tf.compat.v1.fixed_size_partitioner
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Partitioner to specify a fixed number of shards along given axis.
tf . compat . v1 . fixed_size_partitioner (
num_shards , axis = 0
)
Migrate to TF2
Caution: This API was designed for TensorFlow v1.
Continue reading for details on how to migrate from this API to a native
TensorFlow v2 equivalent. See the
TensorFlow v1 to TensorFlow v2 migration guide
for instructions on how to migrate the rest of your code.
This API is deprecated in TF2. In TF2, partitioner is no longer part of
the variable declaration via tf.Variable
.
ParameterServer Training
handles partitioning of variables. The corresponding TF2 partitioner class of
fixed_size_partitioner
is
tf.distribute.experimental.partitioners.FixedShardsPartitioner
.
Check the migration guide
on the differences in treatment of variables and losses between TF1 and TF2.
Before:
x = tf . compat . v1 . get_variable (
"x" , shape = ( 2 ,), partitioner = tf . compat . v1 . fixed_size_partitioner ( 2 )
)
After:
partitioner = (
tf . distribute . experimental . partitioners . FixedShardsPartitioner (
num_shards = 2 )
)
strategy = tf . distribute . experimental . ParameterServerStrategy (
cluster_resolver = cluster_resolver ,
variable_partitioner = partitioner )
with strategy . scope ():
x = tf . Variable ([ 1.0 , 2.0 ])
Description
Args
num_shards
int
, number of shards to partition variable.
axis
int
, axis to partition on.
Returns
A partition function usable as the partitioner
argument to
variable_scope
and get_variable
.
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. Some content is licensed under the numpy license .
Last updated 2024-04-26 UTC.
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