[marmaduke]

Ah right,

> how do you create your models and what do you mean treating them as code

we start with local Jupyter notebooks, and refactor bits of code into modules that get tested, which for our models mainly means recovering parameters from simulations, and then test them on real data, where we assess performance with LOO approximations for Bayesian models (notably PSIS) and some labeling from experts (which is not taken too seriously tbh)

> why slurm and not something like airflow

because the HPC resources we have access to are built with Slurm, which is super fast, supports DAGs of jobs, schedules our jobs reliably and quickly. I don't really want the other stuff on the Airflow feature list to be honest.

[sandGorgon]

super interesting. thanks for sharing.

>we start with local Jupyter notebooks, and refactor bits of code into modules that get tested, which for our models mainly means recovering parameters from simulations, and then test them on real data

This is the part that everyone seems reinventing. Have you looked at PyML (https://eng.uber.com/michelangelo-pyml/). What are some of your learnings around jupyter -> production code. A lot of these are around conventions - "write a function called train(), fit(), test()". Is that the basis of your pipeline as well ?

[marmaduke]

It’s not so simple for our models (hierarchical Bayesian time series models, often nonlinear, which may not be typical): we spend a lot of time digging through the data itself, forward simulations of model, and refactoring/tweaking model structure. PyML (as described in the link you provided) doesn’t appear to support the first two parts, which are prerequisites to improving the model IMO.

Usually when we are doing more of the train/fit/test cycle, there’s an argparse script to quickly try different parameter values succinctly (which is run and tracked by the above CI setup)

I wouldn’t say we’re reinventing since a better solution isn’t very clear (though PyML et al look interesting)

edit forward simulation isn't a frequent thing in posts on generic ML algorithms, so just as an example: suppose you run a model and see an oscillatory component along a temporal dimensions in your residual error, and you add a oscillatory component to your model, and rerun it but still see a residual with an oscillation. You can run a forward simulation of your model to see what frequency it's predicting and check against what's seen in the data, and fix it. This is a contrived example but when you have multiple competing priors or model components, this is an effective way to debug their behavior.

[marmaduke]

this article (https://towardsdatascience.com/uber-introduces-pyml-their-se...) does a better job motivating PyML, or maybe I'm just more awake now. In any case, I see what you mean. The GitLab CI setup we have builds Docker images out of our models, and we use branch names to target datasets, so "production" usage is "just" creating a branch, watching it run, checking results, etc.

Maybe a missing detail is that our models are run-once, once results are QA'd, they are sent to relevant practitioner, so Uber's query-per-second stuff is irrelevant for us (for now), which I can see simplifies the deployment question enormously.

[dsumenkovic]

Hello, Community Advocate from GitLab here. I was reading through your comments and it's great to hear how you use GitLab for your setup. Thanks for sharing your story with the community and we'd love to hear more from you on how GitLab helps you.

[marmaduke]

> we'd love to hear more from you on how GitLab helps you.

do you have specific questions?

[dsumenkovic]

We wanted to hear what features you like using the most and how do those features help you with setting up your project. However, you wrote https://news.ycombinator.com/item?id=18384804 which answers a lot of the questions. Thanks!

[sandGorgon]

Interesting. In that case, why do you even use Docker ? Does it simplify distribution of models easier ?

Would love to know more about your packaging setup - the branch name to divide datasets is a nice trick (I'll use it as well).

How does your CI know where to find models ? Im betting you are using some kind of convention here - one model per py file...so package each py file in a docker container.

If it is possible, would love to see the skeleton structure of one of your pre-packaged files.

Tldr - it seems you invented something like pyml as well. Are the deployment scripts+model skeletons open source ?

[marmaduke]

Our GitLab instance has a lot of projects and it’s been helpful for the users to have a set of template projects each with their own Docker image. Some of those images are many gigabytes in size, tricky env vars etc. Docker “democratized” CI for most of our scientific personnel who aren’t devs, since they can hit the Fork button and have a working CI config to base their project on.

In the ML projects, it serves mainly to package dependencies, and to ensure some basic security constraints: raw datasets are accessible read only, ensuring that if we suspect some issue with cached results (cause our inner orchestrator is Make..) we can nuke all the results and start over from scratch, sure the raw data is intact.

The models and arguments are in the CI config. No magic there, but since it’s all in the repo I’m ok with it.

This whole setup was put together for an upcoming clinical trial as steps toward ISO quality norms compliance, and I can’t share it now. I do intend to reproduce it in an open form alongside our existing software (GitHub.com/the-virtual-brain) when it’s ready.

In any case I appreciate your questions a lot: they drove me to think a little harder and see why stuff like Michelango and PyML is stuff that even we (academic/clinical) group should be using... if we can find the time to do it.