What will you learn?
How you can log and explore time-series forecasting runs/experiments in Neptune.
-
How to add Neptune to your project?
-
How to connect to Neptune and create a run?
-
Logging metadata in a hierarchical structure
-
Uploading the matplotlib figures as images
-
Logging different scores
-
Uploading the model checkpoints
-
How can I visualize all the metadata logged?
-
How does Neptune collect the Git information?
-
Can I stop Neptune from logging my Git info?
-
How to visualize and inspect the training metrics?
-
How to quickly make sense of the most relevant run’s metadata?
-
Exploring the run-level custom dashboards
-
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
Train, Track, and Deploy Your Models: Neptune + Modelbit Integration
.upload(“product_updates_september_2023”)
What is a Project in Neptune?
Model Training: Detectron2 + neptune.ai Integration [Example]
Explore more resources:
Content type
Area of interest
Area of interest
Couldn’t find the use case you were looking for?
Just get in touch, and our ML team will create a custom demo for you.