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.

• Neptune works with common technologies in the data science domain: Python, Jupyter Notebooks, and R, to mention a few.

• It integrates with other tracking tools such as MLflow and TensorBoard or Sacred and many other machine learning and deep learning frameworks.

• It integrates seamlessly with your machine learning infrastructure, be it AWS, GCP, Kubernetes, Azure, or on-prem machines.

• Neptune client is an open source Python library that allows you to integrate your Python scripts with Neptune. Neptune client supports the following cases:

  • Creating and tracking experiments

  • Managing running experiment

  • Querying experiments and projects (search/download)

Get Started

• New user? Register and climb aboard.

• Registered already? Log in here.

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)
  

  Note

  The api_token belongs to the public user Neptuner. After running the code, your experiment will appear here.

  For more information, see the Tracking How To Guide.

• Organize the structure of your project:

  • Code

  • Notebooks

  • Experiment results

  • Model weights

  • Meeting notes

  • Reports

  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 the Organize How To Guide.

• Collaborate in the team:

  • Share your experiments

  • Compare results

  • 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.

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More Resources

In addition to this documentation set, check out the following resources:

• Hands-on 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.

• 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.

• Presentations, talks, podcasts

• Product hunt: A review helps other people find our product.

• Technical support: Should you require further support, or have feature requests, reach out at contact@neptune.ai or click the chat icon in the bottom right corner of the Neptune UI.

Spread the Love

Go ahead and mention us on social media!

• Twitter: Tweet us. Our handle is @neptune.ai.

• Product feedback: File an issue in our GitHub repo.