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Platform For AI:GMM Prediction

Last Updated:Feb 19, 2024

You can use the trained gaussian mixture models (GMMs) of the GMM Prediction component to perform clustering prediction. This topic describes how to configure the GMM Prediction component.

Limits

You can use the GMM Prediction component based only on the computing resources of MaxCompute and Flink of PAI.

Configure the component in the PAI console

You can configure parameters for the GMM Prediction component in the Machine Learning Platform for AI (PAI) console.

Tab

Parameter

Description

Field Setting

vectorCol

The name of the vector column.

reservedCols

The columns to be reserved by the algorithm.

Parameter Setting

predictionCol

The name of the prediction column.

predictionDetailCol

The name of the prediction details column.

numThreads

The number of threads of the component. Default value: 1.

Execution Tuning

Number of Workers

The number of cores. This parameter must be used together with the Memory per worker, unit MB parameter. The value of this parameter must be a positive integer. Valid values: [1,9999]. For more information, see Appendix: How to estimate resource usage.

Memory per worker, unit MB

The memory size of each core. Valid values: 1024 to 64 × 1024. Unit: MB. For more information, see Appendix: How to estimate resource usage.

Appendix: How to estimate resource usage

You can refer to the following section to estimate resource usage.

  • How do I estimate the memory to be used by each node?

    The memory used by each node is approximately the model size times 30.

    For example, if the input model size is 1 GB, the memory of each node can be set to 30 GB.

  • How do I estimate the number of nodes that I need?

    The distributed training task speeds up and then slows down as the number of nodes increases due to communication overhead. If the task slows down, stop increasing the node quantity. This node quantity can be used.