map2map/scripts/example-train.slurm

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#!/bin/bash
#SBATCH --job-name=R2D2
#SBATCH --output=%x-%j.out
#SBATCH --partition=gpu_partition
#SBATCH --gres=gpu:4
#SBATCH --exclusive
#SBATCH --nodes=2
#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
#source $HOME/anaconda3/bin/activate pytorch_env
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