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Merge pull request IGITUGraz#23 from Meta-optimization/experiment
Experiment class added to simplify interface between user and simulation setup/execution.
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,158 @@ | ||
| import logging.config | ||
| import os | ||
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| from l2l.utils.environment import Environment | ||
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| from l2l.logging_tools import create_shared_logger_data, configure_loggers | ||
| from l2l.paths import Paths | ||
| import l2l.utils.JUBE_runner as jube | ||
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| class Experiment(object): | ||
| def __init__(self, root_dir_path): | ||
| """ | ||
| Prepares and starts the l2l simulation. | ||
| :param root_dir_path: str, Path to the results folder. Accepts relative | ||
| paths. Will check if the folder exists and create if not. | ||
| """ | ||
| self.root_dir_path = root_dir_path | ||
| self.logger = logging.getLogger('bin.l2l') | ||
| self.paths = None | ||
| self.env = None | ||
| self.traj = None | ||
| self.optimizee = None | ||
| self.optimizer = None | ||
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| def prepare_experiment(self, **kwargs): | ||
| """ | ||
| Prepare the experiment by creating the enviroment and | ||
| :param kwargs: optional dictionary, contains | ||
| - name: str, name of the run, Default: L2L-run | ||
| - trajectory_name: str, name of the trajectory, Default: trajectory | ||
| - log_stdout: bool, if stdout should be sent to logs, Default:False | ||
| - jube_parameter: dict, User specified parameter for jube. | ||
| See notes section for default jube parameter | ||
| - multiprocessing, bool, enable multiprocessing, Default: False | ||
| :return all_jube_params, dict, a dictionary with all parameters for jube | ||
| given by the user and default ones | ||
| :return traj, trajectory object | ||
| :notes | ||
| Default JUBE parameters are: | ||
| - scheduler: Slurm, | ||
| - submit_cmd: sbatch, | ||
| - job_file: job.run, | ||
| - nodes: 1, | ||
| - walltime: 01:00:00, | ||
| - ppn: 1, | ||
| - cpu_pp: 1, | ||
| - threads_pp: 4, | ||
| - mail_mode: ALL, | ||
| - err_file: stderr, | ||
| - out_file: stdout, | ||
| - tasks_per_job: 1 | ||
| """ | ||
| name = kwargs.get('name', 'L2L-run') | ||
| if not os.path.isdir(self.root_dir_path): | ||
| os.mkdir(os.path.abspath(self.root_dir_path)) | ||
| print('Created a folder at {}'.format(self.root_dir_path)) | ||
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| trajectory_name = kwargs.get('trajectory_name', 'trajectory') | ||
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| self.paths = Paths(name, dict(run_num='test'), | ||
| root_dir_path=self.root_dir_path, | ||
| suffix="-" + trajectory_name) | ||
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| print("All output logs can be found in directory ", | ||
| self.paths.logs_path) | ||
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| # Create an environment that handles running our simulation | ||
| # This initializes an environment | ||
| self.env = Environment( | ||
| trajectory=trajectory_name, | ||
| filename=self.paths.output_dir_path, | ||
| file_title='{} data'.format(name), | ||
| comment='{} data'.format(name), | ||
| add_time=True, | ||
| automatic_storing=True, | ||
| log_stdout=kwargs.get('log_stdout', False), # Sends stdout to logs | ||
| multiprocessing=kwargs.get('multiprocessing', False) | ||
| ) | ||
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| create_shared_logger_data( | ||
| logger_names=['bin', 'optimizers'], | ||
| log_levels=['INFO', 'INFO'], | ||
| log_to_consoles=[True, True], | ||
| sim_name=name, | ||
| log_directory=self.paths.logs_path) | ||
| configure_loggers() | ||
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| # Get the trajectory from the environment | ||
| self.traj = self.env.trajectory | ||
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| # Set JUBE params | ||
| default_jube_params = { | ||
| "scheduler": "Slurm", | ||
| "submit_cmd": "sbatch", | ||
| "job_file": "job.run", | ||
| "nodes": "1", | ||
| "walltime": "01:00:00", | ||
| "ppn": "1", | ||
| "cpu_pp": "1", | ||
| "threads_pp": "4", | ||
| "mail_mode": "ALL", | ||
| "err_file": "stderr", | ||
| "out_file": "stdout", | ||
| "tasks_per_job": "1", | ||
| "exec": "python " + os.path.join(self.paths.simulation_path, | ||
| "run_files/run_optimizee.py"), | ||
| "ready_file": os.path.join(self.paths.root_dir_path, | ||
| "ready_files/ready_w_"), | ||
| "work_path": self.paths.root_dir_path, | ||
| "paths_obj": self.paths, | ||
| } | ||
| # Will contain all jube parameters | ||
| all_jube_params = {} | ||
| self.traj.f_add_parameter_group("JUBE_params", | ||
| "Contains JUBE parameters") | ||
| # Go through the parameter dictionary and add to the trjacectory | ||
| if kwargs.get('jube_parameter'): | ||
| for k, v in kwargs['jube_parameter'].items(): | ||
| self.traj.f_add_parameter_group("JUBE_params", k, v) | ||
| all_jube_params[k] = v | ||
| # Default parameter are added if they are not already set by the user | ||
| for k, v in default_jube_params.items(): | ||
| if k not in kwargs.get('jube_parameter').keys(): | ||
| self.traj.f_add_parameter_to_group("JUBE_params", k, v) | ||
| all_jube_params[k] = v | ||
| print('JUBE parameters used: {}'.format(all_jube_params)) | ||
| return self.traj, all_jube_params | ||
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| def run_experiment(self, optimizee, optimizee_parameters, optimizer, | ||
| optimizer_parameters): | ||
| """ | ||
| Runs the simulation with all parameter combinations | ||
| Optimizee and optimizer object are required as well as their parameters | ||
| as namedtuples. | ||
| :param optimizee: optimizee object | ||
| :param optimizee_parameters: Namedtuple, parameters of the optimizee | ||
| :param optimizer: optimizer object | ||
| :param optimizer_parameters: Namedtuple, parameters of the optimizer | ||
| :return traj, trajectory object | ||
| :return path, Path object | ||
| """ | ||
| self.logger.info("Optimizee parameters: %s", optimizee_parameters) | ||
| self.logger.info("Optimizer parameters: %s", optimizer_parameters) | ||
| jube.prepare_optimizee(optimizee, self.paths.simulation_path) | ||
| # Add post processing | ||
| self.env.add_postprocessing(optimizer.post_process) | ||
| # Run the simulation | ||
| self.env.run(optimizee.simulate) | ||
| # Outer-loop optimizer end | ||
| optimizer.end() | ||
| # Finally disable logging and close all log-files | ||
| self.env.disable_logging() | ||
| return self.traj, self.paths |