JackBurdick

########## Personal aliases

# code ide
alias c="code "

## add ll
alias ll='ls -lGaf'
alias l='ls -lFG'
alias l.='ls -dGF .*'
alias lg='ll |grep --colour=auto '

JackBurdick

%% Initialization
main_directory = 'isbi-segmentation-dataset';     % parent directory

% dialation size
strel_size = 25;

% select one or the other.. test or train
img_type = 'test';
% img_type = 'train';

JackBurdick

[global]
device = gpu
floatX = float32

[cuda]
root = /cm/local/apps/cuda/libs/current

[lib]
cnmem=1

JackBurdick

#!/bin/bash
#SBATCH -t 20                               # time limit of job, in minutes.. USED ONLY	FOR TESTING [REMOVE AS NEEDED]
#SBATCH -p gpu                              # which queue to submit to
#SBATCH --gres=gpu:1                        # tell the sbatch script to allocate GPU resources
#SBATCH -o gpu_python_%j.out                # File to which STDOUT will be written
#SBATCH -e gpu_python_%j.err                # File to which STDERR will be written
#SBATCH --mail-type=END                     # Type of email notification- BEGIN,END,FAIL,ALL
#SBATCH [email protected]    # Email to which notifications will be sent

THEANO_FLAGS=mode=FAST_RUN,device=gpu,config.dnn.enabled=True,floatX=float32 python mnhist_test_fromFile.py

JackBurdick

#!/bin/bash
#SBATCH -t 20                               # time limit of job, in minutes.. just using now for testing
#SBATCH -p gpu                              # which queue to submit to
#SBATCH -o gpu_python_%j.out                    # File to which STDOUT will be written
#SBATCH -e gpu_python_%j.err                    # File to which STDERR will be written
#SBATCH --mail-type=END                     # Type of email notification- BEGIN,END,FAIL,ALL
#SBATCH [email protected]    # Email to which notifications will be sent

THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python test_GPU_or_CPU.py

JackBurdick

[global]
device = gpu
floatX = float32

[cuda]
root = /cm/local/apps/cuda/libs/current

[lib]
cnmem=1

JackBurdick

I can test your workflow if you give me the steps. These are the steps I conducted to verify the environment.
 
salloc –N 1 –p gpu
# Gives me node084
ssh node084
module load cuda75/toolkit/7.5.18
module load python/anaconda
# Testing out CUDA (optional and not required, but this is how you would do it)q
cd $CUDA_SDK
./verify_cuda75.sh

JackBurdick

[global]
device = gpu
floatX = float32

[cuda]
root = /cm/local/apps/cuda/libs/current

JackBurdick

from theano import function, config, shared, sandbox
import theano.tensor as T
import numpy
import time

vlen = 10 * 30 * 768  # 10 x #cores x # threads per core
iters = 1000

rng = numpy.random.RandomState(22)
x = shared(numpy.asarray(rng.rand(vlen), config.floatX))

JackBurdick

#!/bin/bash
#SBATCH -t 20                               # time limit of job, in minutes.. just using now for testing
#SBATCH -p gpu                              # which queue to submit to
#SBATCH -n 40                                   # TEST FROM DR M
#SBATCH -N 2                                    # TEST FROM DR M
#SBATCH -o gpu_python_%j.out                    # File to which STDOUT will be written
#SBATCH -e gpu_python_%j.err                    # File to which STDERR will be written
#SBATCH --mail-type=END                     # Type of email notification- BEGIN,END,FAIL,ALL
#SBATCH [email protected]    # Email to which notifications will be sent