https://github.com/neptune-ai/neptune-client
📘 The experiment tracker for foundation model training
https://github.com/neptune-ai/neptune-client
comparison dl foundation keras learning lightgbm llm logger logging machine ml mlops monitoring optuna pytorch rl tensorflow versioning visualization xgboost
Last synced: 5 days ago
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📘 The experiment tracker for foundation model training
- Host: GitHub
- URL: https://github.com/neptune-ai/neptune-client
- Owner: neptune-ai
- License: apache-2.0
- Created: 2019-02-11T11:25:57.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2025-03-06T17:30:33.000Z (about 1 month ago)
- Last Synced: 2025-04-05T22:25:42.889Z (10 days ago)
- Topics: comparison, dl, foundation, keras, learning, lightgbm, llm, logger, logging, machine, ml, mlops, monitoring, optuna, pytorch, rl, tensorflow, versioning, visualization, xgboost
- Language: Python
- Homepage: https://neptune.ai
- Size: 13 MB
- Stars: 610
- Watchers: 19
- Forks: 65
- Open Issues: 34
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
- Codeowners: .github/CODEOWNERS
- Security: .github/SECURITY.md
Awesome Lists containing this project
- StarryDivineSky - neptune-ai/neptune-client
- awesome-production-machine-learning - Neptune - ai/neptune-client.svg?style=social) - Neptune is a scalable experiment tracker for teams that train foundation models. (Model, Data and Experiment Management)
README
neptune.ai
## What is neptune.ai?
Neptune is an experiment tracker purpose-built for foundation model training.
With Neptune, you can monitor thousands of per-layer metrics—losses, gradients, and activations—at any scale. Visualize them with no lag and no missed spikes. Drill down into logs and debug training issues fast. Keep your model training stable while reducing wasted GPU cycles.
Watch a 3min explainer video →
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Play with a live example project in the Neptune app →
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## Getting started
**Step 1:** Create a **[free account](https://neptune.ai/register)**
**Step 2:** Install the Neptune client library
```bash
pip install neptune
```
**Step 3:** Add an experiment tracking snippet to your code
```python
import neptune
run = neptune.init_run(project="workspace-name/project-name")
run["parameters"] = {"lr": 0.1, "dropout": 0.4}
run["test_accuracy"] = 0.84
```
[](https://colab.research.google.com/github/neptune-ai/examples/blob/master/how-to-guides/hello-neptune/notebooks/hello_neptune.ipynb)
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## Integrate with any MLOps stack
neptune.ai integrates with 25+ frameworks: PyTorch, Lightning, TensorFlow/Keras, LightGBM, scikit-learn, XGBoost, Optuna, Kedro, 🤗 Transformers, fastai, Prophet, detectron2, Airflow, and more.
#### 
PyTorch Lightning
Example:
```python
from pytorch_lightning import Trainer
from lightning.pytorch.loggers import NeptuneLogger
# Create NeptuneLogger instance
from neptune import ANONYMOUS_API_TOKEN
neptune_logger = NeptuneLogger(
api_key=ANONYMOUS_API_TOKEN,
project="common/pytorch-lightning-integration",
tags=["training", "resnet"], # optional
)
# Pass the logger to the Trainer
trainer = Trainer(max_epochs=10, logger=neptune_logger)
# Run the Trainer
trainer.fit(my_model, my_dataloader)
```
[](https://app.neptune.ai/common/pytorch-lightning-integration/experiments?split=tbl&dash=charts&viewId=faa75e77-5bd6-42b9-9379-863fe7a33227)
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[](https://github.com/neptune-ai/examples/tree/main/integrations-and-supported-tools/pytorch-lightning/scripts)
[](https://github.com/neptune-ai/examples/blob/main/integrations-and-supported-tools/pytorch-lightning/notebooks/Neptune_PyTorch_Lightning.ipynb)
[](https://colab.research.google.com/github/neptune-ai/examples/blob/main/integrations-and-supported-tools/pytorch-lightning/notebooks/Neptune_PyTorch_Lightning.ipynb)
[
](https://docs.neptune.ai/integrations/lightning/)
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## neptune.ai is trusted by great companies
Read how various customers use Neptune to improve their workflow.
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## Support
If you get stuck or simply want to talk to us about something, here are your options:
* Check our FAQ page.
* Chat! In the app, click the blue message icon in the bottom-right corner and send a message. A real person will talk to you ASAP (typically very ASAP).
* You can just shoot us an email at [[email protected]](mailto:[email protected]).
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## People behind
Created with :heart: by the [neptune.ai team](https://neptune.ai/jobs#team)