Sample code:
http://paste.ubuntu.com/p/Bw9mgtb9sD/ 8
Running nvprof with the following command:
/usr/local/cuda/bin/nvprof --concurrent-kernels on --print-api-summary --print-gpu-summary --output-profile profile.nvvp -f --profile-from-start off --track-memory-allocations on --demangling on --trace gpu,api python stream.py
Opening nvvp, I see that the kernels runing on the 5 streams one after the other, instead of all at the same time.
This seems wrong, am I misusing the API or is there some other problem?
System info:

PyTorch version: 1.0.1.post2
Is debug build: No
CUDA used to build PyTorch: 10.0.130

OS: Ubuntu 16.04.6 LTS
GCC version: (Ubuntu 5.4.0-6ubuntu1~16.04.11) 5.4.0 20160609
CMake version: version 3.5.1

Python version: 3.6
Is CUDA available: Yes
CUDA runtime version: Could not collect
GPU models and configuration: 
GPU 0: Quadro P5000
GPU 1: Quadro P5000

Nvidia driver version: 418.39
cuDNN version: Could not collect

Versions of relevant libraries:
[pip] numpy==1.15.4
[pip] torch==1.0.1.post2
[pip] torchfile==0.1.0
[pip] torchvision==0.2.2
[conda] blas                      1.0                         mkl  
[conda] mkl                       2019.1                      144  
[conda] mkl_fft                   1.0.6            py36hd81dba3_0  
[conda] mkl_random                1.0.2            py36hd81dba3_0  
[conda] pytorch                   1.0.1           py3.6_cuda10.0.130_cudnn7.4.2_2    pytorch
[conda] torchfile                 0.1.0                     <pip>
[conda] torchvision               0.2.2                      py_3    pytorch


Screenshot at 2019-03-15 11:28:54.png1699×701 56.8 KB