Python util.get_config() Examples

def convert_to_coverage_model(self):
    """Load non-coverage checkpoint, add initialized extra variables for coverage, and save as new checkpoint"""
    tf.logging.info("converting non-coverage model to coverage model..")

    # initialize an entire coverage model from scratch
    sess = tf.Session(config=util.get_config())
    print("initializing everything...")
    sess.run(tf.global_variables_initializer())

    # load all non-coverage weights from checkpoint
    saver = tf.train.Saver([v for v in tf.global_variables() if "coverage" not in v.name and "Adagrad" not in v.name])
    print("restoring non-coverage variables...")
    curr_ckpt = util.load_ckpt(saver, sess)
    print("restored.")

    # save this model and quit
    new_fname = curr_ckpt + '_cov_init'
    print("saving model to %s..." % (new_fname))
    new_saver = tf.train.Saver() # this one will save all variables that now exist
    new_saver.save(sess, new_fname)
    print("saved.")
    exit() 

def convert_to_coverage_model():
  """Load non-coverage checkpoint, add initialized extra variables for coverage, and save as new checkpoint"""
  tf.logging.info("converting non-coverage model to coverage model..")

  # initialize an entire coverage model from scratch
  sess = tf.Session(config=util.get_config())
  print "initializing everything..."
  sess.run(tf.global_variables_initializer())

  # load all non-coverage weights from checkpoint
  saver = tf.train.Saver([v for v in tf.global_variables() if "coverage" not in v.name and "Adagrad" not in v.name])
  print "restoring non-coverage variables..."
  curr_ckpt = util.load_ckpt(saver, sess)
  print "restored."

  # save this model and quit
  new_fname = curr_ckpt + '_cov_init'
  print "saving model to %s..." % (new_fname)
  new_saver = tf.train.Saver() # this one will save all variables that now exist
  new_saver.save(sess, new_fname)
  print "saved."
  exit() 

def restore_best_model():
  """Load bestmodel file from eval directory, add variables for adagrad, and save to train directory"""
  tf.logging.info("Restoring bestmodel for training...")

  # Initialize all vars in the model
  sess = tf.Session(config=util.get_config())
  print "Initializing all variables..."
  sess.run(tf.initialize_all_variables())

  # Restore the best model from eval dir
  saver = tf.train.Saver([v for v in tf.all_variables() if "Adagrad" not in v.name])
  print "Restoring all non-adagrad variables from best model in eval dir..."
  curr_ckpt = util.load_ckpt(saver, sess, "eval")
  print "Restored %s." % curr_ckpt

  # Save this model to train dir and quit
  new_model_name = curr_ckpt.split("/")[-1].replace("bestmodel", "model")
  new_fname = os.path.join(FLAGS.log_root, "train", new_model_name)
  print "Saving model to %s..." % (new_fname)
  new_saver = tf.train.Saver() # this saver saves all variables that now exist, including Adagrad variables
  new_saver.save(sess, new_fname)
  print "Saved."
  exit() 

def __init__(self, model, batcher, vocab):
    """Initialize decoder.

    Args:
      model: a Seq2SeqAttentionModel object.
      batcher: a Batcher object.
      vocab: Vocabulary object
    """
    self._model = model
    self._model.build_graph()
    self._batcher = batcher
    self._vocab = vocab
    self._saver = tf.train.Saver(max_to_keep=3) # we use this to load checkpoints for decoding
    self._sess = tf.Session(config=util.get_config())
    if FLAGS.mode == 'evalall':
      self.prepare_evaluate() 

def __init__(self, model, batcher, vocab):
    """Initialize decoder.

    Args:
      model: a Seq2SeqAttentionModel object.
      batcher: a Batcher object.
      vocab: Vocabulary object
    """
    self._model = model
    self._model.build_graph()
    self._batcher = batcher
    self._vocab = vocab
    self._saver = tf.train.Saver(max_to_keep=3) # we use this to load checkpoints for decoding
    self._sess = tf.Session(config=util.get_config())
    if FLAGS.mode == 'evalall':
      self.prepare_evaluate() 

def zabbix_link_tem(request, arg1):
    work_dir = os.path.dirname(os.path.realpath(__file__))
    service_conf = os.path.join(work_dir, 'service.conf')
    zabbix_config = util.get_config(service_conf, 'zabbix')
    result = []
    tem = []
    template = {}

    data = arg1['params']
    print data
    #       {u'hostids': [u'10157,10158'], u'groupid': u'10001'}
    data_host = data['hostids'].split(',')
    print data_host
    for i in data_host:
        if len(zabbix_api.Zabbix(zabbix_config).hostid_get_template(i)[0]['parentTemplates']) == 0:
            result.append(zabbix_api.Zabbix(zabbix_config).link_template(int(i), data['groupid']))
        else:
            template['templateid'] = data['groupid']
            data_mu = zabbix_api.Zabbix(zabbix_config).hostid_get_template(i)[0]['parentTemplates']
            data_mu.append(template)
            result.append(zabbix_api.Zabbix(zabbix_config).link_template(int(i), data_mu))
    return json.dumps({'code': 0, 'result': result}) 

def zbhost_select(request):
    datadict = {}
    ret = []

    # zbhost表关联cmdb_host by zhoux
    init()
    # update by zhouzx (delete 字段 host)
    fields = ['id', 'cmdb_hostid', 'hostid', 'host', 'ip']
    zabbix_hosts = db.Cursor(util.get_config(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'service.conf'),'api')).get_results('zbhost', fields)
    hostid = [str(zb["cmdb_hostid"]) for zb in zabbix_hosts]
    server_hosts = db.Cursor(util.get_config(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'service.conf'),'api')).get_results('cmdb_host', ["id"])
    for i in server_hosts:
        if str(i["id"]) not in hostid:
            datadict["id"] = i["id"]
            # all_host = app.config['cursor'].get_results('cmdb_host',["ip"],datadict)
            get_ip = db.Cursor(util.get_config(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'service.conf'),'api')).get_where_results('cmdb_host', ["id", "ip"], datadict)
            ret.append(get_ip[0])



    return json.dumps({'code': 0, 'result': ret}) 

def create_maintenance(name, start, stop, hostids, time):
        data = {
            "name": name,
            "active_since": start,
            "active_till": stop,
            "hostids": hostids,
            "timeperiods": [
                {
                    "timeperiod_type": 0,
                    "period": time
                }
            ]
        }
        ret = zabbix_api.Zabbix(
            util.get_config(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'service.conf'),
                            'zabbix')).create_maintenance(data)
        return ret 

def create_zabbix_host(hostid, groupid):
        ret = []
        for host in hostid:
            data = {
                "host": host,
                "interfaces": [
                    {
                        "type": 1,
                        "main": 1,
                        "useip": 1,
                        "ip": host,
                        "dns": "",
                        "port": "10050"
                    }
                ],
                "groups": [
                    {
                        "groupid": groupid
                    }
                ]
            }
            ret.append(zabbix_api.Zabbix(
            util.get_config(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'service.conf'),
                            'zabbix')).create_host(data))
        return ret 

def init_zabbix():
    try:
        # 第一步 取出所有host,要ip,host,id
        # zb_hosts = app.config['zabbix'].get_hosts()
        zb_hosts = zabbix_api.Zabbix(
            util.get_config(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'service.conf'),
                            'zabbix')).get_hosts()
        zb_hosts_interface = zabbix_api.Zabbix(
            util.get_config(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'service.conf'),
                            'zabbix')).get_interface([z['hostid'] for z in zb_hosts])
        data = []
        ret = []
        for h in zb_hosts:
            h['ip'] = zb_hosts_interface[h['hostid']]
            data.append(h)
            ###数据插入数据库
        for i in data:
            result = db.Cursor(
                util.get_config(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'service.conf'),
                                'api')).execute_insert_sql('zbhost', i)

    except:
        return "" 

def init_cmdb():
    try:
        # 取host (在cmdb_host表里)
        # fields = ['id', 'hostname', 'ip', 'vm_status', 'st', 'uuid', 'manufacturers', 'server_type', 'server_cpu', 'os',
        #           'server_disk', 'server_mem', 'mac_address', 'manufacture_date', 'check_update_time', 'server_purpose',
        #           'server_run', 'expire', 'server_up_time']
        fields = ['id','hostname','ip']

        # 将角色对应的p_id都转为name,最终返回的结果p_id的值都是name
        hosts = db.Cursor(util.get_config(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'service.conf'),
                                          'api')).get_results('cmdb_host', fields)
        for h in hosts:
            data = {'cmdb_hostid': h['id']}
            where = {'ip': h['ip']}
            result = db.Cursor(
                util.get_config(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'service.conf'),
                                'api')).execute_update_sql('zbhost', data, where)
    # 更新到cache表， ip
    except:
        return "" 

def setup_training_discriminator(model):
    """Does setup before starting training (run_training)"""
    train_dir = os.path.join(FLAGS.log_root, "train-discriminator")
    if not os.path.exists(train_dir): os.makedirs(train_dir)

    model.build_graph()  # build the graph

    saver = tf.train.Saver(max_to_keep=20)  # we use this to load checkpoints for decoding
    sess = tf.Session(config=util.get_config())
    #init = tf.global_variables_initializer()
    #sess.run(init)
    util.load_ckpt(saver, sess, ckpt_dir="train-discriminator")



    return sess, saver,train_dir 

def setup_training_generator(model):
  """Does setup before starting training (run_training)"""
  train_dir = os.path.join(FLAGS.log_root, "train-generator")
  if not os.path.exists(train_dir): os.makedirs(train_dir)

  model.build_graph() # build the graph

  saver = tf.train.Saver(max_to_keep=20)  # we use this to load checkpoints for decoding
  sess = tf.Session(config=util.get_config())
  #sess.run(tf.train.Saver(max_to_keep=20))
  #init = tf.global_variables_initializer()
  #sess.run(init)

  # Load an initial checkpoint to use for decoding
  util.load_ckpt(saver, sess, ckpt_dir="train-generator")


  return sess, saver,train_dir 

def convert_linear_attn_to_hier_model():
    """Load non-coverage checkpoint, add initialized extra variables for coverage, and save as new checkpoint"""
    tf.logging.info("converting linear model to hier model..")

    # initialize an entire coverage model from scratch
    sess = tf.Session(config=util.get_config())
    print("initializing everything...")
    sess.run(tf.global_variables_initializer())

    # load all non-coverage weights from checkpoint
    saver = tf.train.Saver([v for v in tf.global_variables(
    ) if "Linear--Section-Features" not in v.name and "v_sec" not in v.name and "Adagrad" not in v.name])
    print("restoring variables...")
    curr_ckpt = util.load_ckpt(saver, sess)
    print("restored.")

    # save this model and quit
    new_fname = curr_ckpt
    print(("saving model to %s..." % (new_fname)))
    new_saver = tf.train.Saver()  # this one will save all variables that now exist
    new_saver.save(sess, new_fname)
    print("saved.")
    exit() 

def restore_best_model():
  """Load bestmodel file from eval directory, add variables for adagrad, and save to train directory"""
  tf.logging.info("Restoring bestmodel for training...")

  # Initialize all vars in the model
  sess = tf.Session(config=util.get_config())
  print("Initializing all variables...")
  sess.run(tf.initialize_all_variables())

  # Restore the best model from eval dir
  saver = tf.train.Saver([v for v in tf.all_variables() if "Adagrad" not in v.name])
  print("Restoring all non-adagrad variables from best model in eval dir...")
  curr_ckpt = util.load_ckpt(saver, sess, "eval")
  print("Restored %s." % curr_ckpt)

  # Save this model to train dir and quit
  new_model_name = curr_ckpt.split("/")[-1].replace("bestmodel", "model")
  new_fname = os.path.join(FLAGS.log_root, "train", new_model_name)
  print("Saving model to %s..." % (new_fname))
  new_saver = tf.train.Saver() # this saver saves all variables that now exist, including Adagrad variables
  new_saver.save(sess, new_fname)
  print("Saved.")
  exit() 

def convert_to_coverage_model():
    """Load non-coverage checkpoint, add initialized extra variables for coverage, and save as new checkpoint"""
    tf.logging.info("converting non-coverage model to coverage model..")

    # initialize an entire coverage model from scratch
    sess = tf.Session(config=util.get_config())
    print("initializing everything...")
    sess.run(tf.global_variables_initializer())

    # load all non-coverage weights from checkpoint
    saver = tf.train.Saver([v for v in tf.global_variables(
    ) if "coverage" not in v.name and "Adagrad" not in v.name])
    print("restoring non-coverage variables...")
    curr_ckpt = util.load_ckpt(saver, sess)
    print("restored.")

    # save this model and quit
    new_fname = curr_ckpt + '_cov_init'
    print("saving model to %s..." % (new_fname))
    new_saver = tf.train.Saver()  # this one will save all variables that now exist
    new_saver.save(sess, new_fname)
    print("saved.")
    exit() 

def restore_best_model(self):
    """Load bestmodel file from eval directory, add variables for adagrad, and save to train directory"""
    tf.logging.info("Restoring bestmodel for training...")

    # Initialize all vars in the model
    sess = tf.Session(config=util.get_config())
    print("Initializing all variables...")
    sess.run(tf.initialize_all_variables())

    # Restore the best model from eval dir
    saver = tf.train.Saver([v for v in tf.all_variables() if "Adagrad" not in v.name])
    print("Restoring all non-adagrad variables from best model in eval dir...")
    curr_ckpt = util.load_ckpt(saver, sess, "eval")
    print("Restored %s." % curr_ckpt)

    # Save this model to train dir and quit
    new_model_name = curr_ckpt.split("/")[-1].replace("bestmodel", "model")
    new_fname = os.path.join(FLAGS.log_root, "train", new_model_name)
    print("Saving model to %s..." % (new_fname))
    new_saver = tf.train.Saver() # this saver saves all variables that now exist, including Adagrad variables
    new_saver.save(sess, new_fname)
    print("Saved.")
    exit() 

def convert_to_reinforce_model(self):
    """Load non-reinforce checkpoint, add initialized extra variables for reinforce, and save as new checkpoint"""
    tf.logging.info("converting non-reinforce model to reinforce model..")

    # initialize an entire reinforce model from scratch
    sess = tf.Session(config=util.get_config())
    print("initializing everything...")
    sess.run(tf.global_variables_initializer())

    # load all non-reinforce weights from checkpoint
    saver = tf.train.Saver([v for v in tf.global_variables() if "reinforce" not in v.name and "Adagrad" not in v.name])
    print("restoring non-reinforce variables...")
    curr_ckpt = util.load_ckpt(saver, sess)
    print("restored.")

    # save this model and quit
    new_fname = curr_ckpt + '_rl_init'
    print("saving model to %s..." % (new_fname))
    new_saver = tf.train.Saver() # this one will save all variables that now exist
    new_saver.save(sess, new_fname)
    print("saved.")
    exit() 

def restore_best_model(self):
    """Load bestmodel file from eval directory, add variables for adagrad, and save to train directory"""
    tf.logging.info("Restoring bestmodel for training...")

    # Initialize all vars in the model
    sess = tf.Session(config=util.get_config())
    print("Initializing all variables...")
    sess.run(tf.initialize_all_variables())

    # Restore the best model from eval dir
    saver = tf.train.Saver([v for v in tf.all_variables() if "Adagrad" not in v.name])
    print("Restoring all non-adagrad variables from best model in eval dir...")
    curr_ckpt = util.load_ckpt(saver, sess, "eval")
    print("Restored %s." % curr_ckpt)

    # Save this model to train dir and quit
    new_model_name = curr_ckpt.split("/")[-1].replace("bestmodel", "model")
    new_fname = os.path.join(FLAGS.log_root, "train", new_model_name)
    print("Saving model to %s..." % (new_fname))
    new_saver = tf.train.Saver() # this saver saves all variables that now exist, including Adagrad variables
    new_saver.save(sess, new_fname)
    print("Saved.")
    exit() 

def convert_to_coverage_model(self):
    """Load non-coverage checkpoint, add initialized extra variables for coverage, and save as new checkpoint"""
    tf.logging.info("converting non-coverage model to coverage model..")

    # initialize an entire coverage model from scratch
    sess = tf.Session(config=util.get_config())
    print("initializing everything...")
    sess.run(tf.global_variables_initializer())

    # load all non-coverage weights from checkpoint
    saver = tf.train.Saver([v for v in tf.global_variables() if "coverage" not in v.name and "Adagrad" not in v.name])
    print("restoring non-coverage variables...")
    curr_ckpt = util.load_ckpt(saver, sess)
    print("restored.")

    # save this model and quit
    new_fname = curr_ckpt + '_cov_init'
    print("saving model to %s..." % (new_fname))
    new_saver = tf.train.Saver() # this one will save all variables that now exist
    new_saver.save(sess, new_fname)
    print("saved.")
    exit() 

def convert_to_reinforce_model(self):
    """Load non-reinforce checkpoint, add initialized extra variables for reinforce, and save as new checkpoint"""
    tf.logging.info("converting non-reinforce model to reinforce model..")

    # initialize an entire reinforce model from scratch
    sess = tf.Session(config=util.get_config())
    print("initializing everything...")
    sess.run(tf.global_variables_initializer())

    # load all non-reinforce weights from checkpoint
    saver = tf.train.Saver([v for v in tf.global_variables() if "reinforce" not in v.name and "Adagrad" not in v.name])
    print("restoring non-reinforce variables...")
    curr_ckpt = util.load_ckpt(saver, sess)
    print("restored.")

    # save this model and quit
    new_fname = curr_ckpt + '_rl_init'
    print("saving model to %s..." % (new_fname))
    new_saver = tf.train.Saver() # this one will save all variables that now exist
    new_saver.save(sess, new_fname)
    print("saved.")
    exit() 

def convert_to_coverage_model():
    """Load non-coverage checkpoint, add initialized extra variables for coverage, and save as new checkpoint"""
    tf.logging.info("converting non-coverage model to coverage model..")

    # initialize an entire coverage model from scratch
    sess = tf.Session(config=util.get_config())
    print("initializing everything...")
    sess.run(tf.global_variables_initializer())

    # load all non-coverage weights from checkpoint
    saver = tf.train.Saver([v for v in tf.global_variables() if "coverage" not in v.name and "Adagrad" not in v.name])
    print("restoring non-coverage variables...")
    curr_ckpt = util.load_ckpt(saver, sess, FLAGS.ckpt_dir)
    print("restored.")

    # save this model and quit
    new_fname = curr_ckpt + '_cov_init'
    print("saving model to %s..." % new_fname)
    new_saver = tf.train.Saver()  # this one will save all variables that now exist
    new_saver.save(sess, new_fname)
    print("saved.")
    exit() 

def restore_best_model():
    """Load bestmodel file from eval directory, add variables for adagrad, and save to train directory"""
    tf.logging.info("Restoring best model for training...")

    # Initialize all vars in the model
    sess = tf.Session(config=util.get_config())
    print("Initializing all variables...")
    sess.run(tf.initialize_all_variables())

    # Restore the best model from eval dir
    saver = tf.train.Saver([v for v in tf.all_variables() if "Adagrad" not in v.name])
    print("Restoring all non-adagrad variables from best model in eval dir...")
    curr_ckpt = util.load_ckpt(saver, sess, "eval")
    print("Restored %s." % curr_ckpt)

    # Save this model to train dir and quit
    new_model_name = curr_ckpt.split("/")[-1].replace("bestmodel", "model")
    new_fname = os.path.join(FLAGS.log_root, "train", new_model_name)
    print("Saving model to %s..." % new_fname)
    new_saver = tf.train.Saver()  # this saver saves all variables that now exist, including Adagrad variables
    new_saver.save(sess, new_fname)
    print("Saved.")
    exit() 

def zabbix_unlink_tem(request, arg1):
    work_dir = os.path.dirname(os.path.realpath(__file__))
    service_conf = os.path.join(work_dir, 'service.conf')
    zabbix_config = util.get_config(service_conf, 'zabbix')
    result = []
    data = arg1['params']
    print data

    data_host = data['hostids'].split(',')
    for i in data_host:
        result.append(zabbix_api.Zabbix(zabbix_config).unlink_template(int(i), data['templateid']))
    return json.dumps({'code': 0, 'result': result}) 

def __init__(self, model, batcher, vocab):
    """Initialize decoder.

    Args:
      model: a Seq2SeqAttentionModel object.
      batcher: a Batcher object.
      vocab: Vocabulary object
    """
    self._model = model
    self._model.build_graph()
    self._batcher = batcher
    self._vocab = vocab
    self._saver = tf.train.Saver() # we use this to load checkpoints for decoding
    self._sess = tf.Session(config=util.get_config())

    # Load an initial checkpoint to use for decoding
    ckpt_path = util.load_ckpt(self._saver, self._sess)

    if FLAGS.single_pass:
      # Make a descriptive decode directory name
      ckpt_name = "ckpt-" + ckpt_path.split('-')[-1] # this is something of the form "ckpt-123456"
      self._decode_dir = os.path.join(FLAGS.log_root, get_decode_dir_name(ckpt_name))
      if os.path.exists(self._decode_dir):
        raise Exception("single_pass decode directory %s should not already exist" % self._decode_dir)

    else: # Generic decode dir name
      self._decode_dir = os.path.join(FLAGS.log_root, "decode")

    # Make the decode dir if necessary
    if not os.path.exists(self._decode_dir): os.mkdir(self._decode_dir)

    if FLAGS.single_pass:
      # Make the dirs to contain output written in the correct format for pyrouge
      self._rouge_ref_dir = os.path.join(self._decode_dir, "reference")
      if not os.path.exists(self._rouge_ref_dir): os.mkdir(self._rouge_ref_dir)
      self._rouge_dec_dir = os.path.join(self._decode_dir, "decoded")
      if not os.path.exists(self._rouge_dec_dir): os.mkdir(self._rouge_dec_dir) 

def setup_training(model, batcher):
  """Does setup before starting training (run_training)"""
  train_dir = os.path.join(FLAGS.log_root, "train")
  if not os.path.exists(train_dir): os.makedirs(train_dir)

  model.build_graph() # build the graph
  if FLAGS.convert_to_coverage_model:
    assert FLAGS.coverage, "To convert your non-coverage model to a coverage model, run with convert_to_coverage_model=True and coverage=True"
    convert_to_coverage_model()
  if FLAGS.restore_best_model:
    restore_best_model()
  saver = tf.train.Saver(max_to_keep=3) # keep 3 checkpoints at a time

  sv = tf.train.Supervisor(logdir=train_dir,
                     is_chief=True,
                     saver=saver,
                     summary_op=None,
                     save_summaries_secs=60, # save summaries for tensorboard every 60 secs
                     save_model_secs=60, # checkpoint every 60 secs
                     global_step=model.global_step)
  summary_writer = sv.summary_writer
  tf.logging.info("Preparing or waiting for session...")
  sess_context_manager = sv.prepare_or_wait_for_session(config=util.get_config())
  tf.logging.info("Created session.")
  try:
    run_training(model, batcher, sess_context_manager, sv, summary_writer) # this is an infinite loop until interrupted
  except KeyboardInterrupt:
    tf.logging.info("Caught keyboard interrupt on worker. Stopping supervisor...")
    sv.stop() 

def setup_training(model, batcher):
  """Does setup before starting training (run_training)"""
  train_dir = os.path.join(FLAGS.log_root, "train")
  if not os.path.exists(train_dir): os.makedirs(train_dir)

  default_device = tf.device('/gpu:0')
  with default_device:
    model.build_graph() # build the graph
    if FLAGS.convert_to_coverage_model:
      assert FLAGS.coverage, "To convert your non-coverage model to a coverage model, run with convert_to_coverage_model=True and coverage=True"
      convert_to_coverage_model()

    saver = tf.train.Saver(max_to_keep=FLAGS.model_max_to_keep) # only keep 1 checkpoint at a time

  sv = tf.train.Supervisor(logdir=train_dir,
                     is_chief=True,
                     saver=saver,
                     summary_op=None,
                     save_summaries_secs=60, # save summaries for tensorboard every 60 secs
                     save_model_secs=0, # checkpoint every 60 secs
                     global_step=model.global_step)
  summary_writer = sv.summary_writer
  tf.logging.info("Preparing or waiting for session...")
  sess_context_manager = sv.prepare_or_wait_for_session(config=util.get_config())
  tf.logging.info("Created session.")

  try:
    run_training(model, batcher, sess_context_manager, sv, summary_writer) # this is an infinite loop until interrupted
  except KeyboardInterrupt:
    tf.logging.info("Caught keyboard interrupt on worker. Stopping supervisor...")
    sv.stop() 

def getinfo(self, table_name, fields):
        '''

        查询单个数据表内容，fields首字段为key
        fields为两个字段，返回{v1: v2, ...}，格式为 ['field1','field2'], 例如['id','name'],['name','r_id']
        返回结果一，两列都是字符串如：用户id2name {'1':'tom','2','jerry'}; 组信息id2name {'1':'sa','2':'ask'}
        返回结果二，第二列是个列表如：用户权限信息：{u'songpeng': [u'1', u'2'], u'admin': [u'1', u'2', u'4', u'3']}

        '''
        result = Cursor(util.get_config(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'service.conf'), 'api')).get_results(table_name,fields)
        if fields[1] in ['r_id','p_id','user_all_perm']:  #第二列的结果为列表的字段拼接为字符串
	    result = dict((str(x[fields[0]]), x[fields[1]].split(',')) for x in result)
        else:
	    result = dict((str(x[fields[0]]), x[fields[1]]) for x in result)
        return result 

def zabbix_gettem_select(request):
    work_dir = os.path.dirname(os.path.realpath(__file__))
    service_conf = os.path.join(work_dir, 'service.conf')
    zabbix_config = util.get_config(service_conf, 'zabbix')
    tem = zabbix_api.Zabbix(zabbix_config).get_host_tem()
    print json.dumps({'code': 0, 'result': tem})
    return json.dumps({'code': 0, 'result': tem}) 

def zbhost_allhost_select(request):
    work_dir = os.path.dirname(os.path.realpath(__file__))
    service_conf = os.path.join(work_dir, 'service.conf')
    zabbix_config = util.get_config(service_conf, 'zabbix')
    data = zabbix_api.Zabbix(zabbix_config).get_hosts()
    print json.dumps({'code': 0, 'result': data})
    return json.dumps({'code': 0, 'result': data})