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profile ( activities =, # In this example with wait=1, warmup=1, active=2, # profiler will skip the first step/iteration, # start warming up on the second, record # the third and the forth iterations, # after which the trace will become available # and on_trace_ready (when set) is called # the cycle repeats starting with the next step schedule = torch. table ( sort_by = "self_cuda_time_total", row_limit =- 1 )) # prof.export_chrome_trace("/tmp/test_trace_" + str(prof.step_num) + ".json") with torch. # Non-default profiler schedule allows user to turn profiler on and off # on different iterations of the training loop # trace_handler is called every time a new trace becomes available def trace_handler ( prof ): print ( prof. Note, backward compatibility is not guaranteed. With_flops ( bool) – use formula to estimate the FLOPs (floating point operations) of specific operators Returns ProfilerAction.RECORD_AND_SAVE during the profiling. On_trace_ready ( callable) – callable that is called at each step when schedule

ProfilerAction value that specifies the profiler action to perform at each step. Schedule ( callable) – callable that takes step (int) as a single parameter and returns profile ( *, activities = None, schedule = None, on_trace_ready = None, record_shapes = False, profile_memory = False, with_stack = False, with_flops = False, with_modules = False, experimental_config = None, use_cuda = None ) ¶ To use shape/stack functionality make sure to set record_shapes/with_stack Metric ( str) – metric to use: “self_cpu_time_total” or “self_cuda_time_total”

Path ( str) – save stacks file to this location Save stack traces in a file in a format suitable for visualization. export_stacks ( path, metric = 'self_cpu_time_total' ) ¶ To be used in the trace callback or after the profiling is finished export_chrome_trace ( path ) ¶Įxports the collected trace in Chrome JSON format. Returns the list of unaggregated profiler events, Into the trace file add_metadata_json ( key, value ) ¶Īdds a user defined metadata with a string key and a valid json value add_metadata ( key, value ) ¶Īdds a user defined metadata with a string key and a string value That may further prevent certain optimizations that depend on the reference count and introduceĮxtra tensor copies. When record_shapes=True is specified, profiler will temporarily hold references to the tensors This API is an experimental and subject to change in future.Įnabling shape and stack tracing results in additional overhead. Note that this support exist, at the moment, only for TorchScript modelsĮxperimental_config ( _ExperimentalConfig) – A set of experimental options Module B’s forward which contains an aten::add op,

With_modules ( bool) – record module hierarchy (including function names)Ĭorresponding to the callstack of the op. (matrix multiplication and 2D convolution). With_flops ( bool) – use formula to estimate the FLOPS of specific operators With_stack ( bool) – record source information (file and line number) for the ops. Profile_memory ( bool) – track tensor memory allocation/deallocation. Record_shapes ( bool) – save information about operator’s input shapes. , .ĭefault value: ProfilerActivity.CPU and (when available) ProfilerActivity.CUDA. Low-level profiler wrap the autograd profile ParametersĪctivities ( iterable) – list of activity groups (CPU, CUDA) to use in profiling, supported values: _KinetoProfile ( *, activities = None, record_shapes = False, profile_memory = False, with_stack = False, with_flops = False, with_modules = False, experimental_config = None ) ¶ PyTorch Governance | Persons of InterestĪPI Reference ¶ class torch.profiler.CPU threading and TorchScript inference.CUDA Automatic Mixed Precision examples.