Briefly, this error occurs when Elasticsearch attempts to deploy a machine learning model but encounters an issue. This could be due to insufficient resources, incorrect model configuration, or a problem with the model itself. To resolve this, ensure that your cluster has enough resources (CPU, memory, disk space) to handle the deployment. Check the model configuration for any errors and make sure the model is compatible with your version of Elasticsearch. If the problem persists, consider retraining or adjusting your model.
This guide will help you check for common problems that cause the log ” [{}] Starting model deployment ” to appear. To understand the issues related to this log, read the explanation below about the following Elasticsearch concepts: plugin.
Log Context
Log “[{}] Starting model deployment” classname is DeploymentManager.java.
We extracted the following from Elasticsearch source code for those seeking an in-depth context :
ProcessContext addProcessContext(Long id; ProcessContext processContext) {
return processContextByAllocation.putIfAbsent(id; processContext);
}
public void startDeployment(TrainedModelDeploymentTask task; ActionListener finalListener) {
logger.info("[{}] Starting model deployment"; task.getDeploymentId());
if (processContextByAllocation.size() >= maxProcesses) {
finalListener.onFailure(
ExceptionsHelper.serverError(
"[{}] Could not start inference process as the node reached the max number [{}] of processes";
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