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Neptune app overview#

Overview of the Neptune app

The web app is your UI to the logged metadata. It's where you can have the data visualized, sorted, and organized.

You can watch the training live and, if needed, stop runs remotely. You can also browse tracebacks, system metrics, and source code.

How does it work?#

In each project, you can organize your metadata into the following sections:

Side menu with project sections

We'll walk you through each section below.


You'll typically create a run every time you execute a script that does model training, retraining, or inference. Each tracked run appears in the runs table of your project, where you can display and arrange the metadata to your liking and save views for later.

View modes#

At the very top, you can toggle between three different view modes for your runs.

Choose fields (columns) that are important to you. Filter, group, or sort by any field.

Table view with run metadata

Explore and preview the metadata one run at a time.

Dashboard with multiple visualizations

Select runs with the eye icons () to compare their metadata in this view mode.

Chart displaying multiple graphs

Dashboards are fully customizable and shared project-wide

You can use customizable widgets to create dashboards in the Run details and Compare runs modes (shown in the screenshots above).

This lets you either visualize a single run in great detail or collect metadata from multiple runs into a single view.


Table view with models

Table view with model metadata

The model registry lets you manage the metadata and lifecycle of your models separately from your experimentation runs. For each model, you can create and track model versions. To manage your model lifecycle, you can control the stage of each model version separately.

Project metadata#

To facilitate collaboration, you can track metadata that applies to the whole project. For example, you could store the latest validation dataset, then access that as the source of truth for all of your runs.


With the neptune-notebook extension, you can snapshot and compare Jupyter Notebook checkpoints in a dedicated section of the app.

Browse examples in Neptune#

The below table summarizes some points of interest, provides Neptune examples, and suggests related docs.

Component Example      Description & docs
Runs table View in Neptune → The metadata of the runs organized in a table view. You can customize and save table views for later.
Comparison of runs View in Neptune → Comparison view of selected runs. Contrast the metadata in different ways by switching between the dashboards.
Logged metrics View in Neptune → Series of values are auto-displayed as charts.
Logged artifacts View in Neptune → Track metadata about a file or collection of files.
Logged system metrics View in Neptune → Neptune logs hardware consumption metrics by default.
Logged interactive charts View in Neptune → You can display charts generated with plotting libraries in an interactive way.
Logged images View in Neptune → Preview any uploaded images.
Custom dashboard View in Neptune → You can combine different metadata types and widgets in a single view by creating custom dashboards.
Model registry View in Neptune → The metadata of model versions organized in a table view. You can manage the stage of each version.
Project-level metadata View in Neptune → You can also store and upload metadata common to the entire project.