fix MultiGradientMachine train and infer #2595
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Just to confirm, will swig call c++'s defunctor when a python object is released? |
reyoung
requested changes
Jun 26, 2017
| @@ -326,12 +332,6 @@ void MultiGradientMachine::onPassEnd() { | |||
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| } | |||
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| void MultiGradientMachine::finish() { | |||
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After move thread->stop() to destructor, finish() seems has no use.
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Have add finish back
| @@ -171,6 +171,12 @@ MultiGradientMachine::MultiGradientMachine(const ModelConfig& config, | |||
| } | |||
| } | |||
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| MultiGradientMachine::~MultiGradientMachine() { | |||
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Maybe in destructor, we should check whether MultiGradientMachine is finished or not?
If MultiGradientMachine is not finish, then invoke this->finish();
@typhoonzero not sure, I will check it. |
reyoung
approved these changes
Jun 27, 2017
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fix: #2534 #2565
Problem:
when training or infering with python v2 api, if trainer_count > 1 and call trainer.train or inferer.infer multiple times, the process will hang.
Reason:
when trainer_count > 1, paddle will use MultiGradientMachine and will start multiple worker threads to do the forward/backward work(thread number is trainer_count)
In v2 python API, trainer or inferer will call gradinet_machine.finish() after train/infer, this will stop the worker threads, when you call trianer.train or inferrer.infer the second time, there are no worker thread to handle the task, so it hangs there.
Fix:
Do not close worker threads when gradientMachine.finish() is called, close them in the destructor of gradientMachine.