Software must be recognised as an important output of scholarly research

Jay, Caroline; Haines, Robert; Katz, Daniel S.

doi:10.48550/arxiv.2011.07571

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…Software is a critical component of science, and reproducibility [10,17,18], and scholarly articles are increasingly referencing Jupyter Notebooks with code segments and visualizations. However, it is very cumbersome if not impossible to reproduce the published bundle of notebooks and associated code and data.…”

Section: Use Cases For Portable and Reproducible Notebooks 21 Reprodu...mentioning

confidence: 99%

A Framework to capture and reproduce the Absolute State of Jupyter Notebooks

Wannipurage¹,

Marru²,

Pierce³

2022

Preprint

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Jupyter Notebooks are an enormously popular tool for creating and narrating computational research projects. They also have enormous potential for creating reproducible scientific research artifacts. Capturing the complete state of a notebook has additional benefits; for instance, the notebook execution may be split between local and remote resources, where the latter may have more powerful processing capabilities or store large or access-limited data. There are several challenges for making notebooks fully reproducible when examined in detail. The notebook code must be replicated entirely, and the underlying Python runtime environments must be identical. More subtle problems arise in replicating referenced data, external library dependencies, and runtime variable states. This paper presents solutions to these problems using Juptyer's standard extension mechanisms to create an archivable system state for a running notebook. We show that the overhead for these additional mechanisms, which involve interacting with the underlying Linux kernel, does not introduce substantial execution time overheads, demonstrating the approach's feasibility. CCS Concepts: • Software and its engineering → Software design engineering; Open source model; • Applied computing → Enterprise data management; • Information systems → Data warehouses.

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Section: Use Cases For Portable and Reproducible Notebooks 21 Reprodu...mentioning

confidence: 99%

A Framework to capture and reproduce the Absolute State of Jupyter Notebooks

Wannipurage¹,

Marru²,

Pierce³

2022

Preprint

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“…This creates a barrier of entry in many research tasks and makes it difficult to do iterative research. In other words, this problem makes it difficult for the software to support the provenance of reported results [13].…”

Section: Introductionmentioning

confidence: 99%

TensorX: Extensible API for Neural Network Model Design and Deployment

Nunes¹,

Antunes²

2020

Preprint

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TensorX is a Python library for prototyping, design, and deployment of complex neural network models in TensorFlow. A special emphasis is put on ease of use, performance, and API consistency. It aims to make available high-level components like neural network layers that are, in effect, stateful functions, easy to compose and reuse. Its architecture allows for the expression of patterns commonly found when building neural network models either on research or industrial settings. Incorporating ideas from several other deep learning libraries, it makes it easy to use components commonly found in state-of-the-art models. The library design mixes functional dataflow computation graphs with object-oriented neural network building blocks. TensorX combines the dynamic nature of Python with the high-performance GPU-enabled operations of TensorFlow.This library has minimal core dependencies (TensorFlow and NumPy) and is distributed under Apache License 2.0 licence, encouraging its use in both an academic and commercial settings. Full documentation, source code, and binaries can be found in https://tensorx.org/.

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Software must be recognised as an important output of scholarly research

Cited by 2 publications

References 5 publications

A Framework to capture and reproduce the Absolute State of Jupyter Notebooks

A Framework to capture and reproduce the Absolute State of Jupyter Notebooks

TensorX: Extensible API for Neural Network Model Design and Deployment

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