There's a somehow similar project called Panel: http://panel.pyviz.org/
However it's limited to Bokeh widgets whereas Voila is framework and language agnostic. As I understand it Panel allows to start Bokeh servers from notebooks.
Panel can use a Bokeh server but does not require it; it is equally happy communicating over Bokeh Server's or Jupyter's communication channels. Panel doesn't currently support using ipywidgets, nor does Voila currently support Bokeh plots or widgets, but the maintainers of both Panel and Voila have recently worked out mechanisms for using Panel or Bokeh objects in ipywidgets or using ipywidgets in Panels, which should be ready soon. I'm not sure of the details of how one would use Voila with other languages, but Panel can already show anything that has an interface to Python, e.g. an R ggplot visualization.
I'd also like to know how Voilà compares to Panel.
The recent Panel announcement stated that it was widget/graphing library agnostic and mentioned plotly and Altair explicitly. I have not used it myself though.
Panel and Voila attacked the same problem (moving easily between Jupyter and standalone server contexts) from completely opposite directions. This difference has some implications on their design and function.
Voila is based on ipywidgets running in Jupyter notebooks, and to make a standalone dashboard they had to create a standalone server that can securely execute Jupyter cells and display the results without allowing arbitrary code execution. The server is thus a work in progress, while the Jupyter integration was already solid.
Panel is based on Bokeh models, and because Bokeh models already had a full-blown standalone server, the task for Panel was to make Bokeh models (a) work seamlessly in Jupyter (previously they were awkward and limited in that context), (b) support other plotting libraries (by wrapping everything as a Bokeh model), and (c) have an API that's easier to use than native Bokeh for easy prototyping and design. Solid, secure server support came for free.
Once both libraries support each others models (soon!) and get a bit more polished, then off the top of my head, the main differences will be:
- Panel can use a Jupyter notebook, but it works equally well with a plain Python file; the notebook is just a source of Python code for it. Panel can be used fully even without Jupyter installed. Voila is closely tied to the Jupyter cell-based execution model, which is good or bad depending on your point of view.
- Panel allows you to construct a "server view" of your notebook that can be completely independent of what is shown in the notebook, even though it is specified inside the notebook. I use that capability to have the same notebook go step by step analyzing a given dataset in detail, and then separately designate what should be shown in the server context, which is very handy; the boss sees one view, I work on another, and it all stays in sync. Voila works with notebook cell outputs only, and so I don't think it's possible to have fully different views of your data in the two contexts.
- Panel supports building complex GUIs, with hierarchies of nested objects that each define their own editable parameters, without having to tie any of that code to Jupyter, Bokeh, or any other GUI or plotting system. This approach is really important for building large, complex codebases (e.g. simulators or data-analysis systems) that sometimes are used in dashboards, sometimes in notebooks, sometimes in batch runs, and sometimes on e.g. large remote computing systems.
I'm sure there are lots more differences, but that's enough for now!
Last time I checked you could indeed plug different graphing libraries but Panel comes with its own widgets API which a set of wrappers around Bokeh widgets. So yes for Plotly and Altair but no ipywigets.
