Monitor model training
Time is your most important asset. Maximize it with real-time monitoring.
Stop waiting hours for training to end only to realize your model diverged quickly — and you could have stopped it sooner. Save time and resources with instant feedback on your experiments.
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Model performance monitoring
Better models start with better visibility
Get constant insight into the state of your training from a live feed of your models’ performance.
- Save resources by stopping training early when models start to diverge
- Get better insight into model behavior by watching metrics as they evolve
- Make training more responsive by tweaking hyperparameters or training strategies on the fly if something looks off
Hardware consumption monitoring
Get the most out of your machines
Eliminate bottlenecks in your training by monitoring hardware consumption throughout your experiments.
- Ensure your resources run with maximum efficiency by monitoring usage in real-time
- Prevent crashes by adjusting usage when memory, GPU, or other resources get close to their limits
- Scale your resources smarter by seeing the effects of changing your model or data on your consumption
Get unprecedented visibility into your experiments
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Hubert Brylkowski
Senior Machine Learning Engineer @Brainly
Neptune gives us excellent insight on simple data processing jobs — not just training. Because we can monitor the usage of resources — even when we use all cores of the machines. In a few lines of code, we have much better visibility.
Michał Kardas
Machine Learning Researcher @TensorCell
Without the information in Neptune’s monitoring dashboard, I wouldn’t know my experiments run 10 times slower than they should. All of my experiments are being trained on separate machines which I can access only via ssh. If I needed to download and check all of this separately, I would be rather discouraged.
