Gaussian mixture models (GMMs) are a probabilistic model that represents K Gaussian distributions within a complete population. You can use the GMM Training component to classify models.
Supported computing resources
The GMM Training component runs on one of the following computing resources:
MaxCompute
Realtime Compute for Apache Flink
Deep Learning Containers (DLC) of Platform for AI (PAI)
Estimate resource usage
Use these guidelines to size workers before submitting a training job.
Memory per worker
Calculate the minimum memory per worker (in MB) using:
M × M × K × 8 × 2 × 12 / 1024 / 1024Where:
M = number of vector dimensions
K = number of Gaussian components (the
kparameter)
For most workloads, 8 GB (8192 MB) per worker is sufficient.
Keep vector dimensions (M) below 200. Higher dimensionality significantly increases memory requirements.
Number of workers
As a starting point, use 5 × X workers, where X is the input data size in GB.
If resources are constrained, reduce the worker count. Adding more workers speeds up training up to a point — beyond that, cross-worker communication overhead outweighs the parallelism benefit. Tune the worker count to find the optimal setting for your dataset.