PyQt5 在单独的 QThread 上运行 sklearn 计算 [英] PyQt5 run sklearn calculations on a separate QThread

问题描述

我正在创建一个 PyQt5 应用程序，并希望在单击按钮时在单独的线程中运行一些 ML 代码.我无法这样做.这是我的线程代码:

I am creating a PyQt5 application and want to run some ML code in a separate thread when a button is clicked. I am unable to do so. This is my threading code:

newthread = QtCore.QThread()
runthread = RunThread()
runthread.moveToThread(newthread)
runthread.finished.connect(newthread.quit)
newthread.started.connect(runthread.long_run)
newthread.start()

RunThread 类如下:

And the class RunThread is as follows:

class RunThread(QtCore.QObject):

finished = QtCore.pyqtSignal()

def __init__(self):
    super().__init__()

@pyqtSlot()
def long_run(self):
    #ML code called in the line below
    simulation.start_simulation()
    self.finished.emit()

正常运行是不行的.Pycharm 退出并出现以下错误:

Running it normally doesnt work. Pycharm quits with the following error:

process finished with exit code -1073740791 (0xc0000409)

在调试模式下运行它会抛出 sklearn 抛出的数千个警告说:

Running it in debug mode throws thousands of warnings thrown by sklearn saying:

Multiprocessing-backed parallel loops cannot be nested below threads,
setting n_jobs=1

代码最终会运行，但需要更多时间(至少 4 倍).

The code runs eventually, but it takes a lot more time (at least 4x times) to do so.

有人可以告诉我这里的问题是什么吗?

Could someone please let me know what the issue here is?

推荐答案

sklearn 的警告在这里非常明确.基本上，当从嵌套线程执行时，您不能将超参数 n_jobs 设置为大于 1 的任何值来训练 sklearn 模型.

The warning from sklearn is pretty explicit here. Basically you cannot train sklearn models with hyperparameter n_jobs set to anything greater than 1 when executing from a nested thread.

话虽如此，如果没有看到 simulation.start_simulation() 内部发生了什么，就很难猜测.

That being said, without seeing what is going on inside of simulation.start_simulation() it's hard to conjecture.

我最好的猜测是在 start_simulation() 中查找使用多个作业的任何内容，看看当您将其设置为 n_jobs=1 时会发生什么.

My best guess would be to look for anything in start_simulation() that is using multiple jobs and see what happens when you set that to n_jobs=1.

或者，您可以尝试将 ML 代码编写为独立脚本并使用 QProcess 执行.这可能允许您使用大于 1 的 n_jobs.

Alternatively, you may try writing your ML code as a standalone script and execute it using QProcess. That may allow you to utilize n_jobs greater than 1.

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