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| layout | title | has_children | nav_order |
|---|---|---|---|
| default | ML Commons cluster settings | false | 160 |
ML Commons cluster settings
To enhance and customize your OpenSearch cluster for machine learning (ML), you can add and modify several configuration settings for the ML commons plugin in your 'opensearch.yml' file.
Run tasks and models on ML nodes only
If true, ML Commons tasks and models run machine learning (ML) tasks on ML nodes only. If false, tasks and models run on ML nodes first. If no ML nodes exist, tasks and models run on data nodes. We recommend that you do not set this value to "false" on production clusters.
Setting
plugins.ml_commons.only_run_on_ml_node: true
Values
- Default value:
true - Value range:
trueorfalse
Dispatch tasks to ML node
round_robin dispatches ML tasks to ML nodes using round robin routing. least_load gathers runtime information from all ML nodes, like JVM heap memory usage and running tasks, and then dispatches the tasks to the ML node with the lowest load.
Setting
plugins.ml_commons.task_dispatch_policy: round_robin
Values
- Dafault value:
round_robin - Value range:
round_robinorleast_load
Set number of ML tasks per node
Sets the number of ML tasks that can run on each ML node. When set to 0, no ML tasks run on any nodes.
Setting
plugins.ml_commons.max_ml_task_per_node: 10
Values
- Default value:
10 - Value range: [0, 10,000]
Set number of ML models per node
Sets the number of ML models that can be deployed to each ML node. When set to 0, no ML models can deploy on any node.
Setting
plugins.ml_commons.max_model_on_node: 10
Values
- Default value:
10 - Value range: [0, 10,000]
Set sync job intervals
When returning runtime information with the Profile API, ML Commons will run a regular job to sync newly deployed or undeployed models on each node. When set to 0, ML Commons immediately stops sync-up jobs.
Setting
plugins.ml_commons.sync_up_job_interval_in_seconds: 3
Values
- Default value:
3 - Value range: [0, 86,400]
Predict monitoring requests
Controls how many predict requests are monitored on one node. If set to 0, OpenSearch clears all monitoring predict requests in cache and does not monitor for new predict requests.
Setting
plugins.ml_commons.monitoring_request_count: 100
Value range
- Default value:
100 - Value range: [0, 10,000,000]
Upload model tasks per node
Controls how many upload model tasks can run in parallel on one node. If set to 0, you cannot upload models to any node.
Setting
plugins.ml_commons.max_upload_model_tasks_per_node: 10
Values
- Default value:
10 - Value range: [0, 10]
Load model tasks per node
Controls how many load model tasks can run in parallel on one node. If set to 0, you cannot load models to any node.
Setting
plugins.ml_commons.max_load_model_tasks_per_node: 10
Values
- Default value:
10 - Value range: [0, 10]
Add trusted URL
The default value allows you to upload a model file from any http/https/ftp/local file. You can change this value to restrict trusted model URLs.
Setting
The default URL value for this trusted URL setting is not secure. To ensure the security, please use you own regex string to the trusted repository that contains your models, for example https://github.com/opensearch-project/ml-commons/blob/2.x/ml-algorithms/src/test/resources/org/opensearch/ml/engine/algorithms/text_embedding/*.
{: .warning }
plugins.ml_commons.trusted_url_regex: <model-repository-url>
Values
- Default value:
"^(https?|ftp|file)://[-a-zA-Z0-9+&@#/%?=~_|!:,.;]*[-a-zA-Z0-9+&@#/%=~_|]" - Value range: Java regular expression (regex) string
Assign task timeout
Assigns how long in seconds an ML task will live. After the timeout, the task will fail.
Setting
plugins.ml_commons.ml_task_timeout_in_seconds: 600
Values
- Default value: 600
- Value range: [1, 86,400]
Set native memory threshold
Sets a circuit breaker that checks all system memory usage before running an ML task. If the native memory exceeds the threshold, OpenSearch throws an exception and stops running any ML task.
Values are based on the percentage of memory available. When set to 0, no ML tasks will run. When set to 100, the circuit breaker closes and no threshold exists.
Setting
plugins.ml_commons.native_memory_threshold: 90
Values
- Default value: 90
- Value range: [0, 100]
Allow custom deployment plans
When enabled, this setting grants users the ability to deploy models to specific ML nodes according to that user's permissions.
Setting
plugins.ml_commons.allow_custom_deployment_plan: false
Values
- Default value: false
- Value range: [false, true]
Enable auto redeploy
This setting automatically redeploys deployed or partially deployed models upon cluster failure. If all ML nodes inside a cluster crash, the model switches to the DEPLOYED_FAILED state, and the model must be deployed manually.
Setting
plugins.ml_commons.model_auto_redeploy.enable: false
Values
- Default value: false
- Value range: [false, true]
Set retires for auto redeploy
This setting sets the limit for the number of times a deployed or partially deployed model will try and redeploy when ML nodes in a cluster fail or new ML nodes join the cluster.
Setting
plugins.ml_commons.model_auto_redeploy.lifetime_retry_times: 3
Values
- Default value: 3
- Value range: [0, 100]
Set auto redeploy success ratio
This setting sets the ratio of success for the auto-redeployment of a model based on the available ML nodes in a cluster. For example, if ML nodes crash inside a cluster, the auto redeploy protocol adds another node or retires a crashed node. If the ratio is 0.7 and 70% of all ML nodes successfully redeploy the model on auto-redeploy activation, the redeployment is a success. If the model redeploys on fewer than 70% of available ML nodes, the auto-redeploy retries until the redeployment succeeds or OpenSearch reaches the maximum number of retries.
Setting
plugins.ml_commons.model_auto_redeploy_success_ratio: 0.8
Values
- Default value: 0.8
- Value range: [0, 1]