A collection of benchmarking problems and datasets for testing the performance of advanced optimization algorithms in the field of materials science and chemistry.
Home Page: https://matsci-opt-benchmarks.readthedocs.io/
License: MIT License
Phrased another way, does it change the search bounds of allowable joint size distributions? This is probably something worth double-checking.
Installing single namespace package (e.g., matsci-opt-benchmarks.particle_packing) also didn't work.
CUDA error: CUBLAS_STATUS_EXECUTION_FAILED when calling `cublasSgemmStridedBatched( handle, opa, opb, m, n, k, &alpha, a, lda, stridea, b, ldb, strideb, &beta, c, ldc, stridec, num_batches)`
File "/uufs/chpc.utah.edu/common/home/sparks-thermoelectric/matsci-opt-benchmarks/src/matsci_opt_benchmarks/crabnet_hyperparameter/core.py", line 219, in matbench_metric_calculator
cb.fit(train_df=train_df)
File "/uufs/chpc.utah.edu/common/home/sparks-thermoelectric/matsci-opt-benchmarks/src/matsci_opt_benchmarks/crabnet_hyperparameter/core.py", line 104, in evaluate
results = matbench_metric_calculator(parameters, dummy=dummy)
File "/uufs/chpc.utah.edu/common/home/sparks-thermoelectric/matsci-opt-benchmarks/scripts/crabnet_hyperparameter/crabnet_hyperparameter_submitit.py", line 147, in <module>
evaluate(parameter_batch_sets[0][0], dummy=True)
RuntimeError: CUDA error: CUBLAS_STATUS_EXECUTION_FAILED when calling `cublasSgemmStridedBatched( handle, opa, opb, m, n, k, &alpha, a, lda, stridea, b, ldb, strideb, &beta, c, ldc, stridec, num_batches)`I.e., access the predict method via an API call rather than needing to download the large pickled model or retrain it.
See also sparks-baird/self-driving-lab-demo#132. For example:
cc @kjappelbaum, similar need to that of sparks-baird/matbench-genmetrics#63
pip install crabnet in the meantime
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