def test_run_profile(train, pytestconfig):
net = benchmarks.integrator(3, 2, nengo.RectifiedLinear())
benchmarks.run_profile(
net,
train=train,
n_steps=10,
do_profile=False,
device=pytestconfig.getvalue("--device"),
unroll_simulation=pytest.config.getvalue("--unroll_simulation"),
dtype=(tf.float32 if pytest.config.getvalue("dtype") == "float32" else
tf.float64))
assert net.config[net].inference_only == (not train)
def test_performance(net, train, minibatch_size, eager, min, max):
# performance is based on Azure NC6 VM
# CPU: Intel Xeon E5-2690 v3 @ 2.60Ghz
# GPU: Nvidia Tesla K80
# Python version: 3.6.10
# TensorFlow GPU version: 2.3.0
# Nengo version: 3.1.0
# NengoDL version: 3.3.0
if not eager:
tf.compat.v1.disable_eager_execution()
tf.compat.v1.disable_control_flow_v2()
time = benchmarks.run_profile(
net,
minibatch_size=minibatch_size,
train=train,
n_steps=1000,
unroll_simulation=25,
progress_bar=False,
do_profile=False,
reps=15,
)
assert time > min
assert time < max
def test_run_profile(network, train, pytestconfig, monkeypatch, tmpdir):
monkeypatch.chdir(tmpdir)
if network == "integrator":
net = benchmarks.integrator(3, 2, nengo.SpikingRectifiedLinear())
elif network == "cconv":
net = benchmarks.cconv(3, 10, nengo.LIF())
elif network == "test":
with nengo.Network() as net:
ens = nengo.Ensemble(10, 1)
net.p = nengo.Probe(ens)
benchmarks.run_profile(
net,
train=train,
n_steps=10,
do_profile=True,
device=pytestconfig.getoption("--device"),
unroll_simulation=pytestconfig.getoption("--unroll-simulation"),
dtype=pytestconfig.getoption("dtype"),
)
assert net.config[net].inference_only == (not train)
def test_performance(net, train, minibatch_size, min, max):
# performance is based on Azure NC6 VM
# CPU: Intel Xeon E5-2690 v3 @ 2.60Ghz
# GPU: Nvidia Tesla K80
# Python version: 3.6.10
# TensorFlow GPU version: 2.1.0
# Nengo version: 3.1.0
# NengoDL version: 3.1.0
time = benchmarks.run_profile(
net,
minibatch_size=minibatch_size,
train=train,
n_steps=1000,
unroll_simulation=25,
progress_bar=False,
do_profile=False,
reps=15,
)
assert time > min
assert time < max
def test_performance(net, train, minibatch_size, min, max):
# performance is based on ABR GPU server
# CPU: Intel Xeon E5-1650 v3 @ 3.50GHz
# GPU: GeForce GTX Titan X
# Python version: 3.6.8
# TensorFlow GPU version: 2.0.0
# Nengo version: 3.1.0
# NengoDL version: 3.1.0
time = benchmarks.run_profile(
net,
minibatch_size=minibatch_size,
train=train,
n_steps=1000,
unroll_simulation=25,
progress_bar=False,
do_profile=False,
reps=15,
)
assert time > min
assert time < max