tf.train.ClusterSpec
Represents a cluster as a set of "tasks", organized into "jobs".
tf.train.ClusterSpec(
cluster
)
A tf.train.ClusterSpec represents the set of processes that participate in a distributed TensorFlow computation. Every tf.distribute.Server is constructed in a particular cluster.
To create a cluster with two jobs and five tasks, you specify the mapping from job names to lists of network addresses (typically hostname-port pairs).
cluster = tf.train.ClusterSpec({"worker": ["worker0.example.com:2222",
"worker1.example.com:2222",
"worker2.example.com:2222"],
"ps": ["ps0.example.com:2222",
"ps1.example.com:2222"]})
Each job may also be specified as a sparse mapping from task indices to network addresses. This enables a server to be configured without needing to know the identity of (for example) all other worker tasks:
cluster = tf.train.ClusterSpec({"worker": {1: "worker1.example.com:2222"},
"ps": ["ps0.example.com:2222",
"ps1.example.com:2222"]})
| Args |
|---|
cluster | A dictionary mapping one or more job names to (i) a list of network addresses, or (ii) a dictionary mapping integer task indices to network addresses; or a tf.train.ClusterDef protocol buffer. |
| Raises |
|---|
TypeError | If cluster is not a dictionary mapping strings to lists of strings, and not a tf.train.ClusterDef protobuf. |
| Attributes |
|---|
jobs | Returns a list of job names in this cluster. |
Methods
as_cluster_def
View source
as_cluster_def()
Returns a tf.train.ClusterDef protocol buffer based on this cluster.
as_dict
View source
as_dict()
Returns a dictionary from job names to their tasks.
For each job, if the task index space is dense, the corresponding value will be a list of network addresses; otherwise it will be a dictionary mapping (sparse) task indices to the corresponding addresses.
| Returns |
|---|
| A dictionary mapping job names to lists or dictionaries describing the tasks in those jobs. |
job_tasks
View source
job_tasks(
job_name
)
Returns a mapping from task ID to address in the given job.
Note: For backwards compatibility, this method returns a list. If the given job was defined with a sparse set of task indices, the length of this list may not reflect the number of tasks defined in this job. Use the tf.train.ClusterSpec.num_tasks method to find the number of tasks defined in a particular job.
| Args |
|---|
job_name | The string name of a job in this cluster. |
| Returns |
|---|
A list of task addresses, where the index in the list corresponds to the task index of each task. The list may contain None if the job was defined with a sparse set of task indices. |
| Raises |
|---|
ValueError | If job_name does not name a job in this cluster. |
num_tasks
View source
num_tasks(
job_name
)
Returns the number of tasks defined in the given job.
| Args |
|---|
job_name | The string name of a job in this cluster. |
| Returns |
|---|
| The number of tasks defined in the given job. |
| Raises |
|---|
ValueError | If job_name does not name a job in this cluster. |
task_address
View source
task_address(
job_name, task_index
)
Returns the address of the given task in the given job.
| Args |
|---|
job_name | The string name of a job in this cluster. |
task_index | A non-negative integer. |
| Returns |
|---|
| The address of the given task in the given job. |
| Raises |
|---|
ValueError | If job_name does not name a job in this cluster, or no task with index task_index is defined in that job. |
task_indices
View source
task_indices(
job_name
)
Returns a list of valid task indices in the given job.
| Args |
|---|
job_name | The string name of a job in this cluster. |
| Returns |
|---|
| A list of valid task indices in the given job. |
| Raises |
|---|
ValueError | If job_name does not name a job in this cluster, or no task with index task_index is defined in that job. |
__bool__
View source
__bool__()
__eq__
View source
__eq__(
other
)
Return self==value.
__ne__
View source
__ne__(
other
)
Return self!=value.
__nonzero__
View source
__nonzero__()