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Neptune vs Comet
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
Stand-alone tool with community, self-serve and managed deployment options
Is the product available on-premises and / or in your private/public cloud?
Yes, you can deploy Comet on any cloud environment or on-premise
Is the product delivered as commercial software, open-source software, or a managed cloud service?
Managed cloud service
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?
Security policy and compliance
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
How much do you have to change in your training process?
Minimal. Just a few lines of code needed for tracking. Read more
Minimal. Few lines of code needed for tracking
Does it integrate with the training process via CLI/YAML/Client library?
Yes, through the neptune-client library
Yes, through comet_ml library
Serverless UI
No
No
How can you access model metadata?
– gRPC API
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
– Prediction visualization (image – overlayed prediction masks for image segmentation)
No
No
– Prediction visualization (image – overlayed prediction bounding boxes for object detection)
No
Interactive visualizations for bounding boxes exist
Hardware consumption
– TPU
No
No
System information
– Execution command
No
No
Environment config
Files
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
– Interactive visualization (HTML)
No
No
– Neural Network Histograms
No
No
– Prediction visualization (image, video, audio)
No
No
Code
– Git
No
No
– Source files
No
No
– Notebooks
YesDataset versions
No
Environment
– pip requirements.txt
No
No
– conda env.yml
No
No
– Docker Dockerfile
No
No
Hardware
System information
– Execution command
No
No
Data versions
– Dataset diff
No
No
– External reference version diff (s3)
No
No
Files
– Models
No
No
– CSV
No
No
Custom compare dashboards
Experiment table customization
– Renaming columns in the UI
Yes
Yes
– Adding colors to columns
Yes
Yes
Experiment filtering and searching
– Query language vs fixed selectors
Query language
Query language
Custom dashboards for a single experiment
One-command experiment re-run
No
No
Experiment lineage
– List of datasets used downstream
No
No
– List of other artifacts (models) used downstream
No
Yes, but only using a workaround
– Downstream artifact dependency graph
No
No
Reproducibility protocol
Limited
Limited to git commits
Saving/fetching/caching datasets for experiments
No
No
Interactive project-level reports
No
No
Model Registry
Models/experiments created downstream
No
No
Support for continuous testing
No
No
Users who created a model or downstream experiments
No
No
Locking model version and downstream runs, experiments, and artifacts
No
No
Adding annotations/comments and approvals from the UI
No
No
Model compare (current vs challenger etc)
No
No
Compatibility audit (input/output schema)
No
No
Compliance audit (datasets used, creation process approvals, results/explanations approvals)
No
No
Webhooks
No
No
Model accessibility
No
No
Support for continuous testing
No
No
Integrations with CI/CD tools
No
No
By date
No
Yes
By user/owner
No
Yes
By production stage
No
No
Search query language
No
No
Native packaging system
No
No
Compatibility with packaging protocols (ONNX, etc)
No
No
One model one file or flexible structure
No
No
Integrations with packaging frameworks
No
No
Integrations and Support
CatBoost
No
No
Gluon
No
No
H2O
No
No
Paddle
No
No
Spacy
No
No (only used as dependency for another integration ludwig)
Spark MLlib
No
No
Statsmodel
No
No
Hyperopt
No
No
Ray Tune
No
No
This page was updated on 13 August 2021. Some information may be outdated.
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What are the key advantages of Neptune, then?
- Responsive UI, scalable with thousands of runs
- Integrations with all the important visualization libraries that allow you to log interactive visualizations to Neptune
- Usage-based pricing that scales with model building needs, not team size
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 notebookThousands 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