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https://issues.apache.org/jira/browse/SPARK-38648?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17597397#comment-17597397
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Lee Yang commented on SPARK-38648:
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Agreed, closing.

> SPIP: Simplified API for DL Inferencing
> ---------------------------------------
>
>                 Key: SPARK-38648
>                 URL: https://issues.apache.org/jira/browse/SPARK-38648
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML
>    Affects Versions: 3.0.0
>            Reporter: Lee Yang
>            Priority: Minor
>
> h1. Background and Motivation
> The deployment of deep learning (DL) models to Spark clusters can be a point 
> of friction today.  DL practitioners often aren't well-versed with Spark, and 
> Spark experts often aren't well-versed with the fast-changing DL frameworks.  
> Currently, the deployment of trained DL models is done in a fairly ad-hoc 
> manner, with each model integration usually requiring significant effort.
> To simplify this process, we propose adding an integration layer for each 
> major DL framework that can introspect their respective saved models to 
> more-easily integrate these models into Spark applications.  You can find a 
> detailed proposal here: 
> [https://docs.google.com/document/d/1n7QPHVZfmQknvebZEXxzndHPV2T71aBsDnP4COQa_v0]
> h1. Goals
>  - Simplify the deployment of pre-trained single-node DL models to Spark 
> inference applications.
>  - Follow pandas_udf for simple inference use-cases.
>  - Follow Spark ML Pipelines APIs for transfer-learning use-cases.
>  - Enable integrations with popular third-party DL frameworks like 
> TensorFlow, PyTorch, and Huggingface.
>  - Focus on PySpark, since most of the DL frameworks use Python.
>  - Take advantage of built-in Spark features like GPU scheduling and Arrow 
> integration.
>  - Enable inference on both CPU and GPU.
> h1. Non-goals
>  - DL model training.
>  - Inference w/ distributed models, i.e. "model parallel" inference.
> h1. Target Personas
>  - Data scientists who need to deploy DL models on Spark.
>  - Developers who need to deploy DL models on Spark.



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