def test_cache_compute_dict_speed(self):
f = fungraph.fun(_slow_func, 1, 2)
cache = MemoryCache()
t1 = timeonce(lambda: f.cachedcompute(cache=cache))
t2 = timeonce(lambda: f.cachedcompute(cache=cache))
self.assertGreater(t1, 0.5)
self.assertLess(t2, 0.5)
def test_cache(self):
cachedir = tempfile.mkdtemp()
node = fungraph.fun(_slow_identity, 5, waitseconds=1)
f = lambda: node.cachedcompute(cache=cachedir)
t1 = timeonce(f)
t2 = timeonce(f)
self.assertGreater(t1, 0.5)
self.assertLess(t2, 0.5)
def test_cache_new_object(self):
cachedir = tempfile.mkdtemp()
node1 = fungraph.fun(_slow_identity, 5, waitseconds=1)
f1 = lambda: node1.cachedcompute(cache=cachedir)
t1 = timeonce(f1)
node2 = fungraph.fun(_slow_identity, 5, waitseconds=1)
f2 = lambda: node2.cachedcompute(cache=cachedir)
t2 = timeonce(f2)
self.assertGreater(t1, 0.5)
self.assertLess(t2, 0.5)
def test_paralell(self):
cluster = dask.distributed.LocalCluster(
n_workers=8, processes=True, threads_per_worker=1, memory_limit="auto"
)
with dask.distributed.Client(address=cluster):
def slowfunc(loc):
time.sleep(1)
return random.gauss(loc, 1.0)
N = 8
args = [fungraph.fun(slowfunc, random.uniform(0.0, 1.0)) for _ in range(N)]
jobs = fungraph.fun(lambda *args: sum(args), *args)
t1 = timeonce(lambda: slowfunc(1.0))
tn = timeonce(jobs.cachedcompute)
self.assertLess(tn, (t1 * N) / 2.0)
def test_clone_reuses_cache(self):
cachedir = tempfile.mkdtemp()
node = fungraph.fun(
operator.add,
fungraph.fun(_slow_identity, 2, waitseconds=1),
fungraph.fun(_slow_identity, 3, waitseconds=1),
)
clone = node.clone()
nodefun = lambda: node.cachedcompute(cache=cachedir)
clonefun = lambda: clone.cachedcompute(cache=cachedir)
tn1 = timeonce(nodefun)
tn2 = timeonce(nodefun)
tc1 = timeonce(clonefun)
self.assertGreater(tn1, 0.5)
self.assertLess(tn2, 0.5)
self.assertLess(tc1, 0.5)
def test_paralell_uses_cache(self):
cluster = dask.distributed.LocalCluster(
n_workers=8, processes=True, threads_per_worker=1, memory_limit="auto"
)
with dask.distributed.Client(address=cluster):
def slowfunc(loc):
time.sleep(1)
return random.gauss(loc, 1.0)
N = 8
args = [fungraph.fun(slowfunc, random.uniform(0.0, 1.0)) for _ in range(N)]
jobs = fungraph.fun(lambda *args: sum(args), *args)
with TemporaryDirectory() as d:
with fungraph.cachecontext(d):
t1 = timeonce(jobs.compute)
t2 = timeonce(jobs.compute)
self.assertGreater(t1, 1.0)
self.assertLess(t2, 0.5)
def _timenodeonce(node, cachedir):
return timeonce(lambda: node.cachedcompute(cache=cachedir))
def test_nocache_compute_none(self):
f = fungraph.fun(_slow_func, 1, 2)
t1 = timeonce(lambda: f.compute())
t2 = timeonce(lambda: f.compute())
self.assertAlmostEqual(t1, t2, delta=0.1)