1torch.utils.bottleneck 2====================== 3 4.. automodule:: torch.utils.bottleneck 5.. currentmodule:: torch.utils.bottleneck 6 7`torch.utils.bottleneck` is a tool that can be used as an initial step for 8debugging bottlenecks in your program. It summarizes runs of your script with 9the Python profiler and PyTorch's autograd profiler. 10 11Run it on the command line with 12 13:: 14 15 python -m torch.utils.bottleneck /path/to/source/script.py [args] 16 17where [args] are any number of arguments to `script.py`, or run 18``python -m torch.utils.bottleneck -h`` for more usage instructions. 19 20.. warning:: 21 Because your script will be profiled, please ensure that it exits in a 22 finite amount of time. 23 24.. warning:: 25 Due to the asynchronous nature of CUDA kernels, when running against 26 CUDA code, the cProfile output and CPU-mode autograd profilers may 27 not show correct timings: the reported CPU time reports the amount of time 28 used to launch the kernels but does not include the time the kernel 29 spent executing on a GPU unless the operation does a synchronize. 30 Ops that do synchronize appear to be extremely expensive under regular 31 CPU-mode profilers. 32 In these case where timings are incorrect, the CUDA-mode autograd profiler 33 may be helpful. 34 35.. note:: 36 To decide which (CPU-only-mode or CUDA-mode) autograd profiler output to 37 look at, you should first check if your script is CPU-bound 38 ("CPU total time is much greater than CUDA total time"). 39 If it is CPU-bound, looking at the results of the CPU-mode autograd 40 profiler will help. If on the other hand your script spends most of its 41 time executing on the GPU, then it makes sense to start 42 looking for responsible CUDA operators in the output of the CUDA-mode 43 autograd profiler. 44 45 Of course the reality is much more complicated and your script might not be 46 in one of those two extremes depending on the part of the model you're 47 evaluating. If the profiler outputs don't help, you could try looking at 48 the result of :func:`torch.autograd.profiler.emit_nvtx()` with ``nvprof``. 49 However, please take into account that the NVTX overhead is very high and 50 often gives a heavily skewed timeline. Similarly, ``Intel® VTune™ Profiler`` 51 helps to analyze performance on Intel platforms further with 52 :func:`torch.autograd.profiler.emit_itt()`. 53 54.. warning:: 55 If you are profiling CUDA code, the first profiler that ``bottleneck`` runs 56 (cProfile) will include the CUDA startup time (CUDA buffer allocation cost) 57 in its time reporting. This should not matter if your bottlenecks result 58 in code much slower than the CUDA startup time. 59 60For more complicated uses of the profilers (like in a multi-GPU case), 61please see https://docs.python.org/3/library/profile.html 62or :func:`torch.autograd.profiler.profile()` for more information. 63