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Generic computation implementation on COINSTAC.

Project description

COINSTAC computations development made easy.

A very intuitive wrapper for writing coinstac based computations with:

  • Break down your computations in simple phases with automatic transition between phases.
  • Add as many phases you want.
  • Even run phases that needs to be run multiple rounds of iterations.

PyPi version YourActionName Actions Status versions

Express development(see examples folder for a simple use case):

Commands:

mkdir -p examples/basic/dist        --- Needed only once -------
chmod u+x deploy.sh                 --- Needed only once -------

./deploy.sh examples/basic/dist     --- Needed everytime you make some changes -------

Deployment:

  • Add entry coinstac-computation to requirements.txt.
  • Comment out RUN pip install "/computation/dist/$(ls -t1 dist| head -n 1)" in Dockerfile.

Example: Gather max even numbers from each site

A full working use case is in the examples/basic directory where:

  • Local sites filters out even numbers and sends to the remote.
  • Remote finds the max across sites and returns the final result to each of the sites.
  • Sites save final result.

inputspec.json data:

[
  {
    "data": {
      "value": [10, 3, 5, 6, 7, 8, 12, 38, 32, 789, 776, 441]
    }
  },
  {
    "data": {
      "value": [12, 33, 88, 61, 37, 58, 103, 3386, 312, 9, 77, 41]
    }
  }
]

Local node pipeline:

import os
from coinstac_computation import COINSTACPyNode, ComputationPhase


class PhaseLoadData(ComputationPhase):
    def compute(self):
        data = []
        for d in self.input['data']:
            if d % 2 == 0:
                data.append(d)
        return {'filtered_data': data}


class PhaseSaveResult(ComputationPhase):
    def compute(self):
        with open(f"{self.state['outputDirectory'] + os.sep + 'results.txt'}", 'w') as out:
            out.write(f"{self.input['aggregated_data']}")


local = COINSTACPyNode(mode='local', debug=True)
local.add_phase(PhaseLoadData)
local.add_phase(PhaseSaveResult)

Remote node pipeline:

from coinstac_computation import COINSTACPyNode, ComputationPhase, PhaseEndWithSuccess


class PhaseCollectMaxEvenData(ComputationPhase):
    def compute(self):
        data = []
        for site, site_vars in self.input.items():
            site_max = max(site_vars['filtered_data'])
            data.append(site_max)
        return {'aggregated_data': data}


remote = COINSTACPyNode(mode='remote', debug=True)
remote.add_phase(PhaseCollectMaxEvenData)
remote.add_phase(PhaseEndWithSuccess)

Entry point:

import coinstac

from local_pipeline import local
from remote_pipeline import remote

coinstac.start(local.compute, remote.compute)

Run:

cd example
docker build -t base . && coinstac-simulator

Advanced use case: Phases with multiple iterations.

Overview:

  1. Specify a phase as multi-iterations:
local.add_phase(SomeIterativePhase, multi_iterations=True)
  1. Specify when to end the iterative phase as:
class SomeIterativePhase(ComputationPhase):
    def compute(self):
        """Do all the stuff"""
        
        """Check if the iterative phase is done, and send a phase jump signal."""
        should_jump_to_next_phase = ... 
        return {..., 'jump_to_next': should_jump_to_next_phase}

Full working example where:

  • Each sites cast a vote for multiple(default=51) times.
  • Remote gathers the votes and returns the final voting result at the end.
  • Sites save the final result.

Development notes:

  • Make sure you have:
    • docker installed and running.
    • nodejs installed.
    • coinstac-simulator package installed. npm install --global coinstac-simulator
  • Must set debug=False while deploying.
  • Backward compatible to the older library(compspecVersion=1):
    • Add the following snippet at the end of local and remote pipeline scripts.
    if __name__ == "__main__":
        local.to_stdout()
    
    • Use version 1.0 compspec format.
    • Comment out line CMD ["python", "entry.py"] in the Dockerfile.
    • You can also use a remote debugger in pycharm as here.

Thanks!

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