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# lightning.pytorch==2.4.0
seed_everything: 42
trainer:
accelerator: auto
strategy:
class_path: lightning.pytorch.strategies.FSDPStrategy
init_args:
auto_wrap_policy: [modelgenerator.huggingface_models.fm4bio.modeling_fm4bio.FM4BioLayer]
sharding_strategy: HYBRID_SHARD
devices: 4
num_nodes: 4
precision: 32
logger:
class_path: lightning.pytorch.loggers.WandbLogger
init_args:
name: CP2C9_HUMAN_Amorosi_2021_abundance
save_dir: logs
version: null
offline: false
dir: null
id: null
anonymous: null
project: MGEN_AIDO.Protein-16B_DMS
log_model: false
experiment: null
prefix: ''
checkpoint_name: null
job_type: null
config: null
entity: null
reinit: null
tags: null
group: null
notes: null
magic: null
config_exclude_keys: null
config_include_keys: null
mode: null
allow_val_change: null
resume: null
force: null
tensorboard: null
sync_tensorboard: null
monitor_gym: null
save_code: true
settings: null
callbacks:
- class_path: lightning.pytorch.callbacks.LearningRateMonitor
init_args:
logging_interval: step
log_momentum: false
log_weight_decay: false
- class_path: lightning.pytorch.callbacks.ModelCheckpoint # save ckpt at the end of each epoch, and save the best val_mcc ckpt
init_args:
dirpath: null
filename: epoch_{epoch}-val_mcc:{val_spearman:.3f}
monitor: val_spearman
verbose: false
save_last: true
save_top_k: 1
save_weights_only: false
mode: max
auto_insert_metric_name: true
every_n_train_steps: null
train_time_interval: null
every_n_epochs: 1
save_on_train_epoch_end: null
enable_version_counter: true
- class_path: lightning.pytorch.callbacks.early_stopping.EarlyStopping
dict_kwargs:
monitor: val_spearman
mode: max
patience: 1
fast_dev_run: false
max_epochs: null
min_epochs: 100
max_steps: null
min_steps: null
max_time: null
limit_train_batches: null
limit_val_batches: null
limit_test_batches: null
limit_predict_batches: null
overfit_batches: 0.0
val_check_interval: null
check_val_every_n_epoch: 1
num_sanity_val_steps: null
log_every_n_steps: 50
enable_checkpointing: null
enable_progress_bar: null
enable_model_summary: null
accumulate_grad_batches: 1
gradient_clip_val: 0
gradient_clip_algorithm: null
deterministic: null
benchmark: null
inference_mode: true
use_distributed_sampler: true
profiler:
class_path: lightning.pytorch.profilers.PyTorchProfiler
init_args:
dirpath: null
filename: null
group_by_input_shapes: false
emit_nvtx: false
export_to_chrome: true
row_limit: 20
sort_by_key: null
record_module_names: true
table_kwargs: null
record_shapes: false
dict_kwargs:
profile_memory: true
detect_anomaly: false
barebones: false
plugins: null
sync_batchnorm: false
reload_dataloaders_every_n_epochs: 0
default_root_dir: logs
model:
class_path: modelgenerator.tasks.SequenceRegression
init_args:
backbone:
class_path: modelgenerator.backbones.aido_protein_16b
init_args:
from_scratch: false
use_peft: true
save_peft_only: true
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
config_overwrites: null
model_init_args: null
max_length: 2048
adapter:
class_path: modelgenerator.adapters.MLPPoolAdapter
init_args:
pooling: mean_pooling
hidden_sizes:
- 128
bias: true
dropout: 0.1
dropout_in_middle: false
optimizer:
class_path: torch.optim.AdamW
init_args:
lr: 0.0001
betas:
- 0.9
- 0.95
eps: 1.0e-08
weight_decay: 0.01
amsgrad: false
maximize: false
foreach: null
capturable: false
differentiable: false
fused: null
lr_scheduler:
class_path: modelgenerator.lr_schedulers.CosineWithWarmup
init_args:
warmup_ratio: 0.05
strict_loading: true
reset_optimizer_states: false
data:
class_path: modelgenerator.data.DMSFitnessPrediction
init_args:
path: genbio-ai/ProteinGYM-DMS
train_split_files:
- singles_substitutions/CP2C9_HUMAN_Amorosi_2021_abundance.tsv
normalize: true
train_split_name: train
test_split_files: null
valid_split_files: null
random_seed: 42
batch_size: 2
shuffle: true
sampler: null
num_workers: 0
pin_memory: true
persistent_workers: false
cv_num_folds: 5
cv_test_fold_id: 0
cv_enable_val_fold: true
cv_fold_id_col: fold_id
ckpt_path: null
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