Shortcuts

Source code for ignite.handlers.polyaxon_logger

"""Polyaxon logger and its helper handlers."""

from typing import Any, Callable, List, Optional, Union

from torch.optim import Optimizer

from ignite.engine import Engine, Events

from ignite.handlers.base_logger import BaseLogger, BaseOptimizerParamsHandler, BaseOutputHandler
from ignite.handlers.utils import global_step_from_engine  # noqa

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


[docs]class PolyaxonLogger(BaseLogger): """ `Polyaxon tracking client <https://polyaxon.com/>`_ handler to log parameters and metrics during the training and validation. This class requires `polyaxon <https://github.com/polyaxon/polyaxon/>`_ package to be installed: .. code-block:: bash pip install polyaxon // If you are using polyaxon v0.x pip install polyaxon-client Args: args: Positional arguments accepted from `Experiment <https://polyaxon.com/docs/experimentation/tracking/client/>`_. kwargs: Keyword arguments accepted from `Experiment <https://polyaxon.com/docs/experimentation/tracking/client/>`_. Examples: .. code-block:: python from ignite.handlers.polyaxon_logger import * # Create a logger plx_logger = PolyaxonLogger() # Log experiment parameters: plx_logger.log_inputs(**{ "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 ) # to manually end a run plx_logger.close() """ def __init__(self, *args: Any, **kwargs: Any): try: from polyaxon.tracking import Run self.experiment = Run(*args, **kwargs) except ImportError: try: from polyaxon_client.tracking import Experiment self.experiment = Experiment(*args, **kwargs) except ImportError: raise ModuleNotFoundError( "This contrib module requires polyaxon to be installed.\n" "For Polyaxon v1.x please install it with command: \n pip install polyaxon\n" "For Polyaxon v0.x please install it with command: \n pip install polyaxon-client" ) def close(self) -> None: try: self.experiment.end() except: pass def __getattr__(self, attr: Any) -> Any: return getattr(self.experiment, attr) def _create_output_handler(self, *args: Any, **kwargs: Any) -> "OutputHandler": return OutputHandler(*args, **kwargs) def _create_opt_params_handler(self, *args: Any, **kwargs: Any) -> "OptimizerParamsHandler": return OptimizerParamsHandler(*args, **kwargs)
[docs]class OutputHandler(BaseOutputHandler): """Helper handler to log engine's output and/or metrics. Args: tag: common title for all produced plots. For example, "training" metric_names: list of metric names to plot or a string "all" to plot all available metrics. output_transform: 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: 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.handlers.polyaxon_logger.global_step_from_engine`. state_attributes: list of attributes of the ``trainer.state`` to plot. Examples: .. code-block:: python from ignite.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.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 ) Another example where the State Attributes ``trainer.state.alpha`` and ``trainer.state.beta`` are also logged along with the NLL and Accuracy after each iteration: .. code-block:: python plx_logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED, tag="training", metrics=["nll", "accuracy"], state_attributes=["alpha", "beta"], ) Example of `global_step_transform`: .. code-block:: python def global_step_transform(engine, event_name): return engine.state.get_event_attrib_value(event_name) .. versionchanged:: 0.4.7 accepts an optional list of `state_attributes` """ def __init__( self, tag: str, metric_names: Optional[List[str]] = None, output_transform: Optional[Callable] = None, global_step_transform: Optional[Callable[[Engine, Union[str, Events]], int]] = None, state_attributes: Optional[List[str]] = None, ): super(OutputHandler, self).__init__( tag, metric_names, output_transform, global_step_transform, state_attributes ) def __call__(self, engine: Engine, logger: PolyaxonLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, PolyaxonLogger): raise RuntimeError("Handler 'OutputHandler' works only with PolyaxonLogger") metrics = self._setup_output_metrics_state_attrs(engine, key_tuple=False) global_step = self.global_step_transform(engine, event_name) if not isinstance(global_step, int): raise TypeError( f"global_step must be int, got {type(global_step)}." " Please check the output of global_step_transform." ) metrics.update({"step": global_step}) logger.log_metrics(**metrics)
[docs]class OptimizerParamsHandler(BaseOptimizerParamsHandler): """Helper handler to log optimizer parameters Args: optimizer: torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name: parameter name tag: common title for all produced plots. For example, "generator" Examples: .. code-block:: python from ignite.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 ) """ def __init__(self, optimizer: Optimizer, param_name: str = "lr", tag: Optional[str] = None): super(OptimizerParamsHandler, self).__init__(optimizer, param_name, tag) def __call__(self, engine: Engine, logger: PolyaxonLogger, event_name: Union[str, Events]) -> None: 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 = f"{self.tag}/" if self.tag else "" params = { f"{tag_prefix}{self.param_name}/group_{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)

© Copyright 2024, PyTorch-Ignite Contributors. Last updated on 10/21/2024, 9:43:49 AM.

Built with Sphinx using a theme provided by Read the Docs.