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Source code for ignite.contrib.handlers.wandb_logger

from ignite.contrib.handlers.base_logger import BaseLogger, BaseOptimizerParamsHandler, BaseOutputHandler
from ignite.handlers import global_step_from_engine

__all__ = ["WandBLogger", "OutputHandler", "OptimizerParamsHandler", "global_step_from_engine"]


[docs]class OutputHandler(BaseOutputHandler): """Helper handler to log engine's output and/or metrics Examples: .. code-block:: python from ignite.contrib.handlers.wandb_logger import * # Create a logger. All parameters are optional. See documentation # on wandb.init for details. wandb_logger = WandBLogger( entity="shared", project="pytorch-ignite-integration", name="cnn-mnist", config={"max_epochs": 10}, tags=["pytorch-ignite", "minst"] ) # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # each epoch. We setup `global_step_transform=lambda *_: trainer.state.iteration,` to take iteration value # of the `trainer`: wandb_logger.attach( evaluator, log_handler=OutputHandler( tag="validation", metric_names=["nll", "accuracy"], global_step_transform=lambda *_: trainer.state.iteration, ), event_name=Events.EPOCH_COMPLETED ) # or equivalently wandb_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metric_names=["nll", "accuracy"], global_step_transform=lambda *_: trainer.state.iteration, ) Another example, where model is evaluated every 500 iterations: .. code-block:: python from ignite.contrib.handlers.wandb_logger import * @trainer.on(Events.ITERATION_COMPLETED(every=500)) def evaluate(engine): evaluator.run(validation_set, max_epochs=1) # Create a logger. All parameters are optional. See documentation # on wandb.init for details. wandb_logger = WandBLogger( entity="shared", project="pytorch-ignite-integration", name="cnn-mnist", config={"max_epochs": 10}, tags=["pytorch-ignite", "minst"] ) def global_step_transform(*args, **kwargs): return trainer.state.iteration # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # every 500 iterations. Since evaluator engine does not have access to the training iteration, we # provide a global_step_transform to return the trainer.state.iteration for the global_step, each time # evaluator metrics are plotted on Weights & Biases. wandb_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metrics=["nll", "accuracy"], global_step_transform=global_step_transform ) Args: tag (str): common title for all produced plots. For example, "training" metric_names (list of str, optional): list of metric names to plot or a string "all" to plot all available metrics. output_transform (callable, optional): output transform function to prepare `engine.state.output` as a number. For example, `output_transform = lambda output: output` This function can also return a dictionary, e.g `{"loss": loss1, "another_loss": loss2}` to label the plot with corresponding keys. global_step_transform (callable, optional): global step transform function to output a desired global step. Input of the function is `(engine, event_name)`. Output of function should be an integer. Default is None, global_step based on attached engine. If provided, uses function output as global_step. To setup global step from another engine, please use :meth:`~ignite.contrib.handlers.wandb_logger.global_step_from_engine`. sync (bool, optional): If set to False, process calls to log in a seperate thread. Default (None) uses whatever the default value of wandb.log. Note: Example of `global_step_transform`: .. code-block:: python def global_step_transform(engine, event_name): return engine.state.get_event_attrib_value(event_name) """ def __init__(self, tag, metric_names=None, output_transform=None, global_step_transform=None, sync=None): super().__init__(tag, metric_names, output_transform, global_step_transform) self.sync = sync def __call__(self, engine, logger, event_name): if not isinstance(logger, WandBLogger): raise RuntimeError("Handler '{}' works only with WandBLogger.".format(self.__class__.__name__)) global_step = self.global_step_transform(engine, event_name) if not isinstance(global_step, int): raise TypeError( "global_step must be int, got {}." " Please check the output of global_step_transform.".format(type(global_step)) ) metrics = self._setup_output_metrics(engine) if self.tag is not None: metrics = {"{tag}/{name}".format(tag=self.tag, name=name): value for name, value in metrics.items()} logger.log(metrics, step=global_step, sync=self.sync)
[docs]class OptimizerParamsHandler(BaseOptimizerParamsHandler): """Helper handler to log optimizer parameters Examples: .. code-block:: python from ignite.contrib.handlers.wandb_logger import * # Create a logger. All parameters are optional. See documentation # on wandb.init for details. wandb_logger = WandBLogger( entity="shared", project="pytorch-ignite-integration", name="cnn-mnist", config={"max_epochs": 10}, tags=["pytorch-ignite", "minst"] ) # Attach the logger to the trainer to log optimizer's parameters, e.g. learning rate at each iteration wandb_logger.attach( trainer, log_handler=OptimizerParamsHandler(optimizer), event_name=Events.ITERATION_STARTED ) # or equivalently wandb_logger.attach_opt_params_handler( trainer, event_name=Events.ITERATION_STARTED, optimizer=optimizer ) Args: optimizer (torch.optim.Optimizer): torch optimizer which parameters to log param_name (str): parameter name tag (str, optional): common title for all produced plots. For example, "generator" sync (bool, optional): If set to False, process calls to log in a seperate thread. Default (None) uses whatever the default value of wandb.log. """ def __init__(self, optimizer, param_name="lr", tag=None, sync=None): super(OptimizerParamsHandler, self).__init__(optimizer, param_name, tag) self.sync = sync def __call__(self, engine, logger, event_name): if not isinstance(logger, WandBLogger): raise RuntimeError("Handler OptimizerParamsHandler works only with WandBLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = "{}/".format(self.tag) if self.tag else "" params = { "{}{}/group_{}".format(tag_prefix, self.param_name, i): float(param_group[self.param_name]) for i, param_group in enumerate(self.optimizer.param_groups) } logger.log(params, step=global_step, sync=self.sync)
[docs]class WandBLogger(BaseLogger): """`Weights & Biases <https://app.wandb.ai/>`_ handler to log metrics, model/optimizer parameters, gradients during training and validation. It can also be used to log model checkpoints to the Weights & Biases cloud. .. code-block:: bash pip install wandb This class is also a wrapper for the wandb module. This means that you can call any wandb function using this wrapper. See examples on how to save model parameters and gradients. Args: *args: Positional arguments accepted by `wandb.init`. **kwargs: Keyword arguments accepted by `wandb.init`. Please see `wandb.init <https://docs.wandb.com/library/init>`_ for documentation of possible parameters. Examples: .. code-block:: python from ignite.contrib.handlers.wandb_logger import * # Create a logger. All parameters are optional. See documentation # on wandb.init for details. wandb_logger = WandBLogger( entity="shared", project="pytorch-ignite-integration", name="cnn-mnist", config={"max_epochs": 10}, tags=["pytorch-ignite", "minst"] ) # Attach the logger to the trainer to log training loss at each iteration wandb_logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED, tag="training", output_transform=lambda loss: {"loss": loss} ) # Attach the logger to the evaluator on the training dataset and log NLL, Accuracy metrics after each epoch # We setup `global_step_transform=lambda *_: trainer.state.iteration` to take iteration value # of the `trainer`: wandb_logger.attach_output_handler( train_evaluator, event_name=Events.EPOCH_COMPLETED, tag="training", metric_names=["nll", "accuracy"], global_step_transform=lambda *_: trainer.state.iteration, ) # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # each epoch. We setup `global_step_transform=lambda *_: trainer.state.iteration` to take iteration value # of the `trainer` instead of `evaluator`. wandb_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metric_names=["nll", "accuracy"], global_step_transform=lambda *_: trainer.state.iteration, ) # Attach the logger to the trainer to log optimizer's parameters, e.g. learning rate at each iteration wandb_logger.attach_opt_params_handler( trainer, event_name=Events.ITERATION_STARTED, optimizer=optimizer, param_name='lr' # optional ) If you want to log model gradients, the model call graph, etc., use the logger as wrapper of wandb. Refer to the documentation of wandb.watch for details: .. code-block:: python wandb_logger = WandBLogger( entity="shared", project="pytorch-ignite-integration", name="cnn-mnist", config={"max_epochs": 10}, tags=["pytorch-ignite", "minst"] ) model = torch.nn.Sequential(...) wandb_logger.watch(model) For model checkpointing, Weights & Biases creates a local run dir, and automatically synchronizes all files saved there at the end of the run. You can just use the `wandb_logger.run.dir` as path for the `ModelCheckpoint`: .. code-block:: python from ignite.handlers import ModelCheckpoint def score_function(engine): return engine.state.metrics['accuracy'] model_checkpoint = ModelCheckpoint( wandb_logger.run.dir, n_saved=2, filename_prefix='best', require_empty=False, score_function=score_function, score_name="validation_accuracy", global_step_transform=global_step_from_engine(trainer) ) evaluator.add_event_handler(Events.COMPLETED, model_checkpoint, {'model': model}) """ def __init__(self, *args, **kwargs): try: import wandb self._wandb = wandb except ImportError: raise RuntimeError( "This contrib module requires wandb to be installed. " "You man install wandb with the command:\n pip install wandb\n" ) if kwargs.get("init", True): wandb.init(*args, **kwargs) def __getattr__(self, attr): return getattr(self._wandb, attr) def _create_output_handler(self, *args, **kwargs): return OutputHandler(*args, **kwargs) def _create_opt_params_handler(self, *args, **kwargs): return OptimizerParamsHandler(*args, **kwargs)

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