Neptune vs DagsHub


Neptune

DagsHub
Commercial Requirements
Standalone component

Standalone tool

Only public cloud support for the free plan. On-prem/private cloud installation only for paid plans. Read more about their plans here
Managed cloud service

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.
Minimal. Just a few lines of code needed for tracking. Read more

Minimal. Just a few lines of code need to be added for basic tracking
Yes, through the neptune-client library

Yes, through the Git and DVC CLI
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
Model Registry
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
Integrations and Support
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Report outdated information here.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?

- Customizable metadata structure
- Possibility to log many metadaty types, inlcuding rich format
- Possibility to combine multiple metadata types into dashboards
- Support for distributed training
- Hardware consumption monitoring
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