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Launch VIPP

Open the installed app on macOS

The macOS package creates ~/Applications/VIPP.app. Open your user Applications folder and double-click VIPP; no terminal or separately installed Python is required. The managed CPU-only environment remains under ~/Library/vipp.

First launch may take longer while napari loads. If the package has not been installed yet, follow Install VIPP on macOS, including checksum verification and the Open Anyway steps for this unsigned, unnotarized alpha.

Use the installed shortcut on Windows

The Windows installer creates launchers for the managed installation:

  • a CPU installation provides VIPP;
  • a CUDA installation provides VIPP Automatic, VIPP CPU, and VIPP Prefer GPU.

Begin with VIPP Automatic after a CUDA installation. Startup shows the VIPP brand, real progress milestones, elapsed time, and retained diagnostics. A Prefer-GPU session requests every scientifically and operationally eligible GPU implementation, but operations outside the reviewed region still use CPU with an explanation.

Open the napari widget from a manual environment

Activate the environment used for installation and run:

napari

In napari, choose:

Plugins → VIPP Workflow (napari-vipp)

The VIPP dock widget has a workflow tab bar and three main work regions:

Region What it is for
Workflow tabs Keep several independent graphs, results, caches, histories, inspectors, paths, and Batch workspaces open. Switching tabs restores state without scientific recalculation.
Node library Search or browse operations, then add them to the graph.
Graph canvas Connect and arrange the visible analysis.
Inspector Edit the selected node, calculate manual nodes, and inspect output metadata, images, histograms, or tables.

The top toolbar opens examples and workflows, saves or exports the graph, starts batch processing, and exposes display/execution settings. Its CPU / Auto / Prefer GPU / Custom control is the authored compute request; new sessions default to Auto. After calculation, the toolbar summary and compact node badges show what actually ran. Auto uses exact compatible complete-pipeline timing history when both CPU and accelerated observations exist; without history it uses reviewed safe GPU defaults. Accelerated-only history makes the next global Auto run measure CPU once on the same execution surface. Auto never silently benchmarks multiple implementations. Use Prefer GPU when every reviewed eligible accelerator should run regardless of speed. Use a reviewed Custom choice or apply a Find fastest pipeline… proposal for per-node control and measurement.

VIPP's severity-aware message strip reports graph, workflow, and compute feedback. Napari's own bottom status bar reports viewer coordinates and layer information; the two surfaces have different owners and purposes.

Open a bundled example

Choose Gear menu → Open example…. The chooser opens a new workflow tab, groups complete graph templates by task, and configures their Image Source nodes to use matching bundled samples.

For a first visit choose:

Segmentation & Labels → Red-Channel Label Cleanup

This route does not require an external file or a layer opened through napari's sample menu.

Give the graph enough space

For a long workflow, drag the VIPP Workflow dock title bar out of napari (or double-click the title bar) and maximize the floating window. Hide the node library after adding nodes and keep the inspector visible while tuning. Use graph zoom for overview; use napari layers for full-resolution image comparison.

If VIPP is missing from the Plugins menu

Check that napari is running from the same environment into which VIPP was installed:

python -c "import sys; print(sys.executable)"
python -c "import napari_vipp; print(napari_vipp.__file__)"

If either command uses a different environment, close napari, activate the correct environment, and launch it again. See report a problem if the plugin is installed but still not discovered.

Maintainer launch scripts

Repository scripts such as scripts/launch_vipp_sample.py and scripts/launch_vipp_intensity_workflow.py --list are for development, screenshots, and repeatable review. End users should normally use Gear menu → Open example… or Open inside VIPP.