Resolution: Won't Fix
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
- 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.
- DL model training.
- Inference w/ distributed models, i.e. "model parallel" inference.
- Data scientists who need to deploy DL models on Spark.
- Developers who need to deploy DL models on Spark.