Neptune gives you a blazing-fast, super customizable UI that scales to millions of machine learning experiments, the ability to manage users in a hosted or on-prem application, easy integration with your current codebase/workflow, and more framework integrations than MLflow does. Get Notebook auto snapshots, organize and share the experiments of your entire team and get dedicated user support with Neptune!
Neptune vs MLflow
Which tool is better?
Why Choose Neptune over MLflow?
Neptune can serve all the experiment tracking needs of your team (any language, any framework, any infrastructure)
and lets you manage user access and gives you visibility into your team’s progress at any time with a great user-friendly UI.
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- Free for individuals, non-profit and educational research
- Team: $79 per user
- Team Startup: $39 per user
- Enterprise: custom
- Free: 1 user
- Unlimited private and public projects
The most lightweight experiment management
tool that fits any workflow
Keeping track of machine learning experiments made simple.Get started
Why Neptune is the Best Alternative to Mlflow
Is it easy to set up and maintain MLflow 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.
Can you manage user permissions in MLflow?
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.
Is your MLflow UI loading slowly when you have thousands of runs?
Neptune was built to scale and can support millions of experiment runs both in the back-end and front-end.
Can you save different experiment dashboard views in MLflow?
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.
Can you monitor your hardware in MLflow?
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.
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!
Does MLflow 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.
Does MLflow 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',...])
Does MLflow 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.
Can you use MLflow Interface with Neptune back-end and UI?
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