def test_py_execution_engine(self): advisor = RetiariiAdvisor() set_execution_engine(PurePythonExecutionEngine()) model = Model._load({ '_model': { 'inputs': None, 'outputs': None, 'nodes': { 'layerchoice_1': { 'operation': { 'type': 'LayerChoice', 'parameters': { 'candidates': ['0', '1'] } } } }, 'edges': [] } }) model.python_class = object submit_models(model, model) advisor.stopping = True advisor.default_worker.join() advisor.assessor_worker.join()
def test_py_execution_engine(self): nni.retiarii.integration_api._advisor = None nni.retiarii.execution.api._execution_engine = None advisor = RetiariiAdvisor('ws://_unittest_placeholder_') advisor._channel = LegacyCommandChannel() advisor.default_worker.start() advisor.assessor_worker.start() set_execution_engine(PurePythonExecutionEngine()) model = Model._load({ '_model': { 'inputs': None, 'outputs': None, 'nodes': { 'layerchoice_1': { 'operation': { 'type': 'LayerChoice', 'parameters': { 'candidates': ['0', '1'] } } } }, 'edges': [] } }) model.evaluator = DebugEvaluator() model.python_class = object submit_models(model, model) advisor.stopping = True advisor.default_worker.join() advisor.assessor_worker.join()
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_base_execution_engine(self): advisor = RetiariiAdvisor() set_execution_engine(BaseExecutionEngine()) with open(self.enclosing_dir / 'mnist_pytorch.json') as f: model = Model._load(json.load(f)) submit_models(model, model) advisor.stopping = True advisor.default_worker.join() advisor.assessor_worker.join()
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_submit_models(self): os.makedirs('generated', exist_ok=True) from nni.runtime import protocol protocol._out_file = open( Path(__file__).parent / 'generated/debug_protocol_out_file.py', 'wb') advisor = RetiariiAdvisor() with open('mnist_pytorch.json') as f: model = Model._load(json.load(f)) submit_models(model, model) advisor.stopping = True advisor.default_worker.join() advisor.assessor_worker.join()
def test_base_execution_engine(self): nni.retiarii.integration_api._advisor = None nni.retiarii.execution.api._execution_engine = None advisor = RetiariiAdvisor('ws://_unittest_placeholder_') advisor._channel = LegacyCommandChannel() advisor.default_worker.start() advisor.assessor_worker.start() set_execution_engine(BaseExecutionEngine()) with open(self.enclosing_dir / 'mnist_pytorch.json') as f: model = Model._load(json.load(f)) submit_models(model, model) advisor.stopping = True advisor.default_worker.join() advisor.assessor_worker.join()
def run(self, base_model, applied_mutators, trainer): try: _logger.info('stargety start...') while True: model = base_model _logger.info('apply mutators...') _logger.info('mutators: {}'.format(applied_mutators)) random_sampler = RandomSampler() for mutator in applied_mutators: _logger.info('mutate model...') mutator.bind_sampler(random_sampler) model = mutator.apply(model) # get and apply training approach _logger.info('apply training approach...') model.apply_trainer(trainer['modulename'], trainer['args']) # run models submit_models(model) wait_models(model) _logger.info('Strategy says:', model.metric) except Exception as e: _logger.error(logging.exception('message'))
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()