added info on check_usage

ideasForNextTime

@@ -0,0 +1 @@
+A participant suggested we warn folks in advance that basic understanding of the shell is useful and point them to training materials. 

intro.html

@@ -54,7 +54,7 @@ <h3 class="date">Kunal Mishra and Chris Paciorek</h3>
 <h1 id="introduction">Introduction</h1>
 <p>We'll do this mostly as a demonstration. We encourage you to login to your account and try out the various examples yourself as we go through them.</p>
 <p>Much of this material is based on the extensive Savio documention we have prepared and continue to prepare, available at <a href="http://research-it.berkeley.edu/services/high-performance-computing" class="uri">http://research-it.berkeley.edu/services/high-performance-computing</a>.</p>
-<p>The materials for this tutorial are available using git at <a href="https://github.com/ucberkeley/savio-training-intro-2018" class="uri">https://github.com/ucberkeley/savio-training-intro-2018</a> or simply as a <a href="https://github.com/ucberkeley/savio-training-intro-2018/archive/master.zip">zip file</a>.</p>
+<p>The materials for this tutorial are available using git at the short URL <a href="http://bit.do/F18Savio" class="uri">http://bit.do/F18Savio</a>, the GitHub URL <a href="https://github.com/ucberkeley/savio-training-intro-2018" class="uri">https://github.com/ucberkeley/savio-training-intro-2018</a>, or simply as a <a href="https://github.com/ucberkeley/savio-training-intro-2018/archive/master.zip">zip file</a>.</p>
 <h1 id="outline">Outline</h1>
 <p>This training session will cover the following topics:</p>
 <ul>
@@ -349,7 +349,7 @@ <h1 id="alternatives-to-the-htc-partition-for-collections-of-serial-jobs">Altern
 <li>parallel functionality in MATLAB through <em>parfor</em></li>
 </ul></li>
 </ul>
-<h1 id="monitoring-jobs-and-the-job-queue">Monitoring jobs and the job queue</h1>
+<h1 id="monitoring-jobs-the-job-queue-and-overall-usage">Monitoring jobs, the job queue, and overall usage</h1>
 <p>The basic command for seeing what is running on the system is <code>squeue</code>:</p>
 <pre><code>squeue
 squeue -u SAVIO_USERNAME
@@ -362,6 +362,8 @@ <h1 id="monitoring-jobs-and-the-job-queue">Monitoring jobs and the job queue</h1
 <p>For more information on cores, QoS, and additional (e.g., GPU) resources, here's some syntax:</p>
 <pre><code>squeue -o &quot;%.7i %.12P %.20j %.8u %.2t %.9M %.5C %.8r %.3D %.20R %.8p %.20q %b&quot; </code></pre>
 <p>We provide some <a href="http://research-it.berkeley.edu/services/high-performance-computing/running-your-jobs">tips about monitoring your jobs</a>. (Scroll down to the &quot;Monitoring jobs&quot; section.)</p>
+<p>If you'd like to see how much of an FCA has been used:</p>
+<pre><code>check_usage.sh -a fc_popgen </code></pre>
 <h1 id="example-use-of-standard-software-ipython-and-r-notebooks-through-jupyterhub">Example use of standard software: IPython and R notebooks through JupyterHub</h1>
 <p>Savio allows one to <a href="http://research-it.berkeley.edu/services/high-performance-computing/using-jupyter-notebooks-and-jupyterhub-savio">run Jupyter-based notebooks via a browser-based service called Jupyterhub</a>.</p>
 <p>Let's see a brief demo of an IPython notebook:</p>
@@ -502,6 +504,15 @@ <h1 id="example-use-of-standard-software-r">Example use of standard software: R<
 }
 
 results</code></pre>
+<h1 id="alternative-python-parallelization-dask">Alternative Python Parallelization: Dask</h1>
+<p>In addition to iPyParallel, one of the newer tools in the Python space is <a href="http://dask.pydata.org/en/latest/">Dask</a>, which provides out-of-the-box parallelization more easily without much setup or too much additional. Dask, as a python package, extends Numpy/Pandas syntax for arrays and dataframes that already exists and introduces native parallelization to these data structures, which speeds up analyses. Since Dask dataframes/arrays are descendants of the Pandas dataframe and Numpy array, they are compatible with any existing code and can serve as a plug-in replacement, with performance enhancements for multiple cores/nodes. It's also worth noting that Dask is useful for scaling up to large clusters like Savio but can also be useful for speeding up analyses on your local computer. We're including some articles and documentation that may be helpful in getting started:</p>
+<ul>
+<li><a href="https://dask.pydata.org/en/latest/why.html">Why Dask?</a></li>
+<li><a href="https://www.youtube.com/watch?v=ods97a5Pzw0">Standard Dask Demo</a></li>
+<li><a href="http://dask.pydata.org/en/latest/api.html">Why every Data Scientist should use Dask</a></li>
+<li><a href="https://dask.pydata.org/en/latest/_downloads/daskcheatsheet.pdf">Dask Cheatsheet</a></li>
+<li><a href="https://www.youtube.com/watch?v=mjQ7tCQxYFQ">Detailed Dask overview video</a></li>
+</ul>
 <h1 id="how-to-get-additional-help">How to get additional help</h1>
 <ul>
 <li>For technical issues and questions about using Savio:

intro.md

@@ -442,7 +442,7 @@ Here are some options:
     - parallel Python tools such as  *ipyparallel*, and *Dask*
     - parallel functionality in MATLAB through *parfor*
 
-# Monitoring jobs and the job queue
+# Monitoring jobs, the job queue, and overall usage
 
 The basic command for seeing what is running on the system is `squeue`:
 ```
@@ -469,6 +469,13 @@ squeue -o "%.7i %.12P %.20j %.8u %.2t %.9M %.5C %.8r %.3D %.20R %.8p %.20q %b"
 
 We provide some [tips about monitoring your jobs](http://research-it.berkeley.edu/services/high-performance-computing/running-your-jobs). (Scroll down to the "Monitoring jobs" section.)
 
+If you'd like to see how much of an FCA has been used:
+
+```
+check_usage.sh -a fc_popgen 
+```
+
+
 # Example use of standard software: IPython and R notebooks through JupyterHub
 
 Savio allows one to [run Jupyter-based notebooks via a browser-based service called Jupyterhub](http://research-it.berkeley.edu/services/high-performance-computing/using-jupyter-notebooks-and-jupyterhub-savio). 

intro_slides.html

@@ -63,7 +63,7 @@ <h1 class="title">Savio introductory training: Basic usage of the Berkeley Savio
 <h1>Introduction</h1>
 <p>We'll do this mostly as a demonstration. We encourage you to login to your account and try out the various examples yourself as we go through them.</p>
 <p>Much of this material is based on the extensive Savio documention we have prepared and continue to prepare, available at <a href="http://research-it.berkeley.edu/services/high-performance-computing" class="uri">http://research-it.berkeley.edu/services/high-performance-computing</a>.</p>
-<p>The materials for this tutorial are available using git at <a href="https://github.com/ucberkeley/savio-training-intro-2018" class="uri">https://github.com/ucberkeley/savio-training-intro-2018</a> or simply as a <a href="https://github.com/ucberkeley/savio-training-intro-2018/archive/master.zip">zip file</a>.</p>
+<p>The materials for this tutorial are available using git at the short URL <a href="http://bit.do/F18Savio" class="uri">http://bit.do/F18Savio</a>, the GitHub URL <a href="https://github.com/ucberkeley/savio-training-intro-2018" class="uri">https://github.com/ucberkeley/savio-training-intro-2018</a>, or simply as a <a href="https://github.com/ucberkeley/savio-training-intro-2018/archive/master.zip">zip file</a>.</p>
 </div>
 <div id="outline" class="slide section level1">
 <h1>Outline</h1>
@@ -401,8 +401,8 @@ <h1>Alternatives to the HTC partition for collections of serial jobs</h1>
 </ul></li>
 </ul>
 </div>
-<div id="monitoring-jobs-and-the-job-queue" class="slide section level1">
-<h1>Monitoring jobs and the job queue</h1>
+<div id="monitoring-jobs-the-job-queue-and-overall-usage" class="slide section level1">
+<h1>Monitoring jobs, the job queue, and overall usage</h1>
 <p>The basic command for seeing what is running on the system is <code>squeue</code>:</p>
 <pre><code>squeue
 squeue -u SAVIO_USERNAME
@@ -415,6 +415,8 @@ <h1>Monitoring jobs and the job queue</h1>
 <p>For more information on cores, QoS, and additional (e.g., GPU) resources, here's some syntax:</p>
 <pre><code>squeue -o &quot;%.7i %.12P %.20j %.8u %.2t %.9M %.5C %.8r %.3D %.20R %.8p %.20q %b&quot; </code></pre>
 <p>We provide some <a href="http://research-it.berkeley.edu/services/high-performance-computing/running-your-jobs">tips about monitoring your jobs</a>. (Scroll down to the &quot;Monitoring jobs&quot; section.)</p>
+<p>If you'd like to see how much of an FCA has been used:</p>
+<pre><code>check_usage.sh -a fc_popgen </code></pre>
 </div>
 <div id="example-use-of-standard-software-ipython-and-r-notebooks-through-jupyterhub" class="slide section level1">
 <h1>Example use of standard software: IPython and R notebooks through JupyterHub</h1>
@@ -562,6 +564,17 @@ <h1>Example use of standard software: R</h1>
 
 results</code></pre>
 </div>
+<div id="alternative-python-parallelization-dask" class="slide section level1">
+<h1>Alternative Python Parallelization: Dask</h1>
+<p>In addition to iPyParallel, one of the newer tools in the Python space is <a href="http://dask.pydata.org/en/latest/">Dask</a>, which provides out-of-the-box parallelization more easily without much setup or too much additional. Dask, as a python package, extends Numpy/Pandas syntax for arrays and dataframes that already exists and introduces native parallelization to these data structures, which speeds up analyses. Since Dask dataframes/arrays are descendants of the Pandas dataframe and Numpy array, they are compatible with any existing code and can serve as a plug-in replacement, with performance enhancements for multiple cores/nodes. It's also worth noting that Dask is useful for scaling up to large clusters like Savio but can also be useful for speeding up analyses on your local computer. We're including some articles and documentation that may be helpful in getting started:</p>
+<ul>
+<li><a href="https://dask.pydata.org/en/latest/why.html">Why Dask?</a></li>
+<li><a href="https://www.youtube.com/watch?v=ods97a5Pzw0">Standard Dask Demo</a></li>
+<li><a href="http://dask.pydata.org/en/latest/api.html">Why every Data Scientist should use Dask</a></li>
+<li><a href="https://dask.pydata.org/en/latest/_downloads/daskcheatsheet.pdf">Dask Cheatsheet</a></li>
+<li><a href="https://www.youtube.com/watch?v=mjQ7tCQxYFQ">Detailed Dask overview video</a></li>
+</ul>
+</div>
 <div id="how-to-get-additional-help" class="slide section level1">
 <h1>How to get additional help</h1>
 <ul>