RESUMEN TÉCNICO

CONSIDERACIONES PARA LA IMPLEMENTACIÓN DE LA IA EN EL EDGE

Evalúe si la computación en el edge es adecuada para su infraestructura.

GPU-accelerated libraries, DataFrame and APIs:

  • Layered on top of NVIDIA CUDA, RAPIDS is a suite of open-source software libraries and APIs that provide GPU parallelism and high-bandwidth memory speed through DataFrame and graph operations, achieving speedup factors of 50x or more on typical end-to-end data science workflows. For Spark 3.0, new RAPIDS APIs are used by Spark SQL and DataFrames for GPU accelerated memory efficient columnar data processing and query plans.
  • With Spark 3.0 the Catalyst query optimizer has been modified to identify operators within a query plan that can be accelerated with the RAPIDS API, and to schedule those operators on GPUs within the Spark cluster, when executing the query plan.
  • A new Spark shuffle implementation, built upon GPU accelerated communication libraries including Remote direct memory access (RDMA), dramatically reduces the data transfer among Spark processes. RDMA allows GPUs to communicate directly with each other, across nodes, at up to 100Gb/s, operating as if on one massive server.


GPU-aware Scheduling in Spark

  • Spark 3.0 adds integration with the cluster managers (YARN, Kubernetes, and Standalone) to request GPUs, and plugin points to allow it to be extended to run operations on the GPU. This makes GPUs easier to request and use for Spark application developers, allows for closer integration with deep learning and AI frameworks such as Horovod and TensorFlow on Spark, and allows for better utilization of GPUs.

 

La computación en el edge, el proceso de llevar la potencia informática al lugar donde se recopilan los datos, es una de las tendencias de más rápido crecimiento en la industria de la computación empresarial. Antes de realizar inversiones en la computación en el edge, las organizaciones primero deben evaluar si la computación en el edge es adecuado para las necesidades de su negocio.

En este resumen técnico, aprenderá:

  • Cómo evaluar si la computación en el edge es adecuada para su negocio
  • Qué considerar para implementar la infraestructura en el edge
  • Cómo implementar en el edge
  • Qué recursos necesitas para empezar
Considerations for Deploying AI at the Edge

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