Important: This video was created in July 2021. For the most up-to-date code examples, please refer to the Neptune-fastai integration docs.
What will you get with this integration?
fastai is a deep learning library that provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains and provides researchers with low-level components that can be mixed and matched to build new approaches.
With Neptune + fastai integration the following metadata is logged automatically for you:
- Hyper-parameters
- Losses & metrics
- Training code (Python scripts or Jupyter notebooks) and git information
- Dataset version
- Model configuration, architecture, and weights
neptune.ai is an MLOps stack component for experiment tracking. So we’re constantly working on making it easy to integrate with other parts of the workflow.
It is already integrated with 25+ tools and libraries, and the list is growing. You can check our roadmap to see what’s currently under development.
Other useful resources
See also fastai integration guide.
More about How to Track ML Model Training: fastai + neptune.ai Integration
See also
What is a Project in Neptune?
See also
Model Training: Detectron2 + neptune.ai Integration [Example]
See also
Model Training: Prophet + neptune.ai Integration [Example]
See also
Create AzureML Pipeline – Workshop with Aurimas Griciūnas
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