The most lightweight experiment management tool that fits any workflow

Use as a service or deploy on any cloud or your own hardware.

No credit card required. Takes 5 minutes to get started.

Track experiments

Log metrics, hyperparameters, data versions, hardware usage and more. Work on any infra, any language, scripts or notebooks. 

Record data exploration

Experiments don’t have to stop with running training scripts. Version your exploratory data analysis and share with your team.

Organize teamwork

Manage your team with organizations, projects, and user roles. Organize experiments with tags and custom views. 

"Neptune allows us to keep all of our experiments organized in a single space. Being able to see my team’s work results any time I need makes it effortless to track progress and enables easier coordination."
Michael Ulin
VP, Machine Learning @Zesty.ai
"For me the most important thing about Neptune is its flexibility. Even if I'm training with Keras or Tensorflow on my local laptop, and my colleagues are using fast.ai on a virtual machine, we can share our results in a common environment."
Víctor Peinado
Senior NLP/ML Engineer @reply.ai
"What we like about Neptune is that it easily hooks into multiple frameworks. Keeping track of machine learning experiments systematically over time and visualizing the output adds a lot of value for us."
Ronert Obst
Head of Data Science @New Yorker

Quick and simple setup

Start tracking experiments in minutes, work like you used to... just log it

Insert a few lines of code into your standard training and validation scripts and start logging your experiment data.

Run on your laptop, in the cloud, on Google Colab or wherever you want.

Use in the scripts or in Jupyter notebooks. Run experiments your way just let us track them. 

pip install neptune-client
import neptune

neptune.init('awesome-project') 
neptune.create_experiment('great-idea') 
# any training or validation code you want
neptune.log_metric('auc', score) 
neptune.log_image('diagnostics', 'roc_auc.png')
neptune.log_artifact('model_weights.h5')
python train.py

Integrate with your favourite frameworks and tools

Experiment management

Version and compare experiments

Keep track of your progress and reproduce results easily. Neptune tracks all the details about every single experiment you run. You can tag, filter, group, sort and compare your experiments. 

Notebook Versioning and Diffing

Compare notebooks like source code​

Record your exploration process and analyze diffs between checkpoints. Select two notebooks and compare their content, code and outputs, side-by-side just like source code.

Team collaboration

Communicate progress easily

Discuss data science work in context, right where it is created. Explain your point by adding a comment to a particular object or simply share a link. Mention your teammates, spark discussions and discover insights – everything in a single place.

Example projects

Neptune tutorial

This public project is hands-on tutorial for newcomers. It will guide you from the installation and minimal example to advance use cases.

Hyperparameter Optimisation

In a series of blog posts Jakub is comparing Python hyperparameter optimisation libraries: eg. Scikit-Optimize, Hyperopt, Optuna, hpbanster and more.

Binary classification

In this example project we walk you through data exploration and feature extraction all the way to tuning machine learning model.

Take advantage of 
team collaboration.
seamless tracking.
organized work.
 Start today!