Welcome to Neptune!¶
Neptune is a light-weight experiment management tool that helps you keep track of your machine learning experiments.
Use Neptune to log hyperparameters and output metrics from your runs, then visualize and compare results. Automatically transform tracked data into a knowledge repository, then share and discuss your work with colleagues.
Neptune fits in any workflow, ranging from data exploration and analysis, decision science to machine learning and deep learning.
It integrates seamlessly with your machine learning infrastructure, be it AWS, GCP, Kubernetes, Azure, or on-prem machines.
The Neptune Python Library is an open source package that allows you to integrate your Python scripts with Neptune. Once you have integrated with Neptune, you can:
Create and track experiments
Manage and run experiments
Fetching experiment and project data
Track, Organize, Collaborate¶
The Neptune workflow comprises three iterative phases:
Track all objects in the data science or machine learning project. It can be model training curves, visualizations, input data, calculated features and so on. The snippet below presents an example of integration with Python code.
import neptune neptune.init('shared/onboarding', api_token='ANONYMOUS') neptune.create_experiment() neptune.append_tag('minimal-example') n = 117 for i in range(1, n): neptune.send_metric('iteration', i) neptune.send_metric('loss', 1/i**0.5) neptune.set_property('n_iterations', n)
The api_token belongs to the public user Neptuner. After running the code, your experiment will appear on the experiments dashboard.
For more information, see Experiment Tracking.
Organize the structure of your project:
Everything is in one place, accessible from the application or programmatically. Neptune exposes a Query API, that allows users to access their Neptune data right from the Python code.
For more information, see Experiments View.
Collaborate in the team:
Share your experiments
Comment and communicate your work
Use widgets and mentions to show your progress
Speak your language in our data-science focused interactive wiki!
For more information, see Collaborating in Neptune.
In addition to this documentation set, check out the following resources:
Project tutorial: Covers installation, experiment tracking and comparison, data tracking, and Notebook use.
Sample projects like a comparison of binary classification metrics applied to fraud detection, research on hyperparameter optimization strategies, or a step-by-step experiment tracking tutorial.
YouTube channel: Provides hands-on videos that showcase key Neptune features.
Neptune blog: Provides in-depth articles about best practices and trends in machine learning.
Neptune user community: Meet other Neptune users and developers and start a discussion.
neptune-contrib: Built on top of neptune-client, this is an open-source collection of advanced utilities that make work with Neptune easier.
Product hunt : A review helps other people find our product.
Presentations, talks, podcasts
Technical support: Should you require further support, or have feature requests, contact us by email or click the chat icon in the bottom right corner of the Neptune UI.