[mmq]

Can you elaborate a bit more about your usage of GitLab CI/CD for model management/development. I am currently working on a platform [1] that tries to solve some of the issues mentioned in the article, i.e. improving data scientists' productivity and velocity, compare models, solve reproducibility issues...

[1] https://github.com/polyaxon/polyaxon

[marmaduke]

We uh treat models as code, but also have NFS shares setup for the storage and GitLab runner talking to a Slurm cluster to run the models. Results and cross validation upload to GitLab. Main thing we haven’t built out yet are performance dashboards for showing improvement across commits, but with the GitLab APIs that’s a script away (currently we do it by hand)

[sandGorgon]

Thanks for your reply.

Actually the question was more around "how do you create your models and what do you mean treating them as code", "why slurm and not something like airflow" , "what is the test/performance setup - backtesting, smoke test" etc etc

The Gitlab stuff is easier to understand.

[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.

[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 ?

[ptd]

What do you think are the best dashboard options for showing improvements?

[marmaduke]

I'd probably set up Grafana talking to Elasticsearch or PostGres, but I haven't thought too hard about it.

[marmaduke]

Polyaxon looks nice but we don’t admin the majority of the GPU resources we use (which is why being able to tell GitLab-runner to invoke Slurm is cool)

Pachyderm is another one I’ve looked at but we don’t have the sys admin bandwidth for that stuff right now.