diff --git a/mindinsight/profiler/README.md b/mindinsight/profiler/README.md index c5eb20f09425f15284fe1b4f78515f78f19f1ce9..4943f2f3831c7c099cf007f12d0beceb139c700a 100644 --- a/mindinsight/profiler/README.md +++ b/mindinsight/profiler/README.md @@ -12,16 +12,18 @@ The Profiler enables users to: To enable profiling on MindSpore, the MindInsight Profiler apis should be added to the script: 1. Import MindInsight Profiler - + ``` from mindinsight.profiler import Profiler - -2. Initialize the Profiler before training + ``` +2. Initialize the Profiler after set context, and before the network initialization. Example: - + + context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=int(os.environ["DEVICE_ID"])) profiler = Profiler(output_path="./data", is_detail=True, is_show_op_path=False, subgraph='All') - - Parameters including: + net = Net() + + Parameters of Profiler including: subgraph (str): Defines 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. @@ -31,9 +33,9 @@ To enable profiling on MindSpore, the MindInsight Profiler apis should be added will deal with all op if null. optypes_not_deal (list): Op type names, the data of which optype will not be collected and analysed. -3. Call Profiler.analyse() at the end of the program +3. Call ```Profiler.analyse()``` at the end of the program - Profiler.analyse() will collect profiling data and generate the analysis results. + ```Profiler.analyse()``` will collect profiling data and generate the analysis results. After training, we can open MindInsight UI to analyse the performance. diff --git a/mindinsight/profiler/profiling.py b/mindinsight/profiler/profiling.py index b7385f0294d67c57e478e75e5448a1e382c8a544..abaf456e5985280a043506ee74f656485ac6491d 100644 --- a/mindinsight/profiler/profiling.py +++ b/mindinsight/profiler/profiling.py @@ -50,6 +50,8 @@ class Profiler: Examples: >>> from mindinsight.profiler import Profiler + >>> 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') >>> model = Model(train_network) >>> dataset = get_dataset() @@ -107,6 +109,8 @@ class Profiler: Examples: >>> from mindinsight.profiler import Profiler + >>> 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') >>> model = Model(train_network) >>> dataset = get_dataset()