Neptune vs Polyaxon


Neptune

Polyaxon
Commercial Requirements
Standalone component. ML metadata store that focuses on experiment tracking and model registry

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


Experiment Tracking

No

No



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
No

No
No

No
No

No

No

No
No

No

No

No
No

No
No

No


No

No

No

No

No

No
No

No

No

No

Yes

No
Yes

No



No

No
No

No
Model Registry
No

No
No

No
No

No
Limited

No
No

No
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
No

No
No

No
No

No
No

No
No

No
No

No
No

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Report outdated information here.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
Don’t overthink it