How to combine distributed Tensorflow with TensorFrames?

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#67 8 comments 0 reactions 0 assignees View on GitHub

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
15/100
Issue type
Feature
Clarity
Needs clarification
Activity status
Stale
Tech stack
scala, spark, tensorflow

Research direction

No files, tests, or entry points are named. Start by reviewing the repository's TensorFrames integration with Apache Spark and TensorFlow v0.8's distributed-training support. The issue asks for a plan rather than a defined change, so the scope and what would count as done need to be clarified before implementation.

Written by the indexing model from the issue text.

Description

enhancement

As we all know, TensorFlow v0.8 has supported built-in distributed training, but I can't find any work around combinations of them. So, guys, could you introduce your plan on that?

Dominant language
Scala
Stars
743
Forks
158
PR merge metrics
No merged PRs in 30d

Getting set up

  • Ships a Dockerfile or Docker Compose file
  • No pull request template
  • No contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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