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https://issues.apache.org/jira/browse/SPARK-38648?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Lee Yang updated SPARK-38648:
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Description:
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.
was:
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.
> 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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