47,468 research outputs found
Serving deep learning models in a serverless platform
Serverless computing has emerged as a compelling paradigm for the development
and deployment of a wide range of event based cloud applications. At the same
time, cloud providers and enterprise companies are heavily adopting machine
learning and Artificial Intelligence to either differentiate themselves, or
provide their customers with value added services. In this work we evaluate the
suitability of a serverless computing environment for the inferencing of large
neural network models. Our experimental evaluations are executed on the AWS
Lambda environment using the MxNet deep learning framework. Our experimental
results show that while the inferencing latency can be within an acceptable
range, longer delays due to cold starts can skew the latency distribution and
hence risk violating more stringent SLAs
TPU Cloud-Based Generalized U-Net for Eye Fundus Image Segmentation
Medical images from different clinics are acquired with different instruments and settings.
To perform segmentation on these images as a cloud-based service we need to train with multiple datasets
to increase the segmentation independency from the source. We also require an ef cient and fast segmentation
network. In this work these two problems, which are essential for many practical medical imaging
applications, are studied. As a segmentation network, U-Net has been selected. U-Net is a class of deep
neural networks which have been shown to be effective for medical image segmentation. Many different
U-Net implementations have been proposed.With the recent development of tensor processing units (TPU),
the execution times of these algorithms can be drastically reduced. This makes them attractive for cloud
services. In this paper, we study, using Google's publicly available colab environment, a generalized fully
con gurable Keras U-Net implementation which uses Google TPU processors for training and prediction.
As our application problem, we use the segmentation of Optic Disc and Cup, which can be applied to
glaucoma detection. To obtain networks with a good performance, independently of the image acquisition
source, we combine multiple publicly available datasets (RIM-One V3, DRISHTI and DRIONS). As a result
of this study, we have developed a set of functions that allow the implementation of generalized U-Nets
adapted to TPU execution and are suitable for cloud-based service implementation.Ministerio de Economía y Competitividad TEC2016-77785-
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