使用Apache SystemML进行深度学习
Deep Learning with Apache SystemML
摘要：
企业使用Hadoop和Spark框架运营大型数据湖，这些框架（1）运行大量工具来自动化强大的数据准备/转换管道，（2）在共享的大型集群上运行，（3）执行许多不同的分析任务，包括模型准备，构建，评估和机器学习和深度学习的调整。在这样的共享环境中开发基于数据的机器/深度学习模型具有挑战性。Apache SystemML提供了一个统一的框架，用于在各种共享部署场景中实现机器学习和深度学习算法。SystemML的新颖编译方法自动为机器/深度学习算法生成运行时执行计划，这些算法由单节点和分布式运行时操作组成，具体取决于数据和集群特征，如数据大小、数据稀疏性、集群大小和内存配置，同时仍然利用底层大数据框架的功能。
英文摘要：
Enterprises operate large data lakes using Hadoop and Spark frameworks that (1) run a plethora of tools to automate powerful data preparation/transformation pipelines, (2) run on shared, large clusters to (3) perform many different analytics tasks ranging from model preparation, building, evaluation, and tuning for both machine learning and deep learning. Developing machine/deep learning models on data in such shared environments is challenging. Apache SystemML provides a unified framework for implementing machine learning and deep learning algorithms in a variety of shared deployment scenarios. SystemML's novel compilation approach automatically generates runtime execution plans for machine/deep learning algorithms that are composed of single-node and distributed runtime operations depending on data and cluster characteristics such as data size, data sparsity, cluster size, and memory configurations, while still exploiting the capabilities of the underlying big data frameworks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
