Neptune vs Kubeflow
Which tool is better?

Zero maintenance

Is it easy to set up and maintain Kubeflow server and visualization dashboard for your entire team?

With Neptune you get all your experiment data saved and backed-up on a hosted server or on-prem installation. You can manage user permissions and share experiments in the beautiful UI with no additional overhead. Powerful, simple, and available for you and your team in minutes.

Experiment-focused UI

Is your Kubeflow UI built for experimentation?

Neptune was built with a strong focus on machine learning experiments. It gives you the ability to scroll through model predictions, compare metrics and loss charts, group and compare hyperparameters and much more.

Dashboard Views

Can you save different experiment dashboard views in Kubeflow?

When you have multiple users working on many ideas the things you want to look at change… and so should your experiment dashboard.
Neptune lets you customize, change, and save experiment dashboard views depending on your needs.

Image Channel Display

Can you scroll through your images and charts?

Neptune lets you log images and charts to multiple image channels and scroll through them to quickly see the progress of your model training. Get a full picture of what is happening in your training and validation loops by leveraging more information!

Notebook Autosnapshots

Does Kubeflow snapshot your Jupyter notebooks automatically?

Neptune notebook integration automatically snapshots your .iipynb whenever you run a cell with neptune.create_experiment() in it. Whether you remember to submit your experiment or not everything will be safely versioned and ready to be explored.

Analyze Experiment Dashboard in Jupyter Notebook

Does Kubeflow let you fetch your experiment dashboard directly to a pandas DataFrame?

With Neptune you can fetch whatever information you or your teammates tracked and explore it however you like. We have some nice exploratory features, like HiPlot integration to help you with that.

neptune.init('USERNAME/example-project')

make_parallel_coordinates_plot(

     metrics= ['eval_accuracy', 'eval_loss',...],

     params = ['activation', 'batch_size',...])

Notebook Versioning and Diffing

Does Kubeflow let you track your exploratory analysis?

Neptune goes beyond the tracking of machine learning experiments and allows you to version your exploratory data analysis or results exploration as well!
Once it is saved in Neptune you can name, share, download or see diffs of your notebook checkpoints.

Kubeflow + Neptune

Can you use Kubeflow for orchestration and Neptune for experiment tracking?

Kubeflow is great at orchestration of your machine learning workflows but is missing a lot of the experiment tracking functionality (it’s not the focus of the project). The best thing is you can actually use both tools together! Use Neptune experiment tracking to have a great view of your experiments with run orchestration from Kubeflow!