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https://github.com/RosettaCommons/foundry.git
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5a492032d5
* Initial commit of chiral changes Initial checkin of chiral feature code Add chiral metric * Update the way chiral features are incorporated into the model Move initialization to new func use default pytorch reset parameters fix initialization for chirals config rename argument of confidence head fix initialization for chirals * refactor: src nest, rename rf2aa to modelhub * refactor: initial commit without projects * Initial commit of chiral changes * Initial checkin of chiral feature code * Add chiral metric * Remove option for double residual connection. Add kq_norm oiptions to base (20250125) config. * Restoring flag * config * rename argument of confidence head * Update the way chiral features are incorporated into the model * config * rename argument of confidence head * Update the way chiral features are incorporated into the model * Initial commit of chiral changes Initial checkin of chiral feature code Add chiral metric * Update the way chiral features are incorporated into the model Move initialization to new func use default pytorch reset parameters fix initialization for chirals config rename argument of confidence head fix initialization for chirals * refactor: new modelhub --------- Co-authored-by: fdimaio <dimaio@uw.edu> Co-authored-by: HaotianZhangAI4Science <haotianzhang@zju.edu.cn>
21 lines
574 B
YAML
21 lines
574 B
YAML
defaults:
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- ddp
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- loss: structure_prediction
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- metrics: structure_prediction
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_target_: modelhub.trainers.af3.AF3Trainer
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validate_every_n_epochs: 1
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max_epochs: 10_000
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n_examples_per_epoch: 24000
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prevalidate: True
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# We must pre-specify the number of recycles during training so we can pre-sample recycles per batch consistently for each GPU
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n_recycles_train: ${datasets.n_recycles_train}
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clip_grad_max_norm: 10.0
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output_dir: ${paths.output_dir}
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checkpoint_every_n_epochs: 1
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# precision: bf16-mixed # Mixed precision training with bfloat16 (currently does not work)
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