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Elastic Docs ›Elasticsearch Guide [7.11] ›Set up Elasticsearch ›Configuring Elasticsearch

Machine learning settings in Elasticsearch

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Machine learning settings in Elasticsearch

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You do not need to configure any settings to use machine learning. It is enabled by default.

Machine learning uses SSE4.2 instructions, so it works only on machines whose CPUs support SSE4.2. If you run Elasticsearch on older hardware, you must disable machine learning (by setting xpack.ml.enabled to false).

General machine learning settings

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node.roles: [ ml ]

(Static) Set node.roles to contain ml to identify the node as a machine learning node. If you want to run machine learning jobs, there must be at least one machine learning node in your cluster.

If you set node.roles, you must explicitly specify all the required roles for the node. To learn more, refer to Node.

  • On dedicated coordinating nodes or dedicated master nodes, do not set the ml role.
  • It is strongly recommended that dedicated machine learning nodes also have the remote_cluster_client role; otherwise, cross-cluster search fails when used in machine learning jobs or datafeeds. See Remote-eligible node.
xpack.ml.enabled

(Static) Set to true (default) to enable machine learning APIs on the node.

If set to false, the machine learning APIs are disabled on the node. Therefore the node cannot open jobs, start datafeeds, or receive transport (internal) communication requests related to machine learning APIs. If the node is a coordinating node, machine learning requests from clients (including Kibana) also fail. For more information about disabling machine learning in specific Kibana instances, see Kibana machine learning settings.

If you want to use machine learning features in your cluster, it is recommended that you set xpack.ml.enabled to true on all nodes. This is the default behavior. At a minimum, it must be enabled on all master-eligible nodes. If you want to use machine learning features in clients or Kibana, it must also be enabled on all coordinating nodes.

xpack.ml.inference_model.cache_size
(Static) The maximum inference cache size allowed. The inference cache exists in the JVM heap on each ingest node. The cache affords faster processing times for the inference processor. The value can be a static byte sized value (i.e. "2gb") or a percentage of total allocated heap. The default is "40%". See also Machine learning circuit breaker settings.
xpack.ml.inference_model.time_to_live logo cloud
(Static) The time to live (TTL) for models in the inference model cache. The TTL is calculated from last access. The inference processor attempts to load the model from cache. If the inference processor does not receive any documents for the duration of the TTL, the referenced model is flagged for eviction from the cache. If a document is processed later, the model is again loaded into the cache. Defaults to 5m.
xpack.ml.max_inference_processors
(Dynamic) The total number of inference type processors allowed across all ingest pipelines. Once the limit is reached, adding an inference processor to a pipeline is disallowed. Defaults to 50.
xpack.ml.max_machine_memory_percent
(Dynamic) The maximum percentage of the machine’s memory that machine learning may use for running analytics processes. (These processes are separate to the Elasticsearch JVM.) Defaults to 30 percent. The limit is based on the total memory of the machine, not current free memory. Jobs are not allocated to a node if doing so would cause the estimated memory use of machine learning jobs to exceed the limit.
xpack.ml.max_model_memory_limit
(Dynamic) The maximum model_memory_limit property value that can be set for any job on this node. If you try to create a job with a model_memory_limit property value that is greater than this setting value, an error occurs. Existing jobs are not affected when you update this setting. For more information about the model_memory_limit property, see `analysis_limits`.
xpack.ml.max_open_jobs
(Dynamic) The maximum number of jobs that can run simultaneously on a node. Defaults to 20. In this context, jobs include both anomaly detection jobs and data frame analytics jobs. The maximum number of jobs is also constrained by memory usage. Thus if the estimated memory usage of the jobs would be higher than allowed, fewer jobs will run on a node. Prior to version 7.1, this setting was a per-node non-dynamic setting. It became a cluster-wide dynamic setting in version 7.1. As a result, changes to its value after node startup are used only after every node in the cluster is running version 7.1 or higher. The maximum permitted value is 512.
xpack.ml.nightly_maintenance_requests_per_second
(Dynamic) The rate at which the nightly maintenance task deletes expired model snapshots and results. The setting is a proxy to the requests_per_second parameter used in the delete by query requests and controls throttling. Valid values must be greater than 0.0 or equal to -1.0 where -1.0 means a default value is used. Defaults to -1.0
xpack.ml.node_concurrent_job_allocations
(Dynamic) The maximum number of jobs that can concurrently be in the opening state on each node. Typically, jobs spend a small amount of time in this state before they move to open state. Jobs that must restore large models when they are opening spend more time in the opening state. Defaults to 2.

Advanced machine learning settings

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These settings are for advanced use cases; the default values are generally sufficient:

xpack.ml.enable_config_migration
(Dynamic) Reserved.
xpack.ml.max_anomaly_records
(Dynamic) The maximum number of records that are output per bucket. The default value is 500.
xpack.ml.max_lazy_ml_nodes

(Dynamic) The number of lazily spun up machine learning nodes. Useful in situations where machine learning nodes are not desired until the first machine learning job opens. It defaults to 0 and has a maximum acceptable value of 3. If the current number of machine learning nodes is greater than or equal to this setting, it is assumed that there are no more lazy nodes available as the desired number of nodes have already been provisioned. If a job is opened and this setting has a value greater than zero and there are no nodes that can accept the job, the job stays in the OPENING state until a new machine learning node is added to the cluster and the job is assigned to run on that node.

This setting assumes some external process is capable of adding machine learning nodes to the cluster. This setting is only useful when used in conjunction with such an external process.

xpack.ml.max_ml_node_size
(Dynamic) The maximum node size for machine learning nodes in a deployment that supports automatic cluster scaling. Defaults to 0b, which means this value is ignored. If you set it to the maximum possible size of future machine learning nodes, when a machine learning job is assigned to a lazy node it can check (and fail quickly) when scaling cannot support the size of the job.
xpack.ml.process_connect_timeout
(Dynamic) The connection timeout for machine learning processes that run separately from the Elasticsearch JVM. Defaults to 10s. Some machine learning processing is done by processes that run separately to the Elasticsearch JVM. When such processes are started they must connect to the Elasticsearch JVM. If such a process does not connect within the time period specified by this setting then the process is assumed to have failed. Defaults to 10s. The minimum value for this setting is 5s.
xpack.ml.use_auto_machine_memory_percent

(Dynamic) If this setting is true, the xpack.ml.max_machine_memory_percent setting is ignored. Instead, the maximum percentage of the machine’s memory that can be used for running machine learning analytics processes is calculated automatically and takes into account the total node size and the size of the JVM on the node. The default value is false. If this setting differs between nodes, the value on the current master node is heeded.

If you do not have dedicated machine learning nodes (that is to say, the node has multiple roles), do not enable this setting. Its calculations assume that machine learning analytics are the main purpose of the node.

The calculation assumes that dedicated machine learning nodes have at least 256MB memory reserved outside of the JVM. If you have tiny machine learning nodes in your cluster, you shouldn’t use this setting.

Machine learning circuit breaker settings

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breaker.model_inference.limit
(Dynamic) Limit for the model inference breaker, which defaults to 50% of the JVM heap. If the parent circuit breaker is less than 50% of the JVM heap, it is bound to that limit instead. See Circuit breaker settings.
breaker.model_inference.overhead
(Dynamic) A constant that all accounting estimations are multiplied by to determine a final estimation. Defaults to 1. See Circuit breaker settings.
breaker.model_inference.type
(Static) The underlying type of the circuit breaker. There are two valid options: noop and memory. noop means the circuit breaker does nothing to prevent too much memory usage. memory means the circuit breaker tracks the memory used by inference models and can potentially break and prevent OutOfMemory errors. The default is memory.
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