mlmc.sampling_pool.ProcessPool

class mlmc.sampling_pool.ProcessPool(n_processes, work_dir=None, debug=False)[source]

Sampling pool using multiprocessing for parallel sample execution. Suitable for simulations without external program calls.

__init__(n_processes, work_dir=None, debug=False)[source]

Initialize process-based parallel sampling pool.

Parameters

Methods

`__init__`(n_processes[, work_dir, debug])	Initialize process-based parallel sampling pool.
`calculate_sample`(sample_id, level_sim[, ...])	Execute a single simulation sample.
`change_to_sample_directory`(work_dir, path)	Create and switch to the sample-specific directory.
`compute_seed`(sample_id)	Compute a deterministic seed for a given sample ID.
`copy_sim_files`(files, sample_dir)	Copy shared simulation files to the sample directory.
`get_finished`()	Retrieve all completed (successful and failed) samples.
`handle_sim_files`(work_dir, sample_id, level_sim)	Prepare the sample workspace (create directory, copy common files, set cwd).
`have_permanent_samples`(sample_ids)	Return False, indicating that no samples are stored permanently.
`move_dir`(sample_id, sample_workspace, ...)	Move a sample directory to another location (e.g., failed or successful).
`move_failed_rm`(sample_id, level_sim, ...)	Move failed sample directories and remove originals.
`move_successful_rm`(sample_id, level_sim, ...)	Move successful sample directories and remove originals.
`remove_sample_dir`(sample_id, ...)	Remove the directory for a completed or failed sample.
`res_callback`(result, level_sim)	Callback for handling results from asynchronous execution.
`schedule_sample`(sample_id, level_sim)	Schedule a sample for parallel execution in a separate process.

Attributes