Loading deepchem/models/callbacks.py +1 −34 Original line number Diff line number Diff line Loading @@ -90,36 +90,3 @@ class ValidationCallback(object): if self._best_score is None or score < self._best_score: model.save_checkpoint(model_dir=self.save_dir) self._best_score = score No newline at end of file class WandbCallback(object): """ Weights & Biases Logger """ def __init__(self, **kwargs): try: import wandb except ImportError: raise ImportError( 'You want to use `wandb` logger which is not installed yet,' ' install it with `pip install wandb`.' ) def __call__(self, model, step): """This is invoked by the KerasModel after every step of fitting. Parameters ---------- model: KerasModel the model that is being trained step: int the index of the training step that has just completed """ deepchem/models/keras_model.py +34 −6 Original line number Diff line number Diff line Loading @@ -3,6 +3,7 @@ import tensorflow as tf import time import logging import os try: from collections.abc import Sequence as SequenceCollection except: Loading @@ -13,11 +14,13 @@ from deepchem.metrics import Metric from deepchem.models.losses import Loss from deepchem.models.models import Model from deepchem.models.optimizers import Adam, Optimizer, LearningRateSchedule from deepchem.models.callbacks import ValidationCallback from deepchem.trans import Transformer, undo_transforms from deepchem.utils.evaluate import GeneratorEvaluator from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union from deepchem.utils.typing import ArrayLike, LossFn, OneOrMany from deepchem.models.wandblogger import WandbLogger try: import wandb Loading Loading @@ -131,7 +134,7 @@ class KerasModel(Model): learning_rate: Union[float, LearningRateSchedule] = 0.001, optimizer: Optional[Optimizer] = None, tensorboard: bool = False, wandb: bool = False, wandb: Optional[WandbLogger] = None, log_frequency: int = 100, **kwargs) -> None: """Create a new KerasModel. Loading @@ -158,7 +161,7 @@ class KerasModel(Model): ignored. tensorboard: bool whether to log progress to TensorBoard during training wandb: bool wandb: bool (MODIFY) whether to log progress to Weights & Biases during training log_frequency: int The frequency at which to log data. Data is logged using Loading @@ -182,12 +185,28 @@ class KerasModel(Model): self.tensorboard = tensorboard # W&B logging if wandb and not _has_wandb: if (wandb is not None) and not _has_wandb: logger.warning( "You set wandb to True but W&B is not installed. To use wandb logging, " "You are using a wandb logger but W&B is not installed. To use wandb logging, " "run `pip install wandb; wandb login` see https://docs.wandb.com/huggingface." ) self.wandb = wandb and _has_wandb self.wandb_logger = wandb #Need to save model?? #wandb.save_model(...) #save the tf.keras.Model #update config with KerasModel Params run_params = dict( model=model, loss=loss, output_types=output_types, batch_size=batch_size, model_dir=model_dir, learning_rate=learning_rate, optimizer=optimizer, tensorboard=tensorboard, log_frequency=log_frequency ) self.wandb_logger.wandb.config.update(run_params) # Backwards compatibility if "tensorboard_log_frequency" in kwargs: Loading Loading @@ -394,6 +413,15 @@ class KerasModel(Model): # Main training loop. # Warn if both ValidationCallback and WandbLogger present if wandb is not None: for c in callbacks: if isinstance(c, ValidationCallback): logger.warning( "You are using both WandbLogger and ValidationCallback. WandbLogger is able to log validation metrics" "so there is no need to have a ValidationCallback. Logging validation metrics twice may take longer." ) for batch in generator: self._create_training_ops(batch) if restore: Loading Loading @@ -433,7 +461,7 @@ class KerasModel(Model): if self.tensorboard and should_log: self._log_scalar_to_tensorboard('loss', batch_loss, current_step) if self.wandb and should_log: wandb.log({'loss': batch_loss}, step=current_step) #wandb.log({'loss': batch_loss}, step=current_step) # Report final results. if averaged_batches > 0: Loading deepchem/models/wandblogger.py 0 → 100644 +84 −0 Original line number Diff line number Diff line import torch import tensorflow as tf from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union from deepchem.data import Dataset, NumpyDataset from deepchem.metrics import Metric class WandbLogger(object): """ Weights & Biases Logger """ def __init__(self, datasets: List[Dataset], metrics: List[Metric], log_loss: bool = True, name: Optional[str] = None, save_dir: Optional[str] = None, offline: Optional[bool] = False, id: Optional[str] = None, anonymous: Optional[bool] = None, version: Optional[str] = None, project: Optional[str] = None, log_model: Optional[bool] = False, experiment=None, prefix: Optional[str] = '', **kwargs): try: import wandb except ImportError: raise ImportError( 'You want to use `wandb` logger which is not installed yet,' ' install it with `pip install wandb`.' ) if offline and log_model: # TODO: Different exception type? raise Exception( f'Providing log_model={log_model} and offline={offline} is an invalid configuration' ' since model checkpoints cannot be uploaded in offline mode.\n' 'Hint: Set `offline=False` to log your model.' ) self.base_model = None # will be set in KerasModel init self.datasets = datasets self.metrics = metrics self.log_loss = log_loss self.offline = offline self.log_model = log_model self.prefix = prefix self.experiment = experiment # set wandb init arguments anonymous_lut = {True: 'allow', False: None} self.wandb_init = dict( name=name, project=project, id=version or id, dir=save_dir, resume='allow', anonymous=anonymous_lut.get(anonymous, anonymous) ) self.wandb_init.update(**kwargs) # extract parameters self.save_dir = self.wandb_init.get('dir') self.name = self.wandb_init.get('name') self.id = self.wandb_init.get('id') #Log the parameters of KerasModel self.wandb = wandb.init(**self.wandb_init) if wandb.run is None else wandb.run def save_model(self, model): #model is a tf.keras.Model return None def log_data(self, model, step): #model is a Deepchem KerasModel for dataset in self.datasets: scores = model.evaluate(dataset, self.metrics) self.wandb.log() def update_config(self): return None Loading
deepchem/models/callbacks.py +1 −34 Original line number Diff line number Diff line Loading @@ -90,36 +90,3 @@ class ValidationCallback(object): if self._best_score is None or score < self._best_score: model.save_checkpoint(model_dir=self.save_dir) self._best_score = score No newline at end of file class WandbCallback(object): """ Weights & Biases Logger """ def __init__(self, **kwargs): try: import wandb except ImportError: raise ImportError( 'You want to use `wandb` logger which is not installed yet,' ' install it with `pip install wandb`.' ) def __call__(self, model, step): """This is invoked by the KerasModel after every step of fitting. Parameters ---------- model: KerasModel the model that is being trained step: int the index of the training step that has just completed """
deepchem/models/keras_model.py +34 −6 Original line number Diff line number Diff line Loading @@ -3,6 +3,7 @@ import tensorflow as tf import time import logging import os try: from collections.abc import Sequence as SequenceCollection except: Loading @@ -13,11 +14,13 @@ from deepchem.metrics import Metric from deepchem.models.losses import Loss from deepchem.models.models import Model from deepchem.models.optimizers import Adam, Optimizer, LearningRateSchedule from deepchem.models.callbacks import ValidationCallback from deepchem.trans import Transformer, undo_transforms from deepchem.utils.evaluate import GeneratorEvaluator from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union from deepchem.utils.typing import ArrayLike, LossFn, OneOrMany from deepchem.models.wandblogger import WandbLogger try: import wandb Loading Loading @@ -131,7 +134,7 @@ class KerasModel(Model): learning_rate: Union[float, LearningRateSchedule] = 0.001, optimizer: Optional[Optimizer] = None, tensorboard: bool = False, wandb: bool = False, wandb: Optional[WandbLogger] = None, log_frequency: int = 100, **kwargs) -> None: """Create a new KerasModel. Loading @@ -158,7 +161,7 @@ class KerasModel(Model): ignored. tensorboard: bool whether to log progress to TensorBoard during training wandb: bool wandb: bool (MODIFY) whether to log progress to Weights & Biases during training log_frequency: int The frequency at which to log data. Data is logged using Loading @@ -182,12 +185,28 @@ class KerasModel(Model): self.tensorboard = tensorboard # W&B logging if wandb and not _has_wandb: if (wandb is not None) and not _has_wandb: logger.warning( "You set wandb to True but W&B is not installed. To use wandb logging, " "You are using a wandb logger but W&B is not installed. To use wandb logging, " "run `pip install wandb; wandb login` see https://docs.wandb.com/huggingface." ) self.wandb = wandb and _has_wandb self.wandb_logger = wandb #Need to save model?? #wandb.save_model(...) #save the tf.keras.Model #update config with KerasModel Params run_params = dict( model=model, loss=loss, output_types=output_types, batch_size=batch_size, model_dir=model_dir, learning_rate=learning_rate, optimizer=optimizer, tensorboard=tensorboard, log_frequency=log_frequency ) self.wandb_logger.wandb.config.update(run_params) # Backwards compatibility if "tensorboard_log_frequency" in kwargs: Loading Loading @@ -394,6 +413,15 @@ class KerasModel(Model): # Main training loop. # Warn if both ValidationCallback and WandbLogger present if wandb is not None: for c in callbacks: if isinstance(c, ValidationCallback): logger.warning( "You are using both WandbLogger and ValidationCallback. WandbLogger is able to log validation metrics" "so there is no need to have a ValidationCallback. Logging validation metrics twice may take longer." ) for batch in generator: self._create_training_ops(batch) if restore: Loading Loading @@ -433,7 +461,7 @@ class KerasModel(Model): if self.tensorboard and should_log: self._log_scalar_to_tensorboard('loss', batch_loss, current_step) if self.wandb and should_log: wandb.log({'loss': batch_loss}, step=current_step) #wandb.log({'loss': batch_loss}, step=current_step) # Report final results. if averaged_batches > 0: Loading
deepchem/models/wandblogger.py 0 → 100644 +84 −0 Original line number Diff line number Diff line import torch import tensorflow as tf from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union from deepchem.data import Dataset, NumpyDataset from deepchem.metrics import Metric class WandbLogger(object): """ Weights & Biases Logger """ def __init__(self, datasets: List[Dataset], metrics: List[Metric], log_loss: bool = True, name: Optional[str] = None, save_dir: Optional[str] = None, offline: Optional[bool] = False, id: Optional[str] = None, anonymous: Optional[bool] = None, version: Optional[str] = None, project: Optional[str] = None, log_model: Optional[bool] = False, experiment=None, prefix: Optional[str] = '', **kwargs): try: import wandb except ImportError: raise ImportError( 'You want to use `wandb` logger which is not installed yet,' ' install it with `pip install wandb`.' ) if offline and log_model: # TODO: Different exception type? raise Exception( f'Providing log_model={log_model} and offline={offline} is an invalid configuration' ' since model checkpoints cannot be uploaded in offline mode.\n' 'Hint: Set `offline=False` to log your model.' ) self.base_model = None # will be set in KerasModel init self.datasets = datasets self.metrics = metrics self.log_loss = log_loss self.offline = offline self.log_model = log_model self.prefix = prefix self.experiment = experiment # set wandb init arguments anonymous_lut = {True: 'allow', False: None} self.wandb_init = dict( name=name, project=project, id=version or id, dir=save_dir, resume='allow', anonymous=anonymous_lut.get(anonymous, anonymous) ) self.wandb_init.update(**kwargs) # extract parameters self.save_dir = self.wandb_init.get('dir') self.name = self.wandb_init.get('name') self.id = self.wandb_init.get('id') #Log the parameters of KerasModel self.wandb = wandb.init(**self.wandb_init) if wandb.run is None else wandb.run def save_model(self, model): #model is a tf.keras.Model return None def log_data(self, model, step): #model is a Deepchem KerasModel for dataset in self.datasets: scores = model.evaluate(dataset, self.metrics) self.wandb.log() def update_config(self): return None
