Neptune vs MLflow
Which tool is better?

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.

Neptune lets you manage users easily. You can give people access to projects and organizations. Additionally, Neptune gives you control over the user role: admin, contributor or viewer.

Neptune was built to scale and can support millions of experiment runs both in the back-end and front-end.

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.

Neptune lets you monitor your resource consumption of your CPU, GPU, and Memory.
With that, you can optimize your code to utilize your resources fully.

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!

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.

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',...])

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.

That’s right. Neptune integrates with MLflow to give you the best of both worlds.
You can use MLflow interface for experiment tracking, sync your mlruns folder with Neptune and enjoy the awesome UI that Neptune gives you. No need to keep your mlruns folder backed-up, or firing mlflow UI dashboard on a dedicated server to explore it. Just sync mlruns with Neptune:

neptune mlflow PATH/TO/mlruns \

       --project USER_NAME/PROJECT_NAME