def test_abort():
with pm.Model() as model:
a = pm.Normal("a", shape=1)
pm.HalfNormal("b")
step1 = pm.NUTS([a])
step2 = pm.Metropolis([model.b_log__])
step = pm.CompoundStep([step1, step2])
ctx = multiprocessing.get_context()
proc = ps.ProcessAdapter(
10,
10,
step,
chain=3,
seed=1,
mp_ctx=ctx,
start={"a": 1.0, "b_log__": 2.0},
step_method_pickled=None,
pickle_backend="pickle",
)
proc.start()
proc.write_next()
proc.abort()
proc.join()
def test_explicit_sample():
with pm.Model() as model:
a = pm.Normal("a", shape=1)
pm.HalfNormal("b")
step1 = pm.NUTS([a])
step2 = pm.Metropolis([model.b_log__])
step = pm.CompoundStep([step1, step2])
ctx = multiprocessing.get_context()
proc = ps.ProcessAdapter(
10,
10,
step,
chain=3,
seed=1,
mp_ctx=ctx,
start={"a": 1.0, "b_log__": 2.0},
step_method_pickled=None,
pickle_backend="pickle",
)
proc.start()
while True:
proc.write_next()
out = ps.ProcessAdapter.recv_draw([proc])
view = proc.shared_point_view
for name in view:
view[name].copy()
if out[1]:
break
proc.join()
def test_explicit_sample():
with pm.Model() as model:
a = pm.Normal('a', shape=1)
pm.HalfNormal('b')
step1 = pm.NUTS([a])
step2 = pm.Metropolis([model.b_log__])
step = pm.CompoundStep([step1, step2])
proc = ps.ProcessAdapter(10,
10,
step,
chain=3,
seed=1,
start={
'a': 1.,
'b_log__': 2.
})
proc.start()
while True:
proc.write_next()
out = ps.ProcessAdapter.recv_draw([proc])
view = proc.shared_point_view
for name in view:
view[name].copy()
if out[1]:
break
proc.join()
def test_iterator():
with pm.Model() as model:
a = pm.Normal("a", shape=1)
pm.HalfNormal("b")
step1 = pm.NUTS([a])
step2 = pm.Metropolis([model.b_log__])
step = pm.CompoundStep([step1, step2])
start = {"a": 1.0, "b_log__": 2.0}
sampler = ps.ParallelSampler(10, 10, 3, 2, [2, 3, 4], [start] * 3, step, 0, False)
with sampler:
for draw in sampler:
pass
def test_abort():
with pm.Model() as model:
a = pm.Normal('a', shape=1)
pm.HalfNormal('b')
step1 = pm.NUTS([a])
step2 = pm.Metropolis([model.b_log__])
step = pm.CompoundStep([step1, step2])
proc = ps.ProcessAdapter(10, 10, step, chain=3, seed=1,
start={'a': 1., 'b_log__': 2.})
proc.start()
proc.write_next()
proc.abort()
proc.join()
def test_iterator():
with pm.Model() as model:
a = pm.Normal('a', shape=1)
pm.HalfNormal('b')
step1 = pm.NUTS([a])
step2 = pm.Metropolis([model.b_log__])
step = pm.CompoundStep([step1, step2])
start = time.time()
start = {'a': 1., 'b_log__': 2.}
sampler = ps.ParallelSampler(10, 10, 3, 2, [2, 3, 4], [start] * 3,
step, 0, False)
with sampler:
for draw in sampler:
pass
