psyrun 0.5.2

Easy parameter space evaluation and serial farming.

Psyrun

Psyrun is a Python tool to define parameter spaces and execute an evaluation function for each parameter assignment. In addition Psyrun makes it easy to use serial farming, i.e. evaluating multiple parameter assignments in parallel, on a multicore computers and high-performance clusters.

Documentation

The documentation can be found here.

Overview

Define parameter spaces and evaluate them:

from psyrun import map_pspace, Param

def objective(a, b, c):
    return a * b + c

pspace = (Param(a=np.arange(1, 5))
          * Param(b=np.linspace(0, 1, 10))
          * Param(c=[1., 1.5, 10., 10.5]))
results = map_pspace(objective, pspace)

Or do it in parallel:

from psyrun import map_pspace_parallel
results = map_pspace_parallel(objective, pspace)

Define tasks by placing task_<name>.py files in the psy-tasks` directory:

from psyrun import Param

pspace = (Param(a=np.arange(1, 5))
          * Param(b=np.linspace(0, 1, 10))
          * Param(c=[1., 1.5, 10., 10.5]))

def execute(a, b, c):
    return {'result': a * b + c}

and run them by typing psy run with support for serial farming on high performance clusters.

Installation

pip install psyrun

To be able to use the NPZ store:

pip install numpy
pip install 'psyrun[npz]'

To be able to use the HDF5 store:

pip install numpy
pip install 'psyrun[h5]'

Requirements

• Python >=2.7, >=3.4

• six

Optional requirements

To have faulthandler activated for jobs submitted with psy run in Python 2.7:

• faulthandler

Python 3.4+ already includes the faulthandler module.

To use map_pspace_parallel:

• joblib

To use NPZ files as store:

• NumPy

To use HDF5 files as store:

• NumPy

• pytables

To run the unit tests:

• joblib

• NumPy

• pytables

• pytest >= 2.8

To build the documentation:

• numpydoc

Changes

0.5.2

• Fix incorrect psy status.

• Fix psy run <task1> <task2> ... not running all tasks and run them in order.

0.5.1

Bug fixes

• Fix psy merge always assuming PickleStore.

Documentation improvements

• Add recipe for converting data to Pandas data frame to documentation.

0.5

• Initial release