docs/source/bottleneck.rst

torch.utils.bottleneck
======================

.. automodule:: torch.utils.bottleneck
.. currentmodule:: torch.utils.bottleneck

`torch.utils.bottleneck` is a tool that can be used as an initial step for
debugging bottlenecks in your program. It summarizes runs of your script with
the Python profiler and PyTorch's autograd profiler.

Run it on the command line with

::

    python -m torch.utils.bottleneck /path/to/source/script.py [args]

where [args] are any number of arguments to `script.py`, or run
``python -m torch.utils.bottleneck -h`` for more usage instructions.

.. warning::
    Because your script will be profiled, please ensure that it exits in a
    finite amount of time.

.. warning::
    Due to the asynchronous nature of CUDA kernels, when running against
    CUDA code, the cProfile output and CPU-mode autograd profilers may
    not show correct timings: the reported CPU time reports the amount of time
    used to launch the kernels but does not include the time the kernel
    spent executing on a GPU unless the operation does a synchronize.
    Ops that do synchronize appear to be extremely expensive under regular
    CPU-mode profilers.
    In these case where timings are incorrect, the CUDA-mode autograd profiler
    may be helpful.

.. note::
    To decide which (CPU-only-mode or CUDA-mode) autograd profiler output to
    look at, you should first check if your script is CPU-bound
    ("CPU total time is much greater than CUDA total time").
    If it is CPU-bound, looking at the results of the CPU-mode autograd
    profiler will help. If on the other hand your script spends most of its
    time executing on the GPU, then it makes sense to start
    looking for responsible CUDA operators in the output of the CUDA-mode
    autograd profiler.

    Of course the reality is much more complicated and your script might not be
    in one of those two extremes depending on the part of the model you're
    evaluating. If the profiler outputs don't help, you could try looking at
    the result of :func:`torch.autograd.profiler.emit_nvtx()` with ``nvprof``.
    However, please take into account that the NVTX overhead is very high and
    often gives a heavily skewed timeline. Similarly, Intel VTune Profiler helps
    to analyze performance on Intel platforms further with
    :func:`torch.autograd.profiler.emit_nvtx()`.

.. warning::
    If you are profiling CUDA code, the first profiler that ``bottleneck`` runs
    (cProfile) will include the CUDA startup time (CUDA buffer allocation cost)
    in its time reporting. This should not matter if your bottlenecks result
    in code much slower than the CUDA startup time.

For more complicated uses of the profilers (like in a multi-GPU case),
please see https://docs.python.org/3/library/profile.html
or :func:`torch.autograd.profiler.profile()` for more information.