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Experiment tracking for fastai. 🧩 Log, organize, visualize and compare model metrics, hyperparameters, dataset versions, and more.

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Neptune + fastai integration

Experiment tracking for fastai-trained models.

What will you get with this integration?

  • Log, organize, visualize, and compare ML experiments in a single place
  • Monitor model training live
  • Version and query production-ready models, and associated metadata (e.g. datasets)
  • Collaborate with the team and across the organization

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

image Example dashboard with train-valid metrics and selected parameters

Resources

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 support@neptune.ai