If needed, you can pass them as arguments when initializing Neptune:
Sets a custom run name. To display it in the app, add
sys/nameas a column.
You can also edit and view the name in the Run information view.
run["your/structure"] = some_value run["data/sample"].upload("figure/object/or/path/to/file") run["data/train/samples/images"].upload_files("path/to/directory") run["data/train/version"].track_files("path/to/artifacts") for epoch in range(100): # your training loop loss = ... run["train/loss"].append(loss)
ID, name, and description
Source code and environment
Learn how to enable or disable logging of:
Instead of uploading file contents in full, you can track metadata of files or directories.
Charts, plots, and graphs
You can store metadata of models and manage their lifecycle separately from runs.
For detailed walkthroughs and use cases, check the Tutorials section.
Each tutorial comes with Jupyter notebooks or scripts (usually both) as well as examples in the Neptune web app.
Examples on GitHub#
In the neptune-ai/examples repo on GitHub, you can browse our full library of example scripts, notebooks, use cases, and Neptune projects.
Neptune demo project#
To get an idea of what a Neptune project can be used for, you can browse the
showcase/onboarding-project sample project, which is set up for image segmentation.
Tip: Take a look at the
train.py script in the Source code view to see how the metadata was logged.