Comments (4)
It might be good to use serialization methods mentioned in #16 and #64.
I'm not sure if it would be better to store structured data, or just the bare minimum in the form of arrays.
I'm thinking it may be easier for cross-language compatibility to use JSON for information and scalar parameters and hdf5 as the data format for bond arrays, cross bond dists, etc..
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I endorse the use of the serialization methods! There's a PR up now that allows for GPs to be serialized as JSON files so we could just pass the models around: #64
The hdf5 format could be useful for efficiency for large training data sets, but I think we can cross that bridge once we get there (such as if sparse GPs make huge training sets feasible to work with). JSON isn't the most efficient option, strictly, for storing big arrays, but they are implemented right now and allow for storing everything you need in one .json file (either for a collection of structures representing a trajectory, a set of environments processed from a structure, or an entire GP).
from flare.
No reason we can't have both! hdf5 will be useful for the kernel acceleration that David is looking into now.
from flare.
Sounds good! hdf5 isn't mission critical for me right now, so @dmclark17, feel free to assign yourself to #16 , or we can open a new issue for hdf5 serialization and you can format it in a way that makes the most sense with your benchmarks :)
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Related Issues (20)
- ASE_OTF missing timestep HOT 3
- The `flare` workflow in CI is throwing deprecation warnings
- Can't run the tutorial example HOT 1
- Problem running otf_train.yaml (error message sparse_gp.py) HOT 4
- raise RuntimeError("Failed to retrieve any thermo_style-output") HOT 3
- print step information twice in myotf_thermo.txt HOT 1
- No change when restarting OTF with different training parameters HOT 2
- Active learning hanging there though the cores are still taken up HOT 2
- Compiling LAMMPS with KOKKOS Library Failing with FLARE HOT 6
- MemoryError calling GaussianProcess HOT 1
- Wrong `QE` DFT calculator name in GoogleColab tutorial? HOT 2
- Building with classic intel compilers fails: null pointer
- Coogle colab tutorial is missing HOT 2
- Time of Update GP (s) increases a lot as more DFT calls HOT 1
- otf run hangs at the very first step
- Trajectory extraction HOT 7
- OTF SGP models with force_only=True, not working with OTF.from_checkpint() HOT 2
- Output file inquire HOT 3
- Is the specific coarse-grain version available? HOT 1
- seg fault when predicting local uncertainty with trained sgp calculator HOT 4
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