def make_workloads() -> workload.Stream: trainer = utils.TrainAndValidate() yield from trainer.send(steps=3, validation_freq=3, scheduling_unit=10) training_metrics = trainer.get_avg_training_metrics() _, validation_metrics = trainer.result() batch_size = self.hparams["global_batch_size"] for i, metrics in enumerate(training_metrics): expect = pytorch_onevar_model.TriangleLabelSum.expect( batch_size, 10 * i, 10 * (i + 1)) assert "cls_reducer" in metrics assert metrics["cls_reducer"] == expect assert "fn_reducer" in metrics assert metrics["fn_reducer"] == expect for metrics in validation_metrics: num_batches = len( pytorch_onevar_model.OnesDataset()) // batch_size expect = pytorch_onevar_model.TriangleLabelSum.expect( batch_size, 0, num_batches) assert "cls_reducer" in metrics assert metrics["cls_reducer"] == expect assert "fn_reducer" in metrics assert metrics["fn_reducer"] == expect yield workload.terminate_workload( ), [], workload.ignore_workload_response
def make_workloads() -> workload.Stream: trainer = utils.TrainAndValidate() yield from trainer.send(steps=2, validation_freq=1, scheduling_unit=1) yield workload.terminate_workload( ), [], workload.ignore_workload_response
def make_workloads() -> workload.Stream: trainer = utils.TrainAndValidate() yield from trainer.send(steps=10, validation_freq=1, batches_per_step=100) training_metrics, validation_metrics = trainer.result() # We expect the validation error and training loss to be # monotonically decreasing. # TODO(DET-1597): actually use a model and optimizer where the losses # are monotonically decreasing. for older, newer in zip(training_metrics[::100], training_metrics[::100][1:]): assert newer["loss"] <= older["loss"] for older, newer in zip(validation_metrics, validation_metrics[1:]): assert newer["val_categorical_error"] <= older[ "val_categorical_error"] assert validation_metrics[-1][ "val_categorical_error"] == pytest.approx(0.0) 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: 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) assert metrics["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_workloads() -> workload.Stream: trainer = utils.TrainAndValidate() yield from trainer.send(steps=1, validation_freq=1, batches_per_step=1) yield workload.terminate_workload( ), [], workload.ignore_workload_response
def make_workloads1() -> workload.Stream: nonlocal controller yield workload.train_workload(1, 1, 0), [], workload.ignore_workload_response assert controller is not None, "controller was never set!" assert controller.trial.counter.__dict__ == { "validation_steps_started": 0, "validation_steps_ended": 0, "checkpoints_ended": 0, } yield workload.validation_workload(), [], workload.ignore_workload_response assert controller.trial.counter.__dict__ == { "validation_steps_started": 1, "validation_steps_ended": 1, "checkpoints_ended": 0, } yield workload.checkpoint_workload(), [ checkpoint_dir ], workload.ignore_workload_response assert controller.trial.counter.__dict__ == { "validation_steps_started": 1, "validation_steps_ended": 1, "checkpoints_ended": 1, } yield workload.terminate_workload(), [], workload.ignore_workload_response
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 make_workloads_2() -> workload.Stream: trainer = utils.TrainAndValidate() yield from trainer.send(steps=1, validation_freq=1) yield workload.checkpoint_workload(), [ checkpoint_dir ], workload.ignore_workload_response yield workload.terminate_workload(), [], workload.ignore_workload_response
def make_workloads(checkpoint_dir: pathlib.Path) -> workload.Stream: trainer = utils.TrainAndValidate() yield from trainer.send(steps=10, validation_freq=5, batches_per_step=5) yield workload.checkpoint_workload(), [ checkpoint_dir ], workload.ignore_workload_response yield workload.terminate_workload(), [], workload.ignore_workload_response
def make_workloads() -> workload.Stream: trainer = utils.TrainAndValidate(request_stop_step_id=1) yield from trainer.send(steps=100, validation_freq=2, scheduling_unit=5) tm, vm = trainer.result() yield workload.terminate_workload( ), [], workload.ignore_workload_response
def make_workloads() -> workload.Stream: trainer = utils.TrainAndValidate( request_stop_step_id=request_stop_step_id) yield from trainer.send(steps=2, validation_freq=2, batches_per_step=5) tm, vm = trainer.result() yield workload.terminate_workload( ), [], workload.ignore_workload_response
def make_workloads(tag: str) -> workload.Stream: trainer = utils.TrainAndValidate() yield from trainer.send(steps=1000, validation_freq=100) tm, vm = trainer.result() training_metrics[tag] = tm validation_metrics[tag] = vm yield workload.terminate_workload(), [], workload.ignore_workload_response
def make_workloads() -> workload.Stream: trainer = utils.TrainAndValidate() yield from trainer.send(steps=15, validation_freq=4, scheduling_unit=5) training_metrics, validation_metrics = trainer.result() yield workload.terminate_workload( ), [], workload.ignore_workload_response
def make_workloads() -> workload.Stream: trainer = utils.TrainAndValidate() yield from trainer.send(steps=10, validation_freq=10) training_metrics, validation_metrics = trainer.result() for metrics in training_metrics: assert "accuracy" in metrics yield workload.terminate_workload(), [], workload.ignore_workload_response
def make_workloads(steps: int) -> workload.Stream: trainer = TrainAndValidate() yield from trainer.send(steps, validation_freq=1, scheduling_unit=10) tm, vm = trainer.result() metrics["training"] += tm metrics["validation"] += vm yield workload.terminate_workload( ), [], workload.ignore_workload_response
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) yield workload.terminate_workload(), [], workload.ignore_workload_response
def make_workloads() -> workload.Stream: trainer = utils.TrainAndValidate() yield from trainer.send(steps=1000, validation_freq=100) training_metrics, validation_metrics = trainer.result() # We expect the validation error and training loss to be # monotonically decreasing. for older, newer in zip(training_metrics, training_metrics[1:]): assert newer["loss"] <= older["loss"] yield workload.terminate_workload(), [], workload.ignore_workload_response
def make_workloads(tag: str) -> workload.Stream: nonlocal training_metrics nonlocal validation_metrics trainer = TrainAndValidate() yield from trainer.send(steps, validation_freq, batches_per_step=batches_per_step) tm, vm = trainer.result() training_metrics[tag] = tm validation_metrics[tag] = vm yield workload.terminate_workload(), [], workload.ignore_workload_response
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"] yield workload.checkpoint_workload(), [ checkpoint_dir ], workload.ignore_workload_response yield workload.terminate_workload(), [], workload.ignore_workload_response
def make_workloads(checkpoint_dir: str = "") -> workload.Stream: nonlocal training_metrics trainer = utils.TrainAndValidate() yield from trainer.send(steps=10, validation_freq=10, batches_per_step=1) tm, _ = trainer.result() training_metrics += tm if checkpoint_dir: yield workload.checkpoint_workload(), [ checkpoint_dir ], workload.ignore_workload_response yield workload.terminate_workload(), [], workload.ignore_workload_response
def make_workloads() -> workload.Stream: trainer = utils.TrainAndValidate() yield from trainer.send(steps=100, validation_freq=10) training_metrics, validation_metrics = trainer.result() # Check the gradient update at every step. for idx, batch_metrics in enumerate(training_metrics): pytorch_onevar_model.OneVarTrial.check_batch_metrics(batch_metrics, idx) # We expect the validation error and training loss to be # monotonically decreasing. for older, newer in zip(training_metrics, training_metrics[1:]): assert newer["loss"] <= older["loss"] yield workload.terminate_workload(), [], workload.ignore_workload_response
def make_workloads( steps: int, tag: str, checkpoint_dir: Optional[pathlib.Path] = None ) -> workload.Stream: trainer = TrainAndValidate() yield from trainer.send(steps, validation_freq=1, batches_per_step=100) tm, vm = trainer.result() training_metrics[tag] += tm validation_metrics[tag] += vm if checkpoint_dir is not None: yield workload.checkpoint_workload(), [ checkpoint_dir ], workload.ignore_workload_response yield workload.terminate_workload(), [], workload.ignore_workload_response
def make_workloads() -> workload.Stream: trainer = utils.TrainAndValidate() yield from trainer.send(steps=10, validation_freq=5, batches_per_step=1000) training_metrics, validation_metrics = trainer.result() # We expect the training loss to be monotonically decreasing and the # accuracy to be monotonically increasing. for older, newer in zip(training_metrics, training_metrics[1:]): assert newer["loss"] < older["loss"] for older, newer in zip(validation_metrics, validation_metrics[1:]): assert newer["accuracy"] >= older["accuracy"] # The final accuracy should be 100%. assert validation_metrics[-1]["accuracy"] == pytest.approx(1.0) yield workload.terminate_workload(), [], workload.ignore_workload_response
def make_workloads() -> workload.Stream: trainer = utils.TrainAndValidate() # Test >1 validation to ensure that resetting the allgather_op list is working. yield from trainer.send(steps=2, validation_freq=1, batches_per_step=1) training_metrics, validation_metrics = trainer.result() for metrics in validation_metrics: assert metrics[ "label_sum_fn"] == estimator_linear_model.validation_label_sum( ) assert metrics[ "label_sum_cls"] == estimator_linear_model.validation_label_sum( ) yield workload.terminate_workload( ), [], workload.ignore_workload_response
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.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(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_workloads2() -> workload.Stream: yield workload.terminate_workload( ), [], workload.ignore_workload_response