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Source code for ignite.contrib.handlers.polyaxon_logger

import numbers
import warnings

import torch

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

__all__ = ["PolyaxonLogger", "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.polyaxon_logger import * # Create a logger plx_logger = PolyaxonLogger() # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # each epoch. We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch # of the `trainer`: plx_logger.attach( evaluator, log_handler=OutputHandler( tag="validation", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer) ), event_name=Events.EPOCH_COMPLETED ) # or equivalently plx_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer) ) Another example, where model is evaluated every 500 iterations: .. code-block:: python from ignite.contrib.handlers.polyaxon_logger import * @trainer.on(Events.ITERATION_COMPLETED(every=500)) def evaluate(engine): evaluator.run(validation_set, max_epochs=1) plx_logger = PolyaxonLogger() 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 Polyaxon. plx_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.polyaxon_logger.global_step_from_engine`. 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): super(OutputHandler, self).__init__(tag, metric_names, output_transform, global_step_transform) def __call__(self, engine, logger, event_name): if not isinstance(logger, PolyaxonLogger): raise RuntimeError("Handler 'OutputHandler' works only with PolyaxonLogger") metrics = self._setup_output_metrics(engine) 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)) ) rendered_metrics = {"step": global_step} for key, value in metrics.items(): if isinstance(value, numbers.Number): rendered_metrics["{}/{}".format(self.tag, key)] = value elif isinstance(value, torch.Tensor) and value.ndimension() == 0: rendered_metrics["{}/{}".format(self.tag, key)] = value.item() elif isinstance(value, torch.Tensor) and value.ndimension() == 1: for i, v in enumerate(value): rendered_metrics["{}/{}/{}".format(self.tag, key, i)] = v.item() else: warnings.warn("PolyaxonLogger output_handler can not log " "metrics value type {}".format(type(value))) logger.log_metrics(**rendered_metrics)
[docs]class OptimizerParamsHandler(BaseOptimizerParamsHandler): """Helper handler to log optimizer parameters Examples: .. code-block:: python from ignite.contrib.handlers.polyaxon_logger import * # Create a logger plx_logger = PolyaxonLogger() # Attach the logger to the trainer to log optimizer's parameters, e.g. learning rate at each iteration plx_logger.attach( trainer, log_handler=OptimizerParamsHandler(optimizer), event_name=Events.ITERATION_STARTED ) # or equivalently plx_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" """ def __init__(self, optimizer, param_name="lr", tag=None): super(OptimizerParamsHandler, self).__init__(optimizer, param_name, tag) def __call__(self, engine, logger, event_name): if not isinstance(logger, PolyaxonLogger): raise RuntimeError("Handler OptimizerParamsHandler works only with PolyaxonLogger") 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) } params["step"] = global_step logger.log_metrics(**params)
[docs]class PolyaxonLogger(BaseLogger): """ `Polyaxon <https://polyaxon.com/>`_ tracking client handler to log parameters and metrics during the training and validation. This class requires `polyaxon-client <https://github.com/polyaxon/polyaxon-client/>`_ package to be installed: .. code-block:: bash pip install polyaxon-client Examples: .. code-block:: python from ignite.contrib.handlers.polyaxon_logger import * # Create a logger plx_logger = PolyaxonLogger() # Log experiment parameters: plx_logger.log_params(**{ "seed": seed, "batch_size": batch_size, "model": model.__class__.__name__, "pytorch version": torch.__version__, "ignite version": ignite.__version__, "cuda version": torch.version.cuda, "device name": torch.cuda.get_device_name(0) }) # Attach the logger to the trainer to log training loss at each iteration plx_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=global_step_from_engine(trainer)` to take the epoch # of the `trainer` instead of `train_evaluator`. plx_logger.attach_output_handler( train_evaluator, event_name=Events.EPOCH_COMPLETED, tag="training", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer), ) # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # each epoch. We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch of the # `trainer` instead of `evaluator`. plx_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer)), ) # Attach the logger to the trainer to log optimizer's parameters, e.g. learning rate at each iteration plx_logger.attach_opt_params_handler( trainer, event_name=Events.ITERATION_STARTED, optimizer=optimizer, param_name='lr' # optional ) Args: *args: Positional arguments accepted from `Experiment <https://docs.polyaxon.com/references/polyaxon-tracking-api/experiments/>`_. **kwargs: Keyword arguments accepted from `Experiment <https://docs.polyaxon.com/references/polyaxon-tracking-api/experiments/>`_. """ def __init__(self, *args, **kwargs): try: from polyaxon_client.tracking import Experiment except ImportError: raise RuntimeError( "This contrib module requires polyaxon-client to be installed. " "Please install it with command: \n pip install polyaxon-client" ) self.experiment = Experiment(*args, **kwargs) def __getattr__(self, attr): return getattr(self.experiment, 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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