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author | Reynold Xin <rxin@apache.org> | 2015-09-17 22:11:21 +0000 |
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committer | Reynold Xin <rxin@apache.org> | 2015-09-17 22:11:21 +0000 |
commit | 6f57b0c45a7d1b6255067c6e9bc549baa491acac (patch) | |
tree | dbf7d7a7700e9e6bad3c8289ab831bc9c2c20d62 /site/docs/1.5.0/running-on-mesos.html | |
parent | ee9ffe89d608e7640a2487406b618d27e58026d6 (diff) | |
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diff --git a/site/docs/1.5.0/running-on-mesos.html b/site/docs/1.5.0/running-on-mesos.html new file mode 100644 index 000000000..5e80712d2 --- /dev/null +++ b/site/docs/1.5.0/running-on-mesos.html @@ -0,0 +1,591 @@ +<!DOCTYPE html> +<!--[if lt IE 7]> <html class="no-js lt-ie9 lt-ie8 lt-ie7"> <![endif]--> +<!--[if IE 7]> <html class="no-js lt-ie9 lt-ie8"> <![endif]--> +<!--[if IE 8]> <html class="no-js lt-ie9"> <![endif]--> +<!--[if gt IE 8]><!--> <html class="no-js"> <!--<![endif]--> + <head> + <meta charset="utf-8"> + <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1"> + <title>Running Spark on Mesos - Spark 1.5.0 Documentation</title> + + + + + <link rel="stylesheet" href="css/bootstrap.min.css"> + <style> + body { + padding-top: 60px; + padding-bottom: 40px; + } + </style> + <meta name="viewport" content="width=device-width"> + <link rel="stylesheet" href="css/bootstrap-responsive.min.css"> + <link rel="stylesheet" href="css/main.css"> + + <script src="js/vendor/modernizr-2.6.1-respond-1.1.0.min.js"></script> + + <link rel="stylesheet" href="css/pygments-default.css"> + + + <!-- Google analytics script --> + <script type="text/javascript"> + var _gaq = _gaq || []; + _gaq.push(['_setAccount', 'UA-32518208-2']); + _gaq.push(['_trackPageview']); + + (function() { + var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; + ga.src = ('https:' == document.location.protocol ? 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<li><a href="tuning.html">Tuning Guide</a></li> + <li><a href="job-scheduling.html">Job Scheduling</a></li> + <li><a href="security.html">Security</a></li> + <li><a href="hardware-provisioning.html">Hardware Provisioning</a></li> + <li><a href="hadoop-third-party-distributions.html">3<sup>rd</sup>-Party Hadoop Distros</a></li> + <li class="divider"></li> + <li><a href="building-spark.html">Building Spark</a></li> + <li><a href="https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark">Contributing to Spark</a></li> + <li><a href="https://cwiki.apache.org/confluence/display/SPARK/Supplemental+Spark+Projects">Supplemental Projects</a></li> + </ul> + </li> + </ul> + <!--<p class="navbar-text pull-right"><span class="version-text">v1.5.0</span></p>--> + </div> + </div> + </div> + + <div class="container" id="content"> + + <h1 class="title">Running Spark on Mesos</h1> + + + <ul id="markdown-toc"> + <li><a href="#how-it-works">How it Works</a></li> + <li><a href="#installing-mesos">Installing Mesos</a> <ul> + <li><a href="#from-source">From Source</a></li> + <li><a href="#third-party-packages">Third-Party Packages</a></li> + <li><a href="#verification">Verification</a></li> + </ul> + </li> + <li><a href="#connecting-spark-to-mesos">Connecting Spark to Mesos</a> <ul> + <li><a href="#uploading-spark-package">Uploading Spark Package</a></li> + <li><a href="#using-a-mesos-master-url">Using a Mesos Master URL</a></li> + <li><a href="#client-mode">Client Mode</a></li> + <li><a href="#cluster-mode">Cluster mode</a></li> + </ul> + </li> + <li><a href="#mesos-run-modes">Mesos Run Modes</a></li> + <li><a href="#mesos-docker-support">Mesos Docker Support</a></li> + <li><a href="#running-alongside-hadoop">Running Alongside Hadoop</a></li> + <li><a href="#dynamic-resource-allocation-with-mesos">Dynamic Resource Allocation with Mesos</a></li> + <li><a href="#configuration">Configuration</a> <ul> + <li><a href="#spark-properties">Spark Properties</a></li> + </ul> + </li> + <li><a href="#troubleshooting-and-debugging">Troubleshooting and Debugging</a></li> +</ul> + +<p>Spark can run on hardware clusters managed by <a href="http://mesos.apache.org/">Apache Mesos</a>.</p> + +<p>The advantages of deploying Spark with Mesos include:</p> + +<ul> + <li>dynamic partitioning between Spark and other +<a href="https://mesos.apache.org/documentation/latest/mesos-frameworks/">frameworks</a></li> + <li>scalable partitioning between multiple instances of Spark</li> +</ul> + +<h1 id="how-it-works">How it Works</h1> + +<p>In a standalone cluster deployment, the cluster manager in the below diagram is a Spark master +instance. When using Mesos, the Mesos master replaces the Spark master as the cluster manager.</p> + +<p style="text-align: center;"> + <img src="img/cluster-overview.png" title="Spark cluster components" alt="Spark cluster components" /> +</p> + +<p>Now when a driver creates a job and starts issuing tasks for scheduling, Mesos determines what +machines handle what tasks. Because it takes into account other frameworks when scheduling these +many short-lived tasks, multiple frameworks can coexist on the same cluster without resorting to a +static partitioning of resources.</p> + +<p>To get started, follow the steps below to install Mesos and deploy Spark jobs via Mesos.</p> + +<h1 id="installing-mesos">Installing Mesos</h1> + +<p>Spark 1.5.0 is designed for use with Mesos 0.21.0 and does not +require any special patches of Mesos.</p> + +<p>If you already have a Mesos cluster running, you can skip this Mesos installation step.</p> + +<p>Otherwise, installing Mesos for Spark is no different than installing Mesos for use by other +frameworks. You can install Mesos either from source or using prebuilt packages.</p> + +<h2 id="from-source">From Source</h2> + +<p>To install Apache Mesos from source, follow these steps:</p> + +<ol> + <li>Download a Mesos release from a +<a href="http://www.apache.org/dyn/closer.lua/mesos/0.21.0/">mirror</a></li> + <li>Follow the Mesos <a href="http://mesos.apache.org/gettingstarted">Getting Started</a> page for compiling and +installing Mesos</li> +</ol> + +<p><strong>Note:</strong> If you want to run Mesos without installing it into the default paths on your system +(e.g., if you lack administrative privileges to install it), pass the +<code>--prefix</code> option to <code>configure</code> to tell it where to install. For example, pass +<code>--prefix=/home/me/mesos</code>. By default the prefix is <code>/usr/local</code>.</p> + +<h2 id="third-party-packages">Third-Party Packages</h2> + +<p>The Apache Mesos project only publishes source releases, not binary packages. But other +third party projects publish binary releases that may be helpful in setting Mesos up.</p> + +<p>One of those is Mesosphere. To install Mesos using the binary releases provided by Mesosphere:</p> + +<ol> + <li>Download Mesos installation package from <a href="http://mesosphere.io/downloads/">downloads page</a></li> + <li>Follow their instructions for installation and configuration</li> +</ol> + +<p>The Mesosphere installation documents suggest setting up ZooKeeper to handle Mesos master failover, +but Mesos can be run without ZooKeeper using a single master as well.</p> + +<h2 id="verification">Verification</h2> + +<p>To verify that the Mesos cluster is ready for Spark, navigate to the Mesos master webui at port +<code>:5050</code> Confirm that all expected machines are present in the slaves tab.</p> + +<h1 id="connecting-spark-to-mesos">Connecting Spark to Mesos</h1> + +<p>To use Mesos from Spark, you need a Spark binary package available in a place accessible by Mesos, and +a Spark driver program configured to connect to Mesos.</p> + +<p>Alternatively, you can also install Spark in the same location in all the Mesos slaves, and configure +<code>spark.mesos.executor.home</code> (defaults to SPARK_HOME) to point to that location.</p> + +<h2 id="uploading-spark-package">Uploading Spark Package</h2> + +<p>When Mesos runs a task on a Mesos slave for the first time, that slave must have a Spark binary +package for running the Spark Mesos executor backend. +The Spark package can be hosted at any Hadoop-accessible URI, including HTTP via <code>http://</code>, +<a href="http://aws.amazon.com/s3">Amazon Simple Storage Service</a> via <code>s3n://</code>, or HDFS via <code>hdfs://</code>.</p> + +<p>To use a precompiled package:</p> + +<ol> + <li>Download a Spark binary package from the Spark <a href="https://spark.apache.org/downloads.html">download page</a></li> + <li>Upload to hdfs/http/s3</li> +</ol> + +<p>To host on HDFS, use the Hadoop fs put command: <code>hadoop fs -put spark-1.5.0.tar.gz +/path/to/spark-1.5.0.tar.gz</code></p> + +<p>Or if you are using a custom-compiled version of Spark, you will need to create a package using +the <code>make-distribution.sh</code> script included in a Spark source tarball/checkout.</p> + +<ol> + <li>Download and build Spark using the instructions <a href="index.html">here</a></li> + <li>Create a binary package using <code>make-distribution.sh --tgz</code>.</li> + <li>Upload archive to http/s3/hdfs</li> +</ol> + +<h2 id="using-a-mesos-master-url">Using a Mesos Master URL</h2> + +<p>The Master URLs for Mesos are in the form <code>mesos://host:5050</code> for a single-master Mesos +cluster, or <code>mesos://zk://host:2181</code> for a multi-master Mesos cluster using ZooKeeper.</p> + +<h2 id="client-mode">Client Mode</h2> + +<p>In client mode, a Spark Mesos framework is launched directly on the client machine and waits for the driver output.</p> + +<p>The driver needs some configuration in <code>spark-env.sh</code> to interact properly with Mesos:</p> + +<ol> + <li>In <code>spark-env.sh</code> set some environment variables: + <ul> + <li><code>export MESOS_NATIVE_JAVA_LIBRARY=<path to libmesos.so></code>. This path is typically +<code><prefix>/lib/libmesos.so</code> where the prefix is <code>/usr/local</code> by default. See Mesos installation +instructions above. On Mac OS X, the library is called <code>libmesos.dylib</code> instead of +<code>libmesos.so</code>.</li> + <li><code>export SPARK_EXECUTOR_URI=<URL of spark-1.5.0.tar.gz uploaded above></code>.</li> + </ul> + </li> + <li>Also set <code>spark.executor.uri</code> to <code><URL of spark-1.5.0.tar.gz></code>.</li> +</ol> + +<p>Now when starting a Spark application against the cluster, pass a <code>mesos://</code> +URL as the master when creating a <code>SparkContext</code>. For example:</p> + +<div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">val</span> <span class="n">conf</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SparkConf</span><span class="o">()</span> + <span class="o">.</span><span class="n">setMaster</span><span class="o">(</span><span class="s">"mesos://HOST:5050"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setAppName</span><span class="o">(</span><span class="s">"My app"</span><span class="o">)</span> + <span class="o">.</span><span class="n">set</span><span class="o">(</span><span class="s">"spark.executor.uri"</span><span class="o">,</span> <span class="s">"<path to spark-1.5.0.tar.gz uploaded above>"</span><span class="o">)</span> +<span class="k">val</span> <span class="n">sc</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SparkContext</span><span class="o">(</span><span class="n">conf</span><span class="o">)</span></code></pre></div> + +<p>(You can also use <a href="submitting-applications.html"><code>spark-submit</code></a> and configure <code>spark.executor.uri</code> +in the <a href="configuration.html#loading-default-configurations">conf/spark-defaults.conf</a> file.)</p> + +<p>When running a shell, the <code>spark.executor.uri</code> parameter is inherited from <code>SPARK_EXECUTOR_URI</code>, so +it does not need to be redundantly passed in as a system property.</p> + +<div class="highlight"><pre><code class="language-bash" data-lang="bash">./bin/spark-shell --master mesos://host:5050</code></pre></div> + +<h2 id="cluster-mode">Cluster mode</h2> + +<p>Spark on Mesos also supports cluster mode, where the driver is launched in the cluster and the client +can find the results of the driver from the Mesos Web UI.</p> + +<p>To use cluster mode, you must start the MesosClusterDispatcher in your cluster via the <code>sbin/start-mesos-dispatcher.sh</code> script, +passing in the Mesos master url (e.g: mesos://host:5050).</p> + +<p>From the client, you can submit a job to Mesos cluster by running <code>spark-submit</code> and specifying the master url +to the url of the MesosClusterDispatcher (e.g: mesos://dispatcher:7077). You can view driver statuses on the +Spark cluster Web UI.</p> + +<h1 id="mesos-run-modes">Mesos Run Modes</h1> + +<p>Spark can run over Mesos in two modes: “fine-grained” (default) and “coarse-grained”.</p> + +<p>In “fine-grained” mode (default), each Spark task runs as a separate Mesos task. This allows +multiple instances of Spark (and other frameworks) to share machines at a very fine granularity, +where each application gets more or fewer machines as it ramps up and down, but it comes with an +additional overhead in launching each task. This mode may be inappropriate for low-latency +requirements like interactive queries or serving web requests.</p> + +<p>The “coarse-grained” mode will instead launch only <em>one</em> long-running Spark task on each Mesos +machine, and dynamically schedule its own “mini-tasks” within it. The benefit is much lower startup +overhead, but at the cost of reserving the Mesos resources for the complete duration of the +application.</p> + +<p>To run in coarse-grained mode, set the <code>spark.mesos.coarse</code> property in your +<a href="configuration.html#spark-properties">SparkConf</a>:</p> + +<div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="n">conf</span><span class="o">.</span><span class="n">set</span><span class="o">(</span><span class="s">"spark.mesos.coarse"</span><span class="o">,</span> <span class="s">"true"</span><span class="o">)</span></code></pre></div> + +<p>In addition, for coarse-grained mode, you can control the maximum number of resources Spark will +acquire. By default, it will acquire <em>all</em> cores in the cluster (that get offered by Mesos), which +only makes sense if you run just one application at a time. You can cap the maximum number of cores +using <code>conf.set("spark.cores.max", "10")</code> (for example).</p> + +<p>You may also make use of <code>spark.mesos.constraints</code> to set attribute based constraints on mesos resource offers. By default, all resource offers will be accepted.</p> + +<div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="n">conf</span><span class="o">.</span><span class="n">set</span><span class="o">(</span><span class="s">"spark.mesos.constraints"</span><span class="o">,</span> <span class="s">"tachyon=true;us-east-1=false"</span><span class="o">)</span></code></pre></div> + +<p>For example, Let’s say <code>spark.mesos.constraints</code> is set to <code>tachyon=true;us-east-1=false</code>, then the resource offers will be checked to see if they meet both these constraints and only then will be accepted to start new executors.</p> + +<h1 id="mesos-docker-support">Mesos Docker Support</h1> + +<p>Spark can make use of a Mesos Docker containerizer by setting the property <code>spark.mesos.executor.docker.image</code> +in your <a href="configuration.html#spark-properties">SparkConf</a>.</p> + +<p>The Docker image used must have an appropriate version of Spark already part of the image, or you can +have Mesos download Spark via the usual methods.</p> + +<p>Requires Mesos version 0.20.1 or later.</p> + +<h1 id="running-alongside-hadoop">Running Alongside Hadoop</h1> + +<p>You can run Spark and Mesos alongside your existing Hadoop cluster by just launching them as a +separate service on the machines. To access Hadoop data from Spark, a full <code>hdfs://</code> URL is required +(typically <code>hdfs://<namenode>:9000/path</code>, but you can find the right URL on your Hadoop Namenode web +UI).</p> + +<p>In addition, it is possible to also run Hadoop MapReduce on Mesos for better resource isolation and +sharing between the two. In this case, Mesos will act as a unified scheduler that assigns cores to +either Hadoop or Spark, as opposed to having them share resources via the Linux scheduler on each +node. Please refer to <a href="https://github.com/mesos/hadoop">Hadoop on Mesos</a>.</p> + +<p>In either case, HDFS runs separately from Hadoop MapReduce, without being scheduled through Mesos.</p> + +<h1 id="dynamic-resource-allocation-with-mesos">Dynamic Resource Allocation with Mesos</h1> + +<p>Mesos supports dynamic allocation only with coarse grain mode, which can resize the number of executors based on statistics +of the application. While dynamic allocation supports both scaling up and scaling down the number of executors, the coarse grain scheduler only supports scaling down +since it is already designed to run one executor per slave with the configured amount of resources. However, after scaling down the number of executors the coarse grain scheduler +can scale back up to the same amount of executors when Spark signals more executors are needed.</p> + +<p>Users that like to utilize this feature should launch the Mesos Shuffle Service that +provides shuffle data cleanup functionality on top of the Shuffle Service since Mesos doesn’t yet support notifying another framework’s +termination. To launch/stop the Mesos Shuffle Service please use the provided sbin/start-mesos-shuffle-service.sh and sbin/stop-mesos-shuffle-service.sh +scripts accordingly.</p> + +<p>The Shuffle Service is expected to be running on each slave node that will run Spark executors. One way to easily achieve this with Mesos +is to launch the Shuffle Service with Marathon with a unique host constraint.</p> + +<h1 id="configuration">Configuration</h1> + +<p>See the <a href="configuration.html">configuration page</a> for information on Spark configurations. The following configs are specific for Spark on Mesos.</p> + +<h4 id="spark-properties">Spark Properties</h4> + +<table class="table"> +<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr> +<tr> + <td><code>spark.mesos.coarse</code></td> + <td>false</td> + <td> + If set to "true", runs over Mesos clusters in + <a href="running-on-mesos.html#mesos-run-modes">"coarse-grained" sharing mode</a>, + where Spark acquires one long-lived Mesos task on each machine instead of one Mesos task per + Spark task. This gives lower-latency scheduling for short queries, but leaves resources in use + for the whole duration of the Spark job. + </td> +</tr> +<tr> + <td><code>spark.mesos.extra.cores</code></td> + <td>0</td> + <td> + Set the extra amount of cpus to request per task. This setting is only used for Mesos coarse grain mode. + The total amount of cores requested per task is the number of cores in the offer plus the extra cores configured. + Note that total amount of cores the executor will request in total will not exceed the spark.cores.max setting. + </td> +</tr> +<tr> + <td><code>spark.mesos.mesosExecutor.cores</code></td> + <td>1.0</td> + <td> + (Fine-grained mode only) Number of cores to give each Mesos executor. This does not + include the cores used to run the Spark tasks. In other words, even if no Spark task + is being run, each Mesos executor will occupy the number of cores configured here. + The value can be a floating point number. + </td> +</tr> +<tr> + <td><code>spark.mesos.executor.docker.image</code></td> + <td>(none)</td> + <td> + Set the name of the docker image that the Spark executors will run in. The selected + image must have Spark installed, as well as a compatible version of the Mesos library. + The installed path of Spark in the image can be specified with <code>spark.mesos.executor.home</code>; + the installed path of the Mesos library can be specified with <code>spark.executorEnv.MESOS_NATIVE_LIBRARY</code>. + </td> +</tr> +<tr> + <td><code>spark.mesos.executor.docker.volumes</code></td> + <td>(none)</td> + <td> + Set the list of volumes which will be mounted into the Docker image, which was set using + <code>spark.mesos.executor.docker.image</code>. The format of this property is a comma-separated list of + mappings following the form passed to <tt>docker run -v</tt>. That is they take the form: + + <pre>[host_path:]container_path[:ro|:rw]</pre> + </td> +</tr> +<tr> + <td><code>spark.mesos.executor.docker.portmaps</code></td> + <td>(none)</td> + <td> + Set the list of incoming ports exposed by the Docker image, which was set using + <code>spark.mesos.executor.docker.image</code>. The format of this property is a comma-separated list of + mappings which take the form: + + <pre>host_port:container_port[:tcp|:udp]</pre> + </td> +</tr> +<tr> + <td><code>spark.mesos.executor.home</code></td> + <td>driver side <code>SPARK_HOME</code></td> + <td> + Set the directory in which Spark is installed on the executors in Mesos. By default, the + executors will simply use the driver's Spark home directory, which may not be visible to + them. Note that this is only relevant if a Spark binary package is not specified through + <code>spark.executor.uri</code>. + </td> +</tr> +<tr> + <td><code>spark.mesos.executor.memoryOverhead</code></td> + <td>executor memory * 0.10, with minimum of 384</td> + <td> + The amount of additional memory, specified in MB, to be allocated per executor. By default, + the overhead will be larger of either 384 or 10% of `spark.executor.memory`. If it's set, + the final overhead will be this value. + </td> +</tr> +<tr> + <td><code>spark.mesos.uris</code></td> + <td>(none)</td> + <td> + A list of URIs to be downloaded to the sandbox when driver or executor is launched by Mesos. + This applies to both coarse-grain and fine-grain mode. + </td> +</tr> +<tr> + <td><code>spark.mesos.principal</code></td> + <td>Framework principal to authenticate to Mesos</td> + <td> + Set the principal with which Spark framework will use to authenticate with Mesos. + </td> +</tr> +<tr> + <td><code>spark.mesos.secret</code></td> + <td>Framework secret to authenticate to Mesos</td> + <td> + Set the secret with which Spark framework will use to authenticate with Mesos. + </td> +</tr> +<tr> + <td><code>spark.mesos.role</code></td> + <td>Role for the Spark framework</td> + <td> + Set the role of this Spark framework for Mesos. Roles are used in Mesos for reservations + and resource weight sharing. + </td> +</tr> +<tr> + <td><code>spark.mesos.constraints</code></td> + <td>Attribute based constraints to be matched against when accepting resource offers.</td> + <td> + Attribute based constraints on mesos resource offers. By default, all resource offers will be accepted. Refer to <a href="http://mesos.apache.org/documentation/attributes-resources/">Mesos Attributes & Resources</a> for more information on attributes. + <ul> + <li>Scalar constraints are matched with "less than equal" semantics i.e. value in the constraint must be less than or equal to the value in the resource offer.</li> + <li>Range constraints are matched with "contains" semantics i.e. value in the constraint must be within the resource offer's value.</li> + <li>Set constraints are matched with "subset of" semantics i.e. value in the constraint must be a subset of the resource offer's value.</li> + <li>Text constraints are metched with "equality" semantics i.e. value in the constraint must be exactly equal to the resource offer's value.</li> + <li>In case there is no value present as a part of the constraint any offer with the corresponding attribute will be accepted (without value check).</li> + </ul> + </td> +</tr> +</table> + +<h1 id="troubleshooting-and-debugging">Troubleshooting and Debugging</h1> + +<p>A few places to look during debugging:</p> + +<ul> + <li>Mesos master on port <code>:5050</code> + <ul> + <li>Slaves should appear in the slaves tab</li> + <li>Spark applications should appear in the frameworks tab</li> + <li>Tasks should appear in the details of a framework</li> + <li>Check the stdout and stderr of the sandbox of failed tasks</li> + </ul> + </li> + <li>Mesos logs + <ul> + <li>Master and slave logs are both in <code>/var/log/mesos</code> by default</li> + </ul> + </li> +</ul> + +<p>And common pitfalls:</p> + +<ul> + <li>Spark assembly not reachable/accessible + <ul> + <li>Slaves must be able to download the Spark binary package from the <code>http://</code>, <code>hdfs://</code> or <code>s3n://</code> URL you gave</li> + </ul> + </li> + <li>Firewall blocking communications + <ul> + <li>Check for messages about failed connections</li> + <li>Temporarily disable firewalls for debugging and then poke appropriate holes</li> + </ul> + </li> +</ul> + + + </div> <!-- /container --> + + <script src="js/vendor/jquery-1.8.0.min.js"></script> + <script src="js/vendor/bootstrap.min.js"></script> + <script src="js/vendor/anchor.min.js"></script> + <script src="js/main.js"></script> + + <!-- MathJax Section --> + <script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + TeX: { equationNumbers: { autoNumber: "AMS" } } + }); + </script> + <script> + // Note that we load MathJax this way to work with local file (file://), HTTP and HTTPS. + // We could use "//cdn.mathjax...", but that won't support "file://". + (function(d, script) { + script = d.createElement('script'); + script.type = 'text/javascript'; + script.async = true; + script.onload = function(){ + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ["$", "$"], ["\\\\(","\\\\)"] ], + displayMath: [ ["$$","$$"], ["\\[", "\\]"] ], + processEscapes: true, + skipTags: ['script', 'noscript', 'style', 'textarea', 'pre'] + } + }); + }; + script.src = ('https:' == document.location.protocol ? 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