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| #!/bin/bash | |
| #SBATCH | |
| if [[ "$SLURM_PROCID" = "0" ]]; then | |
| ssh -R 0.0.0.0:9999:localhost:8888 -f -N login-0-1 | |
| jupyter notebook | |
| elif [[ "$SLURM_PROCID" = "1" ]]; then | |
| ipcontroller --ip='*' | |
| elif [[ "$SLURM_PROCID" = "2" ]]; then | |
| mpirun --bind-to=none -n "$((SLURM_NTASKS-2))" ipengine | |
| fi |
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| #!/usr/bin/env python | |
| """ | |
| Usage: python launchable.py | |
| """ | |
| from __future__ import print_function | |
| tpls = {'setup': ''' | |
| mkdir -p $HOME/launchable | |
| tempdir=$(mktemp -d --tmpdir="$HOME/launchable") | |
| TUNNEL_PORT={tunnel_port} | |
| NB_PORT={nb_port} | |
| LOGIN_NODE=$(hostname) | |
| ''', | |
| 'scripts': ''' | |
| batch="$tempdir/batch.sh" | |
| run="$tempdir/run.sh" | |
| cat >"$batch" <<EOL | |
| #!/bin/bash | |
| #SBATCH --account=nn9279k | |
| #SBATCH --time=00:15:00 | |
| #SBATCH --mem-per-cpu=3900M | |
| #SBATCH --ntasks={n} | |
| rm -rf ~/.ipython/profile_default/security | |
| srun "$run" | |
| EOL | |
| cat >"$run" <<EOL | |
| #!/bin/bash | |
| source /etc/profile | |
| source /cluster/bin/jobsetup | |
| source /usit/abel/u1/oyvinev/fenics1.6_jupyter | |
| if [[ "\$SLURM_PROCID" = "0" ]]; then | |
| ssh -R $TUNNEL_PORT:localhost:$NB_PORT $LOGIN_NODE -f -N | |
| jupyter notebook --port $NB_PORT | |
| elif [[ "\$SLURM_PROCID" = "1" ]]; then | |
| ipcontroller --ip='*' | |
| elif [[ "\$SLURM_PROCID" = "2" ]]; then | |
| mpirun --bind-to none -n "\$((SLURM_NTASKS-2))" ipengine --timeout=60 | |
| fi | |
| EOL | |
| chmod +x $run $batch | |
| echo BATCH:$batch | |
| echo RUN:$run | |
| ''', | |
| 'run': ''' | |
| jobid=$(/cluster/bin/sbatch $batch | awk '{{print $4}}') | |
| echo "job id: $jobid" | |
| test -z "$jobid" && exit 1 | |
| state="wait" | |
| while [[ "$state" = "wait" ]]; do | |
| echo . | |
| # check squeue | |
| test -z "$(squeue -u `whoami` | grep $jobid)" && state='FAILED' | |
| curl -s http://localhost:$TUNNEL_PORT 2>&1 >/dev/null && state=AOK || sleep 1 | |
| done | |
| echo "$state" | |
| if [[ "$state" = "FAILED" ]]; then | |
| while [[ ! -f "$HOME/slurm-$jobid.out ]]; do | |
| sleep 1 | |
| end | |
| cat ~/slurm-$jobid.out | |
| exit 1 | |
| fi | |
| while true; do | |
| sleep 30 | |
| done | |
| '''} | |
| import argparse | |
| from random import randint | |
| import sys | |
| import webbrowser | |
| import pexpect | |
| def expect_echo(p): | |
| p.sendline('###END###') | |
| p.expect('###END###') | |
| def run_job(options): | |
| ns = options.__dict__ | |
| print("Tunneling {local_port} to {host}:{tunnel_port}".format(**ns)) | |
| p = pexpect.spawn('ssh', [ | |
| '-L', | |
| '%i:localhost:%i' % (options.local_port, options.tunnel_port), | |
| options.host, | |
| 'bash', | |
| ]) | |
| p.logfile = sys.stderr | |
| print('setup') | |
| p.send(tpls['setup'].format(**ns)) | |
| expect_echo(p) | |
| print('scripts') | |
| p.send(tpls['scripts'].format(**ns)) | |
| expect_echo(p) | |
| print('run') | |
| p.send(tpls['run'].format(**ns)) | |
| expect_echo(p) | |
| p.expect('job id:') | |
| p.expect('\r\n') | |
| jobid = int(p.before) | |
| try: | |
| p.expect(['AOK', 'FAILED'], timeout=600) | |
| except pexpect.EOF: | |
| print("Failed") | |
| print(p.before, file=sys.stderr) | |
| sys.exit(1) | |
| if p.after == 'FAILED': | |
| print("Failed") | |
| # got failed message, wait for EOF and show output | |
| p.expect(pexpect.EOF) | |
| print(p.before, file=sys.stderr) | |
| sys.exit(1) | |
| return p | |
| def main(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--n', type=int, default=4) | |
| parser.add_argument('--port', dest='local_port', type=int, default=9999) | |
| parser.add_argument('--tunnel-port', type=int, default=randint(49152, 65535)) | |
| parser.add_argument('--nb-port', type=int, default=randint(49152, 65535)) | |
| parser.add_argument('--host', type=str, default='abel.uio.no') | |
| options = parser.parse_args() | |
| p = run_job(options) | |
| print("Opening browser") | |
| webbrowser.open('http://localhost:9999') | |
| # wait for exit | |
| p.expect(pexpect.EOF, timeout=None) | |
| if __name__ == '__main__': | |
| main() |
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Load IPython support for working with MPI tasks" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 53, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from ipyparallel import Client, error\n", | |
| "cluster = Client()\n", | |
| "view = cluster[:]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Let's also load the plotting and numerical libraries so we have them ready for visualization later on." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 54, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "%matplotlib inline\n", | |
| "import numpy as np\n", | |
| "import matplotlib.pyplot as plt" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Now, we load the MPI libraries into the engine namespaces, and do a simple printing of their MPI rank information to verify that all nodes are operational and they match our cluster's real capacity. \n", | |
| "\n", | |
| "Here, we are making use of IPython's special `%%px` cell magic, which marks the entire cell for parallel execution. This means that the code below will not run in this notebook's kernel, but instead will be sent to *all* engines for execution there. In this way, IPython makes it very natural to control your entire cluster from within the notebook environment:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 55, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[stdout:0] MPI rank: 1/4\n", | |
| "[stdout:1] MPI rank: 2/4\n", | |
| "[stdout:2] MPI rank: 3/4\n", | |
| "[stdout:3] MPI rank: 0/4\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%%px\n", | |
| "# MPI initialization, library imports and sanity checks on all engines\n", | |
| "from mpi4py import MPI\n", | |
| "# Load data publication API so engines can send data to notebook client\n", | |
| "from ipykernel.datapub import publish_data\n", | |
| "import numpy as np\n", | |
| "import time\n", | |
| "\n", | |
| "mpi = MPI.COMM_WORLD\n", | |
| "bcast = mpi.bcast\n", | |
| "barrier = mpi.barrier\n", | |
| "rank = mpi.rank\n", | |
| "size = mpi.size\n", | |
| "print(\"MPI rank: %i/%i\" % (mpi.rank,mpi.size))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "We write a utility that reorders a list according to the mpi ranks of the engines, since all gather operations will return data in engine id order, not in MPI rank order. We'll need this later on when we want to reassemble in IPython data structures coming from all the engines: IPython will collect the data ordered by engine ID, but our code creates data structures based on MPI rank, so we need to map from one indexing scheme to the other. This simple function does the job:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 56, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "ranks = view['rank']\n", | |
| "engine_mpi = np.argsort(ranks)\n", | |
| "\n", | |
| "def mpi_order(seq):\n", | |
| " \"\"\"Return elements of a sequence ordered by MPI rank.\n", | |
| "\n", | |
| " The input sequence is assumed to be ordered by engine ID.\"\"\"\n", | |
| " return [seq[x] for x in engine_mpi]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "We now define a local (to this notebook) plotting function that fetches data from the engines' global namespace. Once it has retrieved the current state of the relevant variables, it produces and returns a figure:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 58, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "[0, 1, 2, 3]" | |
| ] | |
| }, | |
| "execution_count": 58, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "mpi_order(view.apply_sync(lambda : mpi.rank))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 50, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<AsyncResult: execute>" | |
| ] | |
| }, | |
| "execution_count": 50, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "%%px --noblock\n", | |
| "import numpy as np\n", | |
| "from ipykernel.datapub import publish_data\n", | |
| "N = 1000\n", | |
| "stride = N // size\n", | |
| "x = np.linspace(0, 10, N)[stride * rank:stride*(rank+1)]\n", | |
| "publish_data({'x': x})\n", | |
| "\n", | |
| "for i in range(10):\n", | |
| " y = np.sin(x * i)\n", | |
| " publish_data({'y': y, 'i': i})\n", | |
| " time.sleep(1)\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 51, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "ar = _" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 52, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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+U/JekmZZ8RCtygbudBPsdGOMZxn7YSAP1kzTDLvdeGWw5mpF6cDgYL3JikC1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\n1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VEL/y94AEhJ4AGuISIxA3MPEU6e3RzOG7JY4+DwgRRkeldrCsr3Q9JAFAKHFh2Ib5vE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\nRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x108c794e0>" | |
| ] | |
| }, | |
| "metadata": { | |
| "image/png": { | |
| "height": 381, | |
| "width": 604 | |
| } | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "def stitch_data(ar, key):\n", | |
| " data = mpi_order(ar.data)\n", | |
| " parts = [ d[key] for d in data ]\n", | |
| " return np.concatenate(parts)\n", | |
| " \n", | |
| "\n", | |
| "ar.wait(1)\n", | |
| "while not ar.ready():\n", | |
| " clear_output(wait=True)\n", | |
| " x = stitch_data(ar, 'x')\n", | |
| " y = stitch_data(ar, 'y')\n", | |
| " plt.plot(x, y)\n", | |
| " plt.show()\n", | |
| " ar.wait(1)\n", | |
| " " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 45, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "ename": "CompositeError", | |
| "evalue": "one or more exceptions from call to method: execute\n[0:execute]: NameError: name 'size' is not defined\n[1:execute]: NameError: name 'size' is not defined\n[2:execute]: NameError: name 'size' is not defined\n[3:execute]: NameError: name 'size' is not defined", | |
| "output_type": "error", | |
| "traceback": [ | |
| "[0:execute]: ", | |
| "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)\u001b[1;32m<ipython-input-7-cf61bacbacd5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m", | |
| "\u001b[0;32m 2\u001b[0m \u001b[1;32mfrom\u0001\u001b[0m\u0002 \u001b[0mipykernel\u0001\u001b[0m\u0002\u001b[1;33m.\u0001\u001b[0m\u0002\u001b[0mdatapub\u0001\u001b[0m\u0002 \u001b[1;32mimport\u0001\u001b[0m\u0002 \u001b[0mpublish_data\u0001\u001b[0m\u0002\u001b[1;33m\u0001\u001b[0m\u0002\u0001\u001b[0m\u0002", | |
| "\u001b[0;32m 3\u001b[0m \u001b[0mN\u0001\u001b[0m\u0002 \u001b[1;33m=\u0001\u001b[0m\u0002 \u001b[1;36m1000\u0001\u001b[0m\u0002\u001b[1;33m\u0001\u001b[0m\u0002\u0001\u001b[0m\u0002", | |
| "\u001b[1;32m----> 4\u001b[1;33m \u001b[0mstride\u0001\u001b[0m\u0002 \u001b[1;33m=\u0001\u001b[0m\u0002 \u001b[0mN\u0001\u001b[0m\u0002 \u001b[1;33m//\u0001\u001b[0m\u0002 \u001b[0msize\u0001\u001b[0m\u0002\u001b[1;33m\u0001\u001b[0m\u0002\u0001\u001b[0m\u0002", | |
| "\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0mx\u0001\u001b[0m\u0002 \u001b[1;33m=\u0001\u001b[0m\u0002 \u001b[0mnp\u0001\u001b[0m\u0002\u001b[1;33m.\u0001\u001b[0m\u0002\u001b[0mlinspace\u0001\u001b[0m\u0002\u001b[1;33m(\u0001\u001b[0m\u0002\u001b[1;36m0\u0001\u001b[0m\u0002\u001b[1;33m,\u0001\u001b[0m\u0002 \u001b[1;36m10\u0001\u001b[0m\u0002\u001b[1;33m,\u0001\u001b[0m\u0002 \u001b[0mN\u0001\u001b[0m\u0002\u001b[1;33m)\u0001\u001b[0m\u0002\u001b[1;33m[\u0001\u001b[0m\u0002\u001b[0mstride\u0001\u001b[0m\u0002 \u001b[1;33m*\u0001\u001b[0m\u0002 \u001b[0mrank\u0001\u001b[0m\u0002\u001b[1;33m:\u0001\u001b[0m\u0002\u001b[0mstride\u0001\u001b[0m\u0002\u001b[1;33m*\u0001\u001b[0m\u0002\u001b[1;33m(\u0001\u001b[0m\u0002\u001b[0mrank\u0001\u001b[0m\u0002\u001b[1;33m+\u0001\u001b[0m\u0002\u001b[1;36m1\u0001\u001b[0m\u0002\u001b[1;33m)\u0001\u001b[0m\u0002\u001b[1;33m]\u0001\u001b[0m\u0002\u001b[1;33m\u0001\u001b[0m\u0002\u0001\u001b[0m\u0002", | |
| "\u001b[0;32m 6\u001b[0m \u001b[0mpublish_data\u0001\u001b[0m\u0002\u001b[1;33m(\u0001\u001b[0m\u0002\u001b[1;33m{\u0001\u001b[0m\u0002\u001b[1;34m'x'\u0001\u001b[0m\u0002\u001b[1;33m:\u0001\u001b[0m\u0002 \u001b[0mx\u0001\u001b[0m\u0002\u001b[1;33m}\u0001\u001b[0m\u0002\u001b[1;33m)\u0001\u001b[0m\u0002\u001b[1;33m\u0001\u001b[0m\u0002\u0001\u001b[0m\u0002", | |
| "\u001b[1;31mNameError\u001b[0m: name 'size' is not defined", | |
| "", | |
| "[1:execute]: ", | |
| "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)\u001b[1;32m<ipython-input-7-cf61bacbacd5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m", | |
| "\u001b[0;32m 2\u001b[0m \u001b[1;32mfrom\u0001\u001b[0m\u0002 \u001b[0mipykernel\u0001\u001b[0m\u0002\u001b[1;33m.\u0001\u001b[0m\u0002\u001b[0mdatapub\u0001\u001b[0m\u0002 \u001b[1;32mimport\u0001\u001b[0m\u0002 \u001b[0mpublish_data\u0001\u001b[0m\u0002\u001b[1;33m\u0001\u001b[0m\u0002\u0001\u001b[0m\u0002", | |
| "\u001b[0;32m 3\u001b[0m \u001b[0mN\u0001\u001b[0m\u0002 \u001b[1;33m=\u0001\u001b[0m\u0002 \u001b[1;36m1000\u0001\u001b[0m\u0002\u001b[1;33m\u0001\u001b[0m\u0002\u0001\u001b[0m\u0002", | |
| "\u001b[1;32m----> 4\u001b[1;33m \u001b[0mstride\u0001\u001b[0m\u0002 \u001b[1;33m=\u0001\u001b[0m\u0002 \u001b[0mN\u0001\u001b[0m\u0002 \u001b[1;33m//\u0001\u001b[0m\u0002 \u001b[0msize\u0001\u001b[0m\u0002\u001b[1;33m\u0001\u001b[0m\u0002\u0001\u001b[0m\u0002", | |
| "\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0mx\u0001\u001b[0m\u0002 \u001b[1;33m=\u0001\u001b[0m\u0002 \u001b[0mnp\u0001\u001b[0m\u0002\u001b[1;33m.\u0001\u001b[0m\u0002\u001b[0mlinspace\u0001\u001b[0m\u0002\u001b[1;33m(\u0001\u001b[0m\u0002\u001b[1;36m0\u0001\u001b[0m\u0002\u001b[1;33m,\u0001\u001b[0m\u0002 \u001b[1;36m10\u0001\u001b[0m\u0002\u001b[1;33m,\u0001\u001b[0m\u0002 \u001b[0mN\u0001\u001b[0m\u0002\u001b[1;33m)\u0001\u001b[0m\u0002\u001b[1;33m[\u0001\u001b[0m\u0002\u001b[0mstride\u0001\u001b[0m\u0002 \u001b[1;33m*\u0001\u001b[0m\u0002 \u001b[0mrank\u0001\u001b[0m\u0002\u001b[1;33m:\u0001\u001b[0m\u0002\u001b[0mstride\u0001\u001b[0m\u0002\u001b[1;33m*\u0001\u001b[0m\u0002\u001b[1;33m(\u0001\u001b[0m\u0002\u001b[0mrank\u0001\u001b[0m\u0002\u001b[1;33m+\u0001\u001b[0m\u0002\u001b[1;36m1\u0001\u001b[0m\u0002\u001b[1;33m)\u0001\u001b[0m\u0002\u001b[1;33m]\u0001\u001b[0m\u0002\u001b[1;33m\u0001\u001b[0m\u0002\u0001\u001b[0m\u0002", | |
| "\u001b[0;32m 6\u001b[0m \u001b[0mpublish_data\u0001\u001b[0m\u0002\u001b[1;33m(\u0001\u001b[0m\u0002\u001b[1;33m{\u0001\u001b[0m\u0002\u001b[1;34m'x'\u0001\u001b[0m\u0002\u001b[1;33m:\u0001\u001b[0m\u0002 \u001b[0mx\u0001\u001b[0m\u0002\u001b[1;33m}\u0001\u001b[0m\u0002\u001b[1;33m)\u0001\u001b[0m\u0002\u001b[1;33m\u0001\u001b[0m\u0002\u0001\u001b[0m\u0002", | |
| "\u001b[1;31mNameError\u001b[0m: name 'size' is not defined", | |
| "", | |
| "[2:execute]: ", | |
| "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)\u001b[1;32m<ipython-input-7-cf61bacbacd5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m", | |
| "\u001b[0;32m 2\u001b[0m \u001b[1;32mfrom\u0001\u001b[0m\u0002 \u001b[0mipykernel\u0001\u001b[0m\u0002\u001b[1;33m.\u0001\u001b[0m\u0002\u001b[0mdatapub\u0001\u001b[0m\u0002 \u001b[1;32mimport\u0001\u001b[0m\u0002 \u001b[0mpublish_data\u0001\u001b[0m\u0002\u001b[1;33m\u0001\u001b[0m\u0002\u0001\u001b[0m\u0002", | |
| "\u001b[0;32m 3\u001b[0m \u001b[0mN\u0001\u001b[0m\u0002 \u001b[1;33m=\u0001\u001b[0m\u0002 \u001b[1;36m1000\u0001\u001b[0m\u0002\u001b[1;33m\u0001\u001b[0m\u0002\u0001\u001b[0m\u0002", | |
| "\u001b[1;32m----> 4\u001b[1;33m \u001b[0mstride\u0001\u001b[0m\u0002 \u001b[1;33m=\u0001\u001b[0m\u0002 \u001b[0mN\u0001\u001b[0m\u0002 \u001b[1;33m//\u0001\u001b[0m\u0002 \u001b[0msize\u0001\u001b[0m\u0002\u001b[1;33m\u0001\u001b[0m\u0002\u0001\u001b[0m\u0002", | |
| "\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0mx\u0001\u001b[0m\u0002 \u001b[1;33m=\u0001\u001b[0m\u0002 \u001b[0mnp\u0001\u001b[0m\u0002\u001b[1;33m.\u0001\u001b[0m\u0002\u001b[0mlinspace\u0001\u001b[0m\u0002\u001b[1;33m(\u0001\u001b[0m\u0002\u001b[1;36m0\u0001\u001b[0m\u0002\u001b[1;33m,\u0001\u001b[0m\u0002 \u001b[1;36m10\u0001\u001b[0m\u0002\u001b[1;33m,\u0001\u001b[0m\u0002 \u001b[0mN\u0001\u001b[0m\u0002\u001b[1;33m)\u0001\u001b[0m\u0002\u001b[1;33m[\u0001\u001b[0m\u0002\u001b[0mstride\u0001\u001b[0m\u0002 \u001b[1;33m*\u0001\u001b[0m\u0002 \u001b[0mrank\u0001\u001b[0m\u0002\u001b[1;33m:\u0001\u001b[0m\u0002\u001b[0mstride\u0001\u001b[0m\u0002\u001b[1;33m*\u0001\u001b[0m\u0002\u001b[1;33m(\u0001\u001b[0m\u0002\u001b[0mrank\u0001\u001b[0m\u0002\u001b[1;33m+\u0001\u001b[0m\u0002\u001b[1;36m1\u0001\u001b[0m\u0002\u001b[1;33m)\u0001\u001b[0m\u0002\u001b[1;33m]\u0001\u001b[0m\u0002\u001b[1;33m\u0001\u001b[0m\u0002\u0001\u001b[0m\u0002", | |
| "\u001b[0;32m 6\u001b[0m \u001b[0mpublish_data\u0001\u001b[0m\u0002\u001b[1;33m(\u0001\u001b[0m\u0002\u001b[1;33m{\u0001\u001b[0m\u0002\u001b[1;34m'x'\u0001\u001b[0m\u0002\u001b[1;33m:\u0001\u001b[0m\u0002 \u001b[0mx\u0001\u001b[0m\u0002\u001b[1;33m}\u0001\u001b[0m\u0002\u001b[1;33m)\u0001\u001b[0m\u0002\u001b[1;33m\u0001\u001b[0m\u0002\u0001\u001b[0m\u0002", | |
| "\u001b[1;31mNameError\u001b[0m: name 'size' is not defined", | |
| "", | |
| "[3:execute]: ", | |
| "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)\u001b[1;32m<ipython-input-7-cf61bacbacd5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m", | |
| "\u001b[0;32m 2\u001b[0m \u001b[1;32mfrom\u0001\u001b[0m\u0002 \u001b[0mipykernel\u0001\u001b[0m\u0002\u001b[1;33m.\u0001\u001b[0m\u0002\u001b[0mdatapub\u0001\u001b[0m\u0002 \u001b[1;32mimport\u0001\u001b[0m\u0002 \u001b[0mpublish_data\u0001\u001b[0m\u0002\u001b[1;33m\u0001\u001b[0m\u0002\u0001\u001b[0m\u0002", | |
| "\u001b[0;32m 3\u001b[0m \u001b[0mN\u0001\u001b[0m\u0002 \u001b[1;33m=\u0001\u001b[0m\u0002 \u001b[1;36m1000\u0001\u001b[0m\u0002\u001b[1;33m\u0001\u001b[0m\u0002\u0001\u001b[0m\u0002", | |
| "\u001b[1;32m----> 4\u001b[1;33m \u001b[0mstride\u0001\u001b[0m\u0002 \u001b[1;33m=\u0001\u001b[0m\u0002 \u001b[0mN\u0001\u001b[0m\u0002 \u001b[1;33m//\u0001\u001b[0m\u0002 \u001b[0msize\u0001\u001b[0m\u0002\u001b[1;33m\u0001\u001b[0m\u0002\u0001\u001b[0m\u0002", | |
| "\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0mx\u0001\u001b[0m\u0002 \u001b[1;33m=\u0001\u001b[0m\u0002 \u001b[0mnp\u0001\u001b[0m\u0002\u001b[1;33m.\u0001\u001b[0m\u0002\u001b[0mlinspace\u0001\u001b[0m\u0002\u001b[1;33m(\u0001\u001b[0m\u0002\u001b[1;36m0\u0001\u001b[0m\u0002\u001b[1;33m,\u0001\u001b[0m\u0002 \u001b[1;36m10\u0001\u001b[0m\u0002\u001b[1;33m,\u0001\u001b[0m\u0002 \u001b[0mN\u0001\u001b[0m\u0002\u001b[1;33m)\u0001\u001b[0m\u0002\u001b[1;33m[\u0001\u001b[0m\u0002\u001b[0mstride\u0001\u001b[0m\u0002 \u001b[1;33m*\u0001\u001b[0m\u0002 \u001b[0mrank\u0001\u001b[0m\u0002\u001b[1;33m:\u0001\u001b[0m\u0002\u001b[0mstride\u0001\u001b[0m\u0002\u001b[1;33m*\u0001\u001b[0m\u0002\u001b[1;33m(\u0001\u001b[0m\u0002\u001b[0mrank\u0001\u001b[0m\u0002\u001b[1;33m+\u0001\u001b[0m\u0002\u001b[1;36m1\u0001\u001b[0m\u0002\u001b[1;33m)\u0001\u001b[0m\u0002\u001b[1;33m]\u0001\u001b[0m\u0002\u001b[1;33m\u0001\u001b[0m\u0002\u0001\u001b[0m\u0002", | |
| "\u001b[0;32m 6\u001b[0m \u001b[0mpublish_data\u0001\u001b[0m\u0002\u001b[1;33m(\u0001\u001b[0m\u0002\u001b[1;33m{\u0001\u001b[0m\u0002\u001b[1;34m'x'\u0001\u001b[0m\u0002\u001b[1;33m:\u0001\u001b[0m\u0002 \u001b[0mx\u0001\u001b[0m\u0002\u001b[1;33m}\u0001\u001b[0m\u0002\u001b[1;33m)\u0001\u001b[0m\u0002\u001b[1;33m\u0001\u001b[0m\u0002\u0001\u001b[0m\u0002", | |
| "\u001b[1;31mNameError\u001b[0m: name 'size' is not defined", | |
| "" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "ar.get()" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.5.0" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 0 | |
| } |
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| #SBATCH | |
| env | |
| if [[ "$SLURM_PROCID" = "0" ]]; then | |
| echo "hi" | |
| else | |
| mpirun python -c "import os; print(os.getpid())" | |
| fi |
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