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raycond2-ffhq.sh
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raycond2-ffhq.sh
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#!/bin/bash
#SBATCH -J AFHQ2 # Job name
#SBATCH -o AFHQ2.o # Name of stdout output file (%j expands to jobId)
#SBATCH -e AFHQ2.o # Name of stderr output file (%j expands to jobId)
#SBATCH -N 1 # Total number of nodes (physical machine) requested
#SBATCH -n 6 # Number of cores requested
#SBATCH --mem=30000 # Memory pool (MB)
#SBATCH -t 96:00:00 # Run time (hh:mm:ss)
#SBATCH --partition=cuvl,davis,snavely,gpu
#SBATCH --gres=gpu:a6000:2 # gpu:gpu_type:how_many_gpu
# below is the script that specifies all the commands to run
# or you could just add a single command
# e.g. echo "hello world!"
# better to use absolute path
python /share/phoenix/nfs04/S7/emc348/ray-conditioning/train.py --outdir=/share/phoenix/nfs04/S7/emc348/stylegan3/training-runs --data=/share/phoenix/nfs04/S7/emc348/nerf/eg3d/processed_afhq.zip --cfg=raycond2 --gpus=2 --batch=32 --gamma=1 --snap=20 --cond=1 --aug=noaug --resume=/share/cuvl/emc348/pretrained_models/stylegan2-ffhq-512x512.pkl