Neptune vs TensorBoard

Track and organize the experimentation process of your entire team, from exploratory analysis, to model training runs and hyperparameter sweeps.

Designed for your Team

The difference between Neptune and TensorBoard

TensorBoard is about visualizing and debugging model training for a single person. 

With Neptune you can keep your entire teams’ work organized in one place, accessible to everyone in a powerful UI that scales to millions of runs.

Great UI

UI that scales, super customizable and designed for teams

Log and organize millions of experiment runs.

Create custom views for data scientists or managers, and save them for later.

Search through experiments quickly with a powerful language.

Get Started, It’s Free!

User Management

Organize your projects, give different roles to different people

You can assign people to different organizations and projects. 

You can choose whether they should be able to edit experiment data or simply view what is happening and comment on it.

   params={'lr':0.1, 'epoch_nr':10})
neptune.set_property('data_version', md5)
neptune.log_metric('k_fold_acc', 0.92)
neptune.log_image('ROC_curve', fig)

Flexible Logging

Track whatever information you want with ease

Logging custom information into TensorBoard can be a drag and with Neptune it is super simple. 

You can log metrics, data versions, prediction images, diagnostic charts, model binaries and more and it feels like using a dictionary.

Comparison tools

Inteligent table that shows you diffs and more

When you compare multiple experiment runs sometimes it is difficult to figure out what is different and what you should look for.

We’ve created a table that automatically finds the columns and values that are different and displays them for you! 

Integrate and sync

Easily integrate and convert your runs

Syncing your TensorBoard runs with Neputne is very easy. Just convert your logdir to Neptune experiments and enjoy better teamwork instantly.

You can also integrate your scripts with Neptune with additional 3 lines of code.

That’s it!

neptune tensorboard PATH/TO/logdir \
import neptune_tensorboard as npt_tb

npt_tb.integrate_with_tensorflow() # _with_keras

Import your work into Neptune now

Get Started, It’s Free!

“Neptune allow 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

Manage ML experiments of your team. Create Free Account

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Sponsored by Neptune Labs Inc. As far as we know MLflow is not a registered trademark of Databricks, Inc. but for clarity, Neptune Labs Inc. is neither affiliated with nor sponsored by Databricks, Inc