Documentation

Neptune in 3 minutes

Try Neptune on Colab with zero setup and see results in the UI

Get a quick feel of how monitoring and keeping track of experiments can look like.

What does Neptune do?

Neptune is a lightweight experiment management tool that helps you keep track of your machine learning experiments.

Most common Neptune use cases:

• Monitor ML runs live

• Organize ML experimentation

• Compare and debug ML experiments and models

• Share results of experiments with your team

• Clean up your Jupyter workflow without git

How does Neptune work (in 3 steps)?

Note

Following snippets are just to give you the idea.

If you want to copy paste and run things quickly then go to Quick Starts.

1. Connect it to your script

neptune.init('happy_tom/great-project')

2. Start an experiment

neptune.create_experiment('my-amazing-idea')

3. Log things that you care about

neptune.log_metric('test_auc', 0.92) # metrics, losses
neptune.log_image('charts', roc_curve_fig) # images, charts
neptune.log_artifact('model.h5') # model binaries, predictions, files

4. Run your script normally

python train.py

5. See everything in Neptune UI

Check it for yourself:

• See our Quick Starts

Discover Neptune

• Example project: See how example project looks in Neptune

• YouTube channel: Provides hands-on videos that showcase key Neptune features.

• Neptune blog: Provides in-depth articles about best practices in machine learning experimentation (among other things)

• Neptune community forum: Meet other Neptune users and developers and start a discussion.

• neptune-client: Neptune client is an open source Python library that lets you integrate your Python scripts with Neptune.

• neptune-contrib: Built on top of neptune-client, this is an open-source collection of advanced utilities that make work with Neptune easier.

• Questions? Send an email to contact@neptune.ai by email or click the chat icon in the bottom right corner.