169 lines
8.1 KiB
Plaintext
169 lines
8.1 KiB
Plaintext
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[role="xpack"]
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[[ml-settings]]
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=== Machine learning settings in Elasticsearch
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++++
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<titleabbrev>Machine learning settings</titleabbrev>
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++++
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[[ml-settings-description]]
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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 https://en.wikipedia.org/wiki/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 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>>.
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TIP: Dynamic settings take precedence over settings in the `elasticsearch.yml`
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file.
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// end::ml-settings-description-tag[]
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[discrete]
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[[general-ml-settings]]
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==== General machine learning settings
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`node.ml`::
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deprecated:[7.9.0,"Use <<modules-node,node.roles>> instead."]
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Set to `true` (default) to identify the node as a _machine learning node_. +
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+
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If set to `false` in `elasticsearch.yml`, the node cannot run jobs. If set to
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`true` but `xpack.ml.enabled` is set to `false`, the `node.ml` setting is
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ignored and the node cannot run jobs. If you want to run jobs, there must be at
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least one machine learning node in your cluster. +
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+
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IMPORTANT: On dedicated coordinating nodes or dedicated master nodes, disable
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the `node.ml` role.
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`xpack.ml.enabled`::
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Set to `true` (default) to enable {ml} 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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requests related to {ml} APIs. If the node is a coordinating node, {ml} requests
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from clients (including {kib}) also fail. For more information about disabling
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{ml} in specific {kib} instances, see
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{kibana-ref}/ml-settings-kb.html[{kib} {ml} settings].
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+
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IMPORTANT: If you want to use {ml-features} in your cluster, it is recommended
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that you set `xpack.ml.enabled` to `true` on all nodes. This is the
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default behavior. At a minimum, it must be enabled on all master-eligible nodes.
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If you want to use {ml-features} in clients or {kib}, it must also be enabled on
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all 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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[[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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// 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_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_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_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.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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[discrete]
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[[advanced-ml-settings]]
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==== Advanced machine learning settings
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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.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_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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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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+
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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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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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[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.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.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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