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https://issues.apache.org/jira/browse/BEAM-2085?focusedWorklogId=585423&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-585423
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ASF GitHub Bot logged work on BEAM-2085:
----------------------------------------
Author: ASF GitHub Bot
Created on: 19/Apr/21 22:46
Start Date: 19/Apr/21 22:46
Worklog Time Spent: 10m
Work Description: robertwb commented on a change in pull request #14390:
URL: https://github.com/apache/beam/pull/14390#discussion_r616226045
##########
File path: sdks/python/apache_beam/runners/pipeline_context.py
##########
@@ -118,13 +120,19 @@ def get_by_id(self, id):
def get_by_proto(self, maybe_new_proto, label=None, deduplicate=False):
# type: (message.Message, Optional[str], bool) -> str
+ obj = self._obj_type.from_runner_api(
Review comment:
Did you mean https://github.com/apache/beam/pull/12067? The placement of
label here does look like a bug. It still makes me nervous to unconditionally
call from_runner_api here (from an xlang perspective) just to assign an id to a
proto.
@TheNeuralBit any thoughts on how best to resolve this?
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Issue Time Tracking
-------------------
Worklog Id: (was: 585423)
Time Spent: 21h 50m (was: 21h 40m)
> Transforms could give hints to runners on resource requirements
> ---------------------------------------------------------------
>
> Key: BEAM-2085
> URL: https://issues.apache.org/jira/browse/BEAM-2085
> Project: Beam
> Issue Type: Improvement
> Components: beam-model, runner-core, sdk-java-core
> Affects Versions: Not applicable
> Reporter: Ismaël Mejía
> Assignee: Valentyn Tymofieiev
> Priority: P3
> Labels: Clarified
> Time Spent: 21h 50m
> Remaining Estimate: 0h
>
> As discussed in BEAM-673 runners can allocate workers to accomplish their
> work in a better way if they can take into account some hints from the
> transforms, e.g. a source can hint data locality and with this information
> the runner can allocate the workers in an better way, this can also be the
> case with a particular transform that can suggest to the runner to be
> executed in a worker with a specific resource, e.g. GPU.
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