tf.compat.v1.estimator.tpu.RunConfig RunConfig with TPU support. Inherits From: RunConfig 
tf.compat.v1.estimator.tpu.RunConfig(
    tpu_config=None, evaluation_master=None, master=None, cluster=None, **kwargs
)

 


 Args
  tpu_config   the TPUConfig that specifies TPU-specific configuration.  
  evaluation_master   a string. The address of the master to use for eval. Defaults to master if not set.  
  master   a string. The address of the master to use for training.  
  cluster   a ClusterResolver  
  **kwargs   keyword config parameters.   
 


 Raises
  ValueError   if cluster is not None and the provided session_config has a cluster_def already.   
 


 Attributes
  checkpoint_save_graph_def  
 
  cluster  
 
  cluster_spec  
 
  device_fn   Returns the device_fn. If device_fn is not None, it overrides the default device function used in Estimator. Otherwise the default one is used. 
 
  eval_distribute   Optional tf.distribute.Strategy for evaluation.  
  evaluation_master  
 
  experimental_max_worker_delay_secs  
 
  global_id_in_cluster   The global id in the training cluster. All global ids in the training cluster are assigned from an increasing sequence of consecutive integers. The first id is 0. 
Note: Task id (the property field task_id) is tracking the index of the node among all nodes with the SAME task type. For example, given the cluster definition as follows:

cluster = {'chief': ['host0:2222'],
'ps': ['host1:2222', 'host2:2222'],
'worker': ['host3:2222', 'host4:2222', 'host5:2222']}
 Nodes with task type worker can have id 0, 1, 2. Nodes with task type ps can have id, 0, 1. So, task_id is not unique, but the pair (task_type, task_id) can uniquely determine a node in the cluster. Global id, i.e., this field, is tracking the index of the node among ALL nodes in the cluster. It is uniquely assigned. For example, for the cluster spec given above, the global ids are assigned as: task_type  | task_id  |  global_id
--------------------------------
chief      | 0        |  0
worker     | 0        |  1
worker     | 1        |  2
worker     | 2        |  3
ps         | 0        |  4
ps         | 1        |  5

 
  is_chief  
 
  keep_checkpoint_every_n_hours  
 
  keep_checkpoint_max  
 
  log_step_count_steps  
 
  master  
 
  model_dir  
 
  num_ps_replicas  
 
  num_worker_replicas  
 
  protocol   Returns the optional protocol value.  
  save_checkpoints_secs  
 
  save_checkpoints_steps  
 
  save_summary_steps  
 
  service   Returns the platform defined (in TF_CONFIG) service dict.  
  session_config  
 
  session_creation_timeout_secs  
 
  task_id  
 
  task_type  
 
  tf_random_seed  
 
  tpu_config  
 
  train_distribute   Optional tf.distribute.Strategy for training.    Methods replace View source 
replace(
    **kwargs
)
 Returns a new instance of RunConfig replacing specified properties. Only the properties in the following list are allowed to be replaced:  
model_dir, 
tf_random_seed, 
save_summary_steps, 
save_checkpoints_steps, 
save_checkpoints_secs, 
session_config, 
keep_checkpoint_max, 
keep_checkpoint_every_n_hours, 
log_step_count_steps, 
train_distribute, 
device_fn, 
protocol. 
eval_distribute, 
experimental_distribute, 
experimental_max_worker_delay_secs,  In addition, either save_checkpoints_steps or save_checkpoints_secs can be set (should not be both).
 


 Args
  **kwargs   keyword named properties with new values.   
 


 Raises
  ValueError   If any property name in kwargs does not exist or is not allowed to be replaced, or both save_checkpoints_steps and save_checkpoints_secs are set.   
 


 Returns   a new instance of RunConfig.