--- layout: default title: ML Commons cluster settings has_children: false nav_order: 10 --- # 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. To learn more about static and dynamic settings, see [Configuring OpenSearch]({{site.url}}{{site.baseurl}}/install-and-configure/configuring-opensearch/index/). ## ML node By default, ML tasks and models only run on ML nodes. When configured without the `data` node role, ML nodes do not store any shards and instead calculate resource requirements at runtime. To use an ML node, create a node in your `opensearch.yml` file. Give your node a custom name and define the node role as `ml`: ```yml node.roles: [ ml ] ``` ## Run tasks and models on ML nodes only If `true`, ML Commons tasks and models run 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 setting `plugins.ml_commons.only_run_on_ml_node` to `true` on production clusters. {: .tip} ### Setting ``` plugins.ml_commons.only_run_on_ml_node: true ``` ### Values - Default value: `true` - Value range: `true` or `false` ## 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 - Default value: `round_robin` - Value range: `round_robin` or `least_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]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/profile/), 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] ## Register model tasks per node Controls how many register model tasks can run in parallel on one node. If set to `0`, you cannot run register model tasks on any node. ### Setting ``` plugins.ml_commons.max_register_model_tasks_per_node: 10 ``` ### Values - Default value: `10` - Value range: [0, 10] ## Deploy model tasks per node Controls how many deploy model tasks can run in parallel on one node. If set to 0, you cannot deploy models to any node. ### Setting ``` plugins.ml_commons.max_deploy_model_tasks_per_node: 10 ``` ### Values - Default value: `10` - Value range: [0, 10] ## Register models using URLs This setting gives you the ability to register models using a URL. By default, ML Commons only allows registration of [pretrained]({{site.url}}{{site.baseurl}}//ml-commons-plugin/pretrained-models/) models from the OpenSearch model repository. ### Setting ``` plugins.ml_commons.allow_registering_model_via_url: false ``` ### Values - Default value: false - Valid values: `false`, `true` ## Register models using local files This setting gives you the ability to register a model using a local file. By default, ML Commons only allows registration of [pretrained]({{site.url}}{{site.baseurl}}//ml-commons-plugin/pretrained-models/) models from the OpenSearch model repository. ### Setting ``` plugins.ml_commons.allow_registering_model_via_local_file: false ``` ### Values - Default value: false - Valid values: `false`, `true` ## Add trusted URL The default value allows you to register 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. For security, 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: ``` ### 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 - Valid values: `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 - Valid values: `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](#set-retires-for-auto-redeploy). ### Setting ``` plugins.ml_commons.model_auto_redeploy_success_ratio: 0.8 ``` ### Values - Default value: 0.8 - Value range: [0, 1] ## Run Python-based models When set to `true`, this setting enables the ability to run Python-based models supported by OpenSearch, such as [Metrics correlation]({{site.url}}{{site.baseurl}}/ml-commons-plugin/algorithms/#metrics-correlation). ### Setting ``` plugins.ml_commons.enable_inhouse_python_model: false ``` ### Values - Default value: false - Valid values: `false`, `true` ## Enable access control for connectors When set to `true`, the setting allows admins to control access and permissions to the connector API using `backend_roles`. ### Setting ``` plugins.ml_commons.connector_access_control_enabled: true ``` ### Values - Default value: false - Valid values: `false`, `true`