What will you learn?
How you can log and explore time-series forecasting runs/experiments in Neptune.
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How to add Neptune to your project?
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How to connect to Neptune and create a run?
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Logging metadata in a hierarchical structure
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Uploading the matplotlib figures as images
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Logging different scores
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Uploading the model checkpoints
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How can I visualize all the metadata logged?
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How does Neptune collect the Git information?
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Can I stop Neptune from logging my Git info?
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How to visualize and inspect the training metrics?
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How to quickly make sense of the most relevant run’s metadata?
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Exploring the run-level custom dashboards
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Are the custom dashboards available to all runs in a project?
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 also the docs on what you can log and display in Neptune.
More about How to Explore a Single Run or Experiment
See also
Train, Track, and Deploy Your Models: Neptune + Modelbit Integration
See also
.upload(“product_updates_september_2023”)
See also
What is a Project in Neptune?
See also
Model Training: Detectron2 + neptune.ai Integration [Example]
Explore more resources:
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