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Prediction settings: Graph node regression

To score (predict) new data through the H2O Hydrogen Torch UI (with a built model), you need to specify certain settings refer as prediction settings (which are comprised of certain dataset, prediction, and environment settings similar to those utilized when creating an experiment). Below observe the prediction settings for a graph node regression model.

General settings​

Experiment​

This setting defines the model (experiment) H2O Hydrogen Torch utilizes to score new data.

Prediction name​

This setting defines the name of the prediction.

Dataset settings​

Dataset​

This setting specifies the dataset to score.

Test dataframe​

This setting defines the file containing the test dataset that H2O Hydrogen Torch scores.

note
  • Image regression | 3D image regression | Image classification | 3D image classification | Image metric learning | Text regression | Text classification | Text sequence to sequence | Text span prediction | Text token classification | Text metric learning | Audio regression | Audio classification | Graph node classification | Graph node regression
    • Defines a CSV or Parquet file containing the test dataset that H2O Hydrogen Torch utilizes for scoring.
    note

    The test dataset should have the same format as the train dataset but does not require label columns.

  • Image object detection | Image semantic segmentation | 3D image semantic segmentation | Image instance segmentation
    • Defines a Parquet file containing the test dataset that H2O Hydrogen Torch utilizes for scoring.
      :::

Prediction settings​

Metric​

This setting defines the evaluation metric in which H2O Hydrogen Torch evaluates the model's accuracy on generated predictions.

Neighbor sampling quantile inference​

This setting determines the upper bound of the number of neighbors selected for updating the node features for inference.

tip

The neighbor sampling quantile for inference could differ from the neighbor sampling quantile used for training. By default, consider setting it to the same value as the corresponding parameter during training. Setting this setting to a larger value generally improves the prediction performance at the cost of more memory usage and slower inference speed. However, setting this setting to a smaller value may result in severe degradation in performance.

Batch size inference​

This setting defines the batch size of examples to utilize for inference.

note

Selecting 0 will set the Batch size inference to the same value used for the Batch size setting (utilized during training).

Environment settings​

GPUs​

This setting specifies the list of GPUs H2O Hydrogen Torch can use for scoring. GPUs are listed by name, referring to their system ID (starting from 1). If no GPUs are selected, H2O Hydrogen Torch utilizes CPUs for model scoring.

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