def test_submit_models(self):
_reset()
nni.retiarii.debug_configs.framework = 'pytorch'
os.makedirs('generated', exist_ok=True)
import nni.runtime.platform.test as tt
protocol._set_out_file(
open('generated/debug_protocol_out_file.py', 'wb'))
protocol._set_in_file(
open('generated/debug_protocol_out_file.py', 'rb'))
models = _load_mnist(2)
advisor = RetiariiAdvisor('ws://_unittest_placeholder_')
advisor._channel = protocol.LegacyCommandChannel()
advisor.default_worker.start()
advisor.assessor_worker.start()
remote = RemoteConfig(machine_list=[])
remote.machine_list.append(
RemoteMachineConfig(host='test', gpu_indices=[0, 1, 2, 3]))
cgo_engine = CGOExecutionEngine(training_service=remote,
batch_waiting_time=0)
set_execution_engine(cgo_engine)
submit_models(*models)
time.sleep(3)
if torch.cuda.is_available() and torch.cuda.device_count() >= 2:
cmd, data = protocol.receive()
params = nni.load(data)
tt.init_params(params)
trial_thread = threading.Thread(
target=CGOExecutionEngine.trial_execute_graph)
trial_thread.start()
last_metric = None
while True:
time.sleep(1)
if tt._last_metric:
metric = tt.get_last_metric()
if metric == last_metric:
continue
if 'value' in metric:
metric['value'] = json.dumps(metric['value'])
advisor.handle_report_metric_data(metric)
last_metric = metric
if not trial_thread.is_alive():
trial_thread.join()
break
trial_thread.join()
advisor.stopping = True
advisor.default_worker.join()
advisor.assessor_worker.join()
cgo_engine.join()
def _test_report_final_result(self, in_, out):
nni.report_final_result(in_)
self.assertEqual(
test_platform.get_last_metric(), {
'parameter_id': 'test_param',
'trial_job_id': 'test_trial_job_id',
'type': 'FINAL',
'sequence': 0,
'value': out
})
def test_report_intermediate_result(self):
nni.report_intermediate_result(123)
self.assertEqual(
test_platform.get_last_metric(), {
'parameter_id': 'test_param',
'trial_job_id': 'test_trial_job_id',
'type': 'PERIODICAL',
'sequence': 0,
'value': 123
})
def test_submit_models(self):
_reset()
nni.retiarii.debug_configs.framework = 'pytorch'
os.makedirs('generated', exist_ok=True)
from nni.runtime import protocol
import nni.runtime.platform.test as tt
protocol._set_out_file(
open('generated/debug_protocol_out_file.py', 'wb'))
protocol._set_in_file(
open('generated/debug_protocol_out_file.py', 'rb'))
models = _load_mnist(2)
advisor = RetiariiAdvisor()
cgo_engine = CGOExecutionEngine(devices=[
GPUDevice("test", 0),
GPUDevice("test", 1),
GPUDevice("test", 2),
GPUDevice("test", 3)
],
batch_waiting_time=0)
set_execution_engine(cgo_engine)
submit_models(*models)
time.sleep(3)
if torch.cuda.is_available() and torch.cuda.device_count() >= 2:
cmd, data = protocol.receive()
params = nni.load(data)
tt.init_params(params)
trial_thread = threading.Thread(
target=CGOExecutionEngine.trial_execute_graph)
trial_thread.start()
last_metric = None
while True:
time.sleep(1)
if tt._last_metric:
metric = tt.get_last_metric()
if metric == last_metric:
continue
if 'value' in metric:
metric['value'] = json.dumps(metric['value'])
advisor.handle_report_metric_data(metric)
last_metric = metric
if not trial_thread.is_alive():
trial_thread.join()
break
trial_thread.join()
advisor.stopping = True
advisor.default_worker.join()
advisor.assessor_worker.join()
cgo_engine.join()
def test_report_final_result_nparray(self):
arr = np.array([[1, 2, 3], [4, 5, 6]])
nni.report_final_result(arr)
out = test_platform.get_last_metric()
self.assertEqual(len(arr), 2)
self.assertEqual(len(arr[0]), 3)
self.assertEqual(len(arr[1]), 3)
self.assertEqual(arr[0][0], 1)
self.assertEqual(arr[0][1], 2)
self.assertEqual(arr[0][2], 3)
self.assertEqual(arr[1][0], 4)
self.assertEqual(arr[1][1], 5)
self.assertEqual(arr[1][2], 6)
def test_submit_models(self):
os.environ['CGO'] = 'true'
os.makedirs('generated', exist_ok=True)
from nni.runtime import protocol, platform
import nni.runtime.platform.test as tt
protocol._out_file = open('generated/debug_protocol_out_file.py', 'wb')
protocol._in_file = open('generated/debug_protocol_out_file.py', 'rb')
models = _load_mnist(2)
advisor = RetiariiAdvisor()
submit_models(*models)
if torch.cuda.is_available() and torch.cuda.device_count() >= 2:
cmd, data = protocol.receive()
params = json.loads(data)
params['parameters']['training_kwargs']['max_steps'] = 100
tt.init_params(params)
trial_thread = threading.Thread(
target=CGOExecutionEngine.trial_execute_graph())
trial_thread.start()
last_metric = None
while True:
time.sleep(1)
if tt._last_metric:
metric = tt.get_last_metric()
if metric == last_metric:
continue
advisor.handle_report_metric_data(metric)
last_metric = metric
if not trial_thread.is_alive():
break
trial_thread.join()
advisor.stopping = True
advisor.default_worker.join()
advisor.assessor_worker.join()