"https://distributed.dask.org/en/latest/worker.html#memtrim "
"for more information. "
"-- Unmanaged memory: %s -- Worker memory limit: %s",
format_bytes(memory),
format_bytes(self.memory_limit),
)
break
weight = data.evict()
if weight == -1:
# Failed to evict:
# disk full, spill size limit exceeded, or pickle error
break
total_spilled += weight
count += 1
await asyncio.sleep(0)
memory = worker.monitor.get_process_memory()
if total_spilled > need and memory > target:
# Issue a GC to ensure that the evicted data is actually
# freed from memory and taken into account by the monitor
# before trying to evict even more data.
self._throttled_gc.collect()
memory = worker.monitor.get_process_memory()
self._maybe_pause_or_unpause(worker, memory)
if count:
logger.debug(
"Moved %d tasks worth %s to disk",
count,
format_bytes(total_spilled),
)
def main(args):
# Set up workers on the local machine
if args.protocol == "tcp":
cluster = LocalCUDACluster(
protocol=args.protocol,
n_workers=args.n_workers,
CUDA_VISIBLE_DEVICES=args.devs,
)
else:
enable_infiniband = args.enable_infiniband
enable_nvlink = args.enable_nvlink
enable_tcp_over_ucx = args.enable_tcp_over_ucx
cluster = LocalCUDACluster(
protocol=args.protocol,
n_workers=args.n_workers,
CUDA_VISIBLE_DEVICES=args.devs,
ucx_net_devices="auto",
enable_tcp_over_ucx=enable_tcp_over_ucx,
enable_infiniband=enable_infiniband,
enable_nvlink=enable_nvlink,
)
initialize(
create_cuda_context=True,
enable_tcp_over_ucx=enable_tcp_over_ucx,
enable_infiniband=enable_infiniband,
enable_nvlink=enable_nvlink,
)
client = Client(cluster)
def _worker_setup(initial_pool_size=None):
import rmm
rmm.reinitialize(
pool_allocator=not args.no_rmm_pool,
devices=0,
initial_pool_size=initial_pool_size,
)
cupy.cuda.set_allocator(rmm.rmm_cupy_allocator)
client.run(_worker_setup)
# Create an RMM pool on the scheduler due to occasional deserialization
# of CUDA objects. May cause issues with InfiniBand otherwise.
client.run_on_scheduler(_worker_setup, 1e9)
took_list = []
for _ in range(args.runs - 1):
took_list.append(run(client, args, write_profile=None))
took_list.append(
run(client, args, write_profile=args.profile)
) # Only profiling the last run
# Collect, aggregate, and print peer-to-peer bandwidths
incoming_logs = client.run(lambda dask_worker: dask_worker.incoming_transfer_log)
bandwidths = defaultdict(list)
total_nbytes = defaultdict(list)
for k, L in incoming_logs.items():
for d in L:
if d["total"] >= args.ignore_size:
bandwidths[k, d["who"]].append(d["bandwidth"])
total_nbytes[k, d["who"]].append(d["total"])
bandwidths = {
(cluster.scheduler.workers[w1].name, cluster.scheduler.workers[w2].name): [
"%s/s" % format_bytes(x) for x in numpy.quantile(v, [0.25, 0.50, 0.75])
]
for (w1, w2), v in bandwidths.items()
}
total_nbytes = {
(
cluster.scheduler.workers[w1].name,
cluster.scheduler.workers[w2].name,
): format_bytes(sum(nb))
for (w1, w2), nb in total_nbytes.items()
}
if args.markdown:
print("```")
print("Merge benchmark")
print("-------------------------------")
print(f"backend | {args.backend}")
print(f"rows-per-chunk | {args.chunk_size}")
print(f"protocol | {args.protocol}")
print(f"device(s) | {args.devs}")
print(f"rmm-pool | {(not args.no_rmm_pool)}")
print(f"frac-match | {args.frac_match}")
if args.protocol == "ucx":
print(f"tcp | {args.enable_tcp_over_ucx}")
print(f"ib | {args.enable_infiniband}")
print(f"nvlink | {args.enable_nvlink}")
print(f"data-processed | {format_bytes(took_list[0][0])}")
print("===============================")
print("Wall-clock | Throughput")
print("-------------------------------")
for data_processed, took in took_list:
throughput = int(data_processed / took)
m = format_time(took)
m += " " * (15 - len(m))
print(f"{m}| {format_bytes(throughput)}/s")
print("===============================")
if args.markdown:
print("\n```")
if args.backend == "dask":
if args.markdown:
print("<details>\n<summary>Worker-Worker Transfer Rates</summary>\n\n```")
print("(w1,w2) | 25% 50% 75% (total nbytes)")
print("-------------------------------")
for (d1, d2), bw in sorted(bandwidths.items()):
print(
"(%02d,%02d) | %s %s %s (%s)"
% (d1, d2, bw[0], bw[1], bw[2], total_nbytes[(d1, d2)])
)
if args.markdown:
print("```\n</details>\n")
def main(args):
cluster_options = get_cluster_options(args)
Cluster = cluster_options["class"]
cluster_args = cluster_options["args"]
cluster_kwargs = cluster_options["kwargs"]
scheduler_addr = cluster_options["scheduler_addr"]
if args.sched_addr:
client = Client(args.sched_addr)
else:
filterwarnings("ignore",
message=".*NVLink.*rmm_pool_size.*",
category=UserWarning)
cluster = Cluster(*cluster_args, **cluster_kwargs)
if args.multi_node:
import time
# Allow some time for workers to start and connect to scheduler
# TODO: make this a command-line argument?
time.sleep(15)
client = Client(scheduler_addr if args.multi_node else cluster)
if args.type == "gpu":
client.run(
setup_memory_pool,
pool_size=args.rmm_pool_size,
disable_pool=args.disable_rmm_pool,
log_directory=args.rmm_log_directory,
)
# Create an RMM pool on the scheduler due to occasional deserialization
# of CUDA objects. May cause issues with InfiniBand otherwise.
client.run_on_scheduler(
setup_memory_pool,
pool_size=1e9,
disable_pool=args.disable_rmm_pool,
log_directory=args.rmm_log_directory,
)
scheduler_workers = client.run_on_scheduler(get_scheduler_workers)
n_workers = len(scheduler_workers)
client.wait_for_workers(n_workers)
# Allow the number of chunks to vary between
# the "base" and "other" DataFrames
args.base_chunks = args.base_chunks or n_workers
args.other_chunks = args.other_chunks or n_workers
if args.all_to_all:
all_to_all(client)
took_list = []
for _ in range(args.runs - 1):
took_list.append(run(client, args, n_workers, write_profile=None))
took_list.append(
run(client, args, n_workers,
write_profile=args.profile)) # Only profiling the last run
# Collect, aggregate, and print peer-to-peer bandwidths
incoming_logs = client.run(
lambda dask_worker: dask_worker.incoming_transfer_log)
bandwidths = defaultdict(list)
total_nbytes = defaultdict(list)
for k, L in incoming_logs.items():
for d in L:
if d["total"] >= args.ignore_size:
bandwidths[k, d["who"]].append(d["bandwidth"])
total_nbytes[k, d["who"]].append(d["total"])
bandwidths = {(scheduler_workers[w1].name, scheduler_workers[w2].name): [
"%s/s" % format_bytes(x)
for x in numpy.quantile(v, [0.25, 0.50, 0.75])
]
for (w1, w2), v in bandwidths.items()}
total_nbytes = {(
scheduler_workers[w1].name,
scheduler_workers[w2].name,
): format_bytes(sum(nb))
for (w1, w2), nb in total_nbytes.items()}
broadcast = (False if args.shuffle_join else
(True if args.broadcast_join else "default"))
t_runs = numpy.empty(len(took_list))
if args.markdown:
print("```")
print("Merge benchmark")
print("-------------------------------")
print(f"backend | {args.backend}")
print(f"merge type | {args.type}")
print(f"rows-per-chunk | {args.chunk_size}")
print(f"base-chunks | {args.base_chunks}")
print(f"other-chunks | {args.other_chunks}")
print(f"broadcast | {broadcast}")
print(f"protocol | {args.protocol}")
print(f"device(s) | {args.devs}")
print(f"rmm-pool | {(not args.disable_rmm_pool)}")
print(f"frac-match | {args.frac_match}")
if args.protocol == "ucx":
print(f"tcp | {args.enable_tcp_over_ucx}")
print(f"ib | {args.enable_infiniband}")
print(f"nvlink | {args.enable_nvlink}")
print(f"data-processed | {format_bytes(took_list[0][0])}")
print("===============================")
print("Wall-clock | Throughput")
print("-------------------------------")
for idx, (data_processed, took) in enumerate(took_list):
throughput = int(data_processed / took)
m = format_time(took)
m += " " * (15 - len(m))
print(f"{m}| {format_bytes(throughput)}/s")
t_runs[idx] = float(format_bytes(throughput).split(" ")[0])
print("===============================")
if args.markdown:
print("\n```")
if args.plot is not None:
plot_benchmark(t_runs, args.plot, historical=True)
if args.backend == "dask":
if args.markdown:
print(
"<details>\n<summary>Worker-Worker Transfer Rates</summary>\n\n```"
)
print("(w1,w2) | 25% 50% 75% (total nbytes)")
print("-------------------------------")
for (d1, d2), bw in sorted(bandwidths.items()):
fmt = ("(%s,%s) | %s %s %s (%s)" if args.multi_node
or args.sched_addr else "(%02d,%02d) | %s %s %s (%s)")
print(fmt % (d1, d2, bw[0], bw[1], bw[2], total_nbytes[(d1, d2)]))
if args.markdown:
print("```\n</details>\n")
if args.multi_node:
client.shutdown()
client.close()
def set_chunk_size(num_bytes: Union[int, float] = 128e6):
dask.config.set({'array.chunk_size': format_bytes(num_bytes)})
async def run(args):
cluster_options = get_cluster_options(args)
Cluster = cluster_options["class"]
cluster_args = cluster_options["args"]
cluster_kwargs = cluster_options["kwargs"]
scheduler_addr = cluster_options["scheduler_addr"]
filterwarnings("ignore",
message=".*NVLink.*rmm_pool_size.*",
category=UserWarning)
async with Cluster(*cluster_args, **cluster_kwargs,
asynchronous=True) as cluster:
if args.multi_node:
import time
# Allow some time for workers to start and connect to scheduler
# TODO: make this a command-line argument?
time.sleep(15)
# Use the scheduler address with an SSHCluster rather than the cluster
# object, otherwise we can't shut it down.
async with Client(scheduler_addr if args.multi_node else cluster,
asynchronous=True) as client:
scheduler_workers = await client.run_on_scheduler(
get_scheduler_workers)
await client.run(setup_memory_pool,
disable_pool=args.disable_rmm_pool)
# Create an RMM pool on the scheduler due to occasional deserialization
# of CUDA objects. May cause issues with InfiniBand otherwise.
await client.run_on_scheduler(setup_memory_pool,
1e9,
disable_pool=args.disable_rmm_pool)
took_list = []
for i in range(args.runs):
res = await _run(client, args)
took_list.append((res["took"], res["npartitions"]))
size = res["shape"]
chunksize = res["chunksize"]
# Collect, aggregate, and print peer-to-peer bandwidths
incoming_logs = await client.run(
lambda dask_worker: dask_worker.incoming_transfer_log)
bandwidths = defaultdict(list)
total_nbytes = defaultdict(list)
for k, L in incoming_logs.items():
for d in L:
if d["total"] >= args.ignore_size:
bandwidths[k, d["who"]].append(d["bandwidth"])
total_nbytes[k, d["who"]].append(d["total"])
bandwidths = {(
scheduler_workers[w1].name,
scheduler_workers[w2].name,
): [
"%s/s" % format_bytes(x)
for x in np.quantile(v, [0.25, 0.50, 0.75])
]
for (w1, w2), v in bandwidths.items()}
total_nbytes = {(
scheduler_workers[w1].name,
scheduler_workers[w2].name,
): format_bytes(sum(nb))
for (w1, w2), nb in total_nbytes.items()}
print("Roundtrip benchmark")
print("--------------------------")
print(f"Operation | {args.operation}")
print(f"User size | {args.size}")
print(f"User second size | {args.second_size}")
print(f"User chunk-size | {args.chunk_size}")
print(f"Compute shape | {size}")
print(f"Compute chunk-size | {chunksize}")
print(f"Ignore-size | {format_bytes(args.ignore_size)}")
print(f"Protocol | {args.protocol}")
print(f"Device(s) | {args.devs}")
print(f"Worker Thread(s) | {args.threads_per_worker}")
print("==========================")
print("Wall-clock | npartitions")
print("--------------------------")
for (took, npartitions) in took_list:
t = format_time(took)
t += " " * (11 - len(t))
print(f"{t} | {npartitions}")
print("==========================")
print("(w1,w2) | 25% 50% 75% (total nbytes)")
print("--------------------------")
for (d1, d2), bw in sorted(bandwidths.items()):
fmt = ("(%s,%s) | %s %s %s (%s)"
if args.multi_node or args.sched_addr else
"(%02d,%02d) | %s %s %s (%s)")
print(fmt %
(d1, d2, bw[0], bw[1], bw[2], total_nbytes[(d1, d2)]))
# An SSHCluster will not automatically shut down, we have to
# ensure it does.
if args.multi_node:
await client.shutdown()
