numpipe 0.3.3

Easily run Python functions in parallel and cache the results

NumPipe

NumPipe is a Python software package that makes long-running tasks easier and faster by executing code in embarrassingly parallel and caching the output to HDF5 files.

Features

• Combine computation and visualization code into single scripts. Only re-run computations on request
• Use the yield statement to return data over time that will be periodically cached to file
• Specify dependencies between cached functions
• Progress bars similar to tqdm that work in parallel to show the progress of running tasks
• An optional Telegram Messenger bot that can notify the user of completion and send Matplotlib figures and animations
• Command line arguments to re-run tasks and automatically save Matplotlib figures and animations
• Option to use the Slurm Workload Manager to automatically create and submit an sbatch file (used on many compute clusters)

Installation

NumPipe can be installed with pip

pip install numpipe

Examples

single task

from numpipe import scheduler, pbar
from time import sleep
import matplotlib.pyplot as plt

job = scheduler()

@job.cache
def sim():
    for i in pbar(range(100)):
        sleep(.1)   # long running task...
        yield dict(i=i, x=i**2)

@job.plots
def vis():
    var = job.load(sim)
    plt.plot(var.i, var.x)

if __name__ == '__main__':
    job.run()

parallel tasks

from numpipe import scheduler, pbar
from time import sleep
import matplotlib.pyplot as plt

job = scheduler()

@job.cache
def sim(power):
    for i in pbar(range(100)):
        sleep(.1)   # long running task...
        yield dict(i=i, x=i**power)

@job.plots
def vis():
    for name, var in job.load(sim):
        plt.plot(var.i, var.x)

for i in range(3):
    job.add(sim, power=i)

if __name__ == '__main__':
    job.run()

more examples

See the examples folder for more usage examples

Command line arguments

positional arguments:
  {display,clean,slurm}
    display             display available functions and descriptions
    clean               remove all h5files that are no longer cache functions
    slurm               run on a system with the Slurm Workload Manager

optional arguments:
  -h, --help            show this help message and exit
  -r [RERUN [RERUN ...]], --rerun [RERUN [RERUN ...]]
                        re-run specific cached functions by name
  -f, --force           force over-write any existing cached data
  -d [DELETE [DELETE ...]], --delete [DELETE [DELETE ...]]
                        delete specified cached data
  -e EXCLUDE [EXCLUDE ...], --exclude EXCLUDE [EXCLUDE ...]
                        exclude cached function from being re-run
  --at-end              only run at_end functions
  --no-at-end           don't run at_end functions
  -p [PROCESSES], --processes [PROCESSES]
                        number of processes to use in parallel execution (default: cpu_count)
  -ct CACHE_TIME, --cache_time CACHE_TIME
                        time (in seconds) until data cached data is flushed to file
  --no-deps             do not rerun functions that depend on other reran functions
  --mininterval MININTERVAL
                        time (in seconds) for progress bar mininterval argument
  --notify              send notifications without delay
  --notify-message NOTIFY_MESSAGE
                        send a custom message with other notifications
  --notify-delay NOTIFY_DELAY
                        time (in seconds) before notifications will be sent
  --theme THEME         matplotlib plot theme
  --figures FIGURES [FIGURES ...]
                        which figure numbers to display
  --save [SAVE]         save figures and animations
  --save-format SAVE_FORMAT [SAVE_FORMAT ...]
                        file format for figures
  --save-figs [SAVE_FIGS]
                        save figures
  --save-anims [SAVE_ANIMS]
                        save animations
  --debug               run in debug mode (single process)

License

NumPipe is licensed under the terms of the MIT license.