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| def map_fun(args, ctx): | |
| try: | |
| import tensorflow as tf | |
| #utils | |
| from datetime import datetime | |
| import time | |
| import logging | |
| import numpy as np | |
| logger = logging.getLogger() | |
| tf.logging.set_verbosity(tf.logging.DEBUG) | |
| worker_num = ctx.worker_num | |
| job_name = ctx.job_name | |
| task_index = ctx.task_index | |
| #cluster_spec = ctx.cluster_spec | |
| #num_workers = len(cluster_spec['worker']) | |
| cluster, server = ctx.start_cluster_server(1) | |
| #TFNode.start_cluster_server(ctx) | |
| def get_next_batch(batch): | |
| batch = np.array(batch) | |
| data = batch[:,2:-1].reshape((batch.shape[0],timesteps,num_features)) | |
| labels = batch[:,-1].astype(int) | |
| return data,to_categorical(labels,num_classes=num_classes) | |
| if job_name == "ps": | |
| server.join() | |
| elif job_name == "worker": | |
| #https://www.tensorflow.org/api_docs/python/tf/train/Supervisor | |
| #one task should be identified as chief. This is necessary to handle for exmaple initialization | |
| is_chiefing = (task_index == 0) | |
| with tf.device(tf.train.replica_device_setter( | |
| worker_device="/job:worker/task:%d" % task_index, | |
| cluster=cluster)): | |
| def build_model(): | |
| pass | |
| model_input,\ | |
| model_labels,\ | |
| model_output,\ | |
| tf_global_step,\ | |
| tf_loss,\ | |
| tf_optimizer,\ | |
| tf_metrics = build_model() | |
| hooks=[tf.train.StepCounterHook()] | |
| with tf.train.MonitoredTrainingSession(master=server.target,\ | |
| is_chief=is_chiefing, | |
| checkpoint_dir=arsg['save_dir'],\ | |
| hooks=hooks,\ | |
| save_checkpoint_secs=600.) as mon_sess: | |
| start_time = datetime.now() | |
| tf.logging.info("{0} session ready".format(start_time.isoformat())) | |
| #https://github.com/yahoo/TensorFlowOnSpark/blob/master/tensorflowonspark/TFSparkNode.py | |
| # see TFNODE https://github.com/yahoo/TensorFlowOnSpark/blob/master/tensorflowonspark/TFNode.py | |
| tf_feed = ctx.get_data_feed(train_mode=True) | |
| step = 0 | |
| while not mon_sess.should_stop() and not tf_feed.should_stop() and step < args['steps']: | |
| batch_data, batch_labels = get_next_batch(tf_feed.next_batch(args['batch_size'])) | |
| if len(batch_data) > 0: | |
| feed = {model_input: batch_data, model_labels: batch_labels} | |
| _, logloss, step = mon_sess.run([tf_optimizer, tf_loss,tf_global_step],feed_dict=feed) | |
| if mon_sess.should_stop() or step >= args['steps']: | |
| tf_feed.terminate() | |
| logger.info("{0} stopping supervisor".format(datetime.now().isoformat())) | |
| except Exception as e: | |
| logger.error(e) |
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