Google云平台ML Engine/AI平台上的分布式Keras调谐器

Question

我正在尝试在Google云平台（GCP）ML引擎（又名AI平台）上实现这个分布式Keras调谐器示例： https://github.com/keras-team/keras-tuner/blob/master/docs/templates/tutorials/distributed-tuning.md

这是我的ML培训输入。yaml：

scaleTier : CUSTOM
masterType: standard
masterConfig:
   imageUri: tensorflow/tensorflow:2.1.0-gpu-py3
workerCount: 8
workerType: standard_gpu
workerConfig:
   imageUri: tensorflow/tensorflow:2.1.0-gpu-py3

在python脚本的顶部，我添加了：

tf_config = json.loads(os.environ['TF_CONFIG'])

cluster = tf_config['cluster']
task = tf_config['task']

master_addr = cluster['master'][0].split(':')
os.environ['KERASTUNER_ORACLE_IP'] = master_addr[0]
os.environ['KERASTUNER_ORACLE_PORT'] = '8000'

if task['type'] == 'master':
    os.environ['KERASTUNER_TUNER_ID'] = 'chief'
else:
    os.environ['KERASTUNER_TUNER_ID'] = 'tuner{}'.format(task['index'])

不幸的是，这不起作用。主机返回错误：

server_chttp2.cc:40] {"created":"@1580940408.588629852","description":"No address added out of total 1 resolved","file":"src/core/ext/transport/chttp2/server/chttp2_server.cc","file_line":395,"referenced_errors":[{"created":"@1580940408.588623412","description":"Unable to configure socket","fd":22,"file":"src/core/lib/iomgr/tcp_server_utils_posix_common.cc","file_line":208,"referenced_errors":[{"created":"@1580940408.588609041","description":"Cannot assign requested address","errno":99,"file":"src/core/lib/iomgr/tcp_server_utils_posix_common.cc","file_line":181,"os_error":"Cannot assign requested address","syscall":"bind"}]}]}

因此，主机似乎无法绑定到侦听端口

所以，我想真正的问题是：如何绑定到GCPML引擎上的侦听端口？允许这样做吗

对于现在如何在GCP ML引擎上运行分布式Keras调优的任何见解，我们将不胜感激

Answer 1

KERASTUNER_ORACLE_IP需要IP地址，而不是主机名

这是我在项目中使用的函数，请参见https://github.com/vlasenkoalexey/gcp_runner/blob/master/entry_point.ipynb

import os
import json
import socket

def setup_keras_tuner_config():
    if 'TF_CONFIG' in os.environ:
        try:
            tf_config = json.loads(os.environ['TF_CONFIG'])
            cluster = tf_config['cluster']
            task = tf_config['task']
            chief_addr = cluster['chief'][0].split(':')
            chief_ip = socket.gethostbyname(chief_addr[0])
            chief_port = chief_addr[1]
            os.environ['KERASTUNER_ORACLE_IP'] = chief_ip
            os.environ['KERASTUNER_ORACLE_PORT'] = chief_port
            if task['type'] == 'chief':
                os.environ['KERASTUNER_TUNER_ID'] = 'chief'
            else:
                os.environ['KERASTUNER_TUNER_ID'] = 'tuner{}'.format(task['index'])

            print('set following environment arguments:')
            print('KERASTUNER_ORACLE_IP: %s' % os.environ['KERASTUNER_ORACLE_IP'])
            print('KERASTUNER_ORACLE_PORT: %s' % os.environ['KERASTUNER_ORACLE_PORT'])
            print('KERASTUNER_TUNER_ID: %s' % os.environ['KERASTUNER_TUNER_ID'])
        except Exception as ex:
            print('Error setting up keras tuner config: %s' % str(ex))

对于TF2.x，TF_CONFIG中的“master”替换为“chief”。您可以传递 use-chief-in-tf-config以更新它。 确认它在谷歌人工智能平台和Kubernetes上工作

Answer 2

正如错误消息所说，我和OP有类似的问题。我不确定真正的原因是什么，但对我来说，解决办法是为主管绑定0.0.0.0（即os.environ['KERASTUNER_ORACLE_IP'] = '0.0.0.0'），同时仍然使用来自TF_CONFIG的主管IP