Clean up your Jupyter workflow without using Git

With Neptune you can be in control of your notebook versions with literally one click. It’s good for you and great for your team.

It really is free and takes 5 min to setup

Start collaborating for FREE

Everyone working with Jupyter notebooks needs to version them somehow

And hundreds of data scientists are using Neptune to keep their notebook checkpoints under control.

“Without the information I have in the Monitoring section I wouldn’t know that my experiments are running 10 times slower than they could.
All of my experiments are being trained on separate machines which I can access only via ssh. If I would need to download and check all of this separately I would be rather discouraged :).”

Michał Kordas

Machine Learning Researcher @TensorCell

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

Git

  • Notebooks are shown as .json files.
  • Comparing notebook checkpoints doesn’t work.
  • Saving large notebook checkpoints? They load, load some more, and don’t render.
  • Saving notebooks with interactive charts? They are not interactive anymore.
  • Want to add a quick note after the notebook was committed? Good luck.

Jupytext, Nbdime, ReviewNB

  • Jupytext: Do you really want to create .html and .py with every save or would you rather just have your .ipynb notebooks versioned
  • Nbdime: works nicely for diffs of your local checkpoints but doesn’t help when you want to compare checkpoints on a team, name or share checkpoints easily
  • ReviewNB: works nicely with Github but If you are using Gitlab, Bitbucket or simply don’t want to use git for notebooks you are out of luck

It really is free and takes 5 min to setup

Start collaborating for FREE

Clean up your Jupyter workflow in a few steps

Install notebook extension

Add the extension either from the command line or in the Jupyter interface.
You will get a few additional buttons in your notebook interface.

pip install neptune-notebooks
jupyter labextension install neptune-notebooks

Upload notebook checkpoints to commit your work

When you want to save your notebook checkpoint you just click on the upload button.

Organize notebook checkpoints with names and descriptions

To make finding checkpoints that are important easier you can add names and descriptions.

Do that either in your Jupyter notebook or Neptune app.

Find and compare notebook checkpoints

When every checkpoint your team logged is stored in Neptune fining the analysis you care about is trivial.

You can compare the new version with the previous checkpoint to see what changed.

Share notebooks, comparisons and discuss them with your team

Everything you do in Neptune can be shared with your team.
Just do something in the app, diff checkpoints, for example, copy the link, and send it to your people.

The links are persistent so they will work in a week or a year!

Download your teams’ notebooks directly into your Jupyter Lab or Notebook

You can download every checkpoint your team has logged to Neptune from your Jupyter Lab or Notebook interface.

Just click download, choose the checkpoint and that is it!

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:

“If you need to monitor and manage your machine learning or any other computational experiments, Neptune.ai is a great choice. It has many features that can make your life easier and your research more organized.”

Boaz Shvartzman

Computer vision researcher and developer @TheWolf

“I’m working with deep learning (music information processing), previously I was using Tensorboard to track losses and metrics in TensorFlow, but now I switched to PyTorch so I was looking for alternatives and I found Neptune a bit easier to use, I like the fact that I don’t need to (re)start my own server all the time and also the logging of GPU memory etc. is nice. So far I didn’t have the need to share the results with anyone, but I may in the future, so that will be nice as well.”

Ondřej Cífka

PhD student in Music Information Processing at Télécom Paris

“Without the information I have in the Monitoring section I wouldn’t know that my experiments are running 10 times slower than they could.
All of my experiments are being trained on separate machines which I can access only via ssh. If I would need to download and check all of this separately I would be rather discouraged :).”

Michał Kordas

Machine Learning Researcher @TensorCell

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