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[feature] Provider unified offline batch inference interface #47

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gaocegege opened this issue Jun 14, 2020 · 6 comments
Open

[feature] Provider unified offline batch inference interface #47

gaocegege opened this issue Jun 14, 2020 · 6 comments
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kind/feature Categorizes issue or PR as related to a new feature. priority/P2 Must be staffed and worked on either currently, or very soon, ideally in time for the next release.

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@gaocegege
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gaocegege commented Jun 14, 2020

Is this a BUG REPORT or FEATURE REQUEST?:

Uncomment only one, leave it on its own line:

/kind bug
/kind feature

What happened:

Investigate if we can use https://github.com/uber/neuropod to provide a unified offline batch inference interface for users. They can use ormb python sdk to download the model first then use neuropod to run offline inference.

Thank @terrytangyuan for introducing the project.

What you expected to happen:

How to reproduce it (as minimally and precisely as possible):

Anything else we need to know?:

@gaocegege gaocegege added kind/feature Categorizes issue or PR as related to a new feature. priority/P2 Must be staffed and worked on either currently, or very soon, ideally in time for the next release. labels Jun 14, 2020
@gaocegege
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It is related to our python SDK. Ref #49

@judgeeeeee
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judgeeeeee commented Jul 20, 2020

There are roughly two ways to consider:

  • Transform all models into a unified type (eg onnx. Use onnxruntime to provide inference). At this time, the task needs some resources and dependencies. It is recommended to put it in model-rejistry.
  • Use the interface to unify the model (eg Neuropod). Consider using SDK for this implementation.Using yaml to generate config used by something like Neuropod

@gaocegege
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Transform all models into a unified type (eg onnx. Use onnxruntime to provide inference). At this time, the task needs some resources and dependencies. It is recommended to put it in model-rejistry.

I think we are using this approach in model registry (triton inference server). But we wanna support offline inference here.

@judgeeeeee
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judgeeeeee commented Jul 20, 2020

Transform all models into a unified type (eg onnx. Use onnxruntime to provide inference). At this time, the task needs some resources and dependencies. It is recommended to put it in model-rejistry.

I think we are using this approach in model registry (triton inference server). But we wanna support offline inference here.

We use the converted model for offline inference. But we need convert out model first ,maybe use model registry。
If we use only one model type, we can provide only one library for #40

@gaocegege
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Personally prefer the latter.

If we can unify the API on top of models, we can support multiple framework formats. If we wanna support the offline inference, we always need an SDK, I think.

@judgeeeeee
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/assign

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