clearml_logger#

ClearML logger and its helper handlers.

Classes

`ClearMLLogger`	ClearML handler to log metrics, text, model/optimizer parameters, plots during training and validation.
`ClearMLSaver`	Handler that saves input checkpoint as ClearML artifacts
`GradsHistHandler`	Helper handler to log model's gradients as histograms.
`GradsScalarHandler`	Helper handler to log model's gradients as scalars.
`OptimizerParamsHandler`	Helper handler to log optimizer parameters
`OutputHandler`	Helper handler to log engine's output and/or metrics
`WeightsHistHandler`	Helper handler to log model's weights as histograms.
`WeightsScalarHandler`	Helper handler to log model's weights as scalars.

class ignite.contrib.handlers.clearml_logger.ClearMLLogger(*_, **kwargs)[source]#

ClearML handler to log metrics, text, model/optimizer parameters, plots during training and validation. Also supports model checkpoints logging and upload to the storage solution of your choice (i.e. ClearML File server, S3 bucket etc.)

pip install clearml
clearml-init

Parameters

project_name – The name of the project in which the experiment will be created. If the project does not exist, it is created. If project_name is None, the repository name is used. (Optional)
task_name – The name of Task (experiment). If task_name is None, the Python experiment script’s file name is used. (Optional)
task_type – Optional. The task type. Valid values are: - TaskTypes.training (Default) - TaskTypes.train - TaskTypes.testing - TaskTypes.inference
_ (Any) –
kwargs (Any) –

Examples

from ignite.contrib.handlers.clearml_logger import *

# Create a logger

clearml_logger = ClearMLLogger(
    project_name="pytorch-ignite-integration",
    task_name="cnn-mnist"
)

# Attach the logger to the trainer to log training loss at each iteration
clearml_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`.
clearml_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`.
clearml_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
clearml_logger.attach_opt_params_handler(
    trainer,
    event_name=Events.ITERATION_STARTED,
    optimizer=optimizer,
    param_name='lr'  # optional
)

# Attach the logger to the trainer to log model's weights norm after each iteration
clearml_logger.attach(
    trainer,
    event_name=Events.ITERATION_COMPLETED,
    log_handler=WeightsScalarHandler(model)
)

classmethod bypass_mode()[source]#

Returns the bypass mode state.

Note

GITHUB_ACTIONS env will automatically set bypass_mode to True unless overridden specifically with ClearMLLogger.set_bypass_mode(False).

Returns: If True, all outside communication is skipped.
Return type: bool

classmethod set_bypass_mode(bypass)[source]#

Will bypass all outside communication, and will drop all logs. Should only be used in “standalone mode”, when there is no access to the clearml-server.

Parameters: bypass (bool) – If True, all outside communication is skipped.
Return type: None

class ignite.contrib.handlers.clearml_logger.ClearMLSaver(logger=None, output_uri=None, dirname=None, *args, **kwargs)[source]#

Handler that saves input checkpoint as ClearML artifacts

Parameters

logger (Optional[ClearMLLogger]) – An instance of ClearMLLogger, ensuring a valid ClearML Task has been initialized. If not provided, and a ClearML Task has not been manually initialized, a runtime error will be raised.
output_uri (Optional[str]) – The default location for output models and other artifacts uploaded by ClearML. For more information, see clearml.Task.init.
dirname (Optional[str]) – Directory path where the checkpoint will be saved. If not provided, a temporary directory will be created.
args (Any) –
kwargs (Any) –

Examples

from ignite.contrib.handlers.clearml_logger import *
from ignite.handlers import Checkpoint

clearml_logger = ClearMLLogger(
    project_name="pytorch-ignite-integration",
    task_name="cnn-mnist"
)

to_save = {"model": model}

handler = Checkpoint(
    to_save,
    ClearMLSaver(),
    n_saved=1,
    score_function=lambda e: 123,
    score_name="acc",
    filename_prefix="best",
    global_step_transform=global_step_from_engine(trainer)
)

validation_evaluator.add_event_handler(Events.EVENT_COMPLETED, handler)

get_local_copy(filename)[source]#

Get artifact local copy.

Warning

In distributed configuration this method should be called on rank 0 process.

Parameters: filename (str) – artifact name.
Returns: a local path to a downloaded copy of the artifact
Return type: Optional[str]

remove(filename)[source]#

Method to remove saved checkpoint.

Parameters: filename (str) – filename associated with checkpoint.
Return type: None

class ignite.contrib.handlers.clearml_logger.GradsHistHandler(model, tag=None)[source]#

Helper handler to log model’s gradients as histograms.

Examples

from ignite.contrib.handlers.clearml_logger import *

# Create a logger

clearml_logger = ClearMLLogger(
    project_name="pytorch-ignite-integration",
    task_name="cnn-mnist"
)

# Attach the logger to the trainer to log model's weights norm after each iteration
clearml_logger.attach(
    trainer,
    event_name=Events.ITERATION_COMPLETED,
    log_handler=GradsHistHandler(model)
)

Parameters

model (Module) – model to log weights
tag (Optional[str]) – common title for all produced plots. For example, ‘generator’

class ignite.contrib.handlers.clearml_logger.GradsScalarHandler(model, reduction=<function norm>, tag=None)[source]#

Helper handler to log model’s gradients as scalars. Handler iterates over the gradients of named parameters of the model, applies reduction function to each parameter produce a scalar and then logs the scalar.

Examples

from ignite.contrib.handlers.clearml_logger import *

# Create a logger

clearml_logger = ClearMLLogger(
    project_name="pytorch-ignite-integration",
    task_name="cnn-mnist"
)

# Attach the logger to the trainer to log model's weights norm after each iteration
clearml_logger.attach(
    trainer,
    event_name=Events.ITERATION_COMPLETED,
    log_handler=GradsScalarHandler(model, reduction=torch.norm)
)

Parameters

model (Module) – model to log weights
reduction (Callable) – function to reduce parameters into scalar
tag (Optional[str]) – common title for all produced plots. For example, “generator”

class ignite.contrib.handlers.clearml_logger.OptimizerParamsHandler(optimizer, param_name='lr', tag=None)[source]#

Helper handler to log optimizer parameters

Examples

from ignite.contrib.handlers.clearml_logger import *

# Create a logger

clearml_logger = ClearMLLogger(
    project_name="pytorch-ignite-integration",
    task_name="cnn-mnist"
)

# Attach the logger to the trainer to log optimizer's parameters, e.g. learning rate at each iteration
clearml_logger.attach(
    trainer,
    log_handler=OptimizerParamsHandler(optimizer),
    event_name=Events.ITERATION_STARTED
)
# or equivalently
clearml_logger.attach_opt_params_handler(
    trainer,
    event_name=Events.ITERATION_STARTED,
    optimizer=optimizer
)

Parameters

optimizer (Optimizer) – torch optimizer or any object with attribute param_groups as a sequence.
param_name (str) – parameter name
tag (Optional[str]) – common title for all produced plots. For example, “generator”

class ignite.contrib.handlers.clearml_logger.OutputHandler(tag, metric_names=None, output_transform=None, global_step_transform=None)[source]#

Helper handler to log engine’s output and/or metrics

Examples

from ignite.contrib.handlers.clearml_logger import *

# Create a logger

clearml_logger = ClearMLLogger(
    project_name="pytorch-ignite-integration",
    task_name="cnn-mnist"
)

# 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`:
clearml_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
clearml_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:

from ignite.contrib.handlers.clearml_logger import *

@trainer.on(Events.ITERATION_COMPLETED(every=500))
def evaluate(engine):
    evaluator.run(validation_set, max_epochs=1)

# Create a logger

clearml_logger = ClearMLLogger(
    project_name="pytorch-ignite-integration",
    task_name="cnn-mnist"
)

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 ClearML.

clearml_logger.attach_output_handler(
    evaluator,
    event_name=Events.EPOCH_COMPLETED,
    tag="validation",
    metrics=["nll", "accuracy"],
    global_step_transform=global_step_transform
)

Parameters

tag (str) – common title for all produced plots. For example, “training”
metric_names (Optional[List[str]]) – list of metric names to plot or a string “all” to plot all available metrics.
output_transform (Optional[Callable]) – 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 (Optional[Callable]) – 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 global_step_from_engine().

Note

Example of global_step_transform:

def global_step_transform(engine, event_name):
    return engine.state.get_event_attrib_value(event_name)

class ignite.contrib.handlers.clearml_logger.WeightsHistHandler(model, tag=None)[source]#

Helper handler to log model’s weights as histograms.

Examples

from ignite.contrib.handlers.clearml_logger import *

# Create a logger

clearml_logger = ClearMLLogger(
    project_name="pytorch-ignite-integration",
    task_name="cnn-mnist"
)

# Attach the logger to the trainer to log model's weights norm after each iteration
clearml_logger.attach(
    trainer,
    event_name=Events.ITERATION_COMPLETED,
    log_handler=WeightsHistHandler(model)
)

Parameters

model (Module) – model to log weights
tag (Optional[str]) – common title for all produced plots. For example, ‘generator’

class ignite.contrib.handlers.clearml_logger.WeightsScalarHandler(model, reduction=<function norm>, tag=None)[source]#

Helper handler to log model’s weights as scalars. Handler iterates over named parameters of the model, applies reduction function to each parameter produce a scalar and then logs the scalar.

Examples

from ignite.contrib.handlers.clearml_logger import *

# Create a logger

clearml_logger = ClearMLLogger(
    project_name="pytorch-ignite-integration",
    task_name="cnn-mnist"
)

# Attach the logger to the trainer to log model's weights norm after each iteration
clearml_logger.attach(
    trainer,
    event_name=Events.ITERATION_COMPLETED,
    log_handler=WeightsScalarHandler(model, reduction=torch.norm)
)

Parameters

model (Module) – model to log weights
reduction (Callable) – function to reduce parameters into scalar
tag (Optional[str]) – common title for all produced plots. For example, “generator”

ignite.contrib.handlers.clearml_logger.global_step_from_engine(engine, custom_event_name=None)[source]#

Helper method to setup global_step_transform function using another engine. This can be helpful for logging trainer epoch/iteration while output handler is attached to an evaluator.

Parameters

engine (Engine) – engine which state is used to provide the global step
custom_event_name (Optional[Events]) – registered event name. Optional argument, event name to use.

Returns

global step based on provided engine

Return type

Callable