timeline test

eda/esmm/Model_pipline/train.py

@@ -10,6 +10,7 @@ import os
 import json
 from datetime import date, timedelta
 import tensorflow as tf
+from tensorflow.python.client import timeline
 import subprocess
 import time
 import glob
@@ -338,7 +339,9 @@ def main(_):
         "ctr_task_wgt":FLAGS.ctr_task_wgt
     }
     config = tf.estimator.RunConfig().replace(session_config = tf.ConfigProto(device_count={'GPU':0, 'CPU':FLAGS.num_threads}),
-            log_step_count_steps=FLAGS.log_steps, save_summary_steps=FLAGS.log_steps)
+            log_step_count_steps=FLAGS.log_steps, save_summary_steps=FLAGS.log_steps, graph_options=tf.GraphOptions(
+        optimizer_options=tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)))
+
     Estimator = tf.estimator.Estimator(model_fn=model_fn, model_dir=FLAGS.model_dir, params=model_params, config=config)
 
     if FLAGS.task_type == 'train':
@@ -363,7 +366,13 @@ def main(_):
 if __name__ == "__main__":
     b = time.time()
     path = "hdfs://172.16.32.4:8020/strategy/esmm/"
+    ptions = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
+    run_metadata = tf.RunMetadata()
     tf.logging.set_verbosity(tf.logging.INFO)
-    tf.app.run()
+    tf.app.run(options=ptions, run_metadata=run_metadata)
+    tl = timeline.Timeline(run_metadata.step_stats)
+    ctf = tl.generate_chrome_trace_format()
+    with open(FLAGS.local_dir +'timeline.json', 'w') as wd:
+        wd.write(ctf)
     print("耗时(分钟)：")
     print((time.time()-b)/60)
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