Data science collaboration hub

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.

Neptune allows you to track the entire experimentation process

Everything is backed-up and organized, ready to be accessed, reproduced and shared with others.

Track and version your notebooks

Log all your notebooks directly from Jupyter or Jupyter Lab. 
All you need is to install a Jupyter extension.

Manage your experimentation process

Neptune tracks your work with virtually no interference to the way you like to do it. Decide what is relevant to your project and start tracking:

  • Metrics
  • Hyperparameters
  • Data versions
  • Model files
  • Images
  • Source code

Integrate with your workflow easily 

Neptune is a lightweight extension to your current workflow. Works with all common technologies in data science domain and integrates with other tools. It will take you 5 minutes to get started.

# Track your notebooks

pip install neptune-notebooks

jupyter nbextension enable ——py neptune-notebooks

# Track all experiment-related objects

import neptune


neptune.log_metric(’auc’, score)

neptune.log_image(‘model_diagnostics’, ‘roc_auc.png’)

neptune.log_artifact(’model_weights.h5’, ’.’)

neptune.set_property(‘data_version’, sha1(data_train))

# Host MLflow or TensorBoard runs on Neptune

neptune@ubuntu:~$ neptune tensorboard path/to/logdir

neptune@ubuntu:~$ neptune mlflow path/to/project

Works with your favourite frameworks and tools

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.

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, sort and compare your experiments. 

Collaborate 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.


Hyperparameter Optimisation in Python

In a series of blog posts Jakub is comparing Python hyperparameter optimisation libraries: eg. Scikit-Optimize, Hyperopt, Optuna, hpbanster, BayesianOptimisation, Sherpa and SMAC3.

valid iter auc

Binary classification

In this example project we walk you through data exploration and feature extraction all the way to tuning machine learning model. The whole process is described in the Wiki space.

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