Glossary

  • Batch Grouping

    • Allows you to programmatically decide which samples (out of a buffer) will be put into one batch. See Grouping.

  • Cooking

    • Used to transform crude (raw) samples into a populated instance of a sample data class.

  • Crude Dataset

    • An energon dataset, that does not yield a readily-populated sample (instance of dataclass), but a raw dict.

    • A cooker is used to handle this transformation in the user’s custom task encoder. See Crude Datasets and Auxiliary Data.

  • Grouping

    • See “Batch Grouping”

  • Monolithic Dataset

    • The simple form of putting all your text and media data into the same WebDataset (see Steps to Create a Monolithic Dataset).

    • The other option is to use a “Polylithic Dataset”

  • Packing

    • For Energon, with “packing” we mean “sequence packing”. See “Sequence Packing” below.

  • Polylithic Dataset

    • Used to split the text-based data from the (usually larger) media data.

    • Each modality will be put in its own dataset and one dataset can refer to the other by file names.

    • For more information see Steps to Create a Polylithic Dataset

  • Sample

    • In Energon, by sample we typically mean an instance of Sample (e.g. one of its subclasses)

    • Sometimes we also call the source files that are inside the WebDataset and are used to create that dataclass instance a “sample”

      • For example inside one tar file there may be 004.jpg and 004.txt (image and label) together forming a captioning sample

    • The Sample dataclass has several mandatory and optional fields that describe one piece of training data for your ML workload. Typically it contains the input data to the model and the label data.

  • Sample Part

    • A “sample part” is one of the components of a sample inside the WebDataset tar file. A captioning sample may be created from 004.jpg and 004.txt and each of those files is a sample part. This sample with the key 004 has two parts txt and jpg.

  • Sequence Packing

    • A method to better utilize the available context length / sequence length of a model and reduce padding.

    • Explained in Packing

  • Task Encoder

    • An Energon-specific concept: The TaskEncoder is a user-defined class to customize the steps of the data flow pipeline.

    • See Data Flow and Task Encoder

  • WebDataset