#!/usr/bin/env bash

# Ron the short-list GPU queue

#SBATCH -p slurm_sbel_cmg 

#SBATCH --account=cmg --qos=cmg_owner

## Request one CPU core from the scheduler

#SBATCH -c 1

## Request a GPU from the scheduler, we don't care what kind

#SBATCH --gres=gpu:1

#SBATCH -t 14-3:00 # time (D-HH:MM)

## Create a unique output file for the job

#SBATCH -o cuda_Training-%j.log

## Load CUDA into your environment

## load custimized CUDA and cudaToolkit

module load usermods

module load user/cuda

## Load Euler's OpenCV

## It does not work

## module load opencv/3.3.1

# (1)

# you need to create ssd virtual environment first

# then activate the ssd virtual environment

source activate ssd

# (2)

# install software needed

#conda install -c anaconda --name ssd cudatoolkit>=9.2 --yes

#conda install -c anaconda --name ssd cudnn>=7.0.5 --yes

conda install --name ssd numpy --yes

#conda install --name ssd tensorflow-gpu --yes

conda install --name ssd tensorflow-gpu --yes

conda install -c anaconda --name ssd keras-gpu --yes

#conda install --name ssd keras --yes #>=2.2.0 --yes

#conda install --name ssd keras --yes

conda install -c anaconda --name ssd matplotlib --yes

conda install -c anaconda --name ssd beautifulsoup4 --yes 

conda install -c anaconda --name ssd scikit-learn --yes

conda install -c anaconda --name ssd Pillow --yes

conda install --name ssd opencv --yes

conda install --name ssd tqdm --yes

#conda install -c conda-forge --name ssd opencv --yes

#conda install --name ssd opencv

#conda install -c conda-forge opencv

# (3)

python3 predict_top2.py