wandb_logger#

WandB logger and its helper handlers.

Classes

`OptimizerParamsHandler`	Helper handler to log optimizer parameters
`OutputHandler`	Helper handler to log engine's output and/or metrics
`WandBLogger`	Weights & Biases handler to log metrics, model/optimizer parameters, gradients during training and validation.

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

Helper handler to log optimizer parameters

Examples

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
)

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”
sync (Optional[bool]) – If set to False, process calls to log in a seperate thread. Default (None) uses whatever the default value of wandb.log.

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

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

Examples

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:

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
)

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().
sync (Optional[bool]) – 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:

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

class ignite.contrib.handlers.wandb_logger.WandBLogger(*args, **kwargs)[source]#

Weights & Biases 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.

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.

Parameters

args (Any) – Positional arguments accepted by wandb.init.
kwargs (Any) – Keyword arguments accepted by wandb.init. Please see wandb.init for documentation of possible parameters.

Examples

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
)

# We need to close the logger when we are done
wandb_logger.close()

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:

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:

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})

ignite.contrib.handlers.wandb_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