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17dacheng avatar 17dacheng commented on July 28, 2024

additionally, the config is as follows, our gpu is a100, 80g memory

prep_model self.opt is {'alpha': 2.0, 'cmap_cutoff': 10.0, 'con': {'binary': False, 'cutoff': 14.0, 'num': 2, 'num_pos': inf, 'seqsep': 9}, 'dropout': True, 'fape_cutoff': 10.0, 'fix_pos': ar
ray([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
       17, 18, 19, 20, 21, 22, 23]), 'hard': 0.0, 'i_con': {'binary': False, 'cutoff': 21.6875, 'num': 1, 'num_pos': inf}, 'learning_rate': 0.1, 'norm_seq_grad': True, 'num_models': 1, 'num_recycles': 0, 'pos': array([ 242,  244,  247,  249,  251,  267,  268,  270,  271,  981,  982,                                                                                                           985,  986,  988,  989, 1035, 1036, 1039, 1043, 1044, 1045, 1046,
       1047, 1048]), 'sample_models': True, 'soft': 0.0, 'temp': 1.0, 'template': {'dropout': 0.0, 'rm_ic': False}, 'use_pssm': False, 'weights': {'con': 1.0, 'dgram_cce': 1.0, 'exp_res': 0.0
, 'fape': 0.0, 'helix': 0.0, 'pae': 0.0, 'plddt': 0.0, 'rmsd': 0.0, 'seq_ent': 0.0}}

from colabdesign.

chris-kafka avatar chris-kafka commented on July 28, 2024

As you see, it tries to allocate 206 GB of GPU memory. Generally speaking going beyond 600AAs is not possible for Gradient-based optimisation. You can try the semigreedy protocol, this should work.

from colabdesign.

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