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Standalone component. ML metadata store that focuses on experiment tracking and model registry
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
Email and chat support for individuals and teams. Well defined support SLAs for enterprise customers
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
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
Minimal. Just a few lines of code needed for tracking. Read more
Extensive code and infrastructure changes are required. Check out a few examples here
Yes, through the neptune-client library
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This page was updated on 24 October 2021. Some information may be outdated.
Report outdated information here.
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.
pip install neptune-client
import neptune.new as neptune
run = neptune.init('Me/MyProject')
run['params'] = {'lr':0.1, 'dropout':0.4}
run['test_accuracy'] = 0.84
“Previously used tensorboard and azureml but Neptune is hugely better. In particular, getting started is really easy; documentation is excellent, and the layout of charts and parameters is much clearer.”
“(…) thanks for the great tool, has been really useful for keeping track of the experiments for my Master’s thesis. Way better than the other tools I’ve tried (comet / wandb).
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.”
“While logging experiments is great, what sets Neptune apart for us at the lab is the ease of sharing those logs. The ability to just send a Neptune link in slack and letting my coworkers see the results for themselves is awesome. Previously, we used Tensorboard + locally saved CSVs and would have to send screenshots and CSV files back and forth which would easily get lost. So I’d say Neptune’s ability to facilitate collaboration is the biggest plus.”
“For me the most important thing about Neptune is its flexibility. Even if I’m training with Keras or Tensorflow on my local laptop, and my colleagues are using fast.ai on a virtual machine, we can share our results in a common environment.”
