How to combine distributed Tensorflow with TensorFrames?
Nobody has claimed this yet.
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
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
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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