Roadmap

Where do we see VisPy going in the future? What is development focused on? What can users look forward to in upcoming VisPy major releases? We try to answer these types of “big picture” questions below. For shorter term issues and plans see the GitHub issue tracker.

Road to Version 1.0

At the time of writing VisPy still doesn’t have a 1.x version number despite being used by hundreds of users from graduate students to industry. We believe a 1.0 version should represent stability and feeling of completeness when it comes to interfaces, performance, and functionality. We don’t think we’re there yet, but we have ideas for what a 1.0 release might look like and we’ve described it below.

Performance and Collections

One of the main reasons users come to VisPy (or so we’ve been told) is the performance of visualizing a lot of data or visualizing data that updates quickly. VisPy does a pretty good job covering users for these use cases, but it can do better. The main areas that need improvement are:

  1. SceneCanvas (#1991): In other visualization spaces, a scene graph performs optimizations for when and how components are drawn. If multiple pieces of a visualization have shared logic, let’s not recompile or re-communicate things to the GPU. Let’s be smart.

  2. Collections of Visuals: Along with the SceneCanvas, VisPy needs more Visuals that support defining multiple instances of the same object. VisPy has a very low-level “collections” set of objects, but these aren’t accessible or usable to the higher-level Visuals/SceneCanvas interfaces. A solution to this may be a rewrite to some Visuals to allow for multiple instances or for a complete rewrite of parts of VisPy to detect when multiple of the same object are being created and combine them together for optimized drawing.

  3. Alternative Data Containers: See ‘Dask and CuPy Integration’ below.

  4. Jupyter Widgets (#134, #1989): VisPy’s Jupyter widget does not perform well. It can draw most things nowadays, but eventually lags behind any updates whether they be from timers or user input.

Plotting API

The Plotting API in VisPy has been a dream in the back of the VisPy developers’ minds since the beginning of development. While the plotting interfaces exist, they are not very flexible and at times don’t perform very well. It can be difficult with the plotting APIs to customize all the pieces that you want to or update data after the initial creation. This sometimes requires accessing hidden (_ prefix) attributes and going multiple levels deep into complex (compound) visuals just to change the size or style of something. This deserves a real specification and plan for how users can get the most out of these interfaces so that they stay simple but useful.

Dask and CuPy Integration

Dask and CuPy arrays present a very interesting opportunity for VisPy to display larger data and at faster speeds than previously possible. However, VisPy doesn’t currently make this easier or even possible in some cases. With Dask, VisPy should be able to re-compute or re-load data that the user provides and throw it to the GPU when it is ready. With CuPy users should be able to do all their computations on the GPU and then let VisPy visualize that data without ever needing to copy it back to the CPU.

See #1985 and #1986 for more information and discussion on these topics.

Low-level leakage

VisPy depends heavily on OpenGL for all of its drawing functionality. While this performs well, we’ve had to bring some of the low-level logic of OpenGL into higher levels of VisPy to make things work. As VisPy continues to grow we’d like to make sure that any pieces specific to OpenGL stay in the low level parts of VisPy and are accessed through defined interfaces.

See the “OpenGL, Vulkan, and WebGPU” section below for why this is important.

Primitive Visuals

Along the same lines of preventing low-level APIs leaking into higher levels, we’d like to define a set of “primitive” Visual objects. These primitives would define a set of basic functionality and that interact the closest with the low-level OpenGL layers of VisPy. Using these primitives, users should be able to easily create their own, more complex, visualizations without ever needing to know the complexities of the underlying layers.

See the VisPy Wiki for our attempt at defining these types of primitives.

See the “OpenGL, Vulkan, and WebGPU” section below for why this is important.

Road to Version 2.0

We’ll cross this bridge when we come to it, but maybe we can start planning sooner rather than later.

OpenGL, Vulkan, and WebGPU

VisPy currently strives for compliance with OpenGL 2 and OpenGL ES. This was a goal of early VisPy in order to have compatibility with mobile platforms including web browsers. Over the years this has become a major burden for VisPy. We’ve been able to add functionality for things like Geometry shaders and allow for newer GLSL shaders, but the majority of VisPy is still limited to the features of old OpenGL.

New graphics APIs like Vulkan and WebGPU are meant to provide users with more control, flexibility, and reliability. They are also more supported by industry (ex. gaming). If VisPy wants to keep up with modern technology and still provide its high level interfaces, it needs to be able to adopt new graphics APIs like these. The “low-level leakage” and “primitive visuals” described above are the first steps towards getting VisPy’s source code ready for this type of flexibility.

One library that VisPy is looking to as a future “graphics backend” is Datoviz (https://datoviz.org/) which depends on Vulkan. By implementing a set of primitive visuals, we hope that VisPy can provide the same visualizations but with a completely different graphics technology doing the drawing.

See #1988 to track any discussion and related issues.

Deprecation of “gloo” and GLIR

In the beginning of VisPy development, the GLIR (OpenGL Intermediate Representation) was created to allow for the possibility of “remote” rendering. This would allow users to define what they wanted, but have a remote system do all the actual GPU number crunching. This is great in theory, but in practice becomes extremely difficult to maintain and preserve performance. This was extremely obvious when working with the Jupyter widget. GLIR is also very OpenGL specific. As discussed above, this can only work for so long.

The one major benefit of GLIR is the ability to save the commands for a visualization and then “replay” them. This is really cool, but is almost never used as it is only possible in the javascript vispy.js library (or at least used to be). Put bluntly, VisPy isn’t getting any benefit from GLIR and it will therefore likely be deprecated in coming VisPy versions.

Along with GLIR, the “gloo” interface will also need to be deprecated. VisPy development will focus on higher level functionality and let other libraries like Datoviz focus on the low level. The “gloo” interfaces are extremely useful for having full control over an OpenGL 2/ES visualization, but as described above OpenGL 2 is old. Updating “gloo”, or an interface like it, to work with newer versions of OpenGL would be too much work at this point. What we’re really looking for is the type of control newer graphics libraries like Vulkan can provide.