Neptune + fastai integration

Experiment tracking, model registry, data versioning, and live model monitoring for fastai trained models.

What will you get with this integration?

• Log, display, organize, and compare ML experiments in a single place
• Version, store, manage, and query trained models, and model building metadata
• Record and monitor model training, evaluation, or production runs live

What will be logged to Neptune?

• Hyperparameters
• Losses and metrics
• Training code (Python scripts or Jupyter notebooks) and Git information
• Dataset version
• Model configuration, architecture, and weights
• Other metadata

Example dashboard with train-valid metrics and selected parameters

Resources

• Documentation
• Code example on GitHub
• Example dashboard in the Neptune app
• Run example in Google Colab

Example

On the command line:

pip install neptune-fastai

In Python:

import neptune

# Start a run
run = neptune.init_run(
    project="common/fastai-integration",
    api_token=neptune.ANONYMOUS_API_TOKEN,
)

# Log a single training phase
learn = learner(...)
learn.fit(..., cbs = NeptuneCallback(run=run))

# Log all training phases of the learner
learn = cnn_learner(..., cbs=NeptuneCallback(run=run))
learn.fit(...)
learn.fit(...)

# Stop the run
run.stop()

Support

If you got stuck or simply want to talk to us, here are your options:

• Check our FAQ page
• You can submit bug reports, feature requests, or contributions directly to the repository.
• Chat! When in the Neptune application click on the blue message icon in the bottom-right corner and send a message. A real person will talk to you ASAP (typically very ASAP),
• You can just shoot us an email at [email protected]