diff --git a/mindspore/profiler/profiling.py b/mindspore/profiler/profiling.py index f0716945df..009acca4f3 100644 --- a/mindspore/profiler/profiling.py +++ b/mindspore/profiler/profiling.py @@ -46,17 +46,20 @@ class Profiler: Performance profiling API. Enable MindSpore users to profile the performance of neural network. + Profiler support Ascend and GPU, both of them are used in the same way, + but only output_path in args works on GPU. Args: - subgraph (str): Define which subgraph to monitor and analyse, can be 'all', 'Default', 'Gradients'. - is_detail (bool): Whether to show profiling data for op_instance level, only show optype level if False. - is_show_op_path (bool): Whether to save the full path for each op instance. + subgraph (str): (Ascend only)Define which subgraph to monitor and analyse, can be 'all', 'Default', 'Gradients'. + is_detail (bool): (Ascend only)Whether to show profiling data for op_instance level, + only show optype level if False. + is_show_op_path (bool): (Ascend only)Whether to save the full path for each op instance. output_path (str): Output data path. - optypes_to_deal (str): Op type names, the data of which optype should be collected and analysed, + optypes_to_deal (str): (Ascend only)Op type names, the data of which optype should be collected and analysed, will deal with all op if null; Different op types should be seperated by comma. - optypes_not_deal (str): Op type names, the data of which optype will not be collected and analysed; + optypes_not_deal (str): (Ascend only)Op type names, the data of which optype will not be collected and analysed; Different op types should be seperated by comma. - job_id (str): The directory where the parsed profiling files are located; + job_id (str): (Ascend only)The directory where the parsed profiling files are located; This parameter is used to support offline parsing. Examples: @@ -64,7 +67,7 @@ class Profiler: >>> import mindspore.context >>> context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", >>> device_id=int(os.environ["DEVICE_ID"])) - >>> profiler = Profiler(subgraph='all', is_detail=True, is_show_op_path=False, output_path='./data') + >>> profiler = Profiler() >>> model = Model() >>> model.train() >>> profiler.analyse()