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Cleanlab is data-model and data-framework agnostic, a strong facet of its design. It doesn’t matter if you happen to’re working PyTorch, OpenAI, scikit-learn, or Tensorflow; Cleanlab can work with any classifier. It does, nonetheless, have particular workflows for frequent duties like token classification, multi-labeling, regression, picture segmentation and object detection, outlier detection, and so forth. It’s value perusing the example set to see for your self how the method works and what outcomes you’ll be able to anticipate.
Snakemake
Information science workflows are laborious to arrange, and that’s even more durable to do in a constant, predictable manner. Snakemake was created to automate the method, establishing information evaluation workflows in ways in which guarantee everybody will get the identical outcomes. Many present information science initiatives depend on Snakemake. The extra shifting components you have got in your information science workflow, the extra probably you’ll profit from automating that workflow with Snakemake.
Snakemake workflows resemble GNU Make workflows—you outline the steps of the workflow with guidelines, which specify what they soak up, what they put out, and what instructions to execute to perform that. Workflow guidelines could be multithreaded (assuming that provides them any profit), and configuration information could be piped in from JSON or YAML information. You can too outline capabilities in your workflows to rework information utilized in guidelines, and write the actions taken at every step to logs.