Lee Yang created SPARK-38648:
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Summary: 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
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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