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Documentation for how estimator/graph_transformations/tf-serving work together #1078

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o-90 opened this issue Sep 7, 2018 · 9 comments
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@o-90
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o-90 commented Sep 7, 2018

System information

  • TensorFlow Serving installed from binary
  • TensorFlow Serving version: 1.10.0

Describe the Problem

The documentation describing how create and serve a custom Estimator, how to serve a tensorflow model in general, and how to perform graph transforms is very helpful - each on its own - but it is unclear how all these components fit together in the same ecosystem. This google cloud documentation on deploying models seems to suggest this (create -> transform -> serve) is the intent, I just cannot seem to find any documentation on how to:

  • create a custom estimator
  • perform graph transformations on the created model
  • create a servable from the transformed model
  • serve model
@Harshini-Gadige
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@gautamvasudevan - Hi, any update on this documentation ?

@gautamvasudevan
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No. @lamberta any thoughts on this?

@lamberta
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I've seen an internal doc that addresses some of this (b/116674557), but nothing that is ready to publish.
Might make a nice tutorial for the serving docs.

@r-wheeler
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Looking for this as well.

@r-wheeler
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@lamberta Anything on this you could share? The ideal scenario would be documentation on how to:

  • create a custom estimator
  • perform graph transformations on the created model
  • create a servable from the transformed model
  • serve model

I am able to accomplish many of these steps independently:

  • Familiar with estimator api and writing custom model_fn etc
  • able to perform graph transforms on non estimator based graphs
  • able to create servables from both estimators and using the lower level apis

but pruning / transformer the output of an estimator, and generating a valid servable from this, would be great.

@lamberta
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lamberta commented Dec 3, 2018

The doc I saw became this blog post: Optimizing TensorFlow Models for Serving

We're building out the TFX section which will have more pipelines for serving: https://www.tensorflow.org/tfx/
For TFX, you can see the Chicago Taxi end-to-end example (and notebook).
We'll continue to build this out.

@Harshini-Gadige
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Thank you @lamberta.

@r-wheeler
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@lamberta thanks for posting

@omerasif57
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Please note that above guide is for tf 1.x and most of the packages like sessions, freeze_graph is deprecated now. see here

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