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job.sh
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job.sh
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#!/bin/bash
#PBS -q gpuvolta
#PBS -l ncpus=12
#PBS -l ngpus=1
#PBS -l jobfs=200GB
#PBS -l storage=gdata/dk92+gdata/wb00+gdata/rt52+<YOUR_PROJECT_STORAGE>
#PBS -l mem=200GB
#PBS -l walltime=08:00:00
#PBS -l wd
#PBS -P <YOUR_PROJECT_HERE>
set -eu
module use /g/data/dk92/apps/Modules/modulefiles
module load NCI-ai-ml/23.03
module load cuda/11.7.0
checkpoint_dir='/g/data/wb00/FourCastNet/nvlab/v0/pretrained'
stats_dir='/g/data/wb00/FourCastNet/nvlab/v0/data/stats/'
output_path='./output'
# Run inference AFNO Backbone
python inference/inference.py \
--config=afno_backbone \
--run_num=0 --vis \
--weights '/g/data/wb00/FourCastNet/nvlab/v0/pretrained/backbone.ckpt' \
--override_dir './output'
# Run inference_ensemble AFNO Backbone
python inference/inference_ensemble.py \
--config=afno_backbone \
--run_num=0 \
--n_pert 10 \
--override_dir './output' \
--weights '/g/data/wb00/FourCastNet/nvlab/v0/pretrained/backbone.ckpt'
# Run inference for precipitation
python inference/inference_precip.py \
--config=precip \
--run_num=0 \
--vis \
--weights '/g/data/wb00/FourCastNet/nvlab/v0/pretrained/precip.ckpt' \
--override_dir './output'
# Run inference to generate preds.zarr
python -u ./inference_nci/inference.py \
--start_time=2018-1-01T18:00:00 \
--end_time=2018-1-15T18:00:00 \
--checkpoint_dir=$checkpoint_dir \
--stats_dir=$stats_dir \
--output_path=$output_path \
--prediction_length=20