tenorflow 模型调优

# Create the Timeline object, and write it to a json

from tensorflow.python.client import timeline

tl = timeline.Timeline(run_metadata.step_stats)

ctf = tl.generate_chrome_trace_format()

with tf.gfile.GFile("timeline.json", 'w') as f:

    f.write(ctf)

chrome://tracing/

from tensorflow.core.framework import graph_pb2

from tensorflow.python.profiler import model_analyzer

from tensorflow.python.profiler import option_builder

graph = tf.Graph()

with graph.as_default():

    graph_def = graph_pb2.GraphDef()

    with open(args.input_graph, "rb") as f:

        graph_def.ParseFromString(f.read())

        _ = tf.import_graph_def(graph_def, name='')

        config  = tf.ConfigProto(inter_op_parallelism_threads=args.num_inter_threads,  intra_op_parallelism_threads=args.num_intra_threads)

        with tf.Session(config=config, graph=graph) as sess:

            # warm up

            ...

            # benchmark

            ...

            # profiling

            run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)

            run_metadata = tf.RunMetadata()

            profiler = model_analyzer.Profiler(graph=graph)

            for i in range(10):

                outputs = sess.run(output_data, feed_dict=input_data, options=run_options, run_metadata=run_metadata)

                profiler.add_step(step=i, run_meta=run_metadata)

            profile_op_opt_builder = option_builder.ProfileOptionBuilder()

            profile_op_opt_builder.select(['micros','occurrence'])

            profile_op_opt_builder.order_by('micros')

            profile_op_opt_builder.with_max_depth(50)

            profiler.profile_operations(profile_op_opt_builder.build())

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tenorflow 模型调优

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