Organize your ML experimentation in a single place:
You can actually log any object to Neptune and most of them will be displayed nicely in the UI.
Neptune supports:
You connect Neptune to your experiment inside your code via neptune-client library.
import neptune
neptune.init() # credentials go here
neptune.create_experiment('my-experiment')
# training and evaluation logic here
neptune.log_metric('accuracy', 0.79)
neptune.log_image('diagnostics', roc_curve_fig)
neptune.log_artifact('model_weights.h5')
# there are many other objects you can log
Neptune works with any ML framework in Python and R via the official client libraries (you can also log experiments from other languages).
To make things easier, Neptune has integrations with over 25 major ML frameworks and libraries, including:
#Tensorflow/Keras
model.fit(..., callbacks=[NeptuneMonitor()])
# PyTorch lightning
trainer = pl.Trainer(..., logger=NeptuneLogger())
# lightGBM
gbm = lgb.train(... callbacks = [neptune_monitor()])
# XGBoost
xgb.train(..., callbacks=[neptune_callback()])
# Optuna
study.optimize(…, callbacks=[NeptuneCallback()])
Of course.
You can fetch an existing experiment from Neptune and log new metadata or update logs.
import neptune
project = neptune.init('Me/MyProject') # Get Project
exp = project.get_experiments(id='exp-123')[0] # Get Experiment
exp.log_metric('f1_score', 0.92) # Log things
You can execute experiment code anywhere, and everything will be logged to the central metadata store:
Find the experiments you care about quickly:
Neptune experiment dashboard is fully customizable.
Go from high-level analysis to details in one click.
All the experiment information like code, parameters, evaluation metrics, or model files can be logged and displayed in Neptune.
You can download everything you logged to Neptune either in the UI or programmatically.
You can query:
project = neptune.init('Project')
project.get_leaderboard()
exp = project.get_experiments(id='Proj-123')[0]
exp.get_parameters()
exp.download_artifact('model.pkl')
With Neptune, you can automatically record everything you need to reproduce an experiment:
Find and access information from your past experiments whenever you need them.
neptune.create_experiment(
params={'lr': 0.21,
'batch_size': 128},
upload_source_files=['**.*.py', # scripts
'env.yaml']) # environment
log_data_version('data/train.csv') # data versions
neptune.log_metric('acc', 0.92) # metrics
neptune.log_artifact('model.pkl') # model binaries
Find and access information from your past experiments whenever you need them.
No worries about losing your work or not knowing which model is running in production.
Everything is stored, backed up, and ready for you.
You need to re-run a past experiment for work or research?
Query all the pieces you need from Neptune.
Your code, environment files, data versions, and parameters can be attached to every experiment you run. You can access it programmatically or find it in the UI.
You can execute experiment code on your laptop, cloud environment, or a cluster and every information will be logged to the central storage hosted by us or deployed on-premises.
Works out-of-the-box with Python, R, Jupyter notebooks, and other languages and environments.
You don’t have to log into your workstation to see what is going on.
See all your team’s activity with experiments, metrics, notebooks, and any other information on your desktop or mobile device.
Search, filter, group, and compare experiments in the dashboard.
Find out which ideas people are working on and what is bringing results.
Create and save dashboard views based on tasks, people, or results and link to them from other tools that you are using (looking at you Jira).
Want to discuss what you are seeing right now in the application?
Just share a link, it’s that simple. Experiment details, comparisons, dashboard views, or anything else!
Everything that your team logs to Neptune is automatically accessible to every team member.
You can access experiments or notebooks including the code, parameters, model binary, or other objects via an API.
import neptune
project = neptune.init('MyProject')
experiment = project.get_experiments(id='PRO-332')[0]
experiment.get_parameters()
experiment.download_artifact('model.pkl')
experiment.download_source()
pip install neptune-client
import neptune
neptune.init('Me/MyProject')
neptune.create_experiment(params={'lr':0.1, 'dropout':0.4})
# training and evaluation logic
neptune.log_metric('test_accuracy', 0.84)
Try live notebook
