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https://issues.apache.org/jira/browse/SPARK-38648?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17515623#comment-17515623
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Lee Yang commented on SPARK-38648:
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We've seen a need to help Spark users who are struggling with DL inference with
issues like incorrectly copy/pasting boilerplate code, getting model
serialization failures, having difficulty connecting Spark DataFrames to
various underyling DL framework tensor representations, etc. Many existing
users aren't using MLFlow to train/track their DL models, so that isn't a
specific solution or goal here. The hope is to make inference with third-party
DL models a first-class citizen in the Spark community by adding this to Spark
itself, to gather Spark+DL experts who can contribute to making life easier for
Spark+DL novices.
> 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 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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