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' %}
</code></pre>
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-Although the job runs locally, the output of the job has been saved to Keep, the Arvados file store. The "output" line (third from the bottom) provides the "Keep locator":/user/topics/tutorial-keep-get.html to which the script's output has been saved. Copy the output identifier and use @arv-ls@ to list the contents of your output collection, and @arv-get@ to download it to the current directory:
+Although the job runs locally, the output of the job has been saved to Keep, the Arvados file store. The "output" line (third from the bottom) provides the "Keep locator":{{site.baseurl}}/user/tutorials/tutorial-keep-get.html to which the script's output has been saved. Copy the output identifier and use @arv-ls@ to list the contents of your output collection, and @arv-get@ to download it to the current directory:
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<pre><code>~/tutorial/crunch_scripts$ <span class="userinput">arv-ls d6338df28d6b8e5d14929833b417e20e+107+Adf1ce81222b6992ce5d33d8bfb28a6b5a1497898@53f4bbd4</span>
</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 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.