--- a
+++ b/YOLO/subYOLO.sh
@@ -0,0 +1,67 @@
+#!/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