The breaker.model_inference.limit setting in Elasticsearch controls the memory limit for machine learning model inference operations. This circuit breaker helps prevent out-of-memory errors by limiting the amount of memory that can be used for model inference tasks.
- Default value: 40% of JVM heap
- Possible values: Any percentage (e.g., 50%) or absolute byte size (e.g., 2gb)
- Recommendations: The default value is suitable for most use cases. Adjust based on your specific model inference requirements and available memory.
This setting is part of Elasticsearch's circuit breaker mechanism, which helps maintain cluster stability by preventing operations from consuming excessive memory. The model inference breaker specifically targets memory used by machine learning inference operations.
Example
To change the breaker.model_inference.limit setting using the cluster settings API:
PUT /_cluster/settings
{
"persistent": {
"indices.breaker.model_inference.limit": "60%"
}
}
You might want to increase this limit if you're running large or multiple machine learning models and have sufficient memory available. Increasing the limit allows for more concurrent model inference operations but also increases the risk of out-of-memory errors if not carefully managed.
Common Issues or Misuses
- Setting the limit too high can lead to out-of-memory errors if other operations also require significant memory.
- Setting the limit too low may cause model inference operations to fail unnecessarily.
- Forgetting to account for this breaker when sizing your Elasticsearch cluster can lead to unexpected performance issues or operation failures.
Do's and Don'ts
Do's:
- Monitor the breaker's tripped status and adjust the limit if necessary.
- Consider your overall memory usage and other circuit breaker settings when adjusting this limit.
- Test changes in a non-production environment before applying to production.
Don'ts:
- Don't set the limit arbitrarily high without considering the impact on overall cluster stability.
- Don't ignore breaker trips; they indicate that your current settings may not be optimal for your workload.
- Don't forget to re-evaluate this setting when upgrading Elasticsearch or significantly changing your machine learning workloads.
Frequently Asked Questions
Q: How does the model inference breaker relate to other circuit breakers in Elasticsearch?
A: The model inference breaker is one of several circuit breakers in Elasticsearch. Each breaker targets a specific type of operation or memory usage. They all work together to prevent out-of-memory errors and maintain cluster stability.
Q: Can I disable the model inference breaker?
A: It's not recommended to disable any circuit breaker, including the model inference breaker. Instead, you should adjust the limit to an appropriate value for your use case.
Q: How can I monitor the model inference breaker's status?
A: You can use the /_nodes/stats/breaker API endpoint to view the current status of all circuit breakers, including the model inference breaker.
Q: What happens if the model inference breaker trips?
A: If the breaker trips, the current model inference operation will fail, and Elasticsearch will log an error. This prevents the operation from consuming more memory than allowed and potentially causing an out-of-memory error.
Q: Should I increase the model inference breaker limit if I'm using large machine learning models?
A: If you're using large models and have sufficient memory available, you may need to increase the limit. However, always consider the overall memory usage of your cluster and test changes carefully before applying them to production.
