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This class specifies the configurations for an Estimator
run.
Inherits From: RunConfig
tf.contrib.learn.RunConfig(
master=None, num_cores=0, log_device_placement=False, gpu_memory_fraction=1,
tf_random_seed=None, save_summary_steps=100, save_checkpoints_secs=_USE_DEFAULT,
save_checkpoints_steps=None, keep_checkpoint_max=5,
keep_checkpoint_every_n_hours=10000, log_step_count_steps=100, protocol=None,
evaluation_master='', model_dir=None, session_config=None,
session_creation_timeout_secs=7200
)
This class is a deprecated implementation of tf.estimator.RunConfig
interface.
Args | |
---|---|
master
|
TensorFlow master. Defaults to empty string for local. |
num_cores
|
Number of cores to be used. If 0, the system picks an appropriate number (default: 0). |
log_device_placement
|
Log the op placement to devices (default: False). |
gpu_memory_fraction
|
Fraction of GPU memory used by the process on each GPU uniformly on the same machine. |
tf_random_seed
|
Random seed for TensorFlow initializers. Setting this value allows consistency between reruns. |
save_summary_steps
|
Save summaries every this many steps. |
save_checkpoints_secs
|
Save checkpoints every this many seconds. Can not
be specified with save_checkpoints_steps .
|
save_checkpoints_steps
|
Save checkpoints every this many steps. Can not be
specified with save_checkpoints_secs .
|
keep_checkpoint_max
|
The maximum number of recent checkpoint files to keep. As new files are created, older files are deleted. If None or 0, all checkpoint files are kept. Defaults to 5 (that is, the 5 most recent checkpoint files are kept.) |
keep_checkpoint_every_n_hours
|
Number of hours between each checkpoint to be saved. The default value of 10,000 hours effectively disables the feature. |
log_step_count_steps
|
The frequency, in number of global steps, that the global step/sec will be logged during training. |
protocol
|
An optional argument which specifies the protocol used when starting server. None means default to grpc. |
evaluation_master
|
the master on which to perform evaluation. |
model_dir
|
directory where model parameters, graph etc are saved. If
None , will use model_dir property in TF_CONFIG environment
variable. If both are set, must have same value. If both are None , see
Estimator about where the model will be saved.
|
session_config
|
a ConfigProto used to set session parameters, or None. Note - using this argument, it is easy to provide settings which break otherwise perfectly good models. Use with care. |
session_creation_timeout_secs
|
Max time workers should wait for a session to become available (on initialization or when recovering a session) with MonitoredTrainingSession. Defaults to 7200 seconds, but users may want to set a lower value to detect problems with variable / session (re)-initialization more quickly. |
Attributes | |
---|---|
cluster_spec
|
|
device_fn
|
Returns the device_fn.
If device_fn is not |
environment
|
|
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.
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:
|
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_config
|
|
tf_random_seed
|
|
train_distribute
|
Optional tf.distribute.Strategy for training.
|
Methods
get_task_id
@staticmethod
get_task_id()
Returns task index from TF_CONFIG
environmental variable.
If you have a ClusterConfig instance, you can just access its task_id property instead of calling this function and re-parsing the environmental variable.
Returns | |
---|---|
TF_CONFIG['task']['index'] . Defaults to 0.
|
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 .
|
uid
uid(
whitelist=None
)
Generates a 'Unique Identifier' based on all internal fields. (experimental)
Caller should use the uid string to check RunConfig
instance integrity
in one session use, but should not rely on the implementation details, which
is subject to change.
Args | |
---|---|
whitelist
|
A list of the string names of the properties uid should not
include. If None , defaults to _DEFAULT_UID_WHITE_LIST , which
includes most properties user allowes to change.
|
Returns | |
---|---|
A uid string. |