3254: Typo fix "reproducibilty" -> "reproducibility". Changed use of the term "paral...

[arvados.git] / doc / user / tutorials / tutorial-firstscript.html.textile.liquid

diff --git a/doc/user/tutorials/tutorial-firstscript.html.textile.liquid b/doc/user/tutorials/tutorial-firstscript.html.textile.liquid
index 35710c0540018ee12cf7f7e66b152677478055ef..72aa0b1c0b761d7880dc5fba1196f8be5eed2b8b 100644 (file)
--- a/doc/user/tutorials/tutorial-firstscript.html.textile.liquid
+++ b/doc/user/tutorials/tutorial-firstscript.html.textile.liquid
@@ -5,7 +5,7 @@ navmenu: Tutorials
 title: "Writing a Crunch script"
 ...
 
-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 parallel tasks across nodes.
+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.
 
 {% include 'tutorial_expectations' %}
 
@@ -98,4 +98,4 @@ Although the job runs locally, the output of the job has been saved to Keep, the
 </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 show up 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 reproducibilty; to that you need to "submit a job to the Arvados cluster":/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 show up 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 that you need to "submit a job to the Arvados cluster":/user/tutorials/tutorial-submit-job.html