85 lines
4.0 KiB
Python
85 lines
4.0 KiB
Python
# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
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import grpc
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from tensorflow.tsl.profiler.protobuf import profiler_service_pb2 as tensorflow_dot_tsl_dot_profiler_dot_protobuf_dot_profiler__service__pb2
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class ProfilerServiceStub(object):
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"""The ProfilerService service retrieves performance information about
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the programs running on connected devices over a period of time.
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"""
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def __init__(self, channel):
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"""Constructor.
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Args:
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channel: A grpc.Channel.
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"""
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self.Profile = channel.unary_unary(
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'/tensorflow.ProfilerService/Profile',
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request_serializer=tensorflow_dot_tsl_dot_profiler_dot_protobuf_dot_profiler__service__pb2.ProfileRequest.SerializeToString,
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response_deserializer=tensorflow_dot_tsl_dot_profiler_dot_protobuf_dot_profiler__service__pb2.ProfileResponse.FromString,
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)
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self.Terminate = channel.unary_unary(
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'/tensorflow.ProfilerService/Terminate',
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request_serializer=tensorflow_dot_tsl_dot_profiler_dot_protobuf_dot_profiler__service__pb2.TerminateRequest.SerializeToString,
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response_deserializer=tensorflow_dot_tsl_dot_profiler_dot_protobuf_dot_profiler__service__pb2.TerminateResponse.FromString,
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)
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self.Monitor = channel.unary_unary(
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'/tensorflow.ProfilerService/Monitor',
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request_serializer=tensorflow_dot_tsl_dot_profiler_dot_protobuf_dot_profiler__service__pb2.MonitorRequest.SerializeToString,
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response_deserializer=tensorflow_dot_tsl_dot_profiler_dot_protobuf_dot_profiler__service__pb2.MonitorResponse.FromString,
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)
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class ProfilerServiceServicer(object):
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"""The ProfilerService service retrieves performance information about
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the programs running on connected devices over a period of time.
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"""
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def Profile(self, request, context):
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"""Starts a profiling session, blocks until it completes, and returns data.
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"""
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context.set_code(grpc.StatusCode.UNIMPLEMENTED)
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context.set_details('Method not implemented!')
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raise NotImplementedError('Method not implemented!')
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def Terminate(self, request, context):
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"""Signal to terminate the Profile rpc for a on-going profiling session,
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The Profile rpc will return successfully and prematurely without timeout.
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This is used by programmatic mode to end the session in workers.
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"""
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context.set_code(grpc.StatusCode.UNIMPLEMENTED)
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context.set_details('Method not implemented!')
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raise NotImplementedError('Method not implemented!')
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def Monitor(self, request, context):
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"""Collects profiling data and returns user-friendly metrics.
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"""
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context.set_code(grpc.StatusCode.UNIMPLEMENTED)
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context.set_details('Method not implemented!')
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raise NotImplementedError('Method not implemented!')
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def add_ProfilerServiceServicer_to_server(servicer, server):
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rpc_method_handlers = {
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'Profile': grpc.unary_unary_rpc_method_handler(
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servicer.Profile,
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request_deserializer=tensorflow_dot_tsl_dot_profiler_dot_protobuf_dot_profiler__service__pb2.ProfileRequest.FromString,
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response_serializer=tensorflow_dot_tsl_dot_profiler_dot_protobuf_dot_profiler__service__pb2.ProfileResponse.SerializeToString,
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),
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'Terminate': grpc.unary_unary_rpc_method_handler(
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servicer.Terminate,
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request_deserializer=tensorflow_dot_tsl_dot_profiler_dot_protobuf_dot_profiler__service__pb2.TerminateRequest.FromString,
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response_serializer=tensorflow_dot_tsl_dot_profiler_dot_protobuf_dot_profiler__service__pb2.TerminateResponse.SerializeToString,
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),
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'Monitor': grpc.unary_unary_rpc_method_handler(
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servicer.Monitor,
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request_deserializer=tensorflow_dot_tsl_dot_profiler_dot_protobuf_dot_profiler__service__pb2.MonitorRequest.FromString,
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response_serializer=tensorflow_dot_tsl_dot_profiler_dot_protobuf_dot_profiler__service__pb2.MonitorResponse.SerializeToString,
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),
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}
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generic_handler = grpc.method_handlers_generic_handler(
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'tensorflow.ProfilerService', rpc_method_handlers)
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server.add_generic_rpc_handlers((generic_handler,))
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