Will make devices on the remote host available to use. Note that calling this
more than once will work, but will invalidate any tensor handles on the old
remote devices.
Using the default job_name of worker, you can schedule ops to run remotely as
follows:
# When eager execution is enabled, connect to the remote host.tf.config.experimental_connect_to_host("exampleaddr.com:9876")withops.device("job:worker/replica:0/task:1/device:CPU:0"):# The following tensors should be resident on the remote device, and the op# will also execute remotely.x1=array_ops.ones([2,2])x2=array_ops.ones([2,2])y=math_ops.matmul(x1,x2)
Args
remote_host
a single or a list the remote server addr in host-port format.
job_name
The job name under which the new server will be accessible.
[[["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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-01-23 UTC."],[],[]]