46,792 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
TrIMS: Transparent and Isolated Model Sharing for Low Latency Deep LearningInference in Function as a Service Environments
Deep neural networks (DNNs) have become core computation components within
low latency Function as a Service (FaaS) prediction pipelines: including image
recognition, object detection, natural language processing, speech synthesis,
and personalized recommendation pipelines. Cloud computing, as the de-facto
backbone of modern computing infrastructure for both enterprise and consumer
applications, has to be able to handle user-defined pipelines of diverse DNN
inference workloads while maintaining isolation and latency guarantees, and
minimizing resource waste. The current solution for guaranteeing isolation
within FaaS is suboptimal -- suffering from "cold start" latency. A major cause
of such inefficiency is the need to move large amount of model data within and
across servers. We propose TrIMS as a novel solution to address these issues.
Our proposed solution consists of a persistent model store across the GPU, CPU,
local storage, and cloud storage hierarchy, an efficient resource management
layer that provides isolation, and a succinct set of application APIs and
container technologies for easy and transparent integration with FaaS, Deep
Learning (DL) frameworks, and user code. We demonstrate our solution by
interfacing TrIMS with the Apache MXNet framework and demonstrate up to 24x
speedup in latency for image classification models and up to 210x speedup for
large models. We achieve up to 8x system throughput improvement.Comment: In Proceedings CLOUD 201
Steganographic Generative Adversarial Networks
Steganography is collection of methods to hide secret information ("payload")
within non-secret information "container"). Its counterpart, Steganalysis, is
the practice of determining if a message contains a hidden payload, and
recovering it if possible. Presence of hidden payloads is typically detected by
a binary classifier. In the present study, we propose a new model for
generating image-like containers based on Deep Convolutional Generative
Adversarial Networks (DCGAN). This approach allows to generate more
setganalysis-secure message embedding using standard steganography algorithms.
Experiment results demonstrate that the new model successfully deceives the
steganography analyzer, and for this reason, can be used in steganographic
applications.Comment: 15 pages, 10 figures, 5 tables, Workshop on Adversarial Training
(NIPS 2016, Barcelona, Spain
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