Venkatesh Prasad

The Julia ecosystem has super fast and performant packages, but not always upon first use. This may be alleviated using system images and this talk presents a convenient workflow to ship them. Additionally, it discusses various challenges when doing so and shares our experience in reliably tackling them.

Julia's packages usually compose well with each other. Hence it is common to see people using different packages together. It is often a design choice to split work across different packages.  

Often, users add all the packages used in a tutorial or documentation to a local environment; end up with different versions, often leading to unintended results. Partly, a super-set Manifest.toml could solve it. However, a simple Pkg.up or similar operation could alter it. Did you say you pinned it? Instantiation and precompilation still lurk around. It is unreasonable to keep users waiting from using your highly performant, super-fast package.

With system images, one can start using packages quickly. However, you can't hand them over and expect them to work all the time. System images can sometimes fail to build; and often fail to relocate. Occasionally, they fail to start even on the same machine. We will walk through - how to interpret and fix these errors.

We need to deal with lots of releases of packages. One should ensure that the latest and greatest is delivered while maintaining consistency.

We want to talk about how to alleviate the issues in shipping with a mostly-contained and relocatable bundle that includes various components like a relocatable sysimage, additional packages for `stdlib` and a stackable depot with a pinned environment and more. These bundles should include all the artifacts necessary to run while not being bloated by them.

Distributing private registries and packages is tricky. We will discuss how to enable it without giving away the source code.

Our talk aims at decluttering the workflow of shipping packages by talking about how to set up one and the pitfalls; how to recognize and avoid them. It should give a better idea of how people can reliably build and share relocatable products to increase user adoption.

IsDef.jl: maintainable type inference

32-141

Joris Kraak

Shipping Julia Packages with System-Images

ModelingToolkitStandardLibrary is a library of components to model the world and beyond. We will demonstrate how various Mechanical, Electrical, Magnetic, and Thermal components can be used as basic building blocks to simulate complex models. We will talk about design choices, vision, and future plans for the package.

Component-based acausal modeling is a system for quickly generating large-scale, efficient models by composing elements with known physics. All that one has to do is to define components that represent objects, such as transistors, air conditioning units, or pipes, and connect them to generate accurate physical models of real-world phenomena. However, these models can  easily require hundreds of components. How can one avoid the tedious task of having to understand and write the physics of so many things? 

The answer is ModelingToolkit’s system for component-based modeling. With ModelingToolkit, you can compose components and it will generate the resulting set of equations as simulatable differential equations amenable to simulation with the DifferentialEquations.jl environment.  To aid in the use of modeling standard systems, we introduce a new component to the ModelingToolkit and SciML ecosystem called the ModelingToolkitStandardLibrary.  The ModelingToolkitStandardLibrary is a standard library of pre-built components modeled with ModelingToolkit. We want this package to serve two purposes; as a starting place for anyone who wants to get started with modeling, and as a performant dependency for power users. 

MTKStdLib is structured to provide extendable basic blocks. This talk intends to walk users through the process of composing custom models using these components. We will speak about the internal structure, best practices, and known gotchas.
We will briefly talk about all the signals and shapes we support, the utility and math blocks we provide; the thermal ports, thermal circuit components like convective and thermal- resistors, and inductors, `HeatCapacitors`; the magnetic component: `FluxTubes`; the rotational library; and the electrical circuit components.
 
This talk should leave users supercharged to use MTK and the standard library to model the world and beyond.

Venkatesh Prasad

Talks:

Shipping Julia Packages with System-Images

Intro to modeling with ModelingToolkitStandardLibrary

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