# Getting Started¶

## Simple Job Submission¶

At its simplest, this module can be used by just executing submit(<command>), where command is a function or system command/shell script. The module will autodetect the cluster, generate an intuitive name, run the job, and write all outputs to files in the current directory. These can be cleaned with clean_dir().

To run with dependency tracking, run:

import fyrd
job  = fyrd.submit(<command1>)
job2 = fyrd.submit(<command2>, depends=job1)
out1, out2 = fyrd.get([job, job2])  # Will block until job completes


The submit() function is actually just a wrapper for the Job class. The same behavior as above can be obtained by initializing a Job object directly:

import fyrd
job  = fyrd.Job(<command1>)
job.submit()
job2 = fyrd.Job(<command2>, depends=job1).submit()
out  = job2.get()  # Will block until job completes


Note that as shown above, the submit method returns the Job object, so it can be called on job initialization. Also note that the object returned by calling the submit() function (as in the first example) is also a Job object, so these two examples can be used fully interchangeably.

Similar wrappers allow you to submit and monitor existing job files, such as those made by other pipelines:

import os
import fyrd
jobs = []
job_dir = os.path.abspath('./jobs/')
for job in [os.path.join(job_dir, i) for i in os.listdir(job_dir) if i.endswith('sh')]:
jobs.append(fyrd.submit_file(job))
fyrd.wait(jobs)  # Will block until every job is completed


This type of thing can also be accomplished using the console script:

fyrd run --wait ./jobs/*.sh


## Functions¶

The submit function works well with python functions as well as with shell scripts and shell commands, in fact, this is the most powerful feature of this package. For example:

import fyrd
def raise_me(something, power=2):
return something**power
outs = []
if __name__ == '__main__':
for i in range(80):
outs.append(fyrd.submit(raise_me, (i,), {'power': 4},
mem='10MB', time='00:00:30'))
final_sum = 0
for i in outs:
final_sum += i.get()
print(final_sum)


By default this will submit every instance as a job on the cluster, then get the results and clean up all intermediate files, and the code will work identically on a Mac with no cluster access, a slurm cluster, or a torque cluster, with no need to change syntax.

This is very powerful when combined with simple methods that split files or large python classes, to make this kind of work easier, a number of simple functions are provided in the helpers module, to learn more about that, review the Advanced Usage section of this documentation.

Function submission works equally well for submitting methods, however the original class object will not be updated, the method return value will be accurate, but any changes the method makes to self will not be returned from the cluster and will be lost.

### Possible Infinate Recursion Error¶

Warning: in order for function submission to work, fyrd ends up importing your original script file on the nodes. This means that all code in your file will be executed, so anything that isn’t a function or class must be protected with an if __name__ == '__main__': protecting statement.

If you do not do this you can end up with multi-submission and infinite recursion, which could mess up your jobs or just crash the job, but either way, it won’t be good.

This isn’t true when submitting from an interactive session such as ipython or jupyter.

## Using the Jobify Decorator¶

Function submission can be made much easier by using the jobify decorator.

Using the example above with a decorator, we can do this:

import fyrd
@fyrd.jobify(mem='10MB', time='00:00:30')
def raise_me(something, power=2):
return something**power
outs = []
if __name__ == '__main__':
for i in range(80):
outs.append(raise_me(i, power=4))
final_sum = 0
for i in outs:
final_sum += i.get()
print(final_sum)


Here is a full, if silly, example with outputs:

>>> import fyrd
>>> @fyrd.jobify(name='test_job', mem='1GB')
... def test(string, iterations=4):
...     """This does basically nothing!"""
...     outstring = ""
...     for i in range(iterations):
...         outstring += "Version {0}: {1}".format(i, string)
...     return outstring
...
>>> test?
Signature: test(*args, **kwargs)
Docstring:
This is a fyrd.job.Job decorated function.

When you call it it will return a Job object from which you can get
the results with the .get() method.

Original Docstring:

This does basically nothing!
File:      ~/code/fyrd/fyrd/helpers.py
Type:      function
>>> j = test('hi')
>>> j.get()
'Version 0: hiVersion 1: hiVersion 2: hiVersion 3: hi'


You can see that the decorator also maintains the original docstring if it is implemented.

By default, the returned job will be submitted already, but you can override that behavior:

import fyrd
@fyrd.jobify(mem='10MB', time='00:00:30', submit=False)
def raise_me(something, power=2):
return something**power


## File Submission¶

If you want to just submit a job file that has already been created, either by this software or any other method, that can be done like this:

from fyrd import submit_file
submit_file('/path/to/script', dependencies=[7, 9])


This will return the job number and will enter the job into the queue as dependant on jobs 7 and 9. The dependencies can be omitted.

## Keywords¶

The Job class, and therefore every submission script, accepts a large number of keyword arguments and synonyms to make job submission easy. Some good examples:

• cores
• mem (or memory)
• time (or walltime)
• partition (or queue)

The synonyms are provided to make submission easy for anyone familiar with the arguments used by either torque or slurm. For example:

job = Job('zcat huge_file | parse_file', cores=1, mem='30GB', time='24:00:00')
job = Job(my_parallel_function, cores=28, mem=12000, queue='high_mem')
for i in huge_list:
out.append(submit(parser_function, i, cores=1, mem='1GB', partition='small'))
job = Job('ls /etc')


As you can see, optional keywords make submission very easy and flexible. The whole point of this software it to make working with a remote cluster in python as easy as possible.

For a full list of keyword arguments see the Keyword Arguments section of the documentation.

All options are defined in the fyrd.options module. If you want extra options, just submit an issue or add them yourself and send me a pull request.

## Profiles¶

One of the issues with using keyword options is the nuisance of having to type them every time. More importantly, when writing code to work on any cluster one has to deal with heterogeneity between the clusters, such as the number of cores available on each node, or the name of the submission queue.

Because of this, fyrd makes use of profiles that bundle keyword arguments and give them a name, so that cluster submission can look like this:

job = Job('zcat huge_file | parse_file', profile='large')
job = Job(my_parallel_function, cores=28, profile='high_mem')


These profiles are defined in ~/.fyrd/profiles.txt by default and have the following syntax:

[large]
partition = normal
cores = 16
nodes = 1
time = 24:00:00
mem = 32000


This means that you can now do this:

Job(my_function, profile='large')


You can create as many of these as you like.

While you can edit the profile file directly to add and edit profile, it is easier and more stable to use the console script:

..code:: shell

fyrd profile list fyrd profile edit large time:02-00:00:00 mem=64GB fyrd profile edit DEFAULT partition:normal fyrd profile remove-option DEFAULT cores fyrd profile add silly cores:92 mem:1MB fyrd profile delete silly

The advantage of using the console script is that argument parsing is done on editing the profiles, so any errors are caught at that time. If you edit the file manually, then any mistakes will cause an Exception to be raised when you try to submit a job.

If no arguments are given the default profile (called ‘DEFAULT’ in the config file) is used.

Note: any arguments in the DEFAULT profile are available in all profiles if the are not manually overridden there. The DEFAULT profile cannot be deleted. It is a good place to put the name of the default queue.