83 lines
2.4 KiB
Bash
83 lines
2.4 KiB
Bash
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#!/bin/bash
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CURDIR=`pwd`
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CODEDIR=`mktemp -d -p ${CURDIR}/tmp`
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cp ${CURDIR}/*.py ${CODEDIR}
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cp -r ${CURDIR}/local_dm_control_suite ${CODEDIR}/
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cp -r ${CURDIR}/dmc2gym ${CODEDIR}/
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cp -r ${CURDIR}/agent ${CODEDIR}/
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DOMAIN=${1:-walker}
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TASK=${2:-walk}
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ACTION_REPEAT=${3:-2}
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NOW=${4:-$(date +"%m%d%H%M")}
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ENCODER_TYPE=pixel
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DECODER_TYPE=pixel
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NUM_LAYERS=4
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NUM_FILTERS=32
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IMG_SOURCE=video
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AGENT=bisim
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CDIR=/checkpoint/${USER}/DBC/${DOMAIN}_${TASK}
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mkdir -p ${CDIR}
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for TRANSITION_MODEL_TYPE in 'probabilistic'; do
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for DECODER_TYPE in 'identity'; do
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for SEED in 1 2 3; do
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SUBDIR=${AGENT}_transition${TRANSITION_MODEL_TYPE}_nobg/seed_${SEED}
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SAVEDIR=${CDIR}/${SUBDIR}
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mkdir -p ${SAVEDIR}
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JOBNAME=${NOW}_${DOMAIN}_${TASK}
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SCRIPT=${SAVEDIR}/run.sh
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SLURM=${SAVEDIR}/run.slrm
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CODEREF=${SAVEDIR}/code
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extra=""
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echo "#!/bin/sh" > ${SCRIPT}
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echo "#!/bin/sh" > ${SLURM}
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echo ${CODEDIR} > ${CODEREF}
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echo "#SBATCH --job-name=${JOBNAME}" >> ${SLURM}
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echo "#SBATCH --output=${SAVEDIR}/stdout" >> ${SLURM}
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echo "#SBATCH --error=${SAVEDIR}/stderr" >> ${SLURM}
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echo "#SBATCH --partition=learnfair" >> ${SLURM}
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echo "#SBATCH --nodes=1" >> ${SLURM}
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echo "#SBATCH --time=4000" >> ${SLURM}
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echo "#SBATCH --ntasks-per-node=1" >> ${SLURM}
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echo "#SBATCH --signal=USR1" >> ${SLURM}
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echo "#SBATCH --gres=gpu:volta:1" >> ${SLURM}
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echo "#SBATCH --mem=500000" >> ${SLURM}
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echo "#SBATCH -c 1" >> ${SLURM}
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echo "srun sh ${SCRIPT}" >> ${SLURM}
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echo "echo \$SLURM_JOB_ID >> ${SAVEDIR}/id" >> ${SCRIPT}
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echo "nvidia-smi" >> ${SCRIPT}
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echo "cd ${CODEDIR}" >> ${SCRIPT}
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echo MUJOCO_GL="osmesa" LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu/nvidia-opengl/:$LD_LIBRARY_PATH python train.py \
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--domain_name ${DOMAIN} \
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--task_name ${TASK} \
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--agent ${AGENT} \
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--init_steps 1000 \
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--num_train_steps 1000000 \
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--encoder_type ${ENCODER_TYPE} \
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--decoder_type ${DECODER_TYPE} \
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--action_repeat ${ACTION_REPEAT} \
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--resource_files \'/datasets01/kinetics/070618/400/train/driving_car/*.mp4\' \
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--num_layers ${NUM_LAYERS} \
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--num_filters ${NUM_FILTERS} \
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--transition_model_type ${TRANSITION_MODEL_TYPE} \
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--critic_tau 0.01 \
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--encoder_tau 0.05 \
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--decoder_weight_lambda 0.0000001 \
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--hidden_dim 1024 \
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--batch_size 128 \
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--init_temperature 0.1 \
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--alpha_lr 1e-4 \
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--alpha_beta 0.5\
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--save_model \
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--work_dir ${SAVEDIR} \
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--seed ${SEED} >> ${SCRIPT}
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sbatch ${SLURM}
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done
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done
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done
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