[Stef Countryman -- 2019/07/15] [home] [blogs]

Something that surprises me about physicists is that many of us don't appreciate that good DevOps can massively improve our scientific output with relatively little extra work. For anything other than throwaway scripts, proper DevOps lets you:

There is one superficially plausible (though invalid, as I'll argue) reason for not consciously choosing proper DevOps paradigms for scientific computing: it takes a lot of time to learn, and most physicists--through a failure of our education system--are starting from scratch when they begin their Ph.D.s. By "scratch", I don't just mean zero DevOps experience; I mean close-to-zero programming experience. Many people I've met say that a student has too much to learn straight away, and they consequently don't have time to learn some of the common tools that are used for both the development and operations aspects of good DevOps:

Of course, these naysayers are right that you cannot learn this stuff in-and-out in a week, or a year, or even in the time it takes to do your Ph.D.; your toolchain might also not be amenable to these or other DevOps tools, or you might never end up practically needing all of them. But the thing is, you could say the exact same thing about every other type of expertise demanded by physics research: you start from zero and need to start being productive while being hopelessly ignorant of the full context. Grad students have been handling this challenge for centuries; throwing some code in with the math and physics formulae and hardware engineering skills is no-less reasonable. On the contrary, good development and operations practices actually factor out problems nicely and make it easier to learn how to code! Especially when I was first starting, I learned far more from DevOps adepts' scientific source code repostitories than I did from the scattered, mangled, "simpler" code I found elsewhere. So why do so many physicists view DevOps as some sort of capitalist-tech-decadence that nobody outside of software engineering would care about or benefit from?

Because most of them, even the ones who rely heavily on computation, don't know what they're missing.

Paul Graham has a blog post from two decades ago where he talks about the advantage you can create for yourself by using a powerful programming language (his choice was LISP, but the point is a general one about tooling). In his essay, he presents a fictional language called Blub with a mediocre set of features. If you only know Blub, you don't understand the fancy features of other languages/tools, and they sound like beautiful but useless masturbation by people who don't care about getting work done. More precisely, you don't just use Blub: you think Blub, and Blub's limitations become your own. It's Sapir-Whorf for code in the most rigorous sense: words and concepts with hyper-specific meanings cannot be used unless you understand them deeply, but if this condition is met, your understanding of the world deepens as well. If you know something better than Blub, you don't just use it's nice features: you think in terms of those features, and it makes your coding better across the board. Maybe you still use Blub 90% of the time, but you use it more adeptly, you understand its limitations, and you don't use it when you need something better. This is the same reason we learn powerful, advanced math to actually simplify our understanding of physical laws.

When applied to development practices, however, this seems like hogwash to physicists, who mostly speak Blub dialects. They might know their language of choice (frequently: MATLAB, python2.7, or C++) passably well, but they don't think of code as part of the same hollistic problem-solving ecosystem that hosts Mathematics and Physics. To many physicists, even basic DevOps tools like version control ("why can't we just email code to each other?") or cloud computing ("we should use our allocated time on the school's supercomputer!") often sound like useless tools for tool-fetishists. Of course, they are wrong: these tools exist for a reason, and while you won't use most of them for every project, being ignorant about them means you have to live with the limitations of your favorite Blub dialect. (An immediate example: I chose hand-coded HTML/CSS for this simple website, but my analysis pipeline control systems use Flask for interactivity; in both cases, the solution is efficient given the goal.) If you don't know that things can be better, you're obviously not going to stress about this, but ignorance is only bliss until you're banging your head against the wall trying to run simulations at scale using software that only runs on your laptop.

What's funny to me about all of this, though, is that physicists of all people absolutely should not be fine with this. We were drawn to physics because it's pretty. Keats was wrong in the general case about beauty and truth being equivalent (as one of my favorite math professors, the late Patrick X. Gallagher, put it: Keats got lazy with this one). However, in physics, the truth is often at least describable by something beautiful, its beauty arising from simplicity and elegance of the language, as Dirac so eloquently mused. There is a tradeoff, in the sense that you need to organize and refactor your mathematical tools to see the simplicity of the physical forms you wish to describe, but we do it anyway, precisely because it gives us both the tools to do hard problems and the intuition to reason about things simply. Physicists know that abstractions are useful, but for some reason we draw a boundary when those abstractions are outside the ken of Mathematics and Physics departments. We limit ourselves to the software version of Newton's Second Law, F=ma, cutting ourself off from any hope of solving truly hard problems. And because we willfully remain ignorant, we undermine our chances at improving.

I don't think there's an obvious solution for this cultural rot, but individual reasearchers who rely on any non-trivial amount of code to do their work can at least help themselves and those they mentor by improving DevOps practices. It requires a Bulkington-esque reilience, though, to keep your research projects afloat (please forgive my meager and quotidian shoehorning of this sublime passage into a productivity metaphor):

With all her might she crowds all sail off shore; in so doing, fights 'gainst the very winds that fain would blow her homeward; seeks all the lashed sea's landlessness again; for refuge's sake forlornly rushing into peril; her only friend her bitterest foe!

Know ye now, Bulkington? Glimpses do ye seem to see of that mortally intolerable truth; that all deep, earnest thinking is but the intrepid effort of the soul to keep the open independence of her sea; while the wildest winds of heaven and earth conspire to cast her on the treacherous, slavish shore?

But as in landlessness alone resides highest truth, shoreless, indefinite as God- so better is it to perish in that howling infinite, than be ingloriously dashed upon the lee, even if that were safety! For worm-like, then, oh! who would craven crawl to land! Terrors of the terrible! is all this agony so vain? Take heart, take heart, O Bulkington! Bear thee grimly, demigod! Up from the spray of thy ocean-perishing- straight up, leaps thy apotheosis!

This is pretty much what it feels like to do software development in science (of course, one hopes for an apotheosis free from ocean-perishing). Our general ignorance and accompanying disdain for non-mathematical technologies (and, to be frank, our often hubristic approach to other disciplines) leads us to undervalue DevOps technologies as "engineering", a slur in the mouth of a physicist. This is in spite of the fact that our code and deployment infrastructure (or lack thereof) is what does the heavy-lifting in much of our research: all arguments of practicality aside, if we don't understand the mechanism that produces our results, then we are not doing our job as physicists. Nonetheless, you won't see people get academic credit for authoring open-source scientific libraries that do the work of 10 research groups; you'll only get credit for such an effort if you can get papers out of it, and nobody will understand your work anyway, so you won't get much credit for how elegant and wonderful it is.

We're also all overworked or overambitious (same thing but self-imposed), creating further pressure to cut all corners possible. In my experience, the benefits of good DevOps mentioned above actually reduce your workload while improving your output, but this is not a well-known fact, and in any case, knowing DevOps just makes you smarter about picking corners to cut. This means even the best physicists and scientific programmers working in physics can't always do everything the best way possible (a truism for any human endeavor, but especially harsh as applied in the sciences).

Furthermore, physics academia's fundamental mode of operation runs counter to good production development practices. Academia is like a large collection of startup incubators, trying new things and failing fast, but because of our grant-based funding model and hyper-specialization, we have a very hard time transitioning from minimum-viable-product (MVP) to production-grade code when one of our genuinely good ideas acheives traction. It's a bit terrifying to realize how much mission-critical scientific code is MVP abandonware written a decade ago by a 22-year old who learned python for the first time a year prior to writing it (and who has forgotten the entire architecture since getting her Ph.D. and going into industry).

In short, the winds are conspiring to blow us into the rocks (in the same non-intentional emergent way that the literal winds do, of course), dashing our scientific projects to pieces; you really do need to fight against everyone else to do what's best for the entire community, keeping your project away from infrastructural collapse, and you need to do it strategically enough to get credit for it and persist in the howling infinite of academia without perishing (what happens when you don't publish, as the old adage goes). And since we're all weathering this awful storm, cutting corners and fighting for the survival of our projects, we have to deal with all of the rough edges in each other's scientific coding projects. We're avoiding the rocks while trying not to crash into each other in the gale. It's a miracle that anything gets done at all.

I've dealt with this in my own project, LLAMA, by doing my best to incorporate good practices starting from the most general/fundamental and only learning/using new tools as needed. Of course, I look at other projects both inside and outside of science for inspiration, but I think hard about the simplest way to do things before I start throwing in new features (LLAMA's design requirements are already onerous enough). I can recommend this approach in good conscience to any other physicist (read: programming neophyte) because it's worked very well for me to cut corners deliberately and strategically while designing an overall strategy/architecture/feature list that allows me to stop cutting specific corners when the time comes.

One example of this approach is the computing model of LLAMA. It would be very useful to be able to do burst-computing analyses of new astrophysical multi-messenger sources as data comes in. This requires having a supercomputer reserved for your full-time use (not feasible if your budget isn't in the tens of millions) or, alternatively, using some kind of spot-instance allocation strategy to raise a mountain of computation (and let it die seconds later once the problem is solved).

This architecture has plenty of immediately obvious real-life uses for multi-messenger astrophysics (MMA). If you can run an analysis in low-latency and detect a joint gravitational wave/EM source, for example, you can quickly follow it up with other telescopes, like we did with the first direct kilonova observation. Though the gamma-ray detection localization wasn't good enough to really aid counterpart searches for that event (GW170817), there are other types of candidates (like GW+neutrinos, my current and past project) for which this rapid localization would be a huge improvement. And if you can do really hard things like estimate the GW source parameters before merger (e.g. whether you're looking at a binary neutron star merger, which is likely to emit detectable light if it's close enough), you can try to get fast-slewing telescopes pointing on source at merger time. Not to mention that burst processing would let you do more clever things with your statistics (getting better sensitivity) in low-latency as well. So stuff that makes this easier is really exciting from an experimental physics perspective.

This is, amazingly, a completely feasible architecture for a pipeline maintained by a small team (read: me) thanks to the continuum approximation that is the commercial Cloud, with extreme low-latencies possible through serverless architectures like Amazon's AWS Lambda system. I'm transitioning my pipeline to be able to use these sorts of serverless architectures, but the main reason I can't do this trivially (e.g. my laptop fires 10,000 Lambda requests when it needs to do some real thinking and then reduces the results) is another DevOps problem: I need to use a lot of monolithic, project-wide libraries (read: minimal python installs of many gigabytes), far beyond Lambda's layer-size limitations.

My solution is to split up my own monolithic architecture so that subcomponents can run in limited environments, allowing some steps in my pipeline DAG to be implemented on Lambda; this is the direction I'm moving in (it also has certain development advantages). That said, leaving my own monolith behind requires some careful planning. For one thing, reproducibility is easier if you only need to track a single code version; it's more work to have a fully-reproducible infrastructure (something I'm working on with versioned containers, detailed file-generation metadata, and automated build/test processes). It's also sometimes necessary to hot-fix things in production: this is science, so the APIs I pull data from are not versioned and are subject to breaking changes during production with nothing more than a short email thread to announce them (I get hundreds of emails a day from both major collaborations I'm in (LIGO and IceCube), so tracking these changes is its own hell). This means being able to develop and fix things extremely quickly is paramount, which is also easier on a properly-factored monolith unless you have a proper microservices architecture and deployment system. We also get crazy demands for new features during production from other collaborators and scientists that make my software development friends gasp. And nobody understands anything I do because documentation isn't prioritized, so I have to do all this in a way that's maintainable and extensible by one person. (Seriously, I need to fight to document and properly refactor/maintain my code sometimes because people don't understand these tasks and, therefore, obviously don't want to allocate time for them.)

This is all hard fundamentally because scientific software development is, like I said, a gale of overworked developers and misaligned incentives threatening to cast us on the treacherous, slavish shore. All of our code has 1/3 the manpower it should, so we're all prioritizing ruthlessly with varying degrees of success. Some people have done really amazing, inspiring work using best-practices devops and the rest, and it's slowly getting better in many spots, but the systemics of the dev environment means that unless you're a 10x person, your scientific code is going to be pretty rough around the edges. But even at the local level, DevOps can provide massive benefits and even competitive advantage against the gale. I'm cautiously optimistic that physics academia, which is eventually smart in the way a distributed database can be eventually consistent (albeit much more slowly), will improve.

In the meantime, if you're a physicist working with code, you can start reaping these benefits immediately. There's a lot to learn because the status quo is so terrible. If you want a prioritized checklist of topics/methods useful to the scientific developer, I recommend learning things in roughly the following order:

  1. Put all your code in a git repository and commit every update or change to that repository. Never ever email code again unless it's a throwaway command or example. Use git-lfs if you need to store large files alongside code.
  2. Store your data as HDF5 or JSON. These are standard formats with excellent support in many languages and frameworks, and you will have an easy time writing code to work with these formats. Sometimes you'll need to use a weird legacy format, like FITS files for astronomical images, but in general HDF5 (for numerical data) and JSON (for heirarchical human-readable data) should be your defaults. Giant ASCII files with ad-hoc structures are a mistake that the physics community perenially reinflicts upon itself; save yourself the trouble. (Exception: there's not much data, it's being used in one place, and you *really* need it to be easily human readable.)
  3. Start using Anaconda or pip's virtual environments to install python (or some other similar development environment tracking system for your language of choice). Keep a list of all packages/libraries required by each project in the source code repository mentioned above.
  4. If at all possible, use UNIX environments; most DevOps tools rely on it, and good practices are generally easier in it (and very well-documented thanks to its popularity). This is easier than ever: you can do this on Windows with the Windows Subsystem for Linux; on MacOS, you can use the command line (since MacOS is a BSD UNIX variant). You can also run an actual Linux virtual machine (Debian and Ubuntu are great Linux distributions to start with)
  5. If you need to create a server, don't use hardware; use cloud computing providers like DigitalOcean, Amazon, Google, or Microsoft. They are way cheaper, they can scale up or down as much as you need, they are more reliable than your homemade server will ever be (I know, I know, we all want an excuse to build a PC, but stop lying to yourself about how smart it is), and they make backups and state snapshots easy. On top of all of this, you can easily use scripts to both start as many servers as you need *and* to setup servers so that they actually run your code (and guess what: you can put those scripts in git repositories, too!).
  6. Once you're somewhat comfortable with UNIX system administration, you can start using Docker to "containerize" your code. This is kind of like having a virtual server, but you can run it anywhere (any of the cloud providers above support Docker, and you can also run the images on your laptop; virtual server snapshots, in contrast, are generally only usable with the service provider you chose). It's also more lightweight because Docker images only contain minimal userspace needed for a specific application, reusing the underlying host's kernel (thanks to virtualization advances, this doesn't make it much faster than a true VM, but it does make the images smaller and make startup times imperceptible for locally-stored images; I note pedantically that at time of writing, Docker hosts on MacOS and Windows actually are just host Linux VMs for Docker containers running on your local OS environment as a daemon process, but this extra indirection has no significant performance cost).
  7. If you need to worry about complicated procedures storing large amounts of data, or you're worried about data corruption, start learning about relational databases (RDBs) like SQLite for small jobs where the database can live on your computer or the powerful server-based PostgresQL. (The more historically popular server-based MySQL is more limited and offers no advantages; unless you're writing a throwaway LAMP application, forget about it. You can also ignore closed-source databases--their features and performance are similar in the best cases, but your budget as a scientist makes licensing them utterly foolish.) Databases sound boring and scary to physicists, but they solve tons of really hard and universal problems about storing data and maintaining its consistency. If you want to run things in parallel, one of the easiest things to do is just have a central database with all your data and an arbitrary number of compute nodes that rely on the database to tell them what work to do and then store their results consistently. RDBs are overkill for most workflows, so just use regular old files until, as the old adage warns, the requirements become complex enough that your code is just a buggy reimplementation of an RDB with fewer features; at that point, you can refactor the storage solution to use an RDB. You can probably ignore NoSQL solutions: these were designed for companies like Facebook that process data volumes many orders of magnitude beyond RDB management system (RDBMS) capabilities. Their flexible nature also makes them good tools for prototyping distributed systems, but if your PI demands that you solve a problem that requires organized consistent IO at scales beyond what RDBMSs support, it's time to leave that group running.

This is a rough sketch for how to start, but it's close to the approach I've successfully taken to solve hard technical problems. This is based on a lot of thinking and actual scientific development experience, but of ource you should take it with a grain of salt and read a bit about each of these technologies before implementing them. Some people disagree with me about some of these points, but in a scientific development context, I think these are all pretty frequently true.

Good luck staying afloat.