Comments (3)
Hi @YukeLi-chem , this might be a result from ase parser's failure to read the lammps log file from the latest lammps version. What you can do is to revise ase's parser code. We have been using the bash commands as below:
ase_file="$(dirname $(python3 -c 'import ase; print(ase.__file__)'))/calculators/lammpsrun.py"
sed -i 's/line.startswith(_custom_thermo_mark)/line.strip\(\).startswith\("Step"\)/g' $ase_file
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Hi @YukeLi-chem , this might be a result from ase parser's failure to read the lammps log file from the latest lammps version. What you can do is to revise ase's parser code. We have been using the bash commands as below:
ase_file="$(dirname $(python3 -c 'import ase; print(ase.__file__)'))/calculators/lammpsrun.py" sed -i 's/line.startswith(_custom_thermo_mark)/line.strip\(\).startswith\("Step"\)/g' $ase_file
This solution exactly works and no retrivement issue anymore. However, the check_mgp_match will assert error due to the large forces discrepancy between gp and lammps.
from flare.
Hi @Teng-Martin-Ma, here are a few suggestions you could try:
- do not use restart or warm start, try a fresh start with all previous output files deleted.
- set
export OMP_NUM_THREADS=<number_of_cpus_on_a_single_node>
(from @YukeLi-chem)
from flare.
Related Issues (20)
- 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
- Potential Parallelism for Hyperparameter Tuning
- Execluding some species from training HOT 1
- Bug: Python API cannot save trained model (Segmentation fault, list index out of range) HOT 3
- Adding 'Select' atomic environments to a pre-trained SGP Model using off-line training HOT 3
- Can't clone flare on windows because of tests/test_files/output_files/. Possibly OUTCAR naming not allowed on windows HOT 1
- Google Colab FLARE+LAMMPS crashing HOT 3
- About Mapped GP HOT 1
- Mapped Model in LAMMPS inaccurate compared to ASE SGP Model HOT 4
- Mapped GP Model Grid Options for SGP HOT 2
- How to accelerate training with large dataset HOT 3
- Can't access tutorial notebooks on Colab HOT 2
- Can not exclude stresses from training HOT 4
- Wrong parsing of the results which sticks numbers together.
- Supercell selection in offline training for 2 different systems
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