Neptune vs TensorBoard

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

Neptune vs TensorBoard

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

machine learning experiment dashboard
machine learning experiment dashboard

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.

machine learning experiment dashboard

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.

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.

user management
netune.create_experiment(
   params={'lr':0.1, 'epoch_nr':10})
...
neptune.append_tag('resnet50')
neptune.set_property('data_version', md5)
...
neptune.log_metric('k_fold_acc', 0.92)
neptune.log_image('ROC_curve', fig)
neptune.log_artifact('model.pkl')

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.

netune.create_experiment(
   params={'lr':0.1, 'epoch_nr':10})
...
neptune.append_tag('resnet50')
neptune.set_property('data_version', md5)
...
neptune.log_metric('k_fold_acc', 0.92)
neptune.log_image('ROC_curve', fig)
neptune.log_artifact('model.pkl')

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! 

experiment comparison table

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 it is! 

neptune tensorboard PATH/TO/logdir \
     --project USER_NAME/PROJECT_NAME
import neptune_tensorboard as npt_tb

neptune.init('Your/Project')
npt_tb.integrate_with_tensorflow() # _with_keras
neptune.create_experiment('Your-Run')
...
neptune tensorboard PATH/TO/logdir \
     --project USER_NAME/PROJECT_NAME
import neptune_tensorboard as npt_tb

neptune.init('Your/Project')
npt_tb.integrate_with_tensorflow() # _with_keras
neptune.create_experiment('Your-Run')
...

Import your work into Neptune now

nnaisense and Neptune
Zesty.ai and Neptune
reply.ai and Neptune
NewYorker and Neptune
Intive and Neptune
"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 @Zesty.ai

Manage ML experiments of your team.

Sponsored by Neptune Labs Inc. TensorBoard is a registered trademark of Google LLC. Neptune Labs Inc. is neither affiliated with nor sponsored by Google LLC.