Asynchronous Fitter design pattern for training in IPython notebooks

[Cross-posted from CodeReview@SX]

The problem

When you work with Python interactively (e.g. in an IPython shell or notebook) and run a computationally intensive operation like fitting a machine-learning model that is implemented in a native code, you cannot interrupt the operation since the native code does not return execution control to the Python interpreter until the end of the operation. The problem is not specific to machine learning, although it is typical to run a training process for which you cannot predict the training time. In case it takes longer that you expected, to stop training you need to stop the kernel and thus lose the pre-processed features and other variables stored in the memory, i.e. you cannot interrupt only a particular operation to check a simpler model, which allegedly would be fit faster.

The solution

I propose an Asynchronous Fitter design pattern that runs fitting in a separate process and communicates the results back when they are available. It allows to stop training gracefully by killing the spawned process and then run training of a simpler model. It also allows to train several models simultaneously and work in the IPython notebook during model training. Note that multithreading is probably not an option, since we cannot stop a thread that runs an uncontrolled native code.

Here is a draft implementation:
from multiprocessing import Process, Queue
import time

class AsyncFitter(object):
    def __init__(self, model):
        self.queue = Queue()
        self.model = model
        self.proc = None
        self.start_time = None

    def fit(self, x_train, y_train):
        self.proc = Process(target=AsyncFitter.async_fit_, 
            args=(self.model, x_train, y_train, self.queue))
        self.start_time = time.time()

    def try_get_result(self):
        if self.queue.empty():
            return None

        return self.queue.get()

    def is_alive(self):
        return self.proc is not None and self.proc.is_alive()

    def terminate(self):
        if self.proc is not None and self.proc.is_alive():
        self.proc = None

    def time_elapsed(self):
        if not self.start_time:
            return 0

        return time.time() - self.start_time

    def async_fit_(model, x_train, y_train, queue):, y_train)


It is easy to modify a code that uses scikit-learn to adopt the pattern. Here is an example:
import numpy as np
from sklearn.svm import SVC

model = SVC(C = 1e3, kernel='linear')
fitter = AsyncFitter(model)
x_train = np.random.rand(500, 30)
y_train = np.random.randint(0, 2, size=(500,)), y_train)

You can check if training is still running by calling fitter.is_alive() and check the time currently elapsed by calling fitter.time_elapsed(). Whenever you want, you can terminate() the process or just train another model that will terminate the previous one. Finally, you can obtain the model by try_get_result(), which returns None when training is in progress.

The issues

As far as I understand, the training set is being pickled and copied, which may be a problem if it is large. Is there an easy way to avoid that? Note that training needs only read-only access to the training set.

What happens if someone loses a reference to an AsyncFitter instance that wraps a running process? Is there a way to implement an asynchronous delayed resource cleanup?

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