def test_profile_create_domain_dept():
profiler.profiler_set_config(mode='symbolic', filename='temp.json')
profiler.set_state('run')
domain = profiler.Domain(name='PythonDomain')
print("Domain created: {}".format(str(domain)))
profiler.dump_profile()
profiler.set_state('stop')
def test_profile_create_domain_dept():
profiler.profiler_set_config(mode='symbolic', filename='temp.json')
profiler.set_state('run')
domain = profiler.Domain(name='PythonDomain')
print("Domain created: {}".format(str(domain)))
profiler.dump_profile()
profiler.set_state('stop')
def test_profiler():
profile_filename = "test_profile.json"
iter_num = 100
begin_profiling_iter = 50
end_profiling_iter = 50
profiler.profiler_set_config(mode='symbolic', filename=profile_filename)
print('profile file save to {0}'.format(profile_filename))
A = mx.sym.Variable('A')
B = mx.sym.Variable('B')
C = mx.symbol.dot(A, B)
executor = C.simple_bind(mx.cpu(1),
'write',
A=(4096, 4096),
B=(4096, 4096))
a = mx.random.uniform(-1.0, 1.0, shape=(4096, 4096))
b = mx.random.uniform(-1.0, 1.0, shape=(4096, 4096))
a.copyto(executor.arg_dict['A'])
b.copyto(executor.arg_dict['B'])
flag = False
print("execution begin")
for i in range(iter_num):
if i == begin_profiling_iter:
t0 = time.clock()
profiler.profiler_set_state('run')
if i == end_profiling_iter:
t1 = time.clock()
profiler.profiler_set_state('stop')
executor.forward()
c = executor.outputs[0]
c.wait_to_read()
print("execution end")
duration = t1 - t0
print('duration: {0}s'.format(duration))
print(' {0}ms/operator'.format(duration * 1000 / iter_num))
def test_profiler():
profile_filename = "test_profile.json"
iter_num = 100
begin_profiling_iter = 50
end_profiling_iter = 50
profiler.profiler_set_config(mode='symbolic', filename=profile_filename)
print('profile file save to {0}'.format(profile_filename))
A = mx.sym.Variable('A')
B = mx.sym.Variable('B')
C = mx.symbol.dot(A, B)
executor = C.simple_bind(mx.cpu(1), 'write', A=(4096, 4096), B=(4096, 4096))
a = mx.random.uniform(-1.0, 1.0, shape=(4096, 4096))
b = mx.random.uniform(-1.0, 1.0, shape=(4096, 4096))
a.copyto(executor.arg_dict['A'])
b.copyto(executor.arg_dict['B'])
flag = False
print("execution begin")
for i in range(iter_num):
if i == begin_profiling_iter:
t0 = time.clock()
profiler.profiler_set_state('run')
if i == end_profiling_iter:
t1 = time.clock()
profiler.profiler_set_state('stop')
executor.forward()
c = executor.outputs[0]
c.wait_to_read()
print("execution end")
duration = t1 - t0
print('duration: {0}s'.format(duration))
print(' {0}ms/operator'.format(duration*1000/iter_num))
for i in range(iterations):
mod.forward(batch, is_train=True)
mod.backward()
mod.update()
for output in mod.get_outputs(merge_multi_context=False)[0]:
output.wait_to_read()
profiler.profiler_set_state('stop')
t1 = time.clock()
return (t1 - t0) * 1000.0 / iterations
def executor(num_iteration):
sym, provide_data, provide_label = get_symbol()
ctx = [mx.cpu(0)]
mod = get_module(ctx, sym, provide_data, provide_label, batch_size=128)
return benchmark(mod, iterations=num_iteration)
# In[6]:
iter_num = 10
profile_filename = '/home/ubuntu/results/profiler_mnist_scalar.json'
profiler.profiler_set_config(mode='symbolic', filename=profile_filename)
print('profile file save to {0}'.format(profile_filename))
print('executor num_iteration: {0}'.format(iter_num))
executor_time = executor(iter_num)
print("executor {0} ms / iteration".format(executor_time))
# In[ ]: