cmlkit 2.0.0a26

Machine learning tools for computational chemistry and condensed matter physics

cmlkit 🐫🧰

Publications: repbench: Langer, Gößmann, Rupp (2020)

Plugins: cscribe 🐫🖋️ | mortimer 🎩⏰ | skrrt 🚗💨

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cmlkit is an extensible python package providing clean and concise infrastructure to specify, tune, and evaluate machine learning models for computational chemistry and condensed matter physics. Intended as a common foundation for more specialised systems, not a monolithic user-facing tool, it wants to help you build your own tools! ✨

If you use this code in any scientific work, please mention it in the publication, cite the paper and let me know. Thanks! 🐫

What exactly is cmlkit?

💡 A tutorial introduction to cmlkit courtesy of the NOMAD Analytics Toolkit 💡

Sidenote: If you've come across this from outside the "ML for materials and chemistry" world, this will unfortunately be of limited use for you! However, if you're interested in ML infrastructure in general, please take a look at engine and tune, which are not specific to this domain and might be of interest.

Features

• Reasonably clean, composable, modern codebase with little magic ✨

Representations

cmlkit provides a unified interface for:

• Many-Body Tensor Representation by Huo, Rupp (2017) (qmmlpack and dscribe implementation)
• Smooth Overlap of Atomic Positions representaton by Bartók, Kondor, Csányi (2013) (quippy‡ and dscribe implementations)
• Symmetry Functions representation by Behler (2011) (RuNNer and dscribe implementation), with a semi-automatic parametrisation scheme taken from Gastegger et al. (2018).

‡ The quippy interface was written for an older version that didn't support python3.

Regression methods

• Kernel Ridge Regression as implemented in qmmlpack (supporting both global and local/atomic representations)

Hyper-parameter tuning

• Robust multi-core support (i.e. it can automatically kill timed out external code, even if it ignores SIGTERM)
• No mongodb required
• Extensions to the hyperopt priors (uniform log grids)
• Resumable/recoverable runs backed by a readable, atomically written history of the optimisation (backed by son)
• Search spaces can be defined entirely in text, i.e. they're easily writeable, portable and serialisable
• Possibility to implement multi-step optimisation (experimental at the moment)
• Extensible with custom loss functions or training loops

Various

• Automated loading of datasets by name
• Seamless conversion of properties into per-atom or per-system quantities. Models can do this automatically!
• Plugin system! ☢️ Isolate one-off nightmares! ☢️
• Canonical, stable hashes of models and datasets!
• Automatically train models and compute losses!

But what... is it?

At its core, cmlkit defines a unified dict-based format to specify model components, which can be straightforwardly read and written as yaml. Model components are implemented as pure-ish functions, which is conceptually satisfying and opens the door to easy pipelining and caching. Using this format, cmlkit provides interfaces to many representations and a fast kernel ridge regression implementation.

Here is an example for a SOAP+KRR model:

model:
  per: cell
  regression:
    krr:               # regression method: kernel ridge regression
      kernel:
        kernel_atomic: # soap is a local representation, so we use the appropriate kernel
          kernelf:
            gaussian:  # gaussian kernel
              ls: 80   # ... with length scale 80
      nl: 1.0e-07      # regularisation parameter
  representation:
    ds_soap:           # SOAP representation (dscribe implementation via plugin)
      cutoff: 3	
      elems: [8, 13, 31, 49]
      l_max: 8
      n_max: 2
      sigma: 0.5

Having a canonical model format allows cmlkit to provide a quite pleasant interface to hyperopt. The same mechanism also enables a simple plugin system, making cmlkit easily exensible, so you can isolate one-off task-specific code into separate projects without any problems, while making use of a solid, if opionated, foundation.

For a gentle, detailed tour please check out the tutorial.

Caveats 😬

Okay then, what are the rough parts?

• cmlkit is very inconvenient for interactive and non-automated use: Models cannot be saved and caching is not enabled yet, so all computations (representation, kernel matrices, etc.) must be re-run from scratch upon restart. This is not a problem during HP optimisation, as there the point is to try different models, but it is annoying for exploring a single model in detail. Fixing this is an active consideration, though! After all, the code is written with caching in mind.
• cmlkit is and will remain "scientific research software", i.e. it is prone to somewhat haphazard development practices and periods of hibernation. I'll do my best to avoid breaking changes and abandonement, but you know how it is!
• cmlkit is currently in an "alpha" state. While it's pretty stable and well-tested for some specific usecases (like writing a large-scale benchmarking paper), it's not tested for more everyday use. There's also some internal loose ends that need to be tied up.
• cmlkit is not particularly user friendly at the moment, and expects its users to be python developers. See below for notes on documentation! 😀

Installation and friends

cmlkit is available via pip:

pip install cmlkit

You can also clone this repository! I'd suggest having a look into the codebase in any case, as there is currently no external documentation.

If you want to do any "real" work with cmlkit, you'll need to install qmmlpack on the development branch. It's fairly straightforward!

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In order to compute representations with dscribe, you should install the cscribe plugin:

pip install cscribe

You need to also export CML_PLUGINS=cscribe.

To setup the quippy and RuNNer interface please consult the readmes in cmlkit/representation/soap and cmlkit/representation/sf.

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For details on environment variables and such things, please consult the readme in the cmlkit folder.

"Frequently" Asked Questions

Where is the documentation?

At the moment, I don't think it's feasible for me to maintain separate written docs, and I believe that purely auto-generated docs are basically a worse version of just looking at the formatted source on Github or in your text editor. So I highly encourage to take a look there!

Most submodules in cmlkit have their own README.md documenting what's going on in them, and all "outside facing" classes have extensive docstrings. I hope that's sufficient! Please feel free to file an issue if you have any questions.

I don't work in computational chemistry/condensed matter physics. Should I care?

The short answer is regrettably probably no.

However, I think the architecture of this library is quite neat, so maybe it can provide some marginally interesting reading. The tune component is very general and provides, in my opinion, a delightfully clean interface to hyperopt. The engine is also rather general and provides a nice way to serialise specific kinds of python objects to yaml.

Why should I use this?

Well, maybe if you:

• need to use any of the libraries mentioned above, especially if you want to use them in the same project with the same infrastructure,
• are tired of plain hyperopt,
• would like to be able to save your model parameters in a readable format,
• think it's neat?

My goal with this is to make it slightly easier for you to build up your own infrastructure for studying models and applications in our field! If you're just starting out, just take a look around!