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Neptune vs DVC



Standalone component. ML metadata store that focuses on experiment tracking and model registry
Standalone component
Managed cloud service
Open-source and managed cloud service
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
DVC studio requires DVC initalized github/gitlab/bitbucket repo created and access to the internet if using managed hosting. Contact them here for more information for infrastructure requirements for on-prem deployment
Minimal. Just a few lines of code needed for traking. Read more
Minimal. Just a few lines of code needed for tracking. Read more
Yes, through the neptune-client library
Yes, via .dvc and .json files
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This page was updated on 18 August 2021. Some information may be outdated.
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What are the key advantages of Neptune, then?
- Stable and scalable API
- Hardware consumption monitoring
- More connection modes available (offline and synchronus, but also asynchronous, read-only, and debug)

See these features in action
1. Create a free account
Sign up2. Install Neptune client library
pip install neptune-client
3. Add logging to your script
import neptune.new as neptune
run = neptune.init('Me/MyProject')
run['params'] = {'lr':0.1, 'dropout':0.4}
run['test_accuracy'] = 0.84
4. Or see how it works in a notebook (no registration)
Try live notebookAlready using DVC?
You can display DVC files in the Neptune UI and have all metadata in one place.
You just need to specify which DVC files you would like to log:
# Snapshot all .dvc files from any directory
run = neptune.init(...,
source_files=["**/*.dvc"])

Thousands of ML people already chose their tool

“(…) 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.”
“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.”
“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.”