Overview
Experiment tracking
Track, organize, and compare everything you care about in your ML experiments.
- Monitor experiments as they are running
- Keep track of metrics, parameters, diagnostic charts, and more
- Search, group, and compare experiments with no effort
- Drill down to every experiment information you need
- Share results with your team and access all experiment details programmatically
Integrations
Hugging Face
PyTorch Lightning
PyTorch
TensorFlow/Keras
Scikit-learn
XGBoost
LightGBM
Kedro
Get started
Get started in 5 minutes
1
Sign up to Neptune and install client library
pip install neptune-client2
Track experiments
import neptune.new as neptune
run = neptune.init_run()
run["params"] = {
"lr": 0.1, "dropout": 0.4
}
run["test_accuracy"] = 0.843
Register models
import neptune.new as neptune
model = neptune.init_model()
model["model"] = {
"size_limit": 50.0,
"size_units": "MB",
}
model["model/signature"].upload(
"model_signature.json")
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Resources
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Our users
Trusted by 30000+ ML practitioners and 500+ commercial and research teams
“I’ve been mostly using Neptune just looking at the UI which I have, let’s say, kind of tailored to my needs. So I added some custom columns which will enable me to easily see the interesting parameters and based on this I’m just shifting over the runs and trying to capture what exactly interests me.”
Wojciech Rosiński
CTO at ReSpo.Vision
“Gone are the days of writing stuff down on google docs and trying to remember which run was executed with which parameters and for what reasons. Having everything in Neptune allows us to focus on the results and better algorithms.“
Andreas Malekos
Chief Scientist at Continuum Industries
“Neptune is aesthetic. Therefore we could simply use the visualization it was generating in our reports.
We trained more than 120.000 models in total, for more than 7000 subproblems identified by various combinations of features. Due to Neptune, we were able to filter experiments for given subproblems and compare them to find the best one. Also, we stored a lot of metadata, visualizations of hyperparameters’ tuning, predictions, pickled models, etc. In short, we were saving everything we needed in Neptune.”
Patryk Miziuła
Senior Data Scientist at deepsense.ai
We trained more than 120.000 models in total, for more than 7000 subproblems identified by various combinations of features. Due to Neptune, we were able to filter experiments for given subproblems and compare them to find the best one. Also, we stored a lot of metadata, visualizations of hyperparameters’ tuning, predictions, pickled models, etc. In short, we were saving everything we needed in Neptune.”
“The way we work is that we do not experiment constantly. After checking out both Neptune and Weights and Biases, Neptune made sense to us due to its pay-per-use or usage-based pricing. Now when we are doing active experiments then we can scale up and when we’re busy integrating all our models for a few months that we scale down again.”
Viet Yen Nguyenh
CTO at Hypefactors