llama-recipes/examples/multi_node.slurm

# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the GNU General Public License version 3.


#!/bin/bash

#SBATCH --job-name=Nano-2d-trainer-20b-8nodes

#SBATCH --ntasks=2
#SBATCH --nodes=2
#SBATCH --gpus-per-task=4
#SBATCH --partition=train 
nodes=( $( scontrol show hostnames $SLURM_JOB_NODELIST ) )
nodes_array=($nodes)
head_node=${nodes_array[0]}
head_node_ip=$(srun --nodes=1 --ntasks=1 -w "$head_node" hostname --ip-address)
# Enable for A100
export FI_PROVIDER="efa"

echo Node IP: $head_node_ip
export LOGLEVEL=INFO
# debugging flags (optional)
export NCCL_DEBUG=WARN
export NCCL_DEBUG_SUBSYS=WARN
export PYTHONFAULTHANDLER=1
export LD_LIBRARY_PATH=/opt/amazon/efa/lib:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/usr/local/lib/:$LD_LIBRARY_PATH
export CUDA_LAUNCH_BLOCKING=0

# on your cluster you might need these:
# set the network interface
export NCCL_SOCKET_IFNAME="ens"
export FI_EFA_USE_DEVICE_RDMA=1

srun  torchrun --nproc_per_node 4 --rdzv_id $RANDOM --rdzv_backend c10d --rdzv_endpoint $head_node_ip:29500 examples/finetuning.py  --enable_fsdp --use_peft --peft_method lora