#!/usr/bin/env bash
# Set location of Rail repo here
RAILHOME=~/railclones/rail

# Set input/output bucket here -- must be on S3!
BUCKET=s3://rail-experiments

# Submit 20 at 10, 20, and 40 cores
#python $RAILHOME/src align elastic -i $BUCKET/GEUV20 -a hg19 -o $BUCKET/GEUVADIS_20_datasets_10_c3.2xlarge.nobin -c 10 --master-instance-type c3.2xlarge --core-instance-type c3.2xlarge -m $RAILHOME/eval/GEUVADIS_20_samples.manifest --ec2-key-name rail
#python $RAILHOME/src align elastic -i $BUCKET/GEUV20 -a hg19 -o $BUCKET/GEUVADIS_20_datasets_20_c3.2xlarge.nobin -c 20 --master-instance-type c3.2xlarge --core-instance-type c3.2xlarge -m $RAILHOME/eval/GEUVADIS_20_samples.manifest --ec2-key-name rail
python $RAILHOME/src align elastic -i $BUCKET/GEUV20 -a hg19 -o $BUCKET/GEUVADIS_20_datasets_40_c3.2xlarge.nobin2 -c 40 --master-instance-type c3.2xlarge --core-instance-type c3.2xlarge -m $RAILHOME/eval/GEUVADIS_20_samples.manifest --ec2-key-name rail

# Submit 40 and 80 at 40 cores
#python $RAILHOME/src align elastic -i $BUCKET/GEUV40 -a hg19 -o $BUCKET/GEUVADIS_40_datasets_40_c3.2xlarge.nobin -c 40 --master-instance-type c3.2xlarge --core-instance-type c3.2xlarge -m $RAILHOME/eval/GEUVADIS_40_samples.manifest --ec2-key-name rail
#python $RAILHOME/src align elastic -i $BUCKET/GEUV80 -a hg19 -o $BUCKET/GEUVADIS_80_datasets_40_c3.2xlarge.nobin -c 40 --master-instance-type c3.2xlarge --core-instance-type c3.2xlarge -m $RAILHOME/eval/GEUVADIS_80_samples.manifest --ec2-key-name rail