【发布时间】：2018-06-13 02:52:01
【问题描述】：
参考https://www.tensorflow.org/api_docs/python/tf/estimator/train_and_evaluate 上的 tf.estimator.train_and_evaluate 示例 我想让示例运行分布式，但是训练过程没有以分布式模式开始似乎不起作用。
我的问题是： 为了使源代码分布式运行，我该怎么做？
平台：系统：Ubuntu 18 张量流：v1.8
以下日志是我的源码和操作步骤：
1个源代码：
import tensorflow as tf
import os
import sys
import json
import logging
import numpy as np
x = np.random.rand(1000)
y = np.random.choice([0,1],1000)
def data_input():
ret={}
ret['x'] = x
y_batch = y
print "data"
return ret,y_batch
tf.logging.set_verbosity(tf.logging.DEBUG)
my_feature_columns=[]
v_feature_column = tf.feature_column.numeric_column(key="x",shape=[])
my_feature_columns.append(v_feature_column)
estimator = tf.estimator.DNNClassifier(
feature_columns=my_feature_columns,
hidden_units=[1024, 512, 256],
model_dir='/home/clxman/tf/')
train_spec = tf.estimator.TrainSpec(input_fn=lambda:data_input(), max_steps=1000)
eval_spec = tf.estimator.EvalSpec(input_fn=lambda:data_input())
tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
2 在火车运行良好的单一模式下运行它。查看以下日志
clxman@clxman-VirtualBox:~/test$ python test_c.py
/home/clxman/.local/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_train_distribute': None, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f4e37077890>, '_evaluation_master': '', '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_num_ps_replicas': 0, '_tf_random_seed': None, '_master': '', '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_model_dir': '/home/clxman/tf/', '_global_id_in_cluster': 0, '_save_summary_steps': 100}
INFO:tensorflow:Running training and evaluation locally (non-distributed).
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after 600 secs (eval_spec.throttle_secs) or training is finished.
data
INFO:tensorflow:Calling model_fn.
DEBUG:tensorflow:Transforming feature_column _NumericColumn(key='x', shape=(), default_value=None, dtype=tf.float32, normalizer_fn=None).
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
2018-06-12 23:50:25.702344: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
**INFO:tensorflow:Saving checkpoints for 1 into /home/clxman/tf/model.ckpt**.
**INFO:tensorflow:loss = 693.219, step = 1**
INFO:tensorflow:global_step/sec: 8.12489
**INFO:tensorflow:loss = 691.08575, step = 101 (12.309 sec)**
INFO:tensorflow:global_step/sec: 8.11321
INFO:tensorflow:loss = 690.9834, step = 201 (12.325 sec)
3 分别配置TF_CONFIG和命令行启动脚本
**chief session command line：**
clxman@clxman-VirtualBox:~/test$ TF_CONFIG='{
"cluster": {
"chief": ["192.168.6.99.123:2222"],
"worker": ["192.168.6.99.123:2300"],
"ps": ["192.168.6.99.123:2400"]
},
"task": {"type": "chief", "index": 0}
}' python test_c.py
**ps session comand line:**
clxman@clxman-VirtualBox:~/test$ TF_CONFIG='{
"cluster": {
"chief": ["192.168.6.99.123:2222"],
"worker": ["192.168.6.99.123:2300"],
"ps": ["192.168.6.99.123:2400"]
},
"task": {"type": "ps", "index": 0}
}' python test_c.py
**worker session comand line:**
clxman@clxman-VirtualBox:~/test$ TF_CONFIG='{
"cluster": {
"chief": ["192.168.6.99.123:2222"],
"worker": ["192.168.6.99.123:2300"],
"ps": ["192.168.6.99.123:2400"]
},
"task": {"type": "worker", "index": 0}
}' python test_c.py
**evaluator session command line:**
clxman@clxman-VirtualBox:~/test$ TF_CONFIG='{
"cluster": {
"chief": ["192.168.6.99.123:2222"],
"worker": ["192.168.6.99.123:2300"],
"ps": ["192.168.6.99.123:2400"]
},
"task": {"type": "evaluator", "index": 0}
}' python test_c.py
4 下面的日志是来自于 Chief session，我们可以看到训练过程没有开始。 字符串“data”表示训练过程调用了 data_input 函数，该函数提供训练数据
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_task_type': u'chief', '_train_distribute': None, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fc0024f18d0>, '_evaluation_master': '', '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_num_ps_replicas': 1, '_tf_random_seed': None, '_master': u'grpc://192.168.6.99.123:2222', '_num_worker_replicas': 2, '_task_id': 0, '_log_step_count_steps': 100, '_model_dir': '/home/clxman/tf/', '_global_id_in_cluster': 0, '_save_summary_steps': 100}
INFO:tensorflow:Start Tensorflow server.
2018-06-12 23:23:25.694865: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2018-06-12 23:23:25.697065: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:215] Initialize GrpcChannelCache for job chief -> {0 -> localhost:2222}
2018-06-12 23:23:25.697159: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:215] Initialize GrpcChannelCache for job ps -> {0 -> 192.168.6.99.123:2400}
2018-06-12 23:23:25.697180: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:215] Initialize GrpcChannelCache for job worker -> {0 -> 192.168.6.99.123:2300}
2018-06-12 23:23:25.698882: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:332] Started server with target: grpc://localhost:2222
**data**
INFO:tensorflow:Calling model_fn.
DEBUG:tensorflow:Transforming feature_column _NumericColumn(key='x', shape=(), default_value=None, dtype=tf.float32, normalizer_fn=None).
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
【问题讨论】：
标签： python tensorflow tensorflow-estimator