API reference: PyTorch Ignite integration#

The NeptuneLogger class provided by the PyTorch Ignite library captures metadata that is generated when training and validating models with Ignite.

`NeptuneLogger`#

Creates a Neptune handler to log metrics, model and optimizer parameters, and gradients during the training and validation. It can also log model checkpoints to Neptune.

Parameters

Name	Type	Default	Description
`api_token`	`str`, optional	`None`	User's Neptune API token. If `None`, the value of the `NEPTUNE_API_TOKEN` environment variable is used. To keep your token secure, avoid placing it in the source code. Instead, save it as an environment variable.
`project`	`str`, optional	`None`	Name of a project in the form `workspace-name/project-name`. If `None`, the value of the `NEPTUNE_PROJECT` environment variable is used.
`**neptune_run_kwargs`	`str`, optional	-	Additional keyword arguments to be passed directly to the `init_run()` function, such as `description` and `tags`.

Examples

If you have your Neptune credentials saved as environment variables, the following starts the Neptune logger with default settings:

from ignite.contrib.handlers.neptune_logger import *

neptune_logger = NeptuneLogger()

neptune_logger.attach_output_handler(
    trainer,
    ...,
)

trainer.run()

You can also pass more options to the run created by the logger:

neptune_logger = NeptuneLogger(
    project="workspace-name/project-name", # (1)!
    name="My first Ignite run",
    description="Quick training run with Ignite",
    tags=["training", "low LR"],
)

The full project name. For example, "ml-team/classification".
- You can copy the name from the project details ( → Details & privacy)
- You can also find a pre-filled project string in Experiments → Create a new run.

To log metadata to the Neptune run, access the neptune_logger.experiment attribute. You can then use any logging methods from the Neptune client library to track your metadata, such as append(), assign() (=), and upload().

metadata = ...
neptune_logger.experiment["your/metadata/structure"] = metadata
...
neptune_logger.close()

`NeptuneSaver`#

Handler that saves an input checkpoint to the Neptune server.

Windows note

NeptuneSaver is not supported on Windows.

Parameters

Name	Type	Default	Description
`neptune_logger`	`NeptuneLogger`	-	An instance of the `NeptuneLogger` class.

Example

Set up the logger:

from ignite.contrib.handlers.neptune_logger import *

neptune_logger = NeptuneLogger()
...
evaluator = create_supervised_evaluator(model, metrics=metrics, ...)
...


def score_function(engine):
    return engine.state.metrics["accuracy"]


to_save = {"model": model}

Pass the Neptune logger to the saver:

from ignite.handlers import Checkpoint

handler = Checkpoint(
    to_save,
    NeptuneSaver(neptune_logger), n_saved=2,
    filename_prefix="best", score_function=score_function,
    score_name="validation_accuracy",
    global_step_transform=global_step_from_engine(trainer),
)
evaluator.add_event_handler(Events.COMPLETED, handler)

Close the logger when done:

neptune_logger.close()

You can access example checkpoints and download them from this example run.

API reference: PyTorch Ignite integration#

NeptuneLogger#

NeptuneSaver#

`NeptuneLogger`#

`NeptuneSaver`#