How to Log Computer Vision Experiments Using the Pytorch Lightning Integration
What will you learn?
How you can log computer vision experiments using the Pytorch Lightning integration with Neptune.
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Introduction to the problem
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The concept of namespaces
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How to log metrics using the PTL integration?
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How to log images using the PTL integration?
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How to log misclassified examples to Neptune?
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How to log confusion metrics to Neptune?
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How to log hyperparameters to Neptune?
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Initialize a model checkpoint object in PTL
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How to integrate the Neptune logger in the PTL code?
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UI walkthrough
Important: This video was created in May 2021. For the most up-to-date code examples, please refer to the Neptune-Pytorch Lightning integration docs.
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
Read also Lightning integration guide.
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