--- layout: default navsection: userguide title: "Tutorial: Construct a new pipeline" navorder: 24 --- h1. Tutorial: Construct a new pipeline Here you will write two new crunch scripts, incorporate them into a new pipeline template, run the new pipeline a couple of times using different parameters, and compare the results. One of the new scripts will use the Arvados API to look up trait→human→data relations and use this information to compile a collection of data to analyze. h3. Prerequisites * Log in to a VM "using SSH":ssh-access.html * Put an "API token":api-tokens.html in your @ARVADOS_API_TOKEN@ environment variable * Put the API host name in your @ARVADOS_API_HOST@ environment variable If everything is set up correctly, the command @arv -h user current@ will display your account information. h3. Git repository access Pushing code to your git repository involves using your private key. There are a few ways to arrange this: *Option 1:* Use an SSH agent, and log in to your VM with agent forwarding enabled. With Linux, BSD, MacOS, etc., this looks something like: 
ssh-add -l
eval `ssh-agent` # (only if "ssh-add -l" said it could not open a connection)
ssh-add          # (this adds your private key to the agent)
ssh -A my_vm.arvados
ssh-add -l       # (run this in your VM account to confirm forwarding works)
With PuTTY under Windows, run "pageant", add your key to the agent, and turn on agent forwarding in your PuTTY settings. *Option 2:* Edit code on your workstation and push code to your Arvados repository from there instead of your VM account. Depending on your @.ssh/config@ file, you will use names like @my_vm_name.arvados@ instead of @my_vm_name.{{ site.arvados_api_host }}@ in git and ssh commands. 
git clone git@git.arvados:my_repo_name.git
cd my_repo_name
[...]
git push
*Option 3:* Edit code in your VM, and use git on your workstation as an intermediary. 
git clone git@my_vm_name.arvados:my_repo_name.git
cd my_repo_name
git remote add arvados git@git.arvados:my_repo_name.git

[...make edits and commits in your repository on the VM...]

git pull                        #(update local copy)
git push arvados master:master  #(update arvados hosted copy)
Whichever setup you choose, if everything is working correctly, this command should give you a list of repositories you can access: 
ssh git@git.{{ site.arvados_api_host }}
↓ 
hello your_user_name, the gitolite version here is v2.0.2-17-g66f2065
the gitolite config gives you the following access:
     R   W      your_repo_name
h3. Set some variables Adjust these to match your login account name and the URL of your Arvados repository. The Access→VMs and Access→Repositories pages on Workbench will show the specifics. 
repo_url=git@git.{{ site.arvados_api_host }}:my_repo_name.git
repo_name=my_repo_name
h3. Set up a new branch in your Arvados git repository We will create a new empty branch called "pipeline-tutorial" and add our new crunch scripts there. 
mkdir pipeline-tutorial
cd pipeline-tutorial
git init
git checkout -b pipeline-tutorial
git remote add origin $repo_url
h3. Write the create-collection-by-trait script 
mkdir -p crunch_scripts
touch crunch_scripts/create-collection-by-trait
chmod +x crunch_scripts/create-collection-by-trait
nano crunch_scripts/create-collection-by-trait
Here is the script: 
#!/usr/bin/env python

import arvados
import re
import json

trait_name = arvados.current_job()['script_parameters']['trait_name']

# get UUIDs of all matching traits
trait_uuids = map(lambda t: t['uuid'],
                  filter(lambda t: re.search(trait_name, t['name'], re.IGNORECASE),
                         arvados.service.traits().list(limit=1000).execute()['items']))

# list humans linked to these conditions
trait_links = arvados.service.links().list(limit=10000, where=json.dumps({
            'link_class': 'human_trait',
            'tail_kind': 'arvados#human',
            'head_uuid': trait_uuids
            })).execute()['items']
human_uuids = map(lambda l: l['tail_uuid'], trait_links)

# find collections linked to these humans
provenance_links = arvados.service.links().list(where=json.dumps({
    "link_class": "provenance",
    "name": "provided",
    "tail_uuid": human_uuids
  })).execute()['items']
collection_uuids = map(lambda l: l['head_uuid'], provenance_links)

# pick out all of the "var" files, and build a new collection
out_manifest = ''
for locator in collection_uuids:
    for f in arvados.CollectionReader(locator).all_files():
        if re.search('var-.*\.tsv.bz2', f.name()):
            out_manifest += f.as_manifest()

# output the new collection
arvados.current_task().set_output(arvados.Keep.put(out_manifest))
h3. Write the find-dbsnp-id script 
touch crunch_scripts/find-dbsnp-id
chmod +x crunch_scripts/find-dbsnp-id
nano crunch_scripts/find-dbsnp-id
Here is the script: 
#!/usr/bin/env python

import arvados
import re

arvados.job_setup.one_task_per_input_file(if_sequence=0, and_end_task=True)

this_job = arvados.current_job()
this_task = arvados.current_task()
this_task_input = this_task['parameters']['input']
dbsnp_search_pattern = re.compile("\\bdbsnp\\.\\d+:" +
                                  this_job['script_parameters']['dbsnp_id'] +
                                  "\\b")

input_file = list(arvados.CollectionReader(this_task_input).all_files())[0]
out = arvados.CollectionWriter()
out.set_current_file_name(input_file.decompressed_name())
out.set_current_stream_name(input_file.stream_name())
for line in input_file.readlines():
    if dbsnp_search_pattern.search(line):
        out.write(line)

this_task.set_output(out.finish())
h3. Commit your new code 
git add crunch_scripts/create-collection-by-trait
git add crunch_scripts/find-dbsnp-id
git commit -m 'add scripts from tutorial'
h3. Push your new code to your Arvados git repository Push the new "pipeline-tutorial" branch to your Arvados hosted repository. 
git push origin pipeline-tutorial
h3. Note the commit ID of your latest code Show the latest commit. The first line includes a 40-digit hexadecimal number that uniquely identifies the content of your git tree. You will specify this in your pipeline template in the next step to ensure that Arvados uses the correct version of your git tree when running jobs. 
git show | head
↓ 
commit 37c7faef1b066a2dcdb0667fbe82b7cdd7d0be93
[...]
h3. Write the pipeline template Make a directory called @pipeline_templates@ and create a file called @find-dbsnp-by-trait.json@. 
mkdir pipeline_templates
nano pipeline_templates/find-dbsnp-by-trait.json
Copy the following pipeline template. 
{
  "name":"find_dbsnp_by_trait",
  "components":{
    "create_collection":{
      "script":"create-collection-by-trait",
      "script_parameters":{
        "trait_name":"Non-melanoma skin cancer"
      },
      "script_version":"YOUR_GIT_COMMIT_SHA1_HERE"
    },
    "find_variant":{
      "script":"find-dbsnp-id",
      "script_parameters":{
        "input":{
          "output_of":"create_collection"
        },
        "dbsnp_id":"rs1126809"
      },
      "script_version":"YOUR_GIT_COMMIT_SHA1_HERE"
    }
  }
}
h3. Store the pipeline template in Arvados 
read -rd $'\000' the_pipeline < pipeline_templates/find-dbsnp-by-trait.json

arv pipeline_template create --pipeline-template "$the_pipeline"
@arv@ will output the UUID of the new pipeline template. 
qr1hi-p5p6p-uf9gi9nolgakm85
The new pipeline template will also appear on the Workbench→Compute→Pipeline templates page. h3. Invoke the pipeline using "arv pipeline run" Replace the UUID here with the UUID of your own new pipeline template: 
arv pipeline run --template qr1hi-p5p6p-uf9gi9nolgakm85
This instantiates your pipeline template: it submits the first job, waits for it to finish, submits the next job, etc. h3. Monitor pipeline progress The "arv pipeline run" command displays progress in your terminal until the pipeline instance is finished. 
2013-07-17 05:06:15 +0000 -- pipeline_instance qr1hi-d1hrv-8i4tz440whvwf2o
create_collection qr1hi-8i9sb-haibhu51olihlwp 9e2e489a73e1a918de8ecfc6f59ae5a1+1803+K@qr1hi
find_variant      qr1hi-8i9sb-sqduc932xb1tpff cad082ba4cb174ffbebf751bbe3ed77c+506+K@qr1hi
The new pipeline instance will also show up on your Workbench→Compute→Pipeline instances page. h3. Find the output collection UUID The output of the "find_variant" component is shown in your terminal with the last status update from the "arv pipeline run" command. It is also displayed on the pipeline instance detail page: go to Workbench→Compute→Pipeline instances and click the UUID of your pipeline instance. h3. Compute a summary statistic from the output collection For this step we will use python to read the output manifest and count how many of the inputs produced hits. Type @python@ at a command prompt and paste this script at the prompt: 
import arvados

hash = 'cad082ba4cb174ffbebf751bbe3ed77c+506+K@qr1hi' # Use your result here!

collection = arvados.CollectionReader(hash)
hits = len(filter(lambda f: f.size() > 0, collection.all_files()))
misses = len(filter(lambda f: f.size() == 0, collection.all_files()))
print "%d had the variant, %d did not." % (hits, misses)
↓ 
4 had the variant, 3 did not.
h3. Run the pipeline again using different parameters We can use the same pipeline template to run the jobs again, this time overriding the "trait_name" parameter with a different value: 
arv pipeline run --template qr1hi-p5p6p-uf9gi9nolgakm85 create_collection::trait_name=cancer
When this template instance finishes, run the same Python script on the new output collection to summarize the results.