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more launch scripts

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Robin Rombach 2022-07-09 22:10:20 +00:00 committed by pesser
parent 529bf6a0cf
commit b80ed48bcc
4 changed files with 118 additions and 0 deletions
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20
scripts/slurm/resume_512_improvedaesthetic/launcher.sh Normal file
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#!/bin/bash
export NODE_RANK=${SLURM_NODEID}
echo "##########################################"
echo MASTER_ADDR=${MASTER_ADDR}
echo MASTER_PORT=${MASTER_PORT}
echo NODE_RANK=${NODE_RANK}
echo WORLD_SIZE=${WORLD_SIZE}
echo "##########################################"
# debug environment worked great so we stick with it
# no magic there, just a miniconda python=3.9, pytorch=1.12, cudatoolkit=11.3
# env with pip dependencies from stable diffusion's requirements.txt
eval "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)"
conda activate stable
cd /fsx/stable-diffusion/stable-diffusion
CONFIG=configs/stable-diffusion/txt2img-1p4B-multinode-clip-encoder-high-res-512-improvedaesthetic.yaml
EXTRA="model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/logs/2022-07-07T16-15-18_txt2img-1p4B-multinode-clip-encoder-high-res-512/checkpoints/last.ckpt"
DEBUG="-d True lightning.callbacks.image_logger.params.batch_frequency=5"
python main.py --base $CONFIG --gpus 0,1,2,3,4,5,6,7 -t --num_nodes ${WORLD_SIZE} --scale_lr False $EXTRA #$DEBUG

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scripts/slurm/resume_512_improvedaesthetic/sbatch.sh Normal file
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#!/bin/bash
#SBATCH --partition=compute-od-gpu
#SBATCH --job-name=stable-diffusion-512cont-improvedaesthetic
#SBATCH --nodes=20
#SBATCH --gpus-per-node=8
#SBATCH --cpus-per-gpu=4
#SBATCH --ntasks-per-node=1
#SBATCH --output=%x_%j.%n.out
# nccl / efa stuff
module load intelmpi
source /opt/intel/mpi/latest/env/vars.sh
export LD_LIBRARY_PATH=/opt/aws-ofi-nccl/lib:/opt/amazon/efa/lib64:/usr/local/cuda-11.0/efa/lib:/usr/local/cuda-11.0/lib:/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0:/opt/nccl/build/lib:/opt/aws-ofi-nccl-install/lib:/opt/aws-ofi-nccl/lib:$LD_LIBRARY_PATH
export NCCL_PROTO=simple
export PATH=/opt/amazon/efa/bin:$PATH
export LD_PRELOAD="/opt/nccl/build/lib/libnccl.so"
export FI_EFA_FORK_SAFE=1
export FI_LOG_LEVEL=1
export FI_EFA_USE_DEVICE_RDMA=1 # use for p4dn
export NCCL_DEBUG=info
export PYTHONFAULTHANDLER=1
export CUDA_LAUNCH_BLOCKING=0
export OMPI_MCA_mtl_base_verbose=1
export FI_EFA_ENABLE_SHM_TRANSFER=0
export FI_PROVIDER=efa
export FI_EFA_TX_MIN_CREDITS=64
export NCCL_TREE_THRESHOLD=0
# pytorch multinode vars
# node rank should be set in launcher script
export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
export MASTER_PORT=11338
export WORLD_SIZE=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | wc -l)
echo MASTER_ADDR=${MASTER_ADDR}
echo MASTER_PORT=${MASTER_PORT}
echo WORLD_SIZE=${WORLD_SIZE}
srun --output=%x_%j.%n.out bash /fsx/stable-diffusion/stable-diffusion/scripts/slurm/resume_512_improvedaesthetic/launcher.sh

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scripts/slurm/resume_768_hr/launcher.sh Normal file
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#!/bin/bash
export NODE_RANK=${SLURM_NODEID}
echo "##########################################"
echo MASTER_ADDR=${MASTER_ADDR}
echo MASTER_PORT=${MASTER_PORT}
echo NODE_RANK=${NODE_RANK}
echo WORLD_SIZE=${WORLD_SIZE}
echo "##########################################"
# debug environment worked great so we stick with it
# no magic there, just a miniconda python=3.9, pytorch=1.12, cudatoolkit=11.3
# env with pip dependencies from stable diffusion's requirements.txt
eval "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)"
conda activate stable
cd /fsx/stable-diffusion/stable-diffusion
CONFIG=configs/stable-diffusion/txt2img-multinode-clip-encoder-f16-768-laion-hr.yaml
EXTRA="model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/checkpoints/f16-33k+12k-hr_pruned.ckpt"
DEBUG="-d True lightning.callbacks.image_logger.params.batch_frequency=5"
python main.py --base $CONFIG --gpus 0,1,2,3,4,5,6,7 -t --num_nodes ${WORLD_SIZE} --scale_lr False $EXTRA #$DEBUG

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scripts/slurm/resume_768_hr/sbatch.sh Normal file
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#!/bin/bash
#SBATCH --partition=compute-od-gpu
#SBATCH --job-name=stable-diffusion-768cont-resumehr
#SBATCH --nodes=20
#SBATCH --gpus-per-node=8
#SBATCH --cpus-per-gpu=4
#SBATCH --ntasks-per-node=1
#SBATCH --output=%x_%j.%n.out
# nccl / efa stuff
module load intelmpi
source /opt/intel/mpi/latest/env/vars.sh
export LD_LIBRARY_PATH=/opt/aws-ofi-nccl/lib:/opt/amazon/efa/lib64:/usr/local/cuda-11.0/efa/lib:/usr/local/cuda-11.0/lib:/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0:/opt/nccl/build/lib:/opt/aws-ofi-nccl-install/lib:/opt/aws-ofi-nccl/lib:$LD_LIBRARY_PATH
export NCCL_PROTO=simple
export PATH=/opt/amazon/efa/bin:$PATH
export LD_PRELOAD="/opt/nccl/build/lib/libnccl.so"
export FI_EFA_FORK_SAFE=1
export FI_LOG_LEVEL=1
export FI_EFA_USE_DEVICE_RDMA=1 # use for p4dn
export NCCL_DEBUG=info
export PYTHONFAULTHANDLER=1
export CUDA_LAUNCH_BLOCKING=0
export OMPI_MCA_mtl_base_verbose=1
export FI_EFA_ENABLE_SHM_TRANSFER=0
export FI_PROVIDER=efa
export FI_EFA_TX_MIN_CREDITS=64
export NCCL_TREE_THRESHOLD=0
# pytorch multinode vars
# node rank should be set in launcher script
export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
export MASTER_PORT=11338
export WORLD_SIZE=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | wc -l)
echo MASTER_ADDR=${MASTER_ADDR}
echo MASTER_PORT=${MASTER_PORT}
echo WORLD_SIZE=${WORLD_SIZE}
srun --output=%x_%j.%n.out bash /fsx/stable-diffusion/stable-diffusion/scripts/slurm/resume_768_hr/launcher.sh # srun vs mpirun?