def make_workloads() -> workload.Stream: nonlocal w interceptor = workload.WorkloadResponseInterceptor() for idx, batch in batches: yield from interceptor.send(workload.train_workload(1)) metrics = interceptor.metrics_result() # Calculate what the loss should be. loss = trial_class.calc_loss(w, batch) epsilon = 0.0001 assert abs(metrics["metrics"]["avg_metrics"]["loss"] - loss) < epsilon # Update what the weight should be. w = w - hparams["learning_rate"] * trial_class.calc_gradient( w, batch) if test_checkpointing and idx == 3: # Checkpoint and let the next TrialController finish the work. interceptor = workload.WorkloadResponseInterceptor() yield from interceptor.send(workload.checkpoint_workload()) nonlocal latest_checkpoint, steps_completed latest_checkpoint = interceptor.metrics_result()["uuid"] # steps_completed is unused, but can't be 0. steps_completed = 1 break
def make_workloads() -> workload.Stream: nonlocal w interceptor = workload.WorkloadResponseInterceptor() for idx, batch in batches: yield from interceptor.send(workload.train_workload(1), [1]) metrics = interceptor.metrics_result() # Calculate what the loss should be. loss = trial_class.calc_loss(w, batch) assert metrics["avg_metrics"]["loss"] == pytest.approx(loss) # Update what the weight should be. w = w - hparams["learning_rate"] * trial_class.calc_gradient( w, batch) if test_checkpointing and idx == 3: # Checkpoint and let the next TrialController finish the work.l yield workload.checkpoint_workload(), [ checkpoint_dir ], workload.ignore_workload_response break yield workload.terminate_workload( ), [], workload.ignore_workload_response
def make_test_workloads( checkpoint_dir: pathlib.Path, config: det.ExperimentConfig ) -> workload.Stream: print("Start training a test experiment.") interceptor = workload.WorkloadResponseInterceptor() print("Training 1 step.") yield from interceptor.send(workload.train_workload(1), [config.batches_per_step()]) metrics = interceptor.metrics_result() batch_metrics = metrics["batch_metrics"] check.eq(len(batch_metrics), config.batches_per_step()) print(f"Finished training. Metrics: {batch_metrics}") print("Validating.") yield from interceptor.send(workload.validation_workload(1), []) validation = interceptor.metrics_result() v_metrics = validation["validation_metrics"] print(f"Finished validating. Validation metrics: {v_metrics}") print(f"Saving a checkpoint to {checkpoint_dir}") yield workload.checkpoint_workload(), [checkpoint_dir], workload.ignore_workload_response print(f"Finished saving a checkpoint to {checkpoint_dir}.") yield workload.terminate_workload(), [], workload.ignore_workload_response print("The test experiment passed.")
def make_workloads() -> workload.Stream: training_metrics = [] interceptor = workload.WorkloadResponseInterceptor() total_steps, total_batches_processed = 10, 0 for step_id in range(1, total_steps): num_batches = step_id yield from interceptor.send( workload.train_workload( step_id, num_batches=num_batches, total_batches_processed=total_batches_processed, ), [], ) metrics = interceptor.metrics_result() batch_metrics = metrics["metrics"]["batch_metrics"] assert len( batch_metrics ) == num_batches, "did not run for expected num_batches" training_metrics.extend(batch_metrics) total_batches_processed += num_batches yield workload.terminate_workload( ), [], workload.ignore_workload_response
def send( self, steps: int, validation_freq: int, initial_step_id: int = 1, batches_per_step: int = 1, ) -> workload.Stream: self._training_metrics = [] self._validation_metrics = [] interceptor = workload.WorkloadResponseInterceptor() for step_id in range(initial_step_id, initial_step_id + steps): yield from interceptor.send(workload.train_workload(step_id), [batches_per_step]) metrics = interceptor.metrics_result() batch_metrics = metrics["batch_metrics"] assert len(batch_metrics) == batches_per_step self._training_metrics.extend(batch_metrics) if step_id % validation_freq == 0: yield from interceptor.send( workload.validation_workload(step_id), []) validation = interceptor.metrics_result() v_metrics = validation["validation_metrics"] self._validation_metrics.append(v_metrics)
def make_workloads_1() -> workload.Stream: trainer = utils.TrainAndValidate() yield from trainer.send(steps=1, validation_freq=1) interceptor = workload.WorkloadResponseInterceptor() yield from interceptor.send(workload.checkpoint_workload()) nonlocal latest_checkpoint, steps_completed latest_checkpoint = interceptor.metrics_result()["uuid"] steps_completed = trainer.get_steps_completed()
def make_workloads_2() -> workload.Stream: interceptor = workload.WorkloadResponseInterceptor() yield from interceptor.send(workload.validation_workload()) metrics = interceptor.metrics_result() new_loss = metrics["metrics"]["validation_metrics"]["val_loss"] assert new_loss == pytest.approx(old_loss)
def make_workloads_2() -> workload.Stream: interceptor = workload.WorkloadResponseInterceptor() yield from interceptor.send(workload.validation_workload(), []) metrics = interceptor.metrics_result() new_error = metrics["validation_metrics"]["binary_error"] assert new_error == pytest.approx(old_error) yield workload.terminate_workload(), [], workload.ignore_workload_response
def make_workloads() -> workload.Stream: trainer = utils.TrainAndValidate() yield from trainer.send(steps=1, validation_freq=1, scheduling_unit=10) interceptor = workload.WorkloadResponseInterceptor() yield from interceptor.send(workload.checkpoint_workload()) nonlocal latest_checkpoint latest_checkpoint = interceptor.metrics_result()["uuid"]
def make_workloads1() -> workload.Stream: nonlocal controller assert controller.trial.counter.trial_startups == 1 yield workload.train_workload(1, 1, 0, 4), workload.ignore_workload_response assert controller is not None, "controller was never set!" assert controller.trial.counter.__dict__ == { "trial_startups": 1, "validation_steps_started": 0, "validation_steps_ended": 0, "checkpoints_ended": 0, "training_started_times": 1, "training_epochs_started": 2, "training_epochs_ended": 2, "trial_shutdowns": 0, } assert controller.trial.legacy_counter.__dict__ == { "legacy_on_training_epochs_start_calls": 2 } yield workload.validation_workload( ), workload.ignore_workload_response assert controller.trial.counter.__dict__ == { "trial_startups": 1, "validation_steps_started": 1, "validation_steps_ended": 1, "checkpoints_ended": 0, "training_started_times": 1, "training_epochs_started": 2, "training_epochs_ended": 2, "trial_shutdowns": 0, } assert controller.trial.legacy_counter.__dict__ == { "legacy_on_training_epochs_start_calls": 2 } interceptor = workload.WorkloadResponseInterceptor() yield from interceptor.send(workload.checkpoint_workload()) nonlocal latest_checkpoint, steps_completed latest_checkpoint = interceptor.metrics_result()["uuid"] steps_completed = 1 assert controller.trial.counter.__dict__ == { "trial_startups": 1, "validation_steps_started": 1, "validation_steps_ended": 1, "checkpoints_ended": 1, "training_started_times": 1, "training_epochs_started": 2, "training_epochs_ended": 2, "trial_shutdowns": 0, } assert controller.trial.legacy_counter.__dict__ == { "legacy_on_training_epochs_start_calls": 2 }
def make_workloads_1() -> workload.Stream: nonlocal old_loss trainer = utils.TrainAndValidate() yield from trainer.send(steps=10, validation_freq=10) training_metrics, validation_metrics = trainer.result() old_loss = validation_metrics[-1]["val_loss"] interceptor = workload.WorkloadResponseInterceptor() yield from interceptor.send(workload.checkpoint_workload()) nonlocal latest_checkpoint, steps_completed latest_checkpoint = interceptor.metrics_result()["uuid"] steps_completed = trainer.get_steps_completed()
def send(self, steps: int, validation_freq: int, initial_step_id: int = 1, scheduling_unit: int = 1) -> workload.Stream: self._training_metrics = [] self._avg_training_metrics = [] self._validation_metrics = [] total_batches_processed = 0 interceptor = workload.WorkloadResponseInterceptor() for step_id in range(initial_step_id, initial_step_id + steps): stop_requested = False yield from interceptor.send( workload.train_workload( step_id, num_batches=scheduling_unit, total_batches_processed=total_batches_processed, ), [], ) metrics = interceptor.metrics_result() batch_metrics = metrics["metrics"]["batch_metrics"] assert len(batch_metrics) == scheduling_unit self._training_metrics.extend(batch_metrics) self._avg_training_metrics.append( metrics["metrics"]["avg_metrics"]) total_batches_processed += scheduling_unit if metrics["stop_requested"]: assert step_id == self.request_stop_step_id stop_requested = True if step_id % validation_freq == 0: yield from interceptor.send( workload.validation_workload( step_id, total_batches_processed=total_batches_processed), [], ) validation = interceptor.metrics_result() v_metrics = validation["metrics"]["validation_metrics"] self._validation_metrics.append(v_metrics) if validation["stop_requested"]: assert step_id == self.request_stop_step_id stop_requested = True if stop_requested: break else: assert step_id != self.request_stop_step_id
def make_workloads(steps: int, tag: str, checkpoint: bool) -> workload.Stream: trainer = TrainAndValidate() yield from trainer.send(steps, validation_freq=1, scheduling_unit=100) tm, vm = trainer.result() training_metrics[tag] += tm validation_metrics[tag] += vm if checkpoint is not None: interceptor = workload.WorkloadResponseInterceptor() yield from interceptor.send(workload.checkpoint_workload()) nonlocal latest_checkpoint, steps_completed latest_checkpoint = interceptor.metrics_result()["uuid"] steps_completed = trainer.get_steps_completed()
def _make_test_workloads(config: det.ExperimentConfig) -> workload.Stream: interceptor = workload.WorkloadResponseInterceptor() logging.info("Training one batch") yield from interceptor.send(workload.train_workload(1)) metrics = interceptor.metrics_result() batch_metrics = metrics["metrics"]["batch_metrics"] check.eq(len(batch_metrics), config.scheduling_unit()) logging.info(f"Finished training, metrics: {batch_metrics}") logging.info("Validating one batch") yield from interceptor.send(workload.validation_workload(1)) validation = interceptor.metrics_result() v_metrics = validation["metrics"]["validation_metrics"] logging.info(f"Finished validating, validation metrics: {v_metrics}") logging.info("Saving a checkpoint.") yield workload.checkpoint_workload(), workload.ignore_workload_response logging.info("Finished saving a checkpoint.")
def send(self, steps: int, validation_freq: int, initial_step_id: int = 1, batches_per_step: int = 1) -> workload.Stream: self._training_metrics = [] self._validation_metrics = [] interceptor = workload.WorkloadResponseInterceptor() for step_id in range(initial_step_id, initial_step_id + steps): stop_requested = False yield from interceptor.send(workload.train_workload(step_id), [batches_per_step]) metrics = interceptor.metrics_result() batch_metrics = metrics["metrics"]["batch_metrics"] assert len(batch_metrics) == batches_per_step self._training_metrics.extend(batch_metrics) if metrics["stop_requested"]: assert step_id == self.request_stop_step_id stop_requested = True if step_id % validation_freq == 0: yield from interceptor.send( workload.validation_workload(step_id), []) validation = interceptor.metrics_result() print(validation) v_metrics = validation["metrics"]["validation_metrics"] self._validation_metrics.append(v_metrics) if validation["stop_requested"]: assert step_id == self.request_stop_step_id stop_requested = True if stop_requested: break else: assert step_id != self.request_stop_step_id
def _make_test_workloads(checkpoint_dir: pathlib.Path, config: det.ExperimentConfig) -> workload.Stream: interceptor = workload.WorkloadResponseInterceptor() logging.info("Training one batch") yield from interceptor.send(workload.train_workload(1), []) metrics = interceptor.metrics_result() batch_metrics = metrics["metrics"]["batch_metrics"] check.eq(len(batch_metrics), config.scheduling_unit()) logging.debug(f"Finished training, metrics: {batch_metrics}") logging.info("Validating one step") yield from interceptor.send(workload.validation_workload(1), []) validation = interceptor.metrics_result() v_metrics = validation["metrics"]["validation_metrics"] logging.debug(f"Finished validating, validation metrics: {v_metrics}") logging.info(f"Saving a checkpoint to {checkpoint_dir}.") yield workload.checkpoint_workload(), [checkpoint_dir ], workload.ignore_workload_response logging.info(f"Finished saving a checkpoint to {checkpoint_dir}.") yield workload.terminate_workload(), [], workload.ignore_workload_response logging.info("The test experiment passed.")
def make_workloads() -> workload.Stream: interceptor = workload.WorkloadResponseInterceptor() for i, wkld in enumerate(fake_subprocess_receiver.fake_workload_gen()): yield from interceptor.send(wkld, []) assert interceptor.metrics_result() == {"count": i}