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#!/bin/sh

# recursively removes all .pyc files and __pycache__ directories in the current
# directory

find . | grep -E "(__pycache__|\.pyc$)" | xargs rm -rf

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import radical.pilot as rp
from parsl.app.app import python_app, bash_app

@bash_app
def mpi_simulate(x: float, ptype = rp.MPI,
                           nproc = 3, pre_exec = []):
    return './simulate {0}'.format(x)
  
result = mpi_simulate(2, ptype = rp.MPI, nproc = 3, pre_exec = ['cd simulate_pre_exe'])

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from __future__ import print_function
'''
Basic Multi GPU computation example using TensorFlow library.
Single/Multi-GPU non-MPI Author: Aymeric Damien
Multi-GPU Large scale/Multi-node MPI Author: Aymen Alsaadi
Project: https://github.com/aymericdamien/TensorFlow-Examples/
'''

'''
This tutorial requires your machine to have 2 GPUs

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1. Login to the VM.

• From your terminal ssh to the following IP:

  ssh user_name@149.165.156.107

2. Generate XSEDE certificate.

• In order to be able to login to Bridges from your VM you need to generate XSEDE certificate using the following command:

  myproxy-logon -s myproxy.xsede.org -l user_name -t 72

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#!/bin/bash
# Stop the MPS control daemon for each GPU and clean up /tmp
NGPUS=2 # Number of gpus with compute_capability 3.5 per server
for ((i=0; i< $NGPUS; i++))
do
echo $i
export CUDA_MPS_PIPE_DIRECTORY=/home/aymen/mps_$i
echo "quit" | nvidia-cuda-mps-control
rm -rf /home/aymen/mps_$i
rm -rf /home/aymen/mps_log_$i

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#!/bin/bash
export CUDA_VISIBLE_DEVICES=0
lrank=$OMPI_COMM_WORLD_LOCAL_RANK
case ${lrank} in
[0])
 export CUDA_MPS_PIPE_DIRECTORY=/home/aymen/mps_0; ./vector_add
 ;;
[1])
 export CUDA_MPS_PIPE_DIRECTORY=/home/aymen/mps_1; ./vector_add
 ;;

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#!/bin/bash
NGPUS=2 # Number of gpus with compute_capability 3.5 per server
# Start the MPS server for each GPU
for ((i=0; i< $NGPUS; i++))
do
mkdir /home/aymen/mps_$i
mkdir /home/aymen/mps_log_$i
export CUDA_VISIBLE_DEVICES=$i
export CUDA_MPS_PIPE_DIRECTORY=/home/aymen/mps_$i
export CUDA_MPS_LOG_DIRECTORY=/home/aymen/mps_log_$i

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import os
import radical.pilot as rp
from radical.entk import Pipeline, Stage, Task, AppManager


crop_size = int(360)              #covnvert this to argument later
worker_root = r"/pylon5/mc3bggp/aymen/local_dir/datasets/polygon/"                                                                 #convert this to argument later
weights_path = r"/pylon5/mc3bggp/aymen/local_dir/datasets/logs/ice_wedge_polygon20180823T1403/mask_rcnn_ice_wedge_polygon_0008.h5" #convert this to argument later
imgs_path = r"/pylon5/mc3bggp/aymen/local_dir/datasets/polygon/input_img/"                                                         #convert this to argument later

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from myproxy.client import MyProxyClient
myproxy_clnt = MyProxyClient(hostname="myproxy.somewhere.ac.uk")
cert, private_key = myproxy_clnt.logon(username, password, bootstrap=True)