NeptuneLogger

class pytorch_lightning.loggers.NeptuneLogger(api_key=None, project_name=None, close_after_fit=True, offline_mode=False, experiment_name=None, experiment_id=None, prefix='', **kwargs)

Bases: pytorch_lightning.loggers.base.LightningLoggerBase

Log using Neptune.

Install it with pip:

pip install neptune-client

The Neptune logger can be used in the online mode or offline (silent) mode. To log experiment data in online mode, NeptuneLogger requires an API key. In offline mode, the logger does not connect to Neptune.

ONLINE MODE

from pytorch_lightning import Trainer
from pytorch_lightning.loggers import NeptuneLogger

# arguments made to NeptuneLogger are passed on to the neptune.experiments.Experiment class
# We are using an api_key for the anonymous user "neptuner" but you can use your own.
neptune_logger = NeptuneLogger(
    api_key="ANONYMOUS",
    project_name="shared/pytorch-lightning-integration",
    experiment_name="default",  # Optional,
    params={"max_epochs": 10},  # Optional,
    tags=["pytorch-lightning", "mlp"],  # Optional,
)
trainer = Trainer(max_epochs=10, logger=neptune_logger)

OFFLINE MODE

from pytorch_lightning.loggers import NeptuneLogger

# arguments made to NeptuneLogger are passed on to the neptune.experiments.Experiment class
neptune_logger = NeptuneLogger(
    offline_mode=True,
    project_name="USER_NAME/PROJECT_NAME",
    experiment_name="default",  # Optional,
    params={"max_epochs": 10},  # Optional,
    tags=["pytorch-lightning", "mlp"],  # Optional,
)
trainer = Trainer(max_epochs=10, logger=neptune_logger)

Use the logger anywhere in you LightningModule as follows:

class LitModel(LightningModule):
    def training_step(self, batch, batch_idx):
        # log metrics
        self.logger.experiment.log_metric("acc_train", ...)
        # log images
        self.logger.experiment.log_image("worse_predictions", ...)
        # log model checkpoint
        self.logger.experiment.log_artifact("model_checkpoint.pt", ...)
        self.logger.experiment.whatever_neptune_supports(...)

    def any_lightning_module_function_or_hook(self):
        self.logger.experiment.log_metric("acc_train", ...)
        self.logger.experiment.log_image("worse_predictions", ...)
        self.logger.experiment.log_artifact("model_checkpoint.pt", ...)
        self.logger.experiment.whatever_neptune_supports(...)

If you want to log objects after the training is finished use close_after_fit=False:

neptune_logger = NeptuneLogger(..., close_after_fit=False, ...)
trainer = Trainer(logger=neptune_logger)
trainer.fit()

# Log test metrics
trainer.test(model)

# Log additional metrics
from sklearn.metrics import accuracy_score

accuracy = accuracy_score(y_true, y_pred)
neptune_logger.experiment.log_metric("test_accuracy", accuracy)

# Log charts
from scikitplot.metrics import plot_confusion_matrix
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(16, 12))
plot_confusion_matrix(y_true, y_pred, ax=ax)
neptune_logger.experiment.log_image("confusion_matrix", fig)

# Save checkpoints folder
neptune_logger.experiment.log_artifact("my/checkpoints")

# When you are done, stop the experiment
neptune_logger.experiment.stop()

See also

• An Example experiment showing the UI of Neptune.

• Tutorial on how to use Pytorch Lightning with Neptune.

Parameters

• api_key (Optional[str]) – Required in online mode. Neptune API token, found on https://neptune.ai. Read how to get your API key. It is recommended to keep it in the NEPTUNE_API_TOKEN environment variable and then you can leave api_key=None.

• project_name (Optional[str]) – Required in online mode. Qualified name of a project in a form of “namespace/project_name” for example “tom/minst-classification”. If None, the value of NEPTUNE_PROJECT environment variable will be taken. You need to create the project in https://neptune.ai first.

• offline_mode (bool) – Optional default False. If True no logs will be sent to Neptune. Usually used for debug purposes.

• close_after_fit (Optional[bool]) – Optional default True. If False the experiment will not be closed after training and additional metrics, images or artifacts can be logged. Also, remember to close the experiment explicitly by running neptune_logger.experiment.stop().

• experiment_name (Optional[str]) – Optional. Editable name of the experiment. Name is displayed in the experiment’s Details (Metadata section) and in experiments view as a column.

• experiment_id (Optional[str]) – Optional. Default is None. The ID of the existing experiment. If specified, connect to experiment with experiment_id in project_name. Input arguments “experiment_name”, “params”, “properties” and “tags” will be overriden based on fetched experiment data.

• prefix (str) – A string to put at the beginning of metric keys.

• **kwargs – Additional arguments like params, tags, properties, etc. used by neptune.Session.create_experiment() can be passed as keyword arguments in this logger.

Raises

ImportError – If required Neptune package is not installed on the device.

append_tags(tags)

Appends tags to the neptune experiment.

Parameters

tags (Union[str, Iterable[str]]) – Tags to add to the current experiment. If str is passed, a single tag is added. If multiple - comma separated - str are passed, all of them are added as tags. If list of str is passed, all elements of the list are added as tags.

Return type

None

finalize(status)

Do any processing that is necessary to finalize an experiment.

Parameters

status (str) – Status that the experiment finished with (e.g. success, failed, aborted)

Return type

None

log_artifact(artifact, destination=None)

Save an artifact (file) in Neptune experiment storage.

Parameters

• artifact (str) – A path to the file in local filesystem.

• destination (Optional[str]) – Optional. Default is None. A destination path. If None is passed, an artifact file name will be used.

Return type

None

log_hyperparams(params)

Record hyperparameters.

Parameters

• params (Union[Dict[str, Any], Namespace]) – Namespace containing the hyperparameters

• args – Optional positional arguments, depends on the specific logger being used

• kwargs – Optional keywoard arguments, depends on the specific logger being used

Return type

None

log_image(log_name, image, step=None)

Log image data in Neptune experiment

Parameters

• log_name (str) – The name of log, i.e. bboxes, visualisations, sample_images.

• image (Union[str, Any]) – The value of the log (data-point). Can be one of the following types: PIL image, matplotlib.figure.Figure, path to image file (str)

• step (Optional[int]) – Step number at which the metrics should be recorded, must be strictly increasing

Return type

None

log_metric(metric_name, metric_value, step=None)

Log metrics (numeric values) in Neptune experiments.

Parameters

• metric_name (str) – The name of log, i.e. mse, loss, accuracy.

• metric_value (Union[Tensor, float, str]) – The value of the log (data-point).

• step (Optional[int]) – Step number at which the metrics should be recorded, must be strictly increasing

Return type

None

log_metrics(metrics, step=None)

Log metrics (numeric values) in Neptune experiments.

Parameters

• metrics (Dict[str, Union[Tensor, float]]) – Dictionary with metric names as keys and measured quantities as values

• step (Optional[int]) – Step number at which the metrics should be recorded, currently ignored

Return type

None

log_text(log_name, text, step=None)

Log text data in Neptune experiments.

Parameters

• log_name (str) – The name of log, i.e. mse, my_text_data, timing_info.

• text (str) – The value of the log (data-point).

• step (Optional[int]) – Step number at which the metrics should be recorded, must be strictly increasing

Return type

None

set_property(key, value)

Set key-value pair as Neptune experiment property.

Parameters

• key (str) – Property key.

• value (Any) – New value of a property.

Return type

None

property experiment: neptune.experiments.Experiment

Actual Neptune object. To use neptune features in your LightningModule do the following.

Example:

self.logger.experiment.some_neptune_function()

property name: str

Return the experiment name.

property save_dir: Optional[str]

Return the root directory where experiment logs get saved, or None if the logger does not save data locally.

property version: str

Return the experiment version.