We Raised $8M Series A to Continue Building Experiment Tracking and Model Registry That “Just Works”
Neptune vs DVC
vs
Commercial Requirements
Standalone component or a part of a broader ML platform?
Standalone component. ML metadata store that focuses on experiment tracking and model registry
Standalone component
Is the product available on-premises and / or in your private/public cloud?
Is the product delivered as commercial software, open-source software, or a managed cloud service?
Managed cloud service
Open-source and managed cloud service
What is the pricing model?
SLOs / SLAs: Does the vendor provide guarantees around service levels?
Support: Does the vendor provide 24×7 support?
SSO, ACL: does the vendor provide user access management?
General Capabilities
What are the infrastructure requirements?
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
How much do you have to change in your training process?
Minimal. Just a few lines of code needed for traking. Read more
Minimal. Just a few lines of code needed for tracking. Read more
Does it integrate with the training process via CLI/YAML/Client library?
Yes, through the neptune-client library
Yes, via .dvc and .json files
Serverless UI
No
No
How can you access model metadata?
– gRPC API
No
No
– REST API
No
No
– Java SDK
No
No
– Julia SDK
No
No
Supported operations
Logging modes
Mobile support
No
No
Experiment Tracking
Dataset
– Dataset slicing support
No
No
Code versions
Metrics and losses
Rich format
– Neural Network Histograms
No
No
– Prediction visualization (tabular)
No
No
– Prediction visualization (image)
No
No
Hardware consumption
– TPU
No
No
System information
– Execution command
No
No
Environment config
Files
– Any file
Parameters and metrics
– Parameter Importance plot
No
No
– Slice plot
No
No
– EDF plot
No
No
Rich format (side by side)
– Video
No
No
– Audio
No
No
– Plots
No
No
– Interactive visualization (HTML)
No
No
– Neural Network Histograms
No
No
– Prediction visualization (image, video, audio)
No
No
Code
Environment
Hardware
System information
– Execution command
No
No
Data versions
Files
– Models
No
No
– CSV
No
No
Custom compare dashboards
Experiment table customization
– Renaming columns in the UI
Yes
No
– Adding colors to columns
Yes
No
Experiment filtering and searching
– Query language vs fixed selectors
Query language
Fixed selectors
Custom dashboards for a single experiment
– Custom widgets and plugins
No
No
Experiment lineage
– List of datasets used downstream
No
No
– List of other artifacts (models) used downstream
No
No
– Downstream artifact dependency graph
No
No
Interactive project-level reports
No
No
Model Registry
Environment versions
No
No
Custom stage tags
No
No
Locking model version and downstream runs, experiments, and artifacts
No
No
Adding annotations/comments and approvals from the UI
No
No
Compatibility audit (input/output schema)
No
No
Compliance audit (datasets used, creation process approvals, results/explanations approvals)
No
No
Registered models
No
No
Active models
No
No
By metadata/artifacts used to create it
No
No
By date
No
No
By user/owner
No
No
By production stage
No
No
Search query language
No
No
Native packaging system
No
No
One model one file or flexible structure
No
No
Integrations with packaging frameworks
No
No
Integrations and Support
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.”
Data Science and AI student, Kranenburg, Germany
“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.”
AI Engineer and Data Scientist
“This thing is so much better than Tensorboard, love you guys for creating it!”
Junior Researcher at Rényi Alfréd Institute of Mathematics in Budapest, Hungary
“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.”
Computer Science Undergraduate at Saint Louis University
“Such a fast setup! Love it:)”
PhD student in Music Information Processing at Télécom Paris
“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.”
Senior NLP/ML Engineer