2022-08-01 01:25:03 +02:00
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#!/bin/bash
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# mpi version for node rank
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H=`hostname`
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THEID=`echo -e $HOSTNAMES | python3 -c "import sys;[sys.stdout.write(str(i)) for i,line in enumerate(next(sys.stdin).split(' ')) if line.strip() == '$H'.strip()]"`
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export NODE_RANK=${THEID}
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echo THEID=$THEID
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echo "##########################################"
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echo MASTER_ADDR=${MASTER_ADDR}
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echo MASTER_PORT=${MASTER_PORT}
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echo NODE_RANK=${NODE_RANK}
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echo WORLD_SIZE=${WORLD_SIZE}
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echo "##########################################"
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# debug environment worked great so we stick with it
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# no magic there, just a miniconda python=3.9, pytorch=1.12, cudatoolkit=11.3
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# env with pip dependencies from stable diffusion's requirements.txt
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eval "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)"
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#conda activate stable
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# torch 1.11 to avoid bug in ckpt restoring
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conda activate torch111
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cd /fsx/stable-diffusion/stable-diffusion
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CONFIG=configs/stable-diffusion/v2_pretraining.yaml
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# resume and set new seed to reshuffle data
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2022-08-02 22:32:22 +02:00
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#EXTRA="--seed 542 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/checkpoints/v2-256/216k-256.ckpt"
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EXTRA="--seed 543 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-31T23-35-31_v2_pretraining/checkpoints/last.ckpt"
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2022-08-01 01:25:03 +02:00
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# reduce lr a bit
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#EXTRA="${EXTRA} model.params.scheduler_config.params.f_max=[0.75]"
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# custom logdir
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#EXTRA="${EXTRA} --logdir rlogs"
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# debugging
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#EXTRA="${EXTRA} -d True lightning.callbacks.image_logger.params.batch_frequency=50"
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# detect bad gpus early on
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/bin/bash /fsx/stable-diffusion/stable-diffusion/scripts/test_gpu.sh
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python main.py --base $CONFIG --gpus 0,1,2,3,4,5,6,7 -t --num_nodes ${WORLD_SIZE} --scale_lr False $EXTRA
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