The task visualizer takes as input JSON files that describe work as a series of phases and subphases that contain tasks for each rank, communications, and other user-defined fields (such as memory usage). Documentation on the JSON format can be found in vt's documentation and the JSON schema validator is located in the repository.
The task visualizer, using this input, produces Exodus meshes to describe the ranks and objects over time, which can be visualized using Paraview. Additionally, the task visualizer can produce PNGs directly using a VTK workflow to render a visualization of ranks and tasks over phases.
In order to build the python bindings, make sure you have a Python 3.8 or 3.9 environment, with the nanobind package installed. You can install nanobind with pip:
pip install nanobindYou must have a C++ compiler that supports C++17, and cmake >= 3.17.
Finally, you must have a (C++) VTK build available on your system. We recommend building from source, and the currently tested version is 9.3.0. You can find instructions for building VTK here.
To build the python bindings, you must specify in the VTTV_VTK_DIR environment variable the path to the VTK build directory:
export VTTV_VTK_DIR=/path/to/vtk/buildThen, to install python-environment-wide the binded vt-tv python module, run:
pip install <path/to/vt-tv/source>Optional
To specify the number of parallel jobs to use during the build, you can set the VTTV_J environment variable:
export VTTV_J=8Note
Behind the scenes, the usual cmake and make commands are run. Depending on your system, this can cause the install process to be lengthy as it will be compiling the entire vt-tv library.
