Neptune.ai fast.ai integration library
Project description
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?
- Hyper-parameters
- Losses & 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 fastai neptune-client[fastai]
# In Python:
import neptune.new as neptune
# Start a run
run = neptune.init(project="common/fastai-integration",
api_token="ANONYMOUS",
source_files=["*.py"])
# 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
Project details
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