navmenu: Tutorials
title: "Writing a Crunch script"
...
+{% comment %}
+Copyright (C) The Arvados Authors. All rights reserved.
+
+SPDX-License-Identifier: CC-BY-SA-3.0
+{% endcomment %}
+
+{% include 'pipeline_deprecation_notice' %}
This tutorial demonstrates how to write a script using Arvados Python SDK. The Arvados SDK supports access to advanced features not available using the @run-command@ wrapper, such as scheduling concurrent tasks across nodes.
This tutorial uses @$USER@ to denote your username. Replace @$USER@ with your user name in all the following examples.
-Start by creating a directory called @$USER@ . Next, create a subdirectory called @crunch_scripts@ and change to that directory:
+Start by creating a directory called @tutorial@ in your home directory. Next, create a subdirectory called @crunch_scripts@ and change to that directory:
<notextile>
-<pre><code>~$ <span class="userinput">mkdir -p tutorial/crunch_scripts</span>
+<pre><code>~$ <span class="userinput">cd $HOME</span>
+~$ <span class="userinput">mkdir -p tutorial/crunch_scripts</span>
~$ <span class="userinput">cd tutorial/crunch_scripts</span></code></pre>
</notextile>
</code></pre>
</notextile>
-Running locally is convenient for development and debugging, as it permits a fast iterative development cycle. Your job run is also recorded by Arvados, and will appear in the *Recent jobs and pipelines* panel on the "Workbench Dashboard":https://{{site.arvados_workbench_host}}. This provides limited provenance, by recording the input parameters, the execution log, and the output. However, running locally does not allow you to scale out to multiple nodes, and does not store the complete system snapshot required to achieve reproducibility; to do that you need to "submit a job to the Arvados cluster":{{site.baseurl}}/user/tutorials/tutorial-submit-job.html.
+Running locally is convenient for development and debugging, as it permits a fast iterative development cycle. Your job run is also recorded by Arvados, and will appear in the *Recent jobs and pipelines* panel on the "Workbench Dashboard":{{site.arvados_workbench_host}}. This provides limited provenance, by recording the input parameters, the execution log, and the output. However, running locally does not allow you to scale out to multiple nodes, and does not store the complete system snapshot required to achieve reproducibility; to do that you need to "submit a job to the Arvados cluster":{{site.baseurl}}/user/tutorials/tutorial-submit-job.html.