Here’s a public example project to give you a taste of neptune.ai’s API and the app.
It’s a tabular data project that shows how to use Neptune to keep track of training, finetuning, monitoring, and retraining metadata of XGBoost models.
You can just open the project and play with the app, no registration is needed. And if you want to see the code behind it, go to the example’s GitHub repo.
What’s in this tabular data-based example project?
Here are a few most important things you can see in the project:
- Runs table and comparison charts – when you open the project, you will land in the Compare runs view that shows both the runs table and the comparison charts.
If you want to see only the Runs table, you can change the view by clicking the tabs on the top of the page. The Runs table view shows all the experiments (with associated metadata) that were performed when training the model.
- You can easily change the columns that are visible in the runs table or filter the runs. You can also save custom views for later. In this project, we have saved several custom views, e.g., “debugged”, “finetuning”, “training”, etc. To switch between views, open the dropdown menu above the runs table.
- In the Compare runs tab, you can switch from Charts comparison to Parallel coordinates, Images comparison, or Side-by-side comparison. If you’ve logged artifacts, you can also compare datasets between runs.
By the way, you can choose which runs you want to compare by clicking the eye icon in the runs table.
- You can also inspect a single run. Click the Runs details tab at the top of your screen, and you’ll land in the single run’s view. Just below the name of the run, you can see different dashboards.
The first one is the All metadata dashboard with everything that was logged to Neptune (organized in a folder-like structure). Then, there are Charts, Images, Monitoring, Source code, and more.
- At the end of the list, you can see some custom dashboards that combine different metadata types in one place. In this project, we created four different dashboards, one for every stage of the project. We dive deeper into each of them here.
Other useful resources
Explore other example projects, e.g. text classification example project or time series forecasting example project.
Contact us if you’d like to get a live customized demo specific to your use case.
More about Tabular Data Example Project
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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