Share results of ML experiments with your team

With Neptune you can have all your teams’ experiments and notebooks versioned in an app built for ML teams. Run experiments anywhere, use any framework, have all the results in one place, send a link to share them.

It really is free and takes 5 min to setup

Start collaborating for FREE

ML teams achieve more when they can discuss their experimental results 

And hundreds of them are using Neptune to have a common place to compare and share their experiments.

”Neptune is making it easy to share results with my teammates. I’m sending them a link and telling what to look at, or I’m building a View on the experiments dashboard. I don’t need to generate it by myself, and everyone in my team has access to it.”

Maciej Bartczak

Resarch Lead @Banacha Street

Is this honestly the best way to organize my ML experiments?

Git + Spreadsheets

  • You don’t have to copy experiment information to spreadsheets. It is done automatically every time your team runs an experiment
  • Your team can create many views of the experiment dashboard and save them for later
    You can easily search through thousands of experiments to find the information you want quickly
  • You can compare your teams’ runs with a few clicks. Neptune overlays learning curves and automatically shows you the columns that are different
  • You can drill down to details by clicking on a table row. See model predictions, code, learning curves, html visualizations of your entire team

Open-source solutions like MLflow, Polyaxon, or Kubeflow

  • You don’t have to set up and maintain the database/file system and dashboard UI yourself
  • You can manage user permissions and know who can view or edit projects
  • You can log things like images, html visualizations, model checkpoints, and more
  • You get a premium Jupyter support to keep track of experiments, and data explorations in the same place
  • You can share your running experiments with your team by simply sending a link
  • We are really lightweight -> you can actually use Neptune with any of those tools

It really is free and takes 5 min to setup

Start collaborating for FREE

Share results with your team in a few steps

Add a few lines to your scripts

Connect Neptune to your project by adding literally 3 lines on top of your scripts. Then you just run your training and evaluation and log whatever you care about.

For most machine learning frameworks you don’t even have to write those logging calls -> We created the integrations for you!

neptune.init('Me/MyProject')
neptune.create_experiment(params={'lr':0.1, 'dropout':0.4}, # parameters
                                             upload_source_files=['**/*.py',  # code
                                                                                'requirements.yaml'], # env)
...
log_data_version('path/to/data') # version data
...
neptune.log_metric('test_accuracy', 0.84) # save results
neptune.log_artifact('model.pkl') # version model

Run your experiments the way you usually do

Neptune goes where you work not the other way around.

So if you are running experiments on your laptop, spin up cloud machines, or burn through computational clusters at your university Neptune will keep track of your experiments with no problems.

Find and compare all your teams’ experiments in one place

Search through your experiments, compare them, and find the information you need in minutes.

Every team member can customize the experiment dashboard with metrics, parameters or other info they want to see and save it for later.

Share experiments, notebooks or reports with a link

Compared experiments and see something you want to discuss with other scientists?
Want to share progress with your manager?
Build a model and want to share it with the production team?

Just send a link, it’s that simple. Experiment comparisons, model details, dashboard views, notebook checkpoints or anything else!

Access everything your team logged programmatically

You can download everything you logged to Neptune in the UI or you can do things programmatically.

No problem, fetch everything you need via an API.

import neptune

project = neptune.init('Project')
exp = project.get_experiments(id='Proj-123')[0]

exp.get_parameters()
exp.download_artifact('model.pkl')
exp.download_sources()

It really is free and takes 5 min to setup

Start collaborating for FREE

Start collaborating on experiments in minutes with our integrations

Are you thinking “Ok but, do I have to write the logging/callback functions myself?”

If you are using Keras, XGBoost, Optuna, or one of the 20+ libraries that we integrate with you don’t need to implement anything to monitor your experiments.

What our users say

Over 5,000 ML people started monitoring their experiments with Neptune this year – read what some of them have to say:

“Neptune allows us to keep all of our experiments organized in a single space. Being able to see my team’s work results any time I need makes it effortless to track progress and enables easier coordination.”

Michael Ulin

VP, Machine Learning @Zesty.ai

”Neptune is making it easy to share results with my teammates. I’m sending them a link and telling what to look at, or I’m building a View on the experiments dashboard. I don’t need to generate it by myself, and everyone in my team has access to it.”

Maciej Bartczak

Resarch Lead @Banacha Street

“Even if I’m training with Keras or Tensorflow on my local laptop, and my colleagues are using fast.ai on a virtual machine, we can share our results in a common environment.”

Víctor Peinado

Senior NLP/ML Engineer

They already have their ML experimentation in order.
When will you?

✓ Sign up for a free account
✓ Add a few lines to you code
✓ Get back to running your experiments

Start tracking for FREE