2022-07-29 –, Green
The ODE solver spit out dt<dtmin, what do you do? MethodError Dual{...}, what does it mean? Plenty of people ask these questions every day. In this talk I'll walk through the steps of debugging Julia simulation codes and help you get something working!
Debugging simulation codes can be very different from "standard" or "simple" codes. There's many details that can show up that the user needs to be aware of. Thus while there have been many beginner tutorials in using Julia, and many tutorials on how to using SciML ecosystem tools like DifferentialEquations.jl, there has never been a tutorial that says "okay, I got this error when using Optim.jl, what do I do now?". Some major pieces have been written which condense such information, such as the DifferentialEquations.jl PSA on Discourse (https://discourse.julialang.org/t/psa-how-to-help-yourself-debug-differential-equation-solving-issues/62489), but we believe there remains many things to say.
And also, a video walkthrough is simply the best way to "show some how I do it" so to speak.
So let's do it! But what would this entail? There are many topics to cover, including:
- How to read the gigantic stack traces that arise from dual number issues. Why does f(du,u::Array{Float64},p,t) fail with this error? Why can dual numbers cause issues in some mutating code? How do you use https://github.com/SciML/PreallocationTools.jl to solve these Dual number issues?
- When trying to debug code deep within some package context, how do you do it in a "nice" way (i.e. without the slow interpreted mode of Debugger.jl)? The answer is using Revise with tools like
@showandx=Ref{Any}()and then in the package you can doMain.x[] = .... Never seen this trick before? Well then you'll be interested in this talk. We'll showcase how to use these tricks in real-world contexts where such debugging arises. - When you get dt<dtmin or other ODE solver exit warnings? What are you supposed to do? u'=u^2-u with u(0)=2, oh wait analytically that should error? How do I find out if my model is written incorrectly (it is) and how do I figure out what I should be changing?
- When doing optimization, say using GalacticOptim.jl or Flux.jl, what are these "Zygote does not support mutation" errors? Why do they exist and how do I work around them?
- Everyone is asking for an MWE. How do I make a good MWE? How do I figure out what in the package that is likely causing the issue, and use this to help developers help me?
I will continue to grow the list by keeping tabs on what comes up the most often in Github issues and Discourse posts. At the end of the day, I hope this can be a video that is pasted onto thousands of Discourse questions to give people a much more in-depth view of how to fix issues, and potentially train the next generation of "Discourse answerers".
Research Affiliate and Co-PI of the Julia Lab at the Massachusetts Institute of Technology
Director of Modeling and Simulation at Julia Computing and Creator / Lead Developer of JuliaSim
Director of Scientific Research at Pumas-AI and Creator / Lead Developer of Pumas
Lead Developer of the SciML Open Source Software Organization
Chris Rackauckas
Chris' research and software is focused on Scientific Machine Learning (SciML): the integration of domain models with artificial intelligence techniques like machine learning. By utilizing the structured scientific (differential equation) models together with the unstructured data-driven models of machine learning, our simulators can be accelerated, our science can better approximate the true systems, all while enjoying the robustness and explainability of mechanistic dynamical models.
Chris's recent work is focused on bringing personalized medicine to standard medical practice through the proliferation of software for scientific AI. Chris is at the cutting edge of mathematical methods for scientific simulation. He is the lead developer of the DifferentialEquations.jl solver suite along with over a hundred other Julia packages, earning him the inaugural Julia Community Prize, an outstanding paper award at the IEEE-HPEC conference on computational derivation for the efficient stochastic differential equation solvers, and front page features on many tech community sites. Chris' work on high performance differential equation solving is the engine accelerating many applications from the MIT-CalTech CLiMA climate modeling initiative to the SIAM Dynamical Systems award winning DynamicalSystems.jl toolbox (of which DifferentialEquations.jl was the runner-up). His work is credited for the 15,000x acceleration of NASA Launch Services simulations and recently demonstrated a 60x-570x acceleration over Modelica tools. For these achievements Chris received the United States Department of the Air Force Artificial Intelligence Accelerator Scientific Excellence Award.
Chris brought these enhanced numerical approaches to the domain of pharmaceutical modeling and simulation as the creator and lead developer of Pumas. Pumas is scientific AI in clinical practice. Pumas makes it possible to predict the optimal medication dosage for individuals, reducing the costs and potential complications associated with treatments. Pumas is being used by many major pharmasceuticals to predict personalized safe dosage regimens by incorporating realistic biological models (quantitative systems pharmacology) and deep learning into the traditional nonlinear mixed effects (NLME) modeling framework. These efforts on Pumas led to the International Society of Pharmacology's (ISoP) Mathematical and Computational Special Interest Group Award at the American Conference of Pharmacology (ACoP) 2019 for his work on improved clinical dosing via Koopman Expectations, along with the ACoP 2020 Quality Award for his work with Pfizer on GPU-accelerated quantitative systems pharmacology to accelerate preclinical analysis by 175x. Notably, Moderna adopted Pumas in 2020 to accelerate crucial clinical trials, noting "Pumas has emerged as our 'go-to' tool for most of our analyses in recent months". For these achievements, Chris received the Emerging Scientist award from ISoP, the highest early career award in pharmacometrics.
Chris started this work while completing his Masters and Ph.D. at the University of California, Irvine where he was awarded the Mathematical and Computational Biology institutional fellowship, the Graduate Dean's Fellowship, the National Science Foundation's Graduate Research Fellowship, the Ford Predoctural Fellowship, the NIH T32 Predoctural Training Grant, the Center for Complex Biological Systesms Opportunity Award, and the Data Science Initiative Summer Fellowship. His research with his advisor, Dr. Qing Nie, focused on the methods for simulating stochastic biological models and detailing how the randomness inherent in biological organisms can be controlled using stochastic analysis. Chris bridged the gap between theory and practice by having a "wet lab bench" in Dr. Thomas Schilling's lab, where these methodologies were tested on zebrafish. Fluorescence Light Microscopy (FLIM) measurements of retinoic acid in the zebrafish hindbrain showed that the predicted control proteins could attenuate inherent biological randomness. The result was a verified mathematical theory for controlling the randomness in biological signaling. Chris received the Kovalevsky Outstanding Ph.D. Thesis Award from the Department of Mathematics upon graduation and was showcased in an interview "Interdisciplinary Case Study: How Mathematicians and Biologists Found Order in Cellular Noise" in Cell Press's iScience.
As an undergraduate at Oberlin College, Chris was awarded the NSF S-STEM scholarship and the Margaret C. Etter Student Lecturer Award by the American Crystallographic Association, an award usually given for PhD dissertations, for his work on 3+1 dimensional incommensurate crystal structure identification of H-acid. This award was given for Service Crystallography for its potential impact on industrial dye manufacturing.