Neptune vs Sacred + Omniboard


Neptune

Sacred + Omniboard
Commercial Requirements

Can be deployed both on-premises and/or on the cloud, but has to be self-managed
Managed cloud service

Open-source

No
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. Only a few lines of code need to be added
Yes, through the neptune-client library

Yes, through the sacred python library, and their CLI
No

No

No

No
No

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No

No


No

No
Experiment Tracking

No

No



No

No
No

No
No

No
No

NA
No

NA
No

NA

No

No

No

No



No

No
No

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No

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No

No

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Yes

No
Yes

No

Query language

Fixed Selectors

No

No
No

No

No

No
No

No
No

No
Limited

No
No

No
No

No
Model Registry
No

No
No

No
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No

No
Limited

No
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No

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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

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No

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No

No
No

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Report outdated information here.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

- Highly developed and customizable comparison features
- Scalability with thousands of runs
- User management and team collaboration features
- 25+ out-of-the box integrations with Python libraries and IDEs
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")
Already using Sacred?

Instead of Omniboard, you can integrate Sacred with Neptune and use it’s UI to manage your experiments.
All you need to do is add a NeptuneObserver() to your sacred experiment’s observers:
# Create sacred experiment
ex = Experiment('image_classification', interactive=True)
# Add NeptuneObserver
ex.observers.append(NeptuneObserver(run=neptune_run))
Thousands of ML people already chose their tool
It only takes 5 minutes to integrate Neptune with your code
Don’t overthink it