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