---
layout: default
navsection: userguide
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 concurrent tasks across nodes.

{% include 'tutorial_expectations' %}

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:

<notextile>
<pre><code>~$ <span class="userinput">mkdir -p tutorial/crunch_scripts</span>
~$ <span class="userinput">cd tutorial/crunch_scripts</span></code></pre>
</notextile>

Next, using @nano@ or your favorite Unix text editor, create a new file called @hash.py@ in the @crunch_scripts@ directory.

notextile. <pre>~/tutorial/crunch_scripts$ <code class="userinput">nano hash.py</code></pre>

Add the following code to compute the MD5 hash of each file in a collection:

<notextile> {% code 'tutorial_hash_script_py' as python %} </notextile>

Make the file executable:

notextile. <pre><code>~/tutorial/crunch_scripts$ <span class="userinput">chmod +x hash.py</span></code></pre>

Next, create a submission job record.  This describes a specific invocation of your script:

<notextile>
<pre><code>~/tutorial/crunch_scripts$ <span class="userinput">cat &gt;~/the_job &lt;&lt;EOF
{
 "repository":"",
 "script":"hash.py",
 "script_version":"$HOME/tutorial",
 "script_parameters":{
   "input":"c1bad4b39ca5a924e481008009d94e32+210"
 }
}
EOF</span>
</code></pre>
</notextile>

You can now run your script on your local workstation or VM using @arv-crunch-job@:

<notextile>
<pre><code>~/tutorial/crunch_scripts</span>$ <span class="userinput">arv-crunch-job --job "$(cat ~/the_job)"</span>
2014-08-06_15:16:22 qr1hi-8i9sb-qyrat80ef927lam 14473  check slurm allocation
2014-08-06_15:16:22 qr1hi-8i9sb-qyrat80ef927lam 14473  node localhost - 1 slots
2014-08-06_15:16:23 qr1hi-8i9sb-qyrat80ef927lam 14473  start
2014-08-06_15:16:23 qr1hi-8i9sb-qyrat80ef927lam 14473  script hash.py
2014-08-06_15:16:23 qr1hi-8i9sb-qyrat80ef927lam 14473  script_version $HOME/tutorial
2014-08-06_15:16:23 qr1hi-8i9sb-qyrat80ef927lam 14473  script_parameters {"input":"c1bad4b39ca5a924e481008009d94e32+210"}
2014-08-06_15:16:23 qr1hi-8i9sb-qyrat80ef927lam 14473  runtime_constraints {"max_tasks_per_node":0}
2014-08-06_15:16:23 qr1hi-8i9sb-qyrat80ef927lam 14473  start level 0
2014-08-06_15:16:23 qr1hi-8i9sb-qyrat80ef927lam 14473  status: 0 done, 0 running, 1 todo
2014-08-06_15:16:23 qr1hi-8i9sb-qyrat80ef927lam 14473 0 job_task qr1hi-ot0gb-lptn85mwkrn9pqo
2014-08-06_15:16:23 qr1hi-8i9sb-qyrat80ef927lam 14473 0 child 14478 started on localhost.1
2014-08-06_15:16:23 qr1hi-8i9sb-qyrat80ef927lam 14473  status: 0 done, 1 running, 0 todo
2014-08-06_15:16:24 qr1hi-8i9sb-qyrat80ef927lam 14473 0 stderr crunchstat: Running [stdbuf --output=0 --error=0 /home/$USER/tutorial/crunch_scripts/hash.py]
2014-08-06_15:16:24 qr1hi-8i9sb-qyrat80ef927lam 14473 0 child 14478 on localhost.1 exit 0 signal 0 success=true
2014-08-06_15:16:24 qr1hi-8i9sb-qyrat80ef927lam 14473 0 success in 1 seconds
2014-08-06_15:16:24 qr1hi-8i9sb-qyrat80ef927lam 14473 0 output
2014-08-06_15:16:25 qr1hi-8i9sb-qyrat80ef927lam 14473  wait for last 0 children to finish
2014-08-06_15:16:25 qr1hi-8i9sb-qyrat80ef927lam 14473  status: 1 done, 0 running, 1 todo
2014-08-06_15:16:25 qr1hi-8i9sb-qyrat80ef927lam 14473  start level 1
2014-08-06_15:16:25 qr1hi-8i9sb-qyrat80ef927lam 14473  status: 1 done, 0 running, 1 todo
2014-08-06_15:16:25 qr1hi-8i9sb-qyrat80ef927lam 14473 1 job_task qr1hi-ot0gb-e3obm0lv6k6p56a
2014-08-06_15:16:25 qr1hi-8i9sb-qyrat80ef927lam 14473 1 child 14504 started on localhost.1
2014-08-06_15:16:25 qr1hi-8i9sb-qyrat80ef927lam 14473  status: 1 done, 1 running, 0 todo
2014-08-06_15:16:26 qr1hi-8i9sb-qyrat80ef927lam 14473 1 stderr crunchstat: Running [stdbuf --output=0 --error=0 /home/$USER/tutorial/crunch_scripts/hash.py]
2014-08-06_15:16:35 qr1hi-8i9sb-qyrat80ef927lam 14473 1 child 14504 on localhost.1 exit 0 signal 0 success=true
2014-08-06_15:16:35 qr1hi-8i9sb-qyrat80ef927lam 14473 1 success in 10 seconds
2014-08-06_15:16:35 qr1hi-8i9sb-qyrat80ef927lam 14473 1 output 8c20281b9840f624a486e4f1a78a1da8+105+A234be74ceb5ea31db6e11b6be26f3eb76d288ad0@54987018
2014-08-06_15:16:35 qr1hi-8i9sb-qyrat80ef927lam 14473  wait for last 0 children to finish
2014-08-06_15:16:35 qr1hi-8i9sb-qyrat80ef927lam 14473  status: 2 done, 0 running, 0 todo
2014-08-06_15:16:35 qr1hi-8i9sb-qyrat80ef927lam 14473  release job allocation
2014-08-06_15:16:35 qr1hi-8i9sb-qyrat80ef927lam 14473  Freeze not implemented
2014-08-06_15:16:35 qr1hi-8i9sb-qyrat80ef927lam 14473  collate
2014-08-06_15:16:36 qr1hi-8i9sb-qyrat80ef927lam 14473  collated output manifest text to send to API server is 105 bytes with access tokens
2014-08-06_15:16:36 qr1hi-8i9sb-qyrat80ef927lam 14473  output hash c1b44b6dc41ef334cf1136033ca950e6+54
2014-08-06_15:16:37 qr1hi-8i9sb-qyrat80ef927lam 14473  finish
2014-08-06_15:16:38 qr1hi-8i9sb-qyrat80ef927lam 14473  log manifest is 7fe8cf1d45d438a3ca3ac4a184b7aff4+83
</code></pre>
</notextile>

Although the job runs locally, the output of the job has been saved to Keep, the Arvados file store.  The "output hash" line (third from the bottom) provides the portable data hash of the Arvados collection where the script's output has been saved.  Copy the output hash and use @arv-ls@ to list the contents of your output collection, and @arv-get@ to download it to the current directory:

<notextile>
<pre><code>~/tutorial/crunch_scripts$ <span class="userinput">arv-ls c1b44b6dc41ef334cf1136033ca950e6+54</span>
./md5sum.txt
~/tutorial/crunch_scripts$ <span class="userinput">arv-get c1b44b6dc41ef334cf1136033ca950e6+54/ .</span>
0 MiB / 0 MiB 100.0%
~/tutorial/crunch_scripts$ <span class="userinput">cat md5sum.txt</span>
44b8ae3fde7a8a88d2f7ebd237625b4f c1bad4b39ca5a924e481008009d94e32+210/var-GS000016015-ASM.tsv.bz2
</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":{{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.