Neptune vs Kubeflow
Neptune
Kubeflow
Commercial Requirements
Standalone component
Part of the Kubernetes environment
Almost all popular cloud providers maintain their own distribution of Kubeflow. It can also be installed on-premises manually. Read about the differejnt installation options available here
Managed cloud service
The base product is open source, with managed distributions made available by cloud providers
The open source version is free. Maintainers have their own priving plans for their managed distributions
Community support for the open source version. Different options available for managed distributions
Not for the open source version. Different options available for managed distributions
Different options available for managed distributions. Read more here
Not for the open source version. Different options available for managed distributions
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.
Advanced setup required for manual install. Installation requirements for the managed distribution varies depending on the cloud provider. Read more here
Yes, through the neptune-client library
Yes, through the kubeflow python client
No
No
No
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No
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Experiment Tracking
No
No
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No
No
NA
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NA
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Model Registry
No
No
No
No
No
No
No
No
No
No
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No
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No
Integrations and Support
No
No
No
No
No
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No
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No
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No
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No
No
Report outdated information here.
What are the key advantages of Neptune then?
- Neptune is a paid hosted metadata store with the main focus on experiment tracking and model registry
- Kubeflow is an open-source project created to enable easier deployment of ML workflows on Kubernetes
Neptune and Kubeflow are not mutually exclusive. In fact, Neptune can serve as a great solution for experiment management and model registry inside the Kubeflow Pipelines.
Check how to start using it.
See these features in action
Sign up to Neptune and install client library
pip install neptuneTrack experiments
import neptune
run = neptune.init_run()
run["params"] = {
"lr": 0.1, "dropout": 0.4
}
run["test_accuracy"] = 0.84Register 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
I guess the main reason I prefer neptune is the interface, it is the cleanest and most intuitive in my opinion, the table in the center view just makes a great deal of sense. I like that it’s possible to set up and save the different view configurations as well. Also, the comparison is not as clunky as for instance with wandb. Another plus is the integration with ignite, as that’s what I’m using as the high-level framework for model training.
It only takes 5 minutes to integrate Neptune with your code
Don’t overthink it