{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction\n", "In this notebook, we use the `parallel` submodule of `IPython` for distributing an `OpenTURNSPythonFunction`. This is a very interesting alternative to the `otdistfunc` module that enables a better handling of errors as well as load-balancing. Note that it does not manage third-party files and execution directories though (parsed input files are often needed for running third-party softwares). But this can be handled manually using the `tempfile` module." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "try:\n", " from ipyparallel import Client\n", " from ipyparallel.error import TimeoutError, CompositeError\n", " from ipyparallel.client.view import View\n", "except ImportError:\n", " from Ipython.parallel import Client\n", " from Ipython.parallel.error import TimeoutError, CompositeError\n", " from Ipython.parallel.client.view import View\n", "from IPython.core.display import clear_output\n", "import openturns as ot\n", "from datetime import timedelta\n", "import sys" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In case you've never never heard about IPython, check the [official website](http://www.ipython.org \"IPython\") as well as the following presentation of Fernando Perez." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <iframe\n", " width=\"400\"\n", " height=\"300\"\n", " src=\"https://player.vimeo.com/video/63250251\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "text/plain": [ "<IPython.lib.display.VimeoVideo at 0x7f58ee760f90>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import VimeoVideo\n", "VimeoVideo(\"63250251\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Definition of the `OpenTURNSIPythonParallelFunction`\n", "This is the main contribution of that notebook. This class basically binds the `OpenTURNSPythonFunction` to the IPython parallel computing capabilities.\n", "\n", "Please be advised this is a raw implementation whose unique purpose is to demonstrate the power of `IPython.parallel` for expensive-to-evaluate `Function`'s." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class OpenTURNSIPythonParallelFunction(ot.OpenTURNSPythonFunction):\n", " \"\"\"Distributed Python function using IPython.parallel.\n", "\n", " Parameters\n", " ----------\n", " n : int\n", " The input dimension.\n", "\n", " p : int\n", " The output dimension.\n", "\n", " func : callable\n", " A Python function implementing the calculation for a single\n", " vector (aka NumericalPoint) input X.\n", "\n", " view : IPython.parallel.DirectView or IPython.parallel.LoadBalancedView\n", " A view of your IPython engines (either direct or load-balanced).\n", "\n", " verbose : boolean, optional\n", " Give information on progress of jobs or don't.\n", "\n", " on_error : string, optional\n", " What should be done in case an engine bumps into an error:\n", " - 'abort' prints the first error an engine has bumped into and\n", " aborts the whole stack;\n", " - 'ignore' excepts any error the engines could bump into and\n", " fills the outputs with NaN's.\n", "\n", " interval : float, optional\n", " The time interval in-between two checks of jobs status.\n", " This basically depends on the job duration.\n", " \"\"\"\n", "\n", " def __init__(self, n, p, func, view, verbose=True, on_error='abort', interval=1.):\n", "\n", " assert callable(func)\n", " assert isinstance(view, View)\n", " assert on_error in ['abort', 'ignore']\n", " super(OpenTURNSIPythonParallelFunction, self).__init__(n, p)\n", " self._func = func\n", " self._view = view\n", " self._verbose = bool(verbose)\n", " self._on_error = on_error\n", " self._interval = float(interval)\n", "\n", " def _exec(self, X):\n", "\n", " return self._exec_sample([X])[0]\n", "\n", " def _exec_sample(self, X):\n", "\n", " jobs = self._view.map_async(self._func, X)\n", "\n", " Y, done = [None] * len(jobs), []\n", " while not jobs.ready():\n", " jobs.wait(self._interval)\n", " if self._verbose:\n", " clear_output(wait=True)\n", " print(\"%4i/%i tasks finished after %s.\"\n", " % (jobs.progress, len(jobs),\n", " timedelta(0., jobs.elapsed)))\n", " sys.stdout.flush()\n", " for i in range(len(jobs)):\n", " if i in done:\n", " continue\n", " try:\n", " Y[i] = jobs[i]\n", " done.append(i)\n", " except TimeoutError:\n", " break\n", " except CompositeError as err:\n", " Y.append([np.nan] * self.getOutputDimension())\n", " done.append(i)\n", " if self._on_error == 'abort':\n", " try:\n", " jobs.abort()\n", " except AssertionError:\n", " pass\n", " if float(ot.__version__) < 1.3:\n", " err.print_exception()\n", " raise err\n", " else:\n", " pass\n", "\n", " if self._verbose:\n", " print('\\ndone')\n", "\n", " return Y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also declare a convenience function that will instanciate the final `Function` from our overloaded `OpenTURNSIPythonParallelFunction` instance as it is already done for the `OpenTURNSPythonFunction` (see `ot.PythonFunction`) or the `OpenTURNSDistributedPythonFunction` (see `otdistfunc.DistributedPythonFunction`)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def IPythonParallelFunction(*args, **kwargs):\n", " __doc__ = OpenTURNSIPythonParallelFunction.__doc__\n", "\n", " return ot.Function(OpenTURNSIPythonParallelFunction(*args, **kwargs))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that using a decorator here would be a lot more pythonic." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Practice\n", "## Cluster configuration\n", "We first need to bind to the cluster we have ignited before running this notebook using either:\n", " - the cluster tab in the main menu of the IPython Notebook,\n", " - the `ipcluster` shell command.\n", "\n", "For more information on the configuration of IPython clusters refer to the [official documentation](http://ipython.org/ipython-doc/dev/parallel/ \"IPython.parallel official doc\").\n", "\n", "Anyhow, IPython will create a default profile (called 'default'!) so that the following should work on any fresh install of IPython. This uses localhost as a cluster and the maximum allocatable number of engines is set equal to the number of CPUs you have." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "client = Client(profile='default')\n", "d_view = client.direct_view()\n", "lb_view = client.load_balanced_view()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`d_view` is a direct view of all the engines in the cluster. This can be used for executing statements in parallel on all the engines that are designated by this view (note the direct_view method of the Client object takes a `target` keyword argument that allows you to select a subset of your engines).\n", "\n", "`lb_view` is a load-balanced view of all the engines in the cluster. It features a job manager that will distribute the workload over the engines. This is a very interesting feature in case your jobs and/or the nodes of your cluster are heterogeneous.\n", "\n", "## Function declaration\n", "\n", "We first need to synchronize the dependencies of our Python function over the cluster's engines. For this we use the `sync_imports` method of our direct view." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "importing sin from math on engine(s)\n", "importing sleep from time on engine(s)\n", "importing numpy on engine(s)\n" ] } ], "source": [ "with d_view.sync_imports():\n", " from math import sin\n", " from time import sleep\n", " import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, we define our Python function which is responsible for calculating a single vector output $Y$ from a single vector input $X$ (say $f: X \\mapsto Y$).\n", "\n", "**NB:** *Just don't forget that IPython `\"engines\"` hosted on a same host share the same execution directory. Hence if you plan to parse template file and execute third-party softwares using (*e.g.* the `subprocess` module) you'd better create and cd to your own temporary execution directories using the `tempfile` module.*" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def foo_implementation(X):\n", " \"\"\"This is the function we want to distribute over our clients.\"\"\"\n", " Y = [sin(X[1]) / X[0]]\n", " sleep(2.) # Say it takes time to run...\n", " return Y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We eventually turn this function into an `OpenTURNSIPythonParallelFunction` and then into a `Function` using the convenience function declared above." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "foo = IPythonParallelFunction(n=2, p=1, func=foo_implementation, view=lb_view)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic usage" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "distribution = ot.ComposedDistribution([ot.Uniform(1., 5.)] * 2)\n", "some_random_X = distribution.getSample(10)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 10/10 tasks finished after 0:00:06.456376.\n", "\n", "done\n" ] }, { "data": { "text/html": [ "<TABLE><TR><TD></TD><TH>y0</TH></TR><TR><TH>0</TH><TD>-0.186481669188</TD></TR><TR><TH>1</TH><TD>0.126090922591</TD></TR><TR><TH>2</TH><TD>0.39089767837</TD></TR><TR><TH>3</TH><TD>-0.63941594274</TD></TR><TR><TH>4</TH><TD>-0.412076743441</TD></TR><TR><TH>5</TH><TD>0.16813638093</TD></TR><TR><TH>6</TH><TD>-0.213550881106</TD></TR><TR><TH>7</TH><TD>-0.301883462522</TD></TR><TR><TH>8</TH><TD>-0.448523787815</TD></TR><TR><TH>9</TH><TD>-0.424200919157</TD></TR></TABLE>" ], "text/plain": [ "class=NumericalSample name=Unnamed description=[y0] implementation=class=NumericalSampleImplementation name=Unnamed size=10 dimension=1 data=[class=NumericalPoint name=Unnamed dimension=1 values=[-0.186482],class=NumericalPoint name=Unnamed dimension=1 values=[0.126091],class=NumericalPoint name=Unnamed dimension=1 values=[0.390898],class=NumericalPoint name=Unnamed dimension=1 values=[-0.639416],class=NumericalPoint name=Unnamed dimension=1 values=[-0.412077],class=NumericalPoint name=Unnamed dimension=1 values=[0.168136],class=NumericalPoint name=Unnamed dimension=1 values=[-0.213551],class=NumericalPoint name=Unnamed dimension=1 values=[-0.301883],class=NumericalPoint name=Unnamed dimension=1 values=[-0.448524],class=NumericalPoint name=Unnamed dimension=1 values=[-0.424201]]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "foo(some_random_X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It rocks! Doesn't it?\n", "\n", "**NB:** By the way, note that beautiful HTML rendering of the `NumericalSample` due to the recent implementation of its `_repr_html_` method. Let's do more of these for the other OpenTURNS objects (`_repr_latex_` might also be more relevant for mathematical objects such as `Distribution`'s and `Function`'s).\n", "\n", "## You asked for debug? They thought about it!\n", "Well this is still a bit tricky to trigger, but IPython developpers made it possible to debug the traceback of the last error met by an engine. And I guess this is still work in progress, so this could be improved in the next future!...\n", "\n", "Assume we divide by zero... That's bad!..." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 1/1 tasks finished after 0:00:00.064422.\n" ] }, { "ename": "CompositeError", "evalue": "one or more exceptions from call to method: foo_implementation\n[1:apply]: ZeroDivisionError: float division by zero", "output_type": "error", "traceback": [ "[1:apply]: ", "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)\u001b[1;32m<string>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m", "\u001b[1;32m<ipython-input-7-f0b4feb3c562>\u001b[0m in \u001b[0;36mfoo_implementation\u001b[1;34m(X)\u001b[0m", "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero", "" ] }, { "ename": "RuntimeError", "evalue": "InternalException : Python exception: CompositeError", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-11-bb953f3eb703>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfoo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0.\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/home/dubourg/anaconda/lib/python2.7/site-packages/openturns/func.pyc\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m 3761\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__add__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_func\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNumericalMathFunction___add__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3762\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__sub__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_func\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNumericalMathFunction___sub__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3763\u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_func\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNumericalMathFunction___call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3764\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mgradient\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3765\u001b[0m \"\"\"\n", "\u001b[1;31mRuntimeError\u001b[0m: InternalException : Python exception: CompositeError" ] } ], "source": [ "foo([0., 1.])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see in the traceback (which is now printed in openturns 1.3! ;-)), the `IPython.parallel` module triggered a `CompositeError` that features all the errors met by the engines with headers that look like this: `[%d:apply]`. The integer in front of the column within the brackets is the number of the engine that raised the error detailed after. In the following I assume engine 2 failed.\n", "\n", "Now there are two options.\n", "\n", "First, if you are running a local cluster, you can launch an IPython qtconsole on the engine that raised the error:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/dubourg/anaconda/lib/python2.7/site-packages/IPython/parallel.py:13: ShimWarning: The `IPython.parallel` package has been deprecated. You should import from ipyparallel instead.\n", " \"You should import from ipyparallel instead.\", ShimWarning)\n" ] } ], "source": [ "%%px --target=1\n", "%qtconsole" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and then trigger the debugger in that qtconsole using the `%debug` magic. See:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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+TEEg2qNTiHD7nbiGjnT1etuWWVBACCFCHjV6ywbzgGFDZk7d8O3onxZ81Hpm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\nDC1HJCDelJ3LyapJN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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image('ipython_parallel_function_local_qtconsole.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Second, in the more practical case where your engines are remote, you can ask IPython to tell you how to connect an IPython app (console or qtconsole) to the session ran by the engine. See:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from ipyparallel import bind_kernel\n", "bind_kernel()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The targeted engine is running on host: maybach-l64\n", "\n", "{\n", " \"stdin_port\": 42383, \n", " \"ip\": \"127.0.0.1\", \n", " \"control_port\": 54426, \n", " \"hb_port\": 57028, \n", " \"signature_scheme\": \"hmac-sha256\", \n", " \"key\": \"db7d34cc-372b-40ed-8fd7-b581b9439d7a\", \n", " \"shell_port\": 37302, \n", " \"transport\": \"tcp\", \n", " \"iopub_port\": 48654\n", "}\n", "\n", "Paste the above JSON into a file, and connect with:\n", " $> ipython <app> --existing <file>\n", "or, if you are local, you can connect with just:\n", " $> ipython <app> --existing kernel-3246.json \n", "or even just:\n", " $> ipython <app> --existing \n", "if this is the most recent IPython session you have started.\n" ] } ], "source": [ "%%px --target=1\n", "import socket\n", "print('The targeted engine is running on host: %s\\n' % socket.gethostname())\n", "%connect_info" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then you just need to ssh yourself to the remote host where the engine is running and connect to this engine using the command returned above by `%connect_info`. Once you are in this IPython session, just issue a `%debug` in order to enter the post-mortem mode." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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jFO03mqBb9KzSfyH2i3MHVF1Sa+HhtTWeVDhDgzfHzP6ga4syepS659Wu009Z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\nns8uQQhz2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7S3xHpicHUEQd5Jnh8ACn0GTw26vnbLIxCYCnL05sIsHSUT5F8/HiXtPDPY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\nDAAAYQwAAO4z2ZbPNzMLAAA0of8D6FkFV+/LnioAAAAASUVORK5CYII=\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image('ipython_parallel_function_remote_console.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Happy hacking with IPython!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }