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API reference: Catalyst integration#

You can use the Catalyst NeptuneLogger class to capture model training metadata.


Neptune logger for parameters, metrics, images and artifacts (such as videos, audio, and model checkpoints).


Name         Type Default Description
base_namespace str, optional experiment Namespace under which all metadata logged by the Neptune logger will be stored.
api_token str, optional None User's 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.
run Run, optional None An existing run reference, as returned by neptune.init_run().
log_batch_metrics boolean, optional SETTINGS.log_batch_metrics or False Boolean flag to log batch metrics.
log_epoch_metrics boolean, optional SETTINGS.log_epoch_metrics or True Boolean flag to log epoch metrics.
**neptune_run_kwargs str, optional - Additional keyword arguments to be passed directly to the init_run() function, such as description and tags. If the run parameter is set to None, NeptuneLogger will create a Run object for you.


Add NeptuneLogger through the train() function:

from catalyst import dl
runner = dl.SupervisedRunner()
        "neptune": dl.NeptuneLogger(
            project="workspace-name/project-name", # (1)!
            tags=["pretraining", "retina"],  # kwargs for neptune.init_run()
  1. 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.

Add NeptuneLogger from within custom runner implementation:

from catalyst import dl
class CustomRunner(dl.IRunner):
    # ...
    def get_loggers(self):
        return {
            "console": dl.ConsoleLogger(),
            "neptune": dl.NeptuneLogger(project="workspace-name/project-name"),
    # ...
runner = CustomRunner().run()

You can also add the Neptune logger through Config API and Hydra API:

# Config API
        _target_: NeptuneLogger
        project: workspace-name/project-name
# Hydra API
        _target_: catalyst.dl.NeptuneLogger
        project: workspace-name/project-name
        base_namespace: catalyst

See also