#!/bin/bash
#SBATCH --comment clap
#SBATCH --partition=gpu
#SBATCH --job-name=mclap
#SBATCH --nodes 1
#SBATCH --ntasks-per-node 1
#SBATCH --cpus-per-gpu=6
#SBATCH --gres=gpu:1
#SBATCH --output=%x_%j.out
#SBATCH --exclude gpu-st-p4d-24xlarge-[23,30,31,108,115,134,135,183,185,186,187,188,275,277,290,374]


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_ALGO=ring
export NCCL_DEBUG=info
#export NCCL_DEBUG_SUBSYS=INIT,ENV,GRAPH,COLL

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


#export NCCL_P2P_DISABLE=1
#export NCCL_IBEXT_DISABLE=1
#export NCCL_SOCKET_IFNAME="eth0,en,eth,em,bond"

# sent to sub script
export HOSTNAMES=`scontrol show hostnames "$SLURM_JOB_NODELIST"`
export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
export MASTER_PORT=12802
export COUNT_NODE=`scontrol show hostnames "$SLURM_JOB_NODELIST" | wc -l`

echo go $COUNT_NODE
echo $HOSTNAMES

source /fsx/yusong/clap/bin/activate
cd /fsx/yusong/CLAP/src
export TRANSFORMERS_CACHE=/fsx/yusong/transformers_cache

srun --comment clap --cpu_bind=v --accel-bind=gn python -m evaluate.eval_retrieval_main \
    --save-frequency 5 \
    --save-top-performance 3 \
    --save-most-recent \
    --dataset-type="webdataset" \
    --precision="fp32" \
    --warmup 0 \
    --batch-size=512 \
    --wd=0.0 \
    --epochs=50 \
    --workers=6 \
    --use-bn-sync \
    --freeze-text \
    --amodel HTSAT-tiny \
    --tmodel roberta \
    --report-to "wandb" \
    --wandb-notes "10.17-zeroshot-esc50-dataset-4#" \
    --datasetnames "esc50" \
    --datasetinfos "train" \
    --seed 3407 \
    --remotedata \
    --logs /fsx/clap_logs \
    --gather-with-grad \
    --openai-model-cache-dir /fsx/yusong/transformers_cache \
    --data-filling "repeatpad" \
    --data-truncating "rand_trunc" \
    --class-label-path="../class_labels/ESC50_class_labels_indices_space.json" \
    --pretrained="/fsx/clap_logs/2022_10_17-02_08_21-model_HTSAT-tiny-lr_0.0001-b_96-j_6-p_fp32/checkpoints"