Challenges in migrating imperative deep learning programs to graph execution

Author:

Vélez Tatiana Castro1,Khatchadourian Raffi2,Bagherzadeh Mehdi3,Raja Anita2

Affiliation:

1. City University of New York (CUNY) Graduate Center

2. City University of New York (CUNY) Hunter College

3. Oakland University

Funder

PSC-CUNY

Publisher

ACM

Reference103 articles.

1. Martín Abadi et al. 2016. TensorFlow: a system for large-scale Machine Learning . In Symposium on Operating Systems Design and Implementation. Martín Abadi et al. 2016. TensorFlow: a system for large-scale Machine Learning. In Symposium on Operating Systems Design and Implementation.

2. 2020. Added jitted ncon. Pull request #623. google/TensorNetwork . Xanadu. (May 26, 2020 ). Retrieved 01/10/ 2022 from https://git.io/J9cMx. 2020. Added jitted ncon. Pull request #623. google/TensorNetwork. Xanadu. (May 26, 2020). Retrieved 01/10/2022 from https://git.io/J9cMx.

3. Akshay Agrawal et al. 2019. TensorFlow Eager: a multi-stage Python-embedded DSL for Machine Learning. (2019). arXiv: 1903.01855 [cs.PL]. Akshay Agrawal et al. 2019. TensorFlow Eager: a multi-stage Python-embedded DSL for Machine Learning. (2019). arXiv: 1903.01855 [cs.PL].

4. Software Engineering for Machine Learning: A Case Study

5. Apache. 2018. Customer layers (beginners). Apache MXNet documentation. Retrieved 07/23/2021 from https://mxnet.apache.org/versions/1.7/api/python/docs/tutorials/packages/gluon/blocks/custom_layer_beginners.html. Apache. 2018. Customer layers (beginners). Apache MXNet documentation. Retrieved 07/23/2021 from https://mxnet.apache.org/versions/1.7/api/python/docs/tutorials/packages/gluon/blocks/custom_layer_beginners.html.

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