datamultiproc 0.1.3

A Python multiprocessing library for data processing with pipelines.

datamultiproc: A Python multiprocessing data pipeline library

Create Processors, compose them into pipelines and process your data using Python's multiprocessing library.

Concepts

Sample

A Sample is a data object that is passed through the pipeline. A custom sample can be simply implemented by inheriting from BaseSample and adding the desired data fields. Note that an def __init__(self, ...) method is not required. 'BaseSample' is build in top of Pydantics BaseModel and inherits its functionality. For more information, see Pydantic.

Data fields can be of any type and also Optional. The latter is especially handy when they are filled during the execution of the pipeline.

import numpy as np
from datamultiproc import BaseSample
from typing import Optional


class CustomSample(BaseSample):
    data: int
    optional_data: Optional[str] = None
    array: np.ndarray

BaseSample has two special fields (id and processing_history). The first has to be filled during instantiation of a Sample, the latter is appended to during execution of the pipeline, in order to keep track of all processing steps.

class BaseSample(Aggregate):
    processing_history: List[Tuple[str, str]] = []
    id: str

Processor

A Processor carries out an actual processing step on a Sample. To implement a custom Processor, you simply inherit from the Processorclass and implement the process() method.

# Processor without arguments
class CustomProcessor(Processor):
    def process(self, sample: Sample) -> Sample:
        # do something with sample, e.g.
        sample.data = sample.date + 1
        return sample

    
# Processor with arguments
class CustomProcessor(Processor):
    step_size: int
    _hidden_arg: int = 1

    def process(self, sample: Sample) -> Sample:
        # do something with sample, e.g.
        sample.data = sample.date + self.step_size + self._hidden_arg
        return sample

Compose

Compose chains multiple Processor together. They are executed in the order they are passed to the Compose constructor.

process = Compose(
    CustomProcessor(),
    CustomProcessor(step_size=2),
    CustomProcessor(step_size=3),
)

samples = [...] # some list of samples

processed_samples = [process(s) for s in samples]

Cache and Save

Cache and Save are special Processors that can be used to store intermediate results. Cache stores Samplesafter the processing step, that it is wrapped around as a '.pickle' file. The files are stored inside a subfolder of the specified save_to_dir folder. The subfolder is named after the "Processor" class. If the pipeline is executed again, Cache will first check if the file already exists and if so, it will load the Samples from the file instead of executing the wrapped Processor again.

Save works similarly, but does not load samples from file, if they already exist.

Cache is especially useful for storing intermediate results, that are expensive to compute. Save is useful for storing the final results of a pipeline.

process = Compose(
    Cache(
        processor=CustomProcessor(),
        cache_dir="path/to/cache"
    ),
    CustomProcessor(step_size=2),
    CustomProcessor(step_size=3),
    Save(save_to_dir="path/to/save"),
)

Multiprocessing

The library can be used with Python's multiprocessing library, as follows. A full working example can be found in example.py.

from multiprocessing import Process, Queue

# number of processor cores of your machine
NUM_PROCESSES = 8

# list of samples
samples = [...]

process = Compose([SomeProcessor(), 
                   AnotherProcessor(), 
                   Save(save_to_dir="path/to/save"),
                   ])

# put the samples into a multiprocessing Queue
samples_queue = Queue()
for s in samples:
    samples_queue.put(s)
    

def do_processing(process: Callable, queue: Queue):
    while not queue.empty():
        sample = queue.get()
        try:
            sample = process(sample)
        except ProcessingError:
            print(f"failed processing: {sample.id}")
    
processes = []
for _ in range(NUM_PROCESSES):
    p = Process(target=do_processing, args=(process, samples_queue))
    p.start()
    processes.append(p)

for p in processes:
    p.join()