coinstac-computation 1.0.2

Generic computation implementation on COINSTAC.

COINSTAC computations development made easy.

A very intuitive wrapper for writing coinstac based computations:

• Break down your computations into simple phases with automatic transition between phases.
• Add as many phases as you want.
• Phases alternate between local and remote automatically by default starting from the first phase of the local. See advanced use case example below for extras like:
  • Run phases that needs to be run multiple local-remote trips; Specify multi_iterations=True while adding a phase.
  • Run phases that needs to be run either in local or remote without making a trip(like preprocessing, gathering final results ...); Specify local_only=True while adding a phase.
• Automatic logging that saves what comes and leaves on each phase. Just set debug=True.

Installation:

• Run pip install coinstac-computation
• Add entry coinstac-computation to the requirements.txt.

---

ComputationPhase signature:

from coinstac_computation import  ComputationPhase

class PhaseLoadData(ComputationPhase):
    def _initialize(self):
      """Put anything that needs to be initialized only once here"""
      pass
    
    def compute(self):
        out = {}
        ...
        
        """To end multi-iterative phase, and go to the next phase, in local or remote set:"""
        out['jump_to_next'] = True
        
        """To stop the computation, In remote set:"""
        out['success'] = True
        return out

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.out_dir + 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, remote)

Run:

cd examples/basic/
~/coinstac-computation/examples/basic/$ docker build -t base . && coinstac-simulator

---

Advanced use cases:

1. Multiple local <---> remote iterations example:

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

Overview:

1. Specify when to end the iterative phase with a phase jump signal as jump_to_next=True:

class PhaseSubmitVote(ComputationPhase):

    def _initialize(self):
        """This method runs only once"""
        self.cache['data_index'] = 0
        self.cache['data'] = []
        for line in open(self.base_dir + os.sep + self.input_args['data_source']).readlines():
            self.cache['data'].append(float(line.strip()))

    def compute(self):
        out = {
            'vote': self.cache['data'][self.cache['data_index']] % 2 == 0,
        }
        self.cache['data_index'] += 1
        
        """Send a jump to next phase signal"""
        out['jump_to_next'] = self.cache['data_index'] > len(self.cache['data']) - 1
        return out

2. Add the phase as multi-iterations:

local.add_phase(PhaseSubmitVote, multi_iterations=True)

2. Send data across local <---> remote example:

To make it simple, we send a matrix of size 1000 by 1000 to remote, aggregate it by averaging, and return to each site.

Local:

import os
from coinstac_computation import COINSTACPyNode, ComputationPhase
import numpy as np

class PhaseLoadData(ComputationPhase):
    def compute(self):
        out = {}
        data = np.random.randn(*self.input['matrix_shape'])
        out.update(**self.send("site_matrix", data))
        return out


class PhaseSaveResult(ComputationPhase):
    def compute(self):
        data = self.recv('averaged_matrix')
        np.save(self.out_dir + os.sep + "averaged_matrix.npy", data)


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

Remote:

from coinstac_computation import COINSTACPyNode, ComputationPhase, PhaseEndWithSuccess
import numpy as np


class PhaseAggregateMatrix(ComputationPhase):
    def compute(self):
        out = {}
        data = self.recv("site_matrix")
        mean_data = np.array(data).mean(0)
        out.update(**self.send("averaged_matrix", mean_data))
        return out


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

---

Sample logs from local0

[INPUT] 14:27:36 02/04/2022 
  ->{'data_source': 'data_file.txt'}
[CACHE] 14:27:36 02/04/2022 
  ->{'PIPELINE:LOCAL': {'index': 0, 'iterations': {'PhaseSubmitVote': 0, 'PhaseSaveResult': 0}}}
  <-{'PIPELINE:LOCAL': {'index': 0, 'iterations': {'PhaseSubmitVote': 1, 'PhaseSaveResult': 0}}, 'input_args': {'data_source': 'data_file.txt'}, 'next_phase': 'PhaseSubmitVote', 'data_index': 1, 'data': [712.0, 309.0, 574.0, 838.0, 296.0, 349.0, 781.0, 749.0, 360.0, 702.0, 253.0, 831.0, 911.0, 14.0, 259.0, 805.0, 494.0, 501.0, 549.0, 624.0, 919.0, 836.0, 362.0, 373.0, 563.0, 134.0, 610.0, 875.0, 328.0, 299.0, 874.0, 387.0, 743.0, 233.0, 834.0, 870.0, 685.0, 342.0, 79.0, 270.0, 314.0, 42.0, 364.0, 902.0, 755.0, 248.0, 815.0, 4.0, 21.0, 423.0, 302.0], 'PHASE:PhaseSubmitVote': True}
[OUTPUT] 14:27:36 02/04/2022 
  <-{'output': {'vote': True, 'jump_to_next': False}}

[INPUT] 14:27:37 02/04/2022 
  ->{}
[CACHE] 14:27:37 02/04/2022 
  ->{'PIPELINE:LOCAL': {'index': 0, 'iterations': {'PhaseSubmitVote': 1, 'PhaseSaveResult': 0}}, 'input_args': {'data_source': 'data_file.txt'}, 'next_phase': 'PhaseSubmitVote', 'data_index': 1, 'data': [712.0, 309.0, 574.0, 838.0, 296.0, 349.0, 781.0, 749.0, 360.0, 702.0, 253.0, 831.0, 911.0, 14.0, 259.0, 805.0, 494.0, 501.0, 549.0, 624.0, 919.0, 836.0, 362.0, 373.0, 563.0, 134.0, 610.0, 875.0, 328.0, 299.0, 874.0, 387.0, 743.0, 233.0, 834.0, 870.0, 685.0, 342.0, 79.0, 270.0, 314.0, 42.0, 364.0, 902.0, 755.0, 248.0, 815.0, 4.0, 21.0, 423.0, 302.0], 'PHASE:PhaseSubmitVote': True}
  <-{'PIPELINE:LOCAL': {'index': 0, 'iterations': {'PhaseSubmitVote': 2, 'PhaseSaveResult': 0}}, 'input_args': {'data_source': 'data_file.txt'}, 'next_phase': 'PhaseSubmitVote', 'data_index': 2, 'data': [712.0, 309.0, 574.0, 838.0, 296.0, 349.0, 781.0, 749.0, 360.0, 702.0, 253.0, 831.0, 911.0, 14.0, 259.0, 805.0, 494.0, 501.0, 549.0, 624.0, 919.0, 836.0, 362.0, 373.0, 563.0, 134.0, 610.0, 875.0, 328.0, 299.0, 874.0, 387.0, 743.0, 233.0, 834.0, 870.0, 685.0, 342.0, 79.0, 270.0, 314.0, 42.0, 364.0, 902.0, 755.0, 248.0, 815.0, 4.0, 21.0, 423.0, 302.0], 'PHASE:PhaseSubmitVote': True}
[OUTPUT] 14:27:37 02/04/2022 
  <-{'output': {'vote': False, 'jump_to_next': False}}

[INPUT] 14:27:37 02/04/2022 
  ->{}
[CACHE] 14:27:37 02/04/2022 
  ->{'PIPELINE:LOCAL': {'index': 0, 'iterations': {'PhaseSubmitVote': 2, 'PhaseSaveResult': 0}}, 'input_args': {'data_source': 'data_file.txt'}, 'next_phase': 'PhaseSubmitVote', 'data_index': 2, 'data': [712.0, 309.0, 574.0, 838.0, 296.0, 349.0, 781.0, 749.0, 360.0, 702.0, 253.0, 831.0, 911.0, 14.0, 259.0, 805.0, 494.0, 501.0, 549.0, 624.0, 919.0, 836.0, 362.0, 373.0, 563.0, 134.0, 610.0, 875.0, 328.0, 299.0, 874.0, 387.0, 743.0, 233.0, 834.0, 870.0, 685.0, 342.0, 79.0, 270.0, 314.0, 42.0, 364.0, 902.0, 755.0, 248.0, 815.0, 4.0, 21.0, 423.0, 302.0], 'PHASE:PhaseSubmitVote': True}
  <-{'PIPELINE:LOCAL': {'index': 0, 'iterations': {'PhaseSubmitVote': 3, 'PhaseSaveResult': 0}}, 'input_args': {'data_source': 'data_file.txt'}, 'next_phase': 'PhaseSubmitVote', 'data_index': 3, 'data': [712.0, 309.0, 574.0, 838.0, 296.0, 349.0, 781.0, 749.0, 360.0, 702.0, 253.0, 831.0, 911.0, 14.0, 259.0, 805.0, 494.0, 501.0, 549.0, 624.0, 919.0, 836.0, 362.0, 373.0, 563.0, 134.0, 610.0, 875.0, 328.0, 299.0, 874.0, 387.0, 743.0, 233.0, 834.0, 870.0, 685.0, 342.0, 79.0, 270.0, 314.0, 42.0, 364.0, 902.0, 755.0, 248.0, 815.0, 4.0, 21.0, 423.0, 302.0], 'PHASE:PhaseSubmitVote': True}
[OUTPUT] 14:27:37 02/04/2022 
  <-{'output': {'vote': True, 'jump_to_next': False}}
...

Sample logs from remote

[INPUT] 14:27:37 02/04/2022 
  ->{'local0': {'vote': True, 'jump_to_next': False}, 'local1': {'vote': False, 'jump_to_next': False}, 'local2': {'vote': False, 'jump_to_next': False}, 'local3': {'vote': False, 'jump_to_next': False}}
[CACHE] 14:27:37 02/04/2022 
  ->{'PIPELINE:REMOTE': {'index': 0, 'iterations': {'PhaseCollectVote': 0, 'PhaseSendGlobalResults': 0, 'PhaseEndWithSuccess': 0}}}
  <-{'PIPELINE:REMOTE': {'index': 0, 'iterations': {'PhaseCollectVote': 1, 'PhaseSendGlobalResults': 0, 'PhaseEndWithSuccess': 0}}, 'input_args': {'local0': {'vote': True, 'jump_to_next': False}, 'local1': {'vote': False, 'jump_to_next': False}, 'local2': {'vote': False, 'jump_to_next': False}, 'local3': {'vote': False, 'jump_to_next': False}}, 'next_phase': 'PhaseCollectVote', 'vote_ballot': [[1, 3]], 'PHASE:PhaseCollectVote': True}
[OUTPUT] 14:27:37 02/04/2022 
  <-{'output': {}, 'success': False}

[INPUT] 14:27:37 02/04/2022 
  ->{'local0': {'vote': False, 'jump_to_next': False}, 'local2': {'vote': True, 'jump_to_next': False}, 'local1': {'vote': True, 'jump_to_next': False}, 'local3': {'vote': True, 'jump_to_next': False}}
[CACHE] 14:27:37 02/04/2022 
  ->{'PIPELINE:REMOTE': {'index': 0, 'iterations': {'PhaseCollectVote': 1, 'PhaseSendGlobalResults': 0, 'PhaseEndWithSuccess': 0}}, 'input_args': {'local0': {'vote': True, 'jump_to_next': False}, 'local1': {'vote': False, 'jump_to_next': False}, 'local2': {'vote': False, 'jump_to_next': False}, 'local3': {'vote': False, 'jump_to_next': False}}, 'next_phase': 'PhaseCollectVote', 'vote_ballot': [[1, 3]], 'PHASE:PhaseCollectVote': True}
  <-{'PIPELINE:REMOTE': {'index': 0, 'iterations': {'PhaseCollectVote': 2, 'PhaseSendGlobalResults': 0, 'PhaseEndWithSuccess': 0}}, 'input_args': {'local0': {'vote': True, 'jump_to_next': False}, 'local1': {'vote': False, 'jump_to_next': False}, 'local2': {'vote': False, 'jump_to_next': False}, 'local3': {'vote': False, 'jump_to_next': False}}, 'next_phase': 'PhaseCollectVote', 'vote_ballot': [[1, 3], [3, 1]], 'PHASE:PhaseCollectVote': True}
[OUTPUT] 14:27:37 02/04/2022 
  <-{'output': {}, 'success': False}

[INPUT] 14:27:37 02/04/2022 
  ->{'local0': {'vote': True, 'jump_to_next': False}, 'local3': {'vote': True, 'jump_to_next': False}, 'local2': {'vote': True, 'jump_to_next': False}, 'local1': {'vote': True, 'jump_to_next': False}}
[CACHE] 14:27:37 02/04/2022 
  ->{'PIPELINE:REMOTE': {'index': 0, 'iterations': {'PhaseCollectVote': 2, 'PhaseSendGlobalResults': 0, 'PhaseEndWithSuccess': 0}}, 'input_args': {'local0': {'vote': True, 'jump_to_next': False}, 'local1': {'vote': False, 'jump_to_next': False}, 'local2': {'vote': False, 'jump_to_next': False}, 'local3': {'vote': False, 'jump_to_next': False}}, 'next_phase': 'PhaseCollectVote', 'vote_ballot': [[1, 3], [3, 1]], 'PHASE:PhaseCollectVote': True}
  <-{'PIPELINE:REMOTE': {'index': 0, 'iterations': {'PhaseCollectVote': 3, 'PhaseSendGlobalResults': 0, 'PhaseEndWithSuccess': 0}}, 'input_args': {'local0': {'vote': True, 'jump_to_next': False}, 'local1': {'vote': False, 'jump_to_next': False}, 'local2': {'vote': False, 'jump_to_next': False}, 'local3': {'vote': False, 'jump_to_next': False}}, 'next_phase': 'PhaseCollectVote', 'vote_ballot': [[1, 3], [3, 1], [4, 0]], 'PHASE:PhaseCollectVote': True}
[OUTPUT] 14:27:37 02/04/2022 
  <-{'output': {}, 'success': False}
...

---

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!