[DOCS] Document static machine learning settings (#61382)
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Lisa Cawley
lcawl
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@ -10,15 +10,9 @@
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// tag::ml-settings-description-tag[]
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You do not need to configure any settings to use {ml}. It is enabled by default.
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IMPORTANT: {ml-cap} uses SSE4.2 instructions, so will only work on machines whose
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CPUs {wikipedia}/SSE4#Supporting_CPUs[support] SSE4.2. If you
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run {es} on older hardware you must disable {ml} (by setting `xpack.ml.enabled`
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to `false`).
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All of these settings can be added to the `elasticsearch.yml` configuration
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file. The dynamic settings can also be updated across a cluster with the
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<<cluster-update-settings,cluster update settings API>>. Dynamic settings take
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precedence over settings in the `elasticsearch.yml` file.
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IMPORTANT: {ml-cap} uses SSE4.2 instructions, so it works only on machines whose
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CPUs {wikipedia}/SSE4#Supporting_CPUs[support] SSE4.2. If you run {es} on older
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hardware, you must disable {ml} (by setting `xpack.ml.enabled` to `false`).
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// end::ml-settings-description-tag[]
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@ -27,8 +21,9 @@ precedence over settings in the `elasticsearch.yml` file.
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==== General machine learning settings
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`node.roles: [ ml ]`::
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Set `node.roles` to contain `ml` to identify the node as a _{ml} node_ that is
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capable of running jobs. Every node is a {ml} node by default.+
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(<<static-cluster-setting,Static>>) Set `node.roles` to contain `ml` to identify
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the node as a _{ml} node_ that is capable of running jobs. Every node is a {ml}
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node by default.
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+
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If you use the `node.roles` setting, then all required roles must be explicitly
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set. Consult <<modules-node>> to learn more.
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@ -38,7 +33,8 @@ the `ml` role.
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`xpack.ml.enabled`::
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Set to `true` (default) to enable {ml} APIs on the node.
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(<<static-cluster-setting,Static>>) Set to `true` (default) to enable {ml} APIs
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on the node.
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+
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If set to `false`, the {ml} APIs are disabled on the node. Therefore the node
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cannot open jobs, start {dfeeds}, or receive transport (internal) communication
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@ -54,58 +50,62 @@ want to use {ml-features} in clients or {kib}, it must also be enabled on all
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coordinating nodes.
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`xpack.ml.inference_model.cache_size`::
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The maximum inference cache size allowed. The inference cache exists in the JVM
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heap on each ingest node. The cache affords faster processing times for the
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`inference` processor. The value can be a static byte sized value (i.e. "2gb")
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or a percentage of total allocated heap. The default is "40%".
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See also <<model-inference-circuit-breaker>>.
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(<<static-cluster-setting,Static>>) The maximum inference cache size allowed.
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The inference cache exists in the JVM heap on each ingest node. The cache
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affords faster processing times for the `inference` processor. The value can be
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a static byte sized value (i.e. "2gb") or a percentage of total allocated heap.
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The default is "40%". See also <<model-inference-circuit-breaker>>.
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[[xpack-interference-model-ttl]]
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// tag::interference-model-ttl-tag[]
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`xpack.ml.inference_model.time_to_live` {ess-icon}::
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The time to live (TTL) for models in the inference model cache. The TTL is
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calculated from last access. The `inference` processor attempts to load the
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model from cache. If the `inference` processor does not receive any documents
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for the duration of the TTL, the referenced model is flagged for eviction from
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the cache. If a document is processed later, the model is again loaded into the
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cache. Defaults to `5m`.
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(<<static-cluster-setting,Static>>) The time to live (TTL) for models in the
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inference model cache. The TTL is calculated from last access. The `inference`
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processor attempts to load the model from cache. If the `inference` processor
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does not receive any documents for the duration of the TTL, the referenced model
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is flagged for eviction from the cache. If a document is processed later, the
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model is again loaded into the cache. Defaults to `5m`.
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// end::interference-model-ttl-tag[]
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`xpack.ml.max_inference_processors` (<<cluster-update-settings,Dynamic>>)::
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The total number of `inference` type processors allowed across all ingest
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pipelines. Once the limit is reached, adding an `inference` processor to
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a pipeline is disallowed. Defaults to `50`.
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`xpack.ml.max_inference_processors`::
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(<<cluster-update-settings,Dynamic>>) The total number of `inference` type
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processors allowed across all ingest pipelines. Once the limit is reached,
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adding an `inference` processor to a pipeline is disallowed. Defaults to `50`.
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`xpack.ml.max_machine_memory_percent` (<<cluster-update-settings,Dynamic>>)::
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The maximum percentage of the machine's memory that {ml} may use for running
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analytics processes. (These processes are separate to the {es} JVM.) Defaults to
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`30` percent. The limit is based on the total memory of the machine, not current
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free memory. Jobs will not be allocated to a node if doing so would cause the
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estimated memory use of {ml} jobs to exceed the limit.
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`xpack.ml.max_machine_memory_percent`::
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(<<cluster-update-settings,Dynamic>>) The maximum percentage of the machine's
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memory that {ml} may use for running analytics processes. (These processes are
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separate to the {es} JVM.) Defaults to `30` percent. The limit is based on the
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total memory of the machine, not current free memory. Jobs are not allocated to
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a node if doing so would cause the estimated memory use of {ml} jobs to exceed
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the limit.
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`xpack.ml.max_model_memory_limit` (<<cluster-update-settings,Dynamic>>)::
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The maximum `model_memory_limit` property value that can be set for any job on
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this node. If you try to create a job with a `model_memory_limit` property value
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that is greater than this setting value, an error occurs. Existing jobs are not
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affected when you update this setting. For more information about the
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`model_memory_limit` property, see <<put-analysislimits>>.
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`xpack.ml.max_model_memory_limit`::
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(<<cluster-update-settings,Dynamic>>) The maximum `model_memory_limit` property
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value that can be set for any job on this node. If you try to create a job with
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a `model_memory_limit` property value that is greater than this setting value,
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an error occurs. Existing jobs are not affected when you update this setting.
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For more information about the `model_memory_limit` property, see
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<<put-analysislimits>>.
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[[xpack.ml.max_open_jobs]]
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`xpack.ml.max_open_jobs` (<<cluster-update-settings,Dynamic>>)::
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The maximum number of jobs that can run simultaneously on a node. Defaults to
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`20`. In this context, jobs include both {anomaly-jobs} and {dfanalytics-jobs}.
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The maximum number of jobs is also constrained by memory usage. Thus if the
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estimated memory usage of the jobs would be higher than allowed, fewer jobs will
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run on a node. Prior to version 7.1, this setting was a per-node non-dynamic
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setting. It became a cluster-wide dynamic setting in version 7.1. As a result,
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changes to its value after node startup are used only after every node in the
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cluster is running version 7.1 or higher. The maximum permitted value is `512`.
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`xpack.ml.max_open_jobs`::
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(<<cluster-update-settings,Dynamic>>) The maximum number of jobs that can run
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simultaneously on a node. Defaults to `20`. In this context, jobs include both
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{anomaly-jobs} and {dfanalytics-jobs}. The maximum number of jobs is also
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constrained by memory usage. Thus if the estimated memory usage of the jobs
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would be higher than allowed, fewer jobs will run on a node. Prior to version
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7.1, this setting was a per-node non-dynamic setting. It became a cluster-wide
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dynamic setting in version 7.1. As a result, changes to its value after node
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startup are used only after every node in the cluster is running version 7.1 or
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higher. The maximum permitted value is `512`.
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`xpack.ml.node_concurrent_job_allocations` (<<cluster-update-settings,Dynamic>>)::
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The maximum number of jobs that can concurrently be in the `opening` state on
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each node. Typically, jobs spend a small amount of time in this state before
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they move to `open` state. Jobs that must restore large models when they are
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opening spend more time in the `opening` state. Defaults to `2`.
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`xpack.ml.node_concurrent_job_allocations`::
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(<<cluster-update-settings,Dynamic>>) The maximum number of jobs that can
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concurrently be in the `opening` state on each node. Typically, jobs spend a
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small amount of time in this state before they move to `open` state. Jobs that
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must restore large models when they are opening spend more time in the `opening`
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state. Defaults to `2`.
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[discrete]
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[[advanced-ml-settings]]
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@ -114,52 +114,55 @@ opening spend more time in the `opening` state. Defaults to `2`.
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These settings are for advanced use cases; the default values are generally
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sufficient:
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`xpack.ml.enable_config_migration` (<<cluster-update-settings,Dynamic>>)::
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Reserved.
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`xpack.ml.enable_config_migration`::
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(<<cluster-update-settings,Dynamic>>) Reserved.
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`xpack.ml.max_anomaly_records` (<<cluster-update-settings,Dynamic>>)::
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The maximum number of records that are output per bucket. The default value is
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`500`.
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`xpack.ml.max_anomaly_records`::
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(<<cluster-update-settings,Dynamic>>) The maximum number of records that are
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output per bucket. The default value is `500`.
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`xpack.ml.max_lazy_ml_nodes` (<<cluster-update-settings,Dynamic>>)::
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The number of lazily spun up Machine Learning nodes. Useful in situations
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where ML nodes are not desired until the first Machine Learning Job
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is opened. It defaults to `0` and has a maximum acceptable value of `3`.
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If the current number of ML nodes is `>=` than this setting, then it is
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`xpack.ml.max_lazy_ml_nodes`::
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(<<cluster-update-settings,Dynamic>>) The number of lazily spun up {ml} nodes.
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Useful in situations where {ml} nodes are not desired until the first {ml} job
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opens. It defaults to `0` and has a maximum acceptable value of `3`. If the
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current number of {ml} nodes is greater than or equal to this setting, it is
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assumed that there are no more lazy nodes available as the desired number
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of nodes have already been provisioned. When a job is opened with this
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setting set at `>0` and there are no nodes that can accept the job, then
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the job will stay in the `OPENING` state until a new ML node is added to the
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cluster and the job is assigned to run on that node.
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of nodes have already been provisioned. If a job is opened and this setting has
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a value greater than zero and there are no nodes that can accept the job, the
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job stays in the `OPENING` state until a new {ml} node is added to the cluster
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and the job is assigned to run on that node.
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+
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IMPORTANT: This setting assumes some external process is capable of adding ML nodes
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to the cluster. This setting is only useful when used in conjunction with
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IMPORTANT: This setting assumes some external process is capable of adding {ml}
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nodes to the cluster. This setting is only useful when used in conjunction with
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such an external process.
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`xpack.ml.process_connect_timeout` (<<cluster-update-settings,Dynamic>>)::
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The connection timeout for {ml} processes that run separately from the {es} JVM.
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Defaults to `10s`. Some {ml} processing is done by processes that run separately
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to the {es} JVM. When such processes are started they must connect to the {es}
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JVM. If such a process does not connect within the time period specified by this
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setting then the process is assumed to have failed. Defaults to `10s`. The minimum
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value for this setting is `5s`.
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`xpack.ml.process_connect_timeout`::
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(<<cluster-update-settings,Dynamic>>) The connection timeout for {ml} processes
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that run separately from the {es} JVM. Defaults to `10s`. Some {ml} processing
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is done by processes that run separately to the {es} JVM. When such processes
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are started they must connect to the {es} JVM. If such a process does not
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connect within the time period specified by this setting then the process is
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assumed to have failed. Defaults to `10s`. The minimum value for this setting is
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`5s`.
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[discrete]
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[[model-inference-circuit-breaker]]
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==== {ml-cap} circuit breaker settings
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`breaker.model_inference.limit` (<<cluster-update-settings,Dynamic>>)::
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Limit for the model inference breaker, which defaults to 50% of the JVM heap.
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If the parent circuit breaker is less than 50% of the JVM heap, it is bound
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to that limit instead. See <<circuit-breaker>>.
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`breaker.model_inference.limit`::
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(<<cluster-update-settings,Dynamic>>) Limit for the model inference breaker,
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which defaults to 50% of the JVM heap. If the parent circuit breaker is less
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than 50% of the JVM heap, it is bound to that limit instead. See
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<<circuit-breaker>>.
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`breaker.model_inference.overhead` (<<cluster-update-settings,Dynamic>>)::
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A constant that all accounting estimations are multiplied by to determine
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a final estimation. Defaults to 1. See <<circuit-breaker>>.
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`breaker.model_inference.overhead`::
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(<<cluster-update-settings,Dynamic>>) A constant that all accounting estimations
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are multiplied by to determine a final estimation. Defaults to 1. See
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<<circuit-breaker>>.
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`breaker.model_inference.type`::
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The underlying type of the circuit breaker. There are two valid options: `noop`
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and `memory`. `noop` means the circuit breaker does nothing to prevent too much
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memory usage. `memory` means the circuit breaker tracks the memory used by
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inference models and can potentially break and prevent OutOfMemory errors. The
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default is `memory`.
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(<<static-cluster-setting,Static>>) The underlying type of the circuit breaker.
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There are two valid options: `noop` and `memory`. `noop` means the circuit
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breaker does nothing to prevent too much memory usage. `memory` means the
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circuit breaker tracks the memory used by inference models and can potentially
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break and prevent `OutOfMemory` errors. The default is `memory`.
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@ -10,11 +10,6 @@
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You do not need to configure any settings to use {transforms}. It is enabled by
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default.
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All of these settings can be added to the `elasticsearch.yml` configuration file.
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The dynamic settings can also be updated across a cluster with the
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<<cluster-update-settings,cluster update settings API>>. Dynamic settings take
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precedence over settings in the `elasticsearch.yml` file.
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[discrete]
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[[general-transform-settings]]
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==== General {transforms} settings
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