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cff-version: 1.2.0 |
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message: Please cite this project using these metadata. |
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title: "Gradio: Hassle-free sharing and testing of ML models in the wild" |
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abstract: >- |
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Accessibility is a major challenge of machine learning (ML). |
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Typical ML models are built by specialists and require |
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specialized hardware/software as well as ML experience to |
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validate. This makes it challenging for non-technical |
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collaborators and endpoint users (e.g. physicians) to easily |
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provide feedback on model development and to gain trust in |
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ML. The accessibility challenge also makes collaboration |
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more difficult and limits the ML researcher's exposure to |
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realistic data and scenarios that occur in the wild. To |
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improve accessibility and facilitate collaboration, we |
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developed an open-source Python package, Gradio, which |
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allows researchers to rapidly generate a visual interface |
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for their ML models. Gradio makes accessing any ML model as |
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easy as sharing a URL. Our development of Gradio is informed |
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by interviews with a number of machine learning researchers |
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who participate in interdisciplinary collaborations. Their |
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feedback identified that Gradio should support a variety of |
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interfaces and frameworks, allow for easy sharing of the |
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interface, allow for input manipulation and interactive |
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inference by the domain expert, as well as allow embedding |
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the interface in iPython notebooks. We developed these |
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features and carried out a case study to understand Gradio's |
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usefulness and usability in the setting of a machine |
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learning collaboration between a researcher and a |
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cardiologist. |
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authors: |
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- family-names: Abid |
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given-names: Abubakar |
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- family-names: Abdalla |
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given-names: Ali |
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- family-names: Abid |
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given-names: Ali |
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- family-names: Khan |
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given-names: Dawood |
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- family-names: Alfozan |
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given-names: Abdulrahman |
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- family-names: Zou |
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given-names: James |
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doi: 10.48550/arXiv.1906.02569 |
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date-released: 2019-06-06 |
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url: https: |
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