Get your ML experimentation in order

Keep track of every piece of every experiment or notebook you create and organize them in a single place. Gain control WITHOUT changing your workflow.

It really is free and takes 5 min to setup

Start collaborating for FREE

Get started in a few minutes

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 logic and log whatever you care about. 

import neptune

neptune.create_experiment(params={'lr':0.1, 'dropout':0.4})
# training and evaluation logic
neptune.log_metric('test_accuracy', 0.84)

You can log metrics, parameters, images, interactive charts, model files, code, hardware consumption, and so much more. See other options.  

Run your experiments and notebooks the way you usually do it

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. 

It works with Colab, Kaggle kernels, and integrates nicely with Jupyter Notebooks.

Monitor your experiments live

You can look at the learning curves, hardware consumption, console logs as your model is running. 

If you log ROC curves, image predictions, model checkpoints, or other things after every iteration you can scroll through them and see progress live. 

Search, and compare your experiments and notebooks

You can find your experiments quickly with a fully searchable dashboard. Think of it as an excel spreadsheet designed specifically for ML experiments. 

All experiments can be grouped and compared easily on metrics, parameters, learning curves, and more.

Everything is customizable and can be adjusted so that you always look at things that you know are important. If something changes you can change your dashboard quickly and save it for later.

Drill down to experiment details whenever you want

If you want to explore a particular experiment in more detail just click on it and find what you need. 

See evaluation metrics, ROC curves, model binaries, hardware consumption, console logs, interactive charts, code, and more for every experiment you run.

Share your work and invite people to your projects

Want to show your teammates what you are doing and discuss ideas?

Invite them to your project or simply share your ML experiments or notebooks with a link. No hassle with setting up environments or remote servers. Just send a link and they can see exactly what you see.

Ok but…

Does it really take 5 min to start?

Yeah, if you are fast on your keys it could be even less. Just sign up for a free account, install the client library, paste your API token to the environment variable and you are good to go. 

Even Kobi thinks so 🙂

“Such a fast setup! Love it:)”

Kobi Felton

Also, if you are using Keras, XGBoost, Optuna, or one of the 20+ libraries that we integrate with you don’t need to implement the logging to track your experiments (check the full list of integrations).

Is it really free, like free-free?

Ok, I will be honest with you. Neptune is free for individual use (including commercial), research teams, and non-profit organizations. 

… but If you want to use Neptune on a commercial team we will charge you, sorry. That said you get 1 free project for your team so that you can test it out however you like and see if it is worth it.

What our users say

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

Within the first few tens of runs, I realized how complete the tracking was – not just one or two numbers, but also the exact state of the code, the best-quality model snapshot stored to the cloud, the ability to quickly add notes on a particular experiment. My old methods were such a mess by comparison.”

Edward Dixon

Data Scientist @Intel

If you look for a simple, flexible, and powerful tool or you are tired of using excel sheets or tensorboard to track your results, is a good bet.

Jakub Cieślik

Senior Data Scientist interested in Computer Vision

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