File size: 1,572 Bytes
3b99abb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | setting:
seed: 20000812
os_environ:
WANDB_API_KEY: ~
WANDB_RUN_ID: ~
CUDA_VISIBLE_DEVICES: 0,1,2,3,4,5,6,7
MASTER_ADDR: localhost
MASTER_PORT: 12315
WORLD_SIZE: 1
NODE_RANK: 0
wandb_config:
project: EC
name: SaProt_650M_AF2
model:
# Which model to use
model_py_path: saprot/saprot_annotation_model
kwargs:
# Arguments to initialize the specific class
config_path: weight/PLMs/SaProt_650M_AF2
load_pretrained: True
anno_type: EC
# Arguments to initialize the basic class AbstractModel
lr_scheduler_kwargs:
last_epoch: -1
init_lr: 2.0e-5
# Weather to use this scheduler or not
on_use: false
optimizer_kwargs:
betas: [0.9, 0.98]
weight_decay: 0.01
save_path: weight/EC/SaProt_650M_AF2.pt
dataset:
# Arguments to initialize the basic class LMDBDataset
dataset_py_path: saprot/saprot_annotation_dataset
dataloader_kwargs:
batch_size: 8
num_workers: 8
train_lmdb: scripts/LMDB/EC/AF2/foldseek/train
valid_lmdb: scripts/LMDB/EC/AF2/foldseek/valid
test_lmdb: scripts/LMDB/EC/AF2/foldseek/test
# Arguments to initialize the specific class
kwargs:
tokenizer: weight/PLMs/SaProt_650M_AF2
plddt_threshold: 70
# Arguments to initialize Pytorch Lightning Trainer
Trainer:
max_epochs: 100
log_every_n_steps: 1
strategy:
find_unused_parameters: True
logger: True
enable_checkpointing: false
val_check_interval: 0.1
accelerator: gpu
devices: 8
num_nodes: 1
accumulate_grad_batches: 1
precision: 16
num_sanity_val_steps: 0 |