What will you learn?
How you can log and inspect all the metadata generated during various model training sessions in Neptune.
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How to use Neptune integrations and what do you get by default?
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Analyzing the logged metrics
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What else can I log to Neptune using the Python client?
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You can log the metadata in a hierarchical structure using the namespaces
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You can also log the images and files to Neptune
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Exploring all the logged metadata in the Neptune UI
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Visualizing the model parameters, summary and checkpoints in the Neptune UI
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You can optionally associate the source code with the Neptune run
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Neptune automatically tracks your Git info
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How can I share a particular view inside my run with a team member?
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Can I log the tabular data (i.e. pandas dataframes)?
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You can associate some additional information to your images (i.e. class probability)
Important: This video was created in February 2022. For the most up-to-date code examples, please refer to the Neptune docs.
Other useful resources
Check the docs on what you can log and display
More about How to Log and Analyze Model Training Metadata
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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