#!/usr/bin/env bash

# set up 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:gtx1080:1

#SBATCH -t 14-2:00 # time (D-HH:MM)

## Create a unique output file for the job

#SBATCH -o cuda_Training-%j.log

source activate yolo 

## Load CUDA into your environment

#module load cuda/9.0

## Load CUDA into your environment

module load cuda/9.0

source activate Python3.6

# install cudatoolkit and cudnn

conda install -c anaconda cudatoolkit --yes

conda install -c anaconda cudnn --yes

## Run the installe

pip install numpy

pip install tensorflow-gpu==1.8

pip install numpy scipy scikit-learn pandas matplotlib seaborn

pip install Pillow

pip uninstall cupy

pip install keras

pip install cupy-cuda90

pip install opencv-python

export CUDA_HOME=/usr/local/cuda

export PATH=$PATH:$CUDA_HOME/bin

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_HOME/lib64

# this installs the right pip and dependencies for the fresh python

# maskrcnn_benchmark and coco api dependencies

#export INSTALL_DIR=$PWD

# install pycocotools

#cd $INSTALL_DIR

#git clone https://github.com/cocodataset/cocoapi.git

#cd cocoapi/PythonAPI

#python setup.py build_ext install

# install PyTorch Detection

#cd $INSTALL_DIR

#git clone https://github.com/facebookresearch/maskrcnn-benchmark.git

#cd maskrcnn-benchmark

# follow PyTorch installation in https://pytorch.org/get-started/locally/

# we give the instructions for CUDA 9.0

# conda install -c pytorch pytorch-nightly torchvision cudatoolkit=9.0

#python setup.py build develop

#unset INSTALL_DIR

#cd ..

#pwd

# running scripts

python train.py

#python tools/train_net.py --config-file "configs/defect_detection.yaml" SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0005 SOLVER.MAX_ITER 60000 SOLVER.STEPS "(30000, 40000)" TEST.IMS_PER_BATCH 1