| #!/bin/bash | |
| # | |
| # Evaluates the predictive performance of a predictor | |
| # | |
| #SBATCH --cluster=<clustername> | |
| #SBATCH --partition=<partitionname> | |
| #SBATCH --account=<accountname> | |
| #SBATCH --job-name=evaluate | |
| #SBATCH --output=logs/evaluate.out | |
| #SBATCH --gres=gpu:0 # Number of GPU(s) per node. | |
| #SBATCH --cpus-per-task=16 # CPU cores/threads | |
| #SBATCH --mem=128000M # memory per node | |
| #SBATCH --time=0-24:00 # Max time (DD-HH:MM) | |
| #SBATCH --ntasks=1 # Only set to >1 if you want to use multi-threading | |
| #SBATCH --priority=1 | |
| export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK | |
| dataset=$1 | |
| predictor=$2 | |
| n_seeds=$3 | |
| n_train=$4 | |
| n_threads=16 | |
| kwargs=$5 | |
| python src/evaluate.py $dataset $predictor \ | |
| --n_threads=$n_threads --n_seeds=$n_seeds \ | |
| --n_train=$n_train --joint_training $kwargs | |