fyrd 0.6.1b8

Submit functions and shell scripts to torque, slurm, or local machines

One liner script and function to torque, slurm, or a local machine with dependency tracking using python. Uses the same syntax irrespective of cluster environment!

Author	Michael D Dacre <[e-mail]>
License	MIT License, property of Stanford, use as you wish
Version	0.6.1-beta.8

Allows simple job submission with dependency tracking and queue waiting on either torque, slurm, or locally with the multiprocessing module. It uses simple techniques to avoid overwhelming the queue and to catch bugs on the fly.

It is routinely tested on Mac OS and Linux with slurm and torque clusters, or in the absence of a cluster, on Python versions 2.7.10, 2.7.11, 2.7.12, 3.3.0, 3.4.0, 3.5.2, 3.6.2, and 3.7-dev. The full test suite is available in the tests folder.

Fyrd is pronounced ‘feared’ (sort of), it is an Anglo-Saxon term for an army, particularly an army of freemen (in this case an army of compute nodes). The logo is based on a Saxon shield commonly used by these groups. This software was formerly known as ‘Python Cluster’.

For usage instructions and complete documentation see the documentation site and the fyrd_manual.pdf document in this repository.

Contents

• Overview

  • Installation

    • Prerequisites

    • Cluster Dependencies

• Testing

• Releases

• Issues and Contributing

• Why the Name?

• Documentation

Overview

This library was created to make working with torque or slurm clusters as easy as working with the multiprocessing library. It aims to provide:

• Easy submission of either python functions or shell scripts to torque or slurm from within python.

• Simple dependency tracking for jobs.

• The ability to submit jobs with any of the torque or slurm keyword arguments.

• Easy customization.

• Very simple usage that scales to complex applications.

• A simple queue monitoring API that functions identically with torque and slurm queues.

• A fallback local mode that allows code to run locally using the multiprocessing module without needing any changes to syntax.

To do this, all major torque and slurm keyword arguments are encoded in dictionaries in the fyrd/options.py file using synonyms so that all arguments are standardized on the fly. Job management is handled by the Job class in fyrd/job.py, which accepts any of the keyword arguments in the options file. To make submission as simple as possible, the code makes used of profiles defined in the ~/.fyrd/profiles.txt config file. These allow simple grouping of keyword arguments into named profiles to make submission even easier. Dependency tracking is handled by the depends= argument to Job, which accepts job numbers or Job objects, either singularly or as lists.

To allow simple queue management and job waiting, a Queue class is implemented in fyrd/queue.py. It uses iterators, also defined in that file, to parse torque or slurm queues transparently and allow access to their attributes through the Queue class and the Queue.jobs dictionary. The Job class uses this system to block until the job completes when either the wait() or get() methods are called.

To allow similar functionality on a system not connected to a torque or slurm queue, a local queue that behaves similarly, including allowing dependency tracking, is implemented in the fyrd/jobqueue.py file. It is based on multiprocessing but behaves like torque. It is not a good idea to use this module in place of multiprocessing due to the dependency tracking overhead, it is primarily intended as a fallback, but it does work well enough to use independently.

As all clusters are different, common alterable parameters are defined in a config file located at ~/.fyrd/config.txt. This includes an option for max queue size, which makes job submission block until the queue has opened up, preventing job submission failure on systems with queue limits (most clusters).

To make life easier, a bunch of simple wrapper functions are defined in fyrd/basic.py that allow submission without having to worry about using the class system, or to submit existing job files. Several helper function are also created in fyrd/helpers.py that allow the automation of more complex tasks, like running apply on a pandas dataframe in parallel on the cluster (fyrd.helpers.parapply()).

The end result is that submitting 10 thousand very small jobs to a small cluster can be done like this:

jobs = []
for i in huge_list:
    jobs.append(fyrd.Job(my_function, (i,), profile='small').submit())
results = []
for i in jobs:
    results.append(i.get())

The results list in this example will contain the function outputs, even if those outputs are integers, objects, or other Python types. Similarly, shell scripts can be run like this:

script = r"""zcat {} | grep "#config" | awk '{split($1,a,"."); print a[2]"\t"$2}'"""
jobs   = []
for i in [i for i in os.listdir('.') if i.endswith('.gz')]:
    jobs.append(fyrd.Job(script.format(i), profile='long').submit())
results = []
for i in jobs:
    i.wait()
    results.append(i.stdout)

Results will contain the contents of STDOUT for the submitted script

Here is the same code with dependency tracking:

script = r"""zcat {} | grep "#config" | awk '{split($1,a,"."); print a[2]"\t"$2}'"""
jobs   = []
jobs2  = []
for i in [i for i in os.listdir('.') if i.endswith('.gz')]:
    j = fyrd.Job(script.format(i), profile='long').submit()
    jobs.append(j)
    jobs2.append(fyrd.Job(my_function, depends=j).submit())
results = []
for i in jobs2:
    i.wait()
    results.append(i.out)

As you can see, the profile keyword is not required, if not supplied the default profile is used. It is also important to note that .out will contain the same contents as .stdout for all script submissions, but for function submissions, .out contains the function output, not STDOUT.

Installation

This module will work with Python 2.7+ on Linux and Mac OS systems.

The betas are on PyPI, and can be installed directly from there:

pip install fyrd
fyrd conf init

To install a specific tag from github, do the following:

pip install https://github.com/MikeDacre/fyrd/archive/v0.6.1-beta.7.tar.gz
fyrd conf init

To get the latest version:

pip install https://github.com/MikeDacre/fyrd/tarball/master
fyrd conf init

The fyrd conf init command initializes your environment interactively by asking questions about the local cluster system.

I recommend installing using pyenv in a pyenv anaconda environment, this will make your life much simpler, but is not required.

In general you want either pyenv or user level install (pip install –user) even if you have sudo access, as most cluster environments share /home/<user> across the cluster, making this module available everywhere.

Importing is simple:

import fyrd

Prerequisites

This software requires two external modules: - dill — which makes function submission more stable - tabulate — allows readable printing of help

Cluster Dependencies

In order to submit functions to the cluster, this module must import them on the compute node. This means that all of your python modules must be available on every compute node.

By default, the same python executable used for submission is used on the cluster to run functions, however, this can be overridden by the ‘generic_python’ option on the cluster. If using this option, you must install all of your local modules on the cluster also.

To avoid pain and debugging, you can do this manually by running this on your login node:

freeze --local | grep -v '^\-e' | cut -d = -f 1 > module_list.txt

And then on the compute nodes:

cat module_list.txt | xargs pip install --user

Alternately, if your pyenv is available on the cluster nodes, then all of your modules are already available, so you don’t need to worry about this!

Testing

To fully test this software, I use py.test tests written in the tests folder. Unfortunately, local queue tests do not work with py.test, so I have separated them out into the local_queue.py script. To run all tests, run python tests/run_tests.py.

To ensure sensible testing always, I use buildkite, which is an amazing piece of software. It integrates into this repository and runs tests on all python versions I support on my two clusters (a slurm cluster and a torque cluster) every day and on every push or pull request. I also use travis ci to run local queue tests, and codacy and scrutinizer to monitor code style.

All code in the master branch must pass the travis-ci and buildkite tests, code in dev also usually passes those test, but it is not guaranteed. All other branches are unstable and will often fail the tests.

Releases

I use the following work-flow to release versions of fyrd:

1. Develop new features and fix new bugs in a feature branch

2. Write tests for the new feature

3. When all tests are passing, merge into dev

4. Do more extensive manual testing in dev, possibly add additional commits.

5. Repeat the above for other related features and bugs

6. When a related set of fixes and features are done and well tested, merge into master with a pull request through github, all travis and buildkite tests must pass for the merge to work.

7. At some point after the new features are in master, add a new tagged beta release.

8. After the beta is determined to be stable and all issues attached to that version milestone are resolved, create a non-beta tag

New releases are added when enough features and fixes have accumulated to justify it, new minor version are added only when there are very large changes in the code and are always tracked by milestones.

While this project is still in its infancy, the API cannot be considered stable and the major version will remain 0. once version 1.0 is reached, any API changes will result in a major version change.

As such, and non-beta release can be considered stable, beta releases and the master branch are very likely to be stable, dev is usually but not always stable, all other branches are very unstable.

Issues and Contributing

If you have any trouble with this software add an issue in https://github.com/MikeDacre/fyrd/issues

For peculiar technical questions or help getting the code installed, email me at [e-mail].

I am always looking for help with this software, and I will gladly accept pull requests. In particular, I am looking for help with:

• Testing the code in different cluster environments

• Expanding the list of keyword options

• Adding new clusters other than torque and slurm

• Implementing new features in the issues section

If you are interested in helping out with any of those things, or if you would be willing to give me access to your cluster to allow me to run tests and port fyrd to your environment, please contact me.

If you are planning on contributing and submitting a pull request, please follow these rules:

• Follow the code style as closely as possible, I am a little obsessive about that

• If you add new functions or features: - Add some tests to the test suite that fully test your new feature - Add notes to the documentation on what your feature does and how it works

• Make sure your code passes the full test suite, which means you need to run python tests/run_tests.py from the root of the repository at a bare minimum. Ideally, you will install pyenv and run bash tests/pyenv_tests.py

• Squash all of your commits into a single commit with a well written and informative commit message.

• Send me a pull request to either the dev or master branches.

It may take a few days for me to fully review your pull request, as I will test it extensively. If it is a big new feature implementation I may request that you send the pull request to the dev branch instead of to master.

Why the Name?

I gave this project the name ‘Fyrd’ in honour of my grandmother, Hélène Sandolphen, who was a scholar of old English. It is the old Anglo-Saxon word for ‘army’, and this code gives you an army of workers on any machine so it seemed appropriate.

The project used to be called “Python Cluster”, which is more descriptive but frankly boring. Also, about half a dozen other projects have almost the same name, so it made no sense to keep that name and put the project onto PyPI.

Documentation

This software is much more powerful that this document gives it credit for, to get the most out of it, read the docs at https://fyrd.readthedocs.org or get the PDF version from the file in docs/fyrd.pdf.