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In certain situation, users want more control over when the TensorFlow op is loaded, so we should make it optional (even if it exists). This happens in Ray for example, where we need to make sure that if multiple python workers try to compile and import the TensorFlow op in parallel, there is no race condition (e.g. one worker could try to import a half-built version of the op).
In certain situation, users want more control over when the TensorFlow op is loaded, so we should make it optional (even if it exists). This happens in Ray for example, where we need to make sure that if multiple python workers try to compile and import the TensorFlow op in parallel, there is no race condition (e.g. one worker could try to import a half-built version of the op).
Reporter: Philipp Moritz / @pcmoritz
Assignee: Philipp Moritz / @pcmoritz
PRs and other links:
Note: This issue was originally created as ARROW-3950. Please see the migration documentation for further details.
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