API reference: Lightning integration#

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

`NeptuneLogger`#

Creates a Neptune logger for tracking metadata during a Lightning training loop.

Parameters

Name	Type	Default	Description
`api_key`	`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.
`name`	`str`, optional	`"Untitled"`	Custom name for the run. You can add it as a column in the runs table (`sys/name`).
`run`	`Run` or `Handler`, optional	`None`	An existing `Run` reference, as returned by `neptune.init_run()`, or a namespace handler. If supplied, this run will be used for logging; otherwise `NeptuneLogger` will create a new run for you.
`log_model_checkpoints`	`bool`, optional	`True`	Whether to log model checkpoints to Neptune. Works only if `ModelCheckpoint` is passed to the `Trainer`.
`prefix`	`str`, optional	`"training"`	Namespace under which all metadata logged by the Neptune logger will be stored.
`**neptune_run_kwargs`	`str`, optional	-	Additional keyword arguments to be passed directly to the `init_run()` function, such as `description` and `tags`. Works only if the `run` argument is set to `None` (that is, no existing run is passed).

Examples

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

from pytorch_lightning import Trainer
from pytorch_lightning.loggers import NeptuneLogger

neptune_logger = NeptuneLogger()

trainer = Trainer(logger=neptune_logger)
...

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 PTL run",
    description="Quick training run with Lightning",
    tags=["training", "resnet"],
)

The full project name. For example, "ml-team/classification".

To copy it, navigate to the project settings in the top-right () and select Edit project details.

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

You can also use the Neptune logger anywhere in your LightningModule. Access the run with the self.logger.experiment attribute:

from pytorch_lightning import LightningModule
from neptune.types import File


class LitModel(LightningModule):
    def training_step(self, batch, batch_idx):
        # log metrics
        acc = ...
        self.log("train/loss", loss)

    def any_lightning_module_function_or_hook(self):
        # Log images, using the Neptune client library
        img = ...
        self.logger.experiment["train/misclassified_imgs"].append(File.as_image(img))

API reference: Lightning integration#

NeptuneLogger#

`NeptuneLogger`#