Record exactly how your ML models were created
without changing your workflow

Experiment reproducibility

Track everything you need for every experiment run

Automatically record the code, environment, parameters, model binaries, and evaluation metrics every time you run an experiment. You will not forget to commit your changes because it just happens.

...
neptune.create_experiment(params={'lr':0.21, 
                                                            'data_version': sha('data/train.csv')},
                                             upload_source_files=['**.*.py',
                                                                                 'requirements.yaml')
...
neptune.log_metric('acc', 0.92)
neptune.log_artifact('model.pkl')

Go back to your experiments even months after

Find and access information from your past experiments whenever you need them. No worries about losing your work or not knowing what model is running in production. Everything is stored, backed up, and ready for you.

Re-run every past experiment

You need to re-run a past experiment for work or research? Fetch 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 fetch it programmatically or find it in the UI.

Model traceability

Connect your production models to the experiment training runs

Tag the experiment run that produces the model you put in production. Make it easy to find it later if you need to get any information about it. You can do that programmatically or in the UI.

...
exp = project.get_experiments(id='PRO-332')[0]
exp.append_tag('production-v.2.1)
...

Find out who created a model and talk to them

Do you want to know who created the model that is running in production? Neptune automatically records that info for every experiment that is run. Just find that run, send an experiment link to your colleague and talk.

Find out what data your model was trained

Your production model is behaving unexpectedly and you are wondering what it was trained on? Neptune lets you record data versions and locations for every experiment training run so that when you need it you can find it in a second. You can even record a data snapshot to understand it better.

...
from neptunecontrib.versioning.data import *

log_data_version('/path/to/data/my_data.csv')
log_image_dir_snapshots('/path/to/data/images))

Thousands of Data Scientists already have their ML experimentation in order.
When will you?

✓ Sign up for a free account
✓ Add a few lines to you code
✓ Get back to running your experiments

Start tracking for FREE