Neptune Logger for PyTorch Lightning

This integration allows you to log your PyTorch Lightning training runs to Neptune 3.x, providing seamless experiment tracking.

Changelog

• 2025-09-22 - Renamed NeptuneScaleLogger to NeptuneLogger and existing NeptuneLogger to NeptuneLegacyLogger
• 2025-08-11 - Updated to support ddp-spawn strategy
• 2025-07-25 - Updated to better support nested mappings and dataclasses in log_hyperparameters()
• 2025-06-27 - Initial release

Installation

Important

This integration works only with neptune-scale>=0.18.0, and is built on top of pytorch-lightning==2.5.1rc2

To install the integration, use one of the following options:

Option 1: Install from source (recommended)

Install the custom version of pytorch-lightning from the source:

pip install git+https://github.com/neptune-ai/pytorch-lightning.git

Option 2: Manual file replacement

In your pytorch-lightning/src/lightning/pytorch/loggers folder, replace the __init__.py and neptune.py files with the corresponding files available here

Note

Once the integration is included in the official PyTorch Lightning release, it's not necessary install it separately.

Quickstart

from lightning.pytorch.loggers.neptune import NeptuneLogger
from lightning.pytorch import Trainer

# Initialize the logger
neptune_logger = NeptuneLogger()

# Use with your trainer
trainer = Trainer(
    ...,
    logger=neptune_logger,
)

# Your training will automatically log to Neptune
trainer.fit(model, datamodule, ...)

Usage

1. Import the logger

from lightning.pytorch.loggers.neptune import NeptuneLogger

2. Initialize NeptuneLogger

neptune_logger = NeptuneLogger()

Tip

• Check the class signature and docstring for initialization args.
• Check the Run() init params for additional kwargs that can be passed to NeptuneLogger(): Run | neptune.ai docs

3. Pass to Trainer

trainer = Trainer(
    logger=neptune_logger,
    # ... other trainer arguments
)

4. Log metadata to Neptune

With the Neptune integration, you can combine the default PyTorch Lightning logging methods with the Neptune methods and capabilities.

Use Lightning's logging methods

Use self.log() in your Lightning module as usual – everything will be automatically logged to Neptune.

Use Neptune's logging methods

In addition to the default logging methods, you can use the Neptune Run methods to log more metadata types. You can also use the Neptune methods outside of the training loop.

To access the Neptune run directly, use the neptune_logger.run reference:

neptune_logger.run.assign_files({"dataset/data_sample": "sample_data.csv"})

You can also use the neptune_logger.run reference to log outside the prefix passed to NeptuneLogger.

Add tags

Use tags to identify and organize your runs. Add tags with the neptune_logger.run reference:

neptune_logger.run.add_tags(["notebook", "lightning"])

Log hyperparameters

To log hyperparameters, use the standard log_hyperparams() method from the PyTorch Lightning logger:

PARAMS = {
    "batch_size": 64,
    "lr": 0.07,
    ...
}
neptune_logger.log_hyperparams(PARAMS)

Hyperparameters are logged under the <prefix>/hyperparams namespace. To log outside the prefix, or to use Neptune's automatic casting, use the Neptune's log_configs() method on the Neptune run:

neptune_logger.run.log_configs(
    data={
        "data/batch_size": 64,
        "model/optimizer/name": "adam",
        "model/optimizer/lr": 0.07,
        "model/optimizer/decay_factor": 0.97,
        "model/tokenizer/name": "bert-base-uncased",
    },
)

Log model checkpoint paths

If you have the ModelCheckpoint callback configured, you can log the paths to model checkpoints. Note that:

• All checkpoint paths are logged. The save_last and save_top_k parameters aren't supported.
• Neptune Scale logger doesn't upload the checkpoints to Neptune.

To disable logging of checkpoint paths, set the log_model_checkpoints init parameter to False:

neptune_logger = NeptuneLogger(log_model_checkpoints=False)

Log model summary

To log a text file with the model summary, use:

model = LitModel()
neptune_logger.log_model_summary(model=model, max_depth=-1)

Examples

• Refer to our example Colab notebook to see how this works in practice: Neptune + PyTorch Lightning Notebook
• Play with an interactive example: Neptune + PyTorch Lightning Example

Updating the integration

Updates to this integration are merged to the master branch of the fork rather than being released.

1. Enable notifications

Turn on notifications for all activity to be notified when changes have been merged:

2. Update installation

Update your installation using pip:

pip install git+https://github.com/neptune-ai/pytorch-lightning.git

Support and feedback

Note

This is an early version, and we would appreciate your feedback. Please don't hesitate to reach out if you have any questions, concerns, or suggestions for new features. 🤗

Getting help

• 📖 Support Center
• 🐛 Report Issues