Neptune vs Polyaxon


Neptune

Polyaxon
Commercial Requirements
Standalone component

Standalone tool

Can be hosted both on-prem and on the cloud
Managed cloud service
General Capabilities
No special requirements other than having the neptune-client installed and access to the internet if using managed hosting. Check here for infrastructure requirements for on-prem deployment.

Needs a Kubernetes cluster to be deployed. Read more about core requirements for a local cluster here
Minimal. Just a few lines of code needed for tracking. Read more

Extensive code and infrastructure changes required. Check their quick start to get an overview
Yes, through the neptune-client library

Yes, via their CLI and Python library

No

No
No

No
No

No
No

No


Experiment Tracking




No

No
No

No
No

NA
No

NA
No

NA

No

No

No

No



No

No

No

No
No

No
No

No
No

No
No

No
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No

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No
No

No

No

No
No

No
No

No


No

No


No

No
No

No

No

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No

No
No

No
Model Registry
No

No
No

No
No

No
No

No
Integrations and Support
No

No
No

No
No

No
No

No
No

No
No

No
No

No
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No
No

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No
Report outdated information here.
What are the key advantages of Neptune then?

- Easy to set up, just a few lines of code necessary to start using it
- Customizable metadata structure
- Comparison features (including table format diff, image comparison, and more)
- Possibility to combine multiple metadata types in custom dashboards
- Team collaboration features (e.g. sharing UI links)
See these features in action
Sign up to Neptune and install client library
pip install neptune
Track experiments
import neptune
run = neptune.init_run()
run["params"] = {
"lr": 0.1, "dropout": 0.4
}
run["test_accuracy"] = 0.84
Register models
import neptune
model = neptune.init_model()
model["model"] = {
"size_limit": 50.0,
"size_units": "MB",
}
model["model/signature"].upload(
"model_signature.json")
Thousands of ML people already chose their tool
It only takes 5 minutes to integrate Neptune with your code
Donât overthink it