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.
Training monitoring in real time
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
Forking of runs
Monitor months-long model training with more confidence
The ability to fork runs allows you to:
- Test multiple configs at the same time. Stop the runs that don’t improve accuracy. And continue from the most accurate last step. No more wasting millions on training experiments that won’t converge.
- Restart failed training sessions from any previous step. Your training history is inherited. And you can see your entire experiment on a single chart. No more wasting time on workarounds that give you inconsistent results.
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
(Like these companies)
Hubert Brylkowski
Senior Machine Learning Engineer at 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.
Vadim Markovtsev
Founding Engineer at poolside
Neptune is great for monitoring LLM training. I really appreciate that I’ve never seen any outage in Neptune. And since we’re training an LLM, that it’s super critical to not have any outages in our loss curve. Other than that, there are things you often take for granted in a product: reliability, flexibility, quality of support. Neptune nails those and gives us the confidence.
Esben Toke Christensen
Principal Data Scientist at Visma
We use Neptune for keeping track of all our research work and monitoring of on-going model training. Since everything is tracked in Neptune it is super easy to keep track of what we did, how we did it, and what the results were. It makes it a lot easier also direct future research directions.