Detach Other Words Writing my tensor detach numpy is simply saying I m going to do some non tracked computations based on the value of this tensor in a numpy array The Dive into Deep Learning d2l textbook has a nice section describing the detach method although it doesn t talk about why a detach makes sense before converting to a numpy array
In addition coupled with detach as detach clone the better order to do it btw it creates a completely new tensor that has been detached with the old history and thus stops gradient flow through that path Detach x detached x detach creates a new python reference the only one that does not is doing x new x of course When you detach thread it means that you don t have to join it before exiting main Thread library will actually wait for each such thread below main but you should not care about it
Detach Other Words
Detach Other Words
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For further explanation on why the detach method is needed please see this question on which I awarded a bounty Do not detach the thread Instantiate it in main Add a bool value and a std mutex the bool gets initialized to false Each time through the thread s inner loop lock the mutex using a std unique lock take the bool s value then unlock the mutex After unlocking the mutex if the bool was true break out of the loop and return
Tensor detach returns a new Tensor detached from the current computation graph The result will never require gradient tensor requires grad changes if autograd should record operations on this tensor sets this tensor s requires grad attribute in place I am adding some text from the link for the sake of completeness torch tensor always copies data If you have a Tensor data and want to avoid a copy use torch Tensor requires grad or torch Tensor detach When data is a tensor x torch tensor reads out the data from whatever it is passed and constructs a leaf variable
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The detach option on the docker command line indicates that the docker client docker will make a request to the server dockerd and then the client will exit while that request continues on the server Part of the confusion may be that docker looks like a single process where in reality it is a client server application where the client is just a thin frontend on a The std thread is a handle you can wait on but detaching it frees it so once the thread has ended and you detach or join the std thread instance there s nothing else you need to do all the resources associated with a thread are freed
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Writing my tensor detach numpy is simply saying I m going to do some non tracked computations based on the value of this tensor in a numpy array The Dive into Deep Learning d2l textbook has a nice section describing the detach method although it doesn t talk about why a detach makes sense before converting to a numpy array
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In addition coupled with detach as detach clone the better order to do it btw it creates a completely new tensor that has been detached with the old history and thus stops gradient flow through that path Detach x detached x detach creates a new python reference the only one that does not is doing x new x of course
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Detach Other Words - For further explanation on why the detach method is needed please see this question on which I awarded a bounty
