"""Polyaxon logger and its helper handlers."""
import numbers
import warnings
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from torch.optim import Optimizer
from ignite.contrib.handlers.base_logger import BaseLogger, BaseOptimizerParamsHandler, BaseOutputHandler
from ignite.engine import Engine, Events
from ignite.handlers import global_step_from_engine
__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
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://polyaxon.com/docs/experimentation/tracking/client/>`_.
kwargs: Keyword arguments accepted from
`Experiment <https://polyaxon.com/docs/experimentation/tracking/client/>`_.
"""
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 RuntimeError(
"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 __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.
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: 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.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: str,
metric_names: Optional[List[str]] = None,
output_transform: Optional[Callable] = None,
global_step_transform: Optional[Callable] = None,
):
super(OutputHandler, self).__init__(tag, metric_names, output_transform, global_step_transform)
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(engine)
global_step = self.global_step_transform(engine, event_name) # type: ignore[misc]
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."
)
rendered_metrics = {"step": global_step} # type: Dict[str, Union[float, numbers.Number]]
for key, value in metrics.items():
if isinstance(value, numbers.Number):
rendered_metrics[f"{self.tag}/{key}"] = value
elif isinstance(value, torch.Tensor) and value.ndimension() == 0:
rendered_metrics[f"{self.tag}/{key}"] = value.item()
elif isinstance(value, torch.Tensor) and value.ndimension() == 1:
for i, v in enumerate(value):
rendered_metrics[f"{self.tag}/{key}/{i}"] = v.item()
else:
warnings.warn(f"PolyaxonLogger output_handler can not log metrics value type {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 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"
"""
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)
