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 | |
---|---|
cluster | |
cluster_spec | |
device_fn | Returns the device_fn. If device_fn is not |
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
cluster = {'chief': ['host0:2222'], 'ps': ['host1:2222', 'host2:2222'], 'worker': ['host3:2222', 'host4:2222', 'host5:2222']} Nodes with task type 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. |
replace
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 . |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/compat/v1/estimator/tpu/RunConfig