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Performance Modeling and Evaluation of Distributed Deep Learning Frameworks on GPUs
Deep learning frameworks have been widely deployed on GPU servers for de...
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SymJAX: symbolic CPU/GPU/TPU programming
SymJAX is a symbolic programming version of JAX simplifying graph input/...
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Benchmarking TPU, GPU, and CPU Platforms for Deep Learning
Training deep learning models is compute-intensive and there is an indus...
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Security Risks in Deep Learning Implementations
Advance in deep learning algorithms overshadows their security risk in s...
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Evaluating Performance of an Adult Pornography Classifier for Child Sexual Abuse Detection
The information technology revolution has facilitated reaching pornograp...
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Time-Based Roofline for Deep Learning Performance Analysis
Deep learning applications are usually very compute-intensive and requir...
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Empirical Evaluation of Deep Learning Model Compression Techniques on the WaveNet Vocoder
WaveNet is a state-of-the-art text-to-speech vocoder that remains challe...
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Performance Evaluation of Deep Learning Tools in Docker Containers
With the success of deep learning techniques in a broad range of application domains, many deep learning software frameworks have been developed and are being updated frequently to adapt to new hardware features and software libraries, which bring a big challenge for end users and system administrators. To address this problem, container techniques are widely used to simplify the deployment and management of deep learning software. However, it remains unknown whether container techniques bring any performance penalty to deep learning applications. The purpose of this work is to systematically evaluate the impact of docker container on the performance of deep learning applications. We first benchmark the performance of system components (IO, CPU and GPU) in a docker container and the host system and compare the results to see if there's any difference. According to our results, we find that computational intensive jobs, either running on CPU or GPU, have small overhead indicating docker containers can be applied to deep learning programs. Then we evaluate the performance of some popular deep learning tools deployed in a docker container and the host system. It turns out that the docker container will not cause noticeable drawbacks while running those deep learning tools. So encapsulating deep learning tool in a container is a feasible solution.
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