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Neptune vs TensorBoard



Standalone component. ML metadata store that focuses on experiment tracking and model registry
Open source tool which is a part of the TensorFlow ecosystem
Managed cloud service
TensorBoard is open-source, while TensorBoard.dev is available as a free managed cloud service
Email and chat support for individuals and teams. Well defined support SLAs for enterprise customers
Community support only
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
Basic logging can be done by having just TensorBoard installed. However, most advanced logging also requires TensorFlow to be installed
Minimal. Just a few lines of code needed for traking. Read more
Minimal if already using the TensorFlow framework, else significant
Yes, through the neptune-client library
TensorBoard is available both as a client library and CLI. TensorBoard .dev is available only as a CLI
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Query language
Regex with limited query language on the TensorBoard.dev experiments homepage
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This page was updated on 8 August 2021. Some information may be outdated.
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What are the key advantages of Neptune, then?
- Easy, one-time setup to have all metadata in one place
- Intuitive, out-of-the box runs comparison
- Hosted and sharable environment
- User management and team collaboration features
- Scalability with thousands of runs

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 TensorBoard?
Convert your TensorBoard logs directory with Neptune:
neptune tensorboard --project USER_NAME/PROJECT_NAME
Or connect a new project in 5 minutes.
You can use a cloud-hosted service or have Neptune on your own servers.

Want to know why others switched to Neptune?
InstaDeep, an EMEA leader in delivering decision-making AI products, switched from TensorBoard to Neptune.
Read about the biggest challenges they faced with TensorBoard, and why Neptune helped them manage the experimentation process better.

Thousands of ML people already chose their tool

“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.”
“I’d say the advantage (of TensorBoard) is that it’s free and it works pretty well but anytime an engineer wanted to show the team some training curve, they’d need to start the VM (Virtual Machine) containing the logs, or make their localhost port available, expose it to the internet, it was not very secure… When you end up having to start a VM just to visualize some logs, you realize there should be a better tool.”
“This thing is so much better than Tensorboard, love you guys for creating it!”
“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.”
“(…) 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.”
“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.”