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How to convert ndarray to autograd variable in GPU format?


How do I return multiple values from a function?How do I pass a variable by reference?Proper way to declare custom exceptions in modern Python?How does Python's super() work with multiple inheritance?How to access environment variable values?How to set environment variables in PythonCatch multiple exceptions in one line (except block)“Large data” work flows using pandasHow to test multiple variables against a value?Why is “1000000000000000 in range(1000000000000001)” so fast in Python 3?






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-1















I am trying to do something like this,



data = torch.autograd.Variable(torch.from_numpy(nd_array))



It comes under the type as Variable[torch.FloatTensor], But I need Variable[torch.cuda.FloatTensor] also I want to do this in pytorch version 0.3.0 which lacks few methods like to(device) or set_default_device










share|improve this question






























    -1















    I am trying to do something like this,



    data = torch.autograd.Variable(torch.from_numpy(nd_array))



    It comes under the type as Variable[torch.FloatTensor], But I need Variable[torch.cuda.FloatTensor] also I want to do this in pytorch version 0.3.0 which lacks few methods like to(device) or set_default_device










    share|improve this question


























      -1












      -1








      -1








      I am trying to do something like this,



      data = torch.autograd.Variable(torch.from_numpy(nd_array))



      It comes under the type as Variable[torch.FloatTensor], But I need Variable[torch.cuda.FloatTensor] also I want to do this in pytorch version 0.3.0 which lacks few methods like to(device) or set_default_device










      share|improve this question














      I am trying to do something like this,



      data = torch.autograd.Variable(torch.from_numpy(nd_array))



      It comes under the type as Variable[torch.FloatTensor], But I need Variable[torch.cuda.FloatTensor] also I want to do this in pytorch version 0.3.0 which lacks few methods like to(device) or set_default_device







      python numpy gpu pytorch autograd






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Mar 28 at 12:30









      Arjun SankarlalArjun Sankarlal

      1161 silver badge9 bronze badges




      1161 silver badge9 bronze badges

























          1 Answer
          1






          active

          oldest

          votes


















          1
















          You can use cuda() method of your tensor.



          If you'd like to use specific device you could go with context manager, e.g.



          with torch.cuda.device(device_index):
          t = torch.FloatTensor(1.).cuda()


          For more specific information check documentation for version 0.3.0.






          share|improve this answer
























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            1 Answer
            1






            active

            oldest

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            active

            oldest

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            active

            oldest

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            1
















            You can use cuda() method of your tensor.



            If you'd like to use specific device you could go with context manager, e.g.



            with torch.cuda.device(device_index):
            t = torch.FloatTensor(1.).cuda()


            For more specific information check documentation for version 0.3.0.






            share|improve this answer





























              1
















              You can use cuda() method of your tensor.



              If you'd like to use specific device you could go with context manager, e.g.



              with torch.cuda.device(device_index):
              t = torch.FloatTensor(1.).cuda()


              For more specific information check documentation for version 0.3.0.






              share|improve this answer



























                1














                1










                1









                You can use cuda() method of your tensor.



                If you'd like to use specific device you could go with context manager, e.g.



                with torch.cuda.device(device_index):
                t = torch.FloatTensor(1.).cuda()


                For more specific information check documentation for version 0.3.0.






                share|improve this answer













                You can use cuda() method of your tensor.



                If you'd like to use specific device you could go with context manager, e.g.



                with torch.cuda.device(device_index):
                t = torch.FloatTensor(1.).cuda()


                For more specific information check documentation for version 0.3.0.







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered Mar 28 at 13:49









                Szymon MaszkeSzymon Maszke

                4,9482 gold badges7 silver badges32 bronze badges




                4,9482 gold badges7 silver badges32 bronze badges



















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