Paropt

Paropt enables automating the process of finding optimal tool configurations using Parsl and BayesianOptimization.

See paropt-service for using this package in a RESTful API to launch optimization tasks.

Setup

pip install git+https://[email protected]/globus-labs/ParaOpt

Usage

Optimizing a tool requires a few things. First, is the experiment which describes the tool we are testing and provides it's context. An experiment is composed of a name, parameters that you will tune to optimize the tool, and a script which uses those parameters to actually run the tool. Notice that the template script and parameters have matching names - this is required for them to get used in the script at runtime. Information about these classes can be found in the storage/entities directory.

from paropt.storage.entities import Parameter, PARAMETER_TYPE_INT, PARAMETER_TYPE_FLOAT, Experiment, LocalCompute

# This is a template script for running the tool to be optimized
command_template_string = """
#! /bin/bash

echo "Sleeping for ${foo} + ${bar} seconds"
sleep $(echo ${foo} + ${bar} | bc)
"""

experiment = Experiment(
  tool_name='my-tool',
  parameters=[
    Parameter(name="foo", type=PARAMETER_TYPE_INT, minimum=0, maximum=10),
    Parameter(name="bar", type=PARAMETER_TYPE_FLOAT, minimum=0, maximum=10)
  ],
  command_template_string=command_template_string,
  compute=LocalCompute(max_threads=8)
)

To persist the results after running trials we need storage. Right now we support any database that SQLAlchemy can use.

from paropt.storage import RelationalDB

storage = RelationalDB(
  dialect='postgresql',
  username=os.environ['DB_USERNAME'],
  password=os.environ['DB_PASSWORD'],
  host_url=os.environ['DB_HOSTNAME'],
  dbname=os.environ['DB_NAME'],
)

An optimizer is used to determine which configurations of the tool to test. Right now we just have grid search and bayesian optimization - both of which only accept numeric types.

from paropt.optimizer import BayesianOptimizer

bayesian_optimizer = BayesianOptimizer(
  n_init=2,
  n_iter=2,
)

This is all tied together by a runner, which we use Parsl for. Note that we need to provide a Parsl app that will wrap our template script. For jobs that run on the order of hours, you can use the provided timeCmd.

from paropt.runner import ParslRunner
from paropt.runner.parsl import timeCmd


po = ParslRunner(
  parsl_app=timeCmd,
  optimizer=bayesian_optimizer,
  storage=storage,
  experiment=experiment)

po.run()
print(f'Run result: {po.run_result}')