An empirical study on program failures of deep learning jobs-Reference-Cited by-同舟云学术

An empirical study on program failures of deep learning jobs

Published:2020-06-27 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering
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Author:

Zhang Ru¹,Xiao Wencong²,Zhang Hongyu³,Liu Yu¹,Lin Haoxiang¹,Yang Mao¹

Affiliation:

1. Microsoft Research

2. Alibaba Group

3. The University of Newcastle

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3377811.3380362

Reference50 articles.

1. 2019. Amazon SageMaker. https://aws.amazon.com/sagemaker. 2019. Amazon SageMaker. https://aws.amazon.com/sagemaker.

2. 2019. Google Cloud AI. https://cloud.google.com/products/ai. 2019. Google Cloud AI. https://cloud.google.com/products/ai.

3. 2019. Microsoft Azure Machine Learning. https://azure.microsoft.com/en-us/services/machine-learning-service. 2019. Microsoft Azure Machine Learning. https://azure.microsoft.com/en-us/services/machine-learning-service.

4. 2019. The "swap_memory" argument in TensorFlow operator "tf.nn.dynamic_rnns" example. https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/nn/dynamic_rnn. 2019. The "swap_memory" argument in TensorFlow operator "tf.nn.dynamic_rnns" example. https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/nn/dynamic_rnn.

5. 2019. TensorBoard: TensorFlow's Visualization Toolkit. https://github.com/tensorflow/tensorboard. 2019. TensorBoard: TensorFlow's Visualization Toolkit. https://github.com/tensorflow/tensorboard.

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