ae_job

#!/bin/sh
#SBATCH --partition=cms-uhh             ## or allgpu / cms / cms-uhh / maxgpu
#SBATCH --time=24:00:00
#SBATCH --nodes=1
#SBATCH --job-name auto_encoder           # give job unique name
#SBATCH --output ./out/%j-std.out      # terminal output
#SBATCH --error ./out/%j-std.err
#S BATCH --mail-type END
#S BATCH --mail-user lennart.kaemmle@desy.de
#SBATCH --constraint=GPU

# #SBATCH --nodelist=max-cmsg004         # you can select specific nodes, if necessary
# #SBATCH --constraint="P100|V100"        # ask for specific types of GPUs
 
# (GPU drivers, anaconda, .bashrc, etc) --> likely not necessary if you're installing your own cudatoolkit & cuDNN versions
#source /etc/profile.d/modules.sh
#module load maxwell
#module load cuda
#module load anaconda/3

. ~/.bashrc
export PATH="/home/kaemmlel/miniconda3/envs/torch/bin:$PATH"

set -x

# go to your folder with your python scripts
cd ~/Projects/amazing_ai

# run
# time python -m auto_encoder -o "./out/$SLURM_JOBID" -e 300
     # --data @events_b_sr_images_54_cutoff=-10.0_sigma=0.6_bs=15_center.h5 \
time python -m amazing_ai.auto_encoder \
     -o "./models/42_center/${SLURM_JOBID}_model={model}_npix={npix}_ts={train_size}_data={data}_noise={noise}_ls={latent_space_size}" \
     -e 300 \
     --npix 42 \
     --batch_size 512 \
     --data @events_b_sr_images_42_center_hist.h5 \
     --learning_rate 1e-3 \
     --noise 8e-4 \
     --model 1 \
     --latent_space_size 40
     # --noise 7e-3
# time python -m amazing_ai.auto_encoder -o "./out/$SLURM_JOBID" -e 300 --data @kristian_events_b_sr_images_33px_rotated+flipped