tf.contrib.training.RandomStrategy
Stay organized with collections
Save and categorize content based on your preferences.
Returns a random PS task for op placement.
tf.contrib.training.RandomStrategy(
num_ps_tasks, seed=0
)
This may perform better than the default round-robin placement if you
have a large number of variables. Depending on your architecture and
number of parameter servers, round-robin can lead to situations where
all of one type of variable is placed on a single PS task, which may
lead to contention issues.
This strategy uses a hash function on the name of each op for deterministic
placement.
Methods
__call__
View source
__call__(
op
)
Chooses a ps task index for the given Operation
.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2020-10-01 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2020-10-01 UTC."],[],[]]