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[[package]] name = "pandocfilters" version = "1.5.1" @@ -4469,17 +4258,6 @@ files = [ {file = "python_rapidjson-1.20.tar.gz", hash = "sha256:115f08c86d2df7543c02605e77c84727cdabc4b08310d2f097e953efeaaa73eb"}, ] -[[package]] -name = "pytz" -version = "2024.2" -description = "World timezone definitions, modern and historical" -optional = false -python-versions = "*" -files = [ - {file = "pytz-2024.2-py2.py3-none-any.whl", hash = "sha256:31c7c1817eb7fae7ca4b8c7ee50c72f93aa2dd863de768e1ef4245d426aa0725"}, - {file = "pytz-2024.2.tar.gz", hash = "sha256:2aa355083c50a0f93fa581709deac0c9ad65cca8a9e9beac660adcbd493c798a"}, -] - [[package]] name = "pywin32" version = "307" @@ -5455,17 +5233,6 @@ files = [ {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"}, ] -[[package]] -name = "tzdata" -version = "2024.2" -description = "Provider of IANA time zone data" -optional = false -python-versions = ">=2" -files = [ - {file = "tzdata-2024.2-py2.py3-none-any.whl", hash = "sha256:a48093786cdcde33cad18c2555e8532f34422074448fbc874186f0abd79565cd"}, - 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[[package]] name = "ledger" version = "0.1.0" @@ -2924,7 +2919,6 @@ files = [] develop = true [package.dependencies] -bilby = "^2.2.2" h5py = "^3.10.0" numpy = "^1.26.4" pycbc = "^2.5.1" diff --git a/projects/train/config.yaml b/projects/train/config.yaml index ec2526fb..809a1608 100644 --- a/projects/train/config.yaml +++ b/projects/train/config.yaml @@ -28,8 +28,6 @@ model: weight_decay: 0.0 learning_rate: 0.000585 pct_lr_ramp: 0.115 - # early stop - patience: null data: class_path: train.data.supervised.TimeDomainSupervisedAframeDataset init_args: @@ -83,6 +81,23 @@ trainer: init_args: # save_dir: flush_logs_every_n_steps: 10 + callbacks: + - class_path: lightning.pytorch.callbacks.EarlyStopping + init_args: + monitor: "valid_auroc" + mode: "max" + patience: null + # custom model checkpoint for saving and + # tracing best model at end of traiing + # that will be used for downstream export + - class_path: train.callbacks.ModelCheckpoint + init_args: + monitor: "valid_auroc" + mode: "max" + save_top_k: 1 + save_last: true + auto_insert_metric_name: false + - class_path: train.callbacks.SaveAugmentedBatch # uncomment below if you want to profile # profiler: # class_path: lightning.pytorch.profilers.PyTorchProfiler diff --git a/projects/train/configs/bbh.yaml b/projects/train/configs/bbh.yaml index 3db22cad..08897d82 100644 --- a/projects/train/configs/bbh.yaml +++ b/projects/train/configs/bbh.yaml @@ -28,8 +28,6 @@ model: weight_decay: 0.0 learning_rate: 0.000585 pct_lr_ramp: 0.115 - # early stop - patience: 50 data: class_path: train.data.supervised.TimeDomainSupervisedAframeDataset init_args: @@ -85,6 +83,23 @@ trainer: # save_dir: flush_logs_every_n_steps: 10 name: "train_logs" + callbacks: + - class_path: lightning.pytorch.callbacks.EarlyStopping + init_args: + monitor: "valid_auroc" + mode: "max" + patience: null + # custom model checkpoint for saving and + # tracing best model at end of traiing + # that will be used for downstream export + - class_path: train.callbacks.ModelCheckpoint + init_args: + monitor: "valid_auroc" + mode: "max" + save_top_k: 1 + save_last: true + auto_insert_metric_name: false + - class_path: train.callbacks.SaveAugmentedBatch # uncomment below if you want to profile # profiler: # class_path: lightning.pytorch.profilers.PyTorchProfiler diff --git a/projects/train/configs/bns.yaml b/projects/train/configs/bns.yaml index a5ca984e..19991d4e 100644 --- a/projects/train/configs/bns.yaml +++ b/projects/train/configs/bns.yaml @@ -29,7 +29,7 @@ model: learning_rate: 0.000585 pct_lr_ramp: 0.115 # early stop - patience: 50 + patience: null data: class_path: train.data.supervised.SpectrogramDomainSupervisedAframeDataset init_args: @@ -87,6 +87,24 @@ trainer: # save_dir: flush_logs_every_n_steps: 10 name: "train_logs" + + callbacks: + - class_path: lightning.pytorch.callbacks.EarlyStopping + init_args: + monitor: "valid_auroc" + mode: "max" + patience: 50 + # custom model checkpoint for saving and + # tracing best model at end of traiing + # that will be used for downstream export + - class_path: train.callbacks.ModelCheckpoint + init_args: + monitor: "valid_auroc" + mode: "max" + save_top_k: 1 + save_last: true + auto_insert_metric_name: false + - class_path: train.callbacks.SaveAugmentedBatch # uncomment below if you want to profile # profiler: # class_path: lightning.pytorch.profilers.PyTorchProfiler diff --git a/projects/train/poetry.lock b/projects/train/poetry.lock index bd8090c0..deadbfea 100644 --- a/projects/train/poetry.lock +++ b/projects/train/poetry.lock @@ -13,7 +13,7 @@ develop = true boto3 = "^1.34.4" cloudpathlib = "^0.18.1" kr8s = "^0.10.0" -law = {git = "https://github.com/riga/law.git", branch = "master"} +law = ">=0.1.19" luigi = "^3.0" ml4gw-hermes = ">=0.2.0" numpy = "^1.26.4" @@ -28,24 +28,24 @@ url = "../.." 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-import ray import torch from architectures import Architecture -from train.callbacks import ModelCheckpoint, SaveAugmentedBatch from train.metrics import TimeSlideAUROC Tensor = torch.Tensor @@ -40,8 +38,6 @@ def __init__( learning_rate: float, pct_lr_ramp: float, weight_decay: float = 0.0, - patience: Optional[int] = None, - save_top_k_models: int = 10, verbose: bool = False, ) -> None: super().__init__() @@ -154,6 +150,7 @@ def training_step(self, batch: tuple[Tensor, Tensor]) -> Tensor: def validation_step(self, batch, _) -> None: shift, X_bg, X_inj = batch + y_bg = self.score(X_bg) # compute predictions over multiple views of @@ -181,37 +178,6 @@ def validation_step(self, batch, _) -> None: sync_dist=True, ) - def configure_callbacks(self) -> Sequence[pl.Callback]: - # checkpoint for saving best model - # that will be used for downstream export - # and inference tasks - # checkpoint for saving multiple best models - callbacks = [] - callbacks.append(SaveAugmentedBatch()) - - # if using ray tune don't append lightning - # model checkpoint since we'll be using ray's - checkpoint = ModelCheckpoint( - monitor="valid_auroc", - save_top_k=self.hparams.save_top_k_models, - save_last=True, - auto_insert_metric_name=False, - mode="max", - ) - - if not ray.is_initialized(): - callbacks.append(checkpoint) - - if self.hparams.patience is not None: - early_stop = pl.callbacks.EarlyStopping( - monitor="valid_auroc", - patience=self.hparams.patience, - mode="max", - min_delta=0.00, - ) - callbacks.append(early_stop) - return callbacks - def configure_optimizers(self): if not torch.distributed.is_initialized(): world_size = 1