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What is

Neptune is a lightweight experiment tracker for ML teams that struggle with debugging and reproducing experiments, sharing results, and messy model handover. It offers a single place to track, compare, store, and collaborate on experiments and models.

With Neptune, Data Scientists can develop production-ready models faster, and ML Engineers can access model artifacts instantly in order to deploy them to production.  

Watch a 3min explainer video →  

Watch a 20min product demo →  

Play with a live example project in the Neptune app →  

Getting started

Step 1: Create a free account

Step 2: Install the Neptune client library

pip install neptune

Step 3: Add an experiment tracking snippet to your code

import neptune

run = neptune.init_run(project="workspace-name/project-name")
run["parameters"] = {"lr": 0.1, "dropout": 0.4}
run["test_accuracy"] = 0.84

Open in Colab  


Core features

Log and display

Add a snippet to any step of your ML pipeline once. Decide what and how you want to log. Run a million times.

  • Any framework: any code, fastai, PyTorch, Lightning, TensorFlow/Keras, scikit-learn, 🤗 Transformers, XGBoost, Optuna.

  • Any metadata type: metrics, parameters, dataset and model versions, images, interactive plots, videos, hardware (GPU, CPU, memory), code state.

  • From anywhere in your ML pipeline: multinode pipelines, distributed computing, log during or after execution, log offline, and sync when you are back online.  


all metadata metrics


Organize experiments

Organize logs in a fully customizable nested structure. Display model metadata in user-defined dashboard templates.

  • Nested metadata structure: the flexible API lets you customize the metadata logging structure however you want. Organize nested parameter configs or the results on k-fold validation splits the way they should be.

  • Custom dashboards: combine different metadata types in one view. Define it for one run. Use anywhere. Look at GPU, memory consumption, and load times to debug training speed. See learning curves, image predictions, and confusion matrix to debug model quality.

  • Table views: create different views of the runs table and save them for later. You can have separate table views for debugging, comparing parameter sets, or best experiments.  


organize dashboards


Compare results

Visualize training live in the web app. See how different parameters and configs affect the results. Optimize models quicker.

  • Compare: learning curves, parameters, images, datasets.

  • Search, sort, and filter: experiments by any field you logged. Use our query language to filter runs based on parameter values, metrics, execution times, or anything else.

  • Visualize and display: runs table, interactive display, folder structure, dashboards.

  • Monitor live: hardware consumption metrics, GPU, CPU, memory.

  • Group by: dataset versions, parameters.  


compare, search, filter


Version models

Version, review, and access production-ready models and metadata associated with them in a single place.

  • Version models: register models, create model versions, version external model artifacts.

  • Review and change stages: look at the validation, test metrics and other model metadata. You can move models between None/Staging/Production/Archived.

  • Access and share models: every model and model version is accessible via the web app or through the API.  


register models


Share results

Have a single place where your team can see the results and access all models and experiments.

  • Send a link: share every chart, dashboard, table view, or anything else you see in the app by copying and sending persistent URLs.

  • Query API: access all model metadata via API. Whatever you logged, you can query in a similar way.

  • Manage users and projects: create different projects, add users to them, and grant different permissions levels.

  • Add your entire org: you can collaborate with a team on every plan, even the Free one. So, invite your entire organization, including product managers and subject matter experts, to increase the visibility from the very beginning.  


share persistent link


Integrate with any MLOps stack integrates with 25+ frameworks: PyTorch, Lightning, TensorFlow/Keras, LightGBM, scikit-learn, XGBoost, Optuna, Kedro, 🤗 Transformers, fastai, Prophet, detectron2, Airflow, and more.

PyTorch Lightning


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

# Create NeptuneLogger instance
from neptune import ANONYMOUS_API_TOKEN

neptune_logger = NeptuneLogger(
    tags=["training", "resnet"],  # optional

# Pass the logger to the Trainer
trainer = Trainer(max_epochs=10, logger=neptune_logger)

# Run the Trainer, my_dataloader)


github-code jupyter-code Open In Colab is trusted by great companies


Read how various customers use Neptune to improve their workflow.  



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