File size: 1,558 Bytes
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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: esm2_t33_650M_UR50D
model:
# Which model to use
model_py_path: saprot/saprot_annotation_model
kwargs:
# Arguments to initialize the specific class
config_path: weight/PLMs/esm2_t33_650M_UR50D
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/esm2_t33_650M_UR50D.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/normal/train
valid_lmdb: scripts/LMDB/EC/AF2/normal/valid
test_lmdb: scripts/LMDB/EC/AF2/normal/test
# Arguments to initialize the specific class
kwargs:
tokenizer: weight/PLMs/esm2_t33_650M_UR50D
# 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 |