Polyaxon vs Neptune
Which tool is better?

Neptune gives you a lot of flexibility and control on what you want to track and analyse and how you want to do it. It fits into any workflow and is adaptable. Manage users in a hosted or on-prem application, and get dedicated user support with Neptune!

Why Choose Neptune over polyaxon?

Neptune is lightweight (quick to learn and easy to master) and can serve all the experiment tracking needs of your team (any language, any framework, any infrastructure).
It lets you manage user access and gives you visibility into your team’s progress at any time with a great user-friendly UI.
Polyaxon tries to cover a larger part of the ML lifecycle and because of that is a less focused and heavier solution.

Show differences only

Polyaxon
Neptune
Pricing
Polyaxon
Neptune
Pricing

n.a.

  • Free for individuals, non-profit and educational research
  • Team: $79 per user
  • Team Startup: $39 per user
  • Enterprise: custom
Free plan limitations

n.a.

  • Free: 1 user
  • Unlimited private and public projects
Open-Source

Limited

Experiment Tracking Features
Polyaxon
Neptune
Data Versioning

Limited

Notebook Versioning
Model Versioning

Limited

Environment Versioning

Limited

Logging Images and Charts

Limited

UI Features
Polyaxon
Neptune
Customizable Experiment Dashboard

Limited

Experiment Organization

Limited

Saving Experiment Views

Limited

View Sharing

Limited

Run Comparison

Limited

Notebook Diffs
Comments
Reports

Limited

Integrations
Polyaxon
Neptune
MLflow
Sacred
Amazon SageMaker
Kubeflow
LightGBM
XGBoost
skorch
PyTorch Lightning
Catalyst
Optuna
Scikit-Optimize
HiPlot
Sklearn

The most lightweight experiment management
tool that fits any workflow

Keeping track of machine learning experiments made simple.

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Why Neptune is the Best Alternative to polyaxon

QUICK START

Is it instantaneous to start using polyaxon without contacting sales or setting up a server cluster?

With Neptune you simply register, install an open source neptune-client and you are ready to track experiments and collaborate in a team. 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.

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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!

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NOTEBOOK AUTOSNAPSHOTS

Does polyaxon 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.

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ANALYZE EXPERIMENT DASHBOARD IN JUPYTER NOTEBOOK

Does polyaxon 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',...])
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NOTEBOOK VERSIONING AND DIFFING

Does polyaxon 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.

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jupyter notebook compare
PYTORCH ECOSYSTEM INTEGRATIONS

Do you have to create and maintain custom loggers for Skorch or PyTorch Ignite?

Neptune has integrations with every PyTorch Ecosystem library to let you start tracking your experiments in minutes!

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# Skorch
net = NeuralNetClassifier(...callbacks=[NeptuneLogger(...)])
net.fit(X, y)

# Pytorch Ignite
npt_logger = NeptuneLogger(...)
npt_logger.attach(trainer)
trainer.run(...)

# Pytorch Lightning
trainer = Trainer(logger=NeptuneLogger(...)) 

# Catalyst
runner = SupervisedNeptuneRunner()
runner.train()

# Fastai
learn.callbacks.append(NeptuneMonitor())
learn.fit_one_cycle(...)