47 lines
1.3 KiB
Bash
47 lines
1.3 KiB
Bash
#!/bin/bash
|
|
|
|
#SBATCH --job-name=R2D2
|
|
#SBATCH --output=%x-%j.out
|
|
#SBATCH --partition=gpu_partition
|
|
#SBATCH --nodes=2
|
|
#SBATCH --gres=gpu:4
|
|
#SBATCH --exclusive
|
|
#SBATCH --time=1-00:00:00
|
|
|
|
|
|
echo "This is a minimal example. See --help or args.py for more," \
|
|
"e.g. on augmentation, cropping, padding, and data division."
|
|
echo "Training on 2 nodes with 8 GPUs."
|
|
echo "input data: {train,val,test}/R{0,1}-*.npy"
|
|
echo "target data: {train,val,test}/D{0,1}-*.npy"
|
|
echo "normalization functions: {R,D}{0,1} in ./RnD.py," \
|
|
"see map2map/data/norms/*.py for examples"
|
|
echo "model: Net in ./model.py, see map2map/models/*.py for examples"
|
|
echo "Training with placeholder learning rate 1e-4 and batch size 1."
|
|
|
|
|
|
hostname; pwd; date
|
|
|
|
|
|
# set computing environment, e.g. with module or anaconda
|
|
|
|
#module load python
|
|
#module list
|
|
|
|
#source $HOME/anaconda3/bin/activate pytorch_env
|
|
#conda info
|
|
|
|
|
|
srun m2m.py train \
|
|
--train-in-patterns "train/R0-*.npy,train/R1-*.npy" \
|
|
--train-tgt-patterns "train/D0-*.npy,train/D1-*.npy" \
|
|
--val-in-patterns "val/R0-*.npy,val/R1-*.npy" \
|
|
--val-tgt-patterns "val/D0-*.npy,val/D1-*.npy" \
|
|
--in-norms RnD.R0,RnD.R1 --tgt-norms RnD.D0,RnD.D1 \
|
|
--model model.Net --callback-at . \
|
|
--lr 1e-4 --batch-size 1 \
|
|
--epochs 1024 --seed $RANDOM
|
|
|
|
|
|
date
|