ubatch 0.0.2

Micro batch solution for improve throughput in SIMD processes

uBatch

uBatch is a simple, yet elegant library for processing any data in batch.

uBatch allow to process multiple inputs data from different threads as a single block of data, this is useful when process data in a batch mode has a lower cost than processing it independently, for example process data in GPU or take advantage from optimization of libraries written in C. Ideally, the code that processes the batches should release the Python GIL for allowing others threads/coroutines to run, this is true in many C libraries wrapped in Python.

Example

>>> import threading
>>>
>>> from typing import List
>>> from ubatch import UBatch
>>>
>>>
>>> class Squeared:
...     def in_batch(self, data_inputs: List[int]) -> List[int]:
...         return [x ** 2 for x in data_inputs]
...
>>>
>>> squared = Squeared()
>>>
>>> squared_batch: UBatch[int, int] = UBatch(max_size=100, timeout=0.02)
>>> squared_batch.set_handler(handler=squared.in_batch)
>>> squared_batch.start()
>>>
>>>
>>> def thread_function(number: int) -> None:
...     result = squared_batch.ubatch(number)
...     print(f"Input: {number}, Output: {result}")
...
>>>
>>> threads = []
>>> for i in range(10):
...     t = threading.Thread(target=thread_function, args=(i,))
...     threads.append(t)
...     t.start()
...
>>> for t in threads:
...     t.join()
Input: 0, Output: 0
Input: 4, Output: 16
Input: 9, Output: 81
Input: 5, Output: 25
Input: 2, Output: 4
Input: 1, Output: 1
Input: 6, Output: 36
Input: 3, Output: 9
Input: 8, Output: 64
Input: 7, Output: 49

The example above shows 10 threads calculating the square of a number, using uBatch the threads delegate the calculation task to a single process that calculates them in batch.

Example using ubatch decorator

>>> import threading
>>>
>>> from typing import List
>>> from ubatch import ubatch_decorator
>>>
>>>
>>> @ubatch_decorator(max_size=5, timeout=0.01)
... def squared(a: List[int]) -> List[int]:
...     print(a)
...     return [x ** 2 for x in a]
...
>>>
>>> inputs = list(range(10))
>>>
>>> # Run squared as usual
... _ = squared(inputs)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>>
>>>
>>> def thread_function(number: int) -> None:
...     _ = squared.ubatch(number)
...
>>>
>>> # Multiple threads squared individual inputs
... threads = []
>>> for i in inputs:
...     t = threading.Thread(target=thread_function, args=(i,))
...     threads.append(t)
...     t.start()
...
[0, 1, 2, 3, 4]
[5, 6, 7, 8, 9]
>>> for t in threads:
...     t.join()

Installing uBatch and Supported Versions

$ pip install ubatch

uBatch officially supports Python 3.6+.

Why using uBatch?

When data is processed offline it is easy to collect data to be processed at same time, the same does not happen when requests are attended online as example using Flask, this is where the uBatch potential comes in.

TensorFlow or Scikit-learn are just some of the libraries that can take advantage of this functionality.

uBatch and application server

Python application servers work like this:

When the server is initialized multiple processes are created and each process create a bunch of threads for handling requests. Taking advantage of those threads that run in parallel uBatch can be used to group several inputs and process them in a single block.

Let's see a Flask example:

import numpy as np

from typing import List, Dict
from flask import Flask, request as flask_request
from flask_restx import Resource, Api

from ubatch import UBatch
from model import load_model


app = Flask(__name__)
api = Api(app)

model = load_model()

predict_batch: UBatch[np.array, np.array] = UBatch(max_size=50, timeout=0.01)
predict_batch.set_handler(handler=model.batch_predict)
predict_batch.start()


@api.route("/predict")
class Predict(Resource):
    def post(self) -> Dict[str, List[float]]:
        received_input = np.array(flask_request.json["input"])
        result = predict_batch.ubatch(received_input)

        return {"prediction": result.tolist()}

Start application server:

$ gunicorn -k gevent app:app

Another example using uBatch to join multiple requests into one:

import requests

from typing import List, Dict
from flask import Flask, request as flask_request
from flask_restx import Resource, Api

from ubatch import UBatch


app = Flask(__name__)
api = Api(app)

# FAKE_TITLE_MPI is am mpi that determines if a title is fake or not
FAKE_TITLE_MPI_URL = "http://my_mpi_url/predict"


def batch_fake_title_post(titles: List[str]) -> List[bool]:
    """Post a list of titles to MPI and return responses in a list"""

    # json_post example: {"predict": ["title1", "title2", "title3"]}
    json_post = {"predict": titles}

    # response example: {"predictions": [False, True. False]}
    response = requests.post(FAKE_TITLE_MPI_URL, json=json_post).json()

    # return: [False, True, False]
    return [x for x in response["predictions"]]


fake_title_batch: UBatch[str, bool] = UBatch(max_size=100, timeout=0.02)
fake_title_batch.set_handler(handler=batch_fake_title_post)
fake_title_batch.start()


@api.route("/predict")
class Predict(Resource):
    def post(self) -> Dict[str, bool]:
        # title example: "Title1"
        title = flask_request.json["title"]

        # prediction example: False
        prediction = fake_title_batch.ubatch(title)

        return {"prediction": prediction}

Start application server:

$ gunicorn -k gevent app:app

Start developing uBatch

Install poetry

$ curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python -

Clone repository

$ git clone [e-mail]:mercadolibre/ubatch.git

Start shell

$ cd ubatch
$ poetry shell
$ poetry install

Run tests

$ pytest