File size: 1,923 Bytes
94391f2 |
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 |
data_path="./test_datasets"
n_gpu=1
batch_size=1
batch_size_valid=1
epoch=20
update_freq=1
#lr=1e-3
#MASTER_PORT=10075
#arch=pocket_ranking
export NCCL_ASYNC_ERROR_HANDLING=1
export OMP_NUM_THREADS=1
arch=${1} # model architecture
weight_path=${2} # path for pretrained model
results_path=${3} #
result_file=${4} #
lr=${5} # learning rate
MASTER_PORT=${6}
train_ligf=${7} # !! input path for training ligands file (.csv format)
test_ligf=${8} # !! input path for test ligands file (.csv format)
device=${9} # cuda device
if [[ "$arch" == "pocketregression" ]] || [[ "$arch" == "DTA" ]]; then
loss="mseloss"
else
loss="rank_softmax"
fi
CUDA_VISIBLE_DEVICES=${device} python -m torch.distributed.launch --nproc_per_node=$n_gpu --master_port=$MASTER_PORT $(which unicore-train) $data_path --user-dir ./unimol --train-subset train --valid-subset valid \
--results-path $results_path \
--num-workers 8 --ddp-backend=c10d \
--task train_task --loss ${loss} --arch $arch \
--max-pocket-atoms 256 \
--optimizer adam --adam-betas "(0.9, 0.999)" --adam-eps 1e-8 --clip-norm 1.0 \
--lr-scheduler polynomial_decay --lr $lr --max-epoch $epoch --batch-size $batch_size --batch-size-valid $batch_size_valid \
--update-freq $update_freq --seed 1 \
--log-interval 1 --log-format simple \
--validate-interval 1 --validate-begin-epoch 15 \
--best-checkpoint-metric valid_mean_r2 --patience 100 --all-gather-list-size 2048000 \
--no-save --save-dir $results_path --tmp-save-dir $results_path \
--find-unused-parameters \
--maximize-best-checkpoint-metric \
--valid-set TYK2 \
--max-lignum 512 --test-max-lignum 10000 \
--restore-model $weight_path --few-shot true \
--fp16 --fp16-init-scale 4 --fp16-scale-window 256 \
--active-learning-resfile ${result_file} \
--case-train-ligfile ${train_ligf} --case-test-ligfile ${test_ligf}
|