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If the xpack.ml.use_auto_machine_memory_percent setting is true, and xpack.ml.max_model_memory_limit is not set then xpack.ml.max_model_memory_limit is now considered to be set to the largest size that could be assigned in the cluster. This functionality will be crucial for Cloud once the Elasticsearch startup code is setting the Elasticsearch JVM heap size. Then the Cloud code will no longer be able to accurately set xpack.ml.max_model_memory_limit, so will not set it at all. Instead the Cloud code will just set xpack.ml.use_auto_machine_memory_percent and the ML code will calculate the appropriate maximum model_memory_limit that should be permitted.
239 lines
12 KiB
Text
239 lines
12 KiB
Text
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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 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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[discrete]
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[[general-ml-settings]]
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==== General machine learning settings
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`node.roles: [ ml ]`::
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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_. If you want to run {ml} jobs, there must be at least
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one {ml} node in your cluster.
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If you set `node.roles`, you must explicitly specify all the required roles for
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the node. To learn more, refer to <<modules-node>>.
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[IMPORTANT]
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====
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* On dedicated coordinating nodes or dedicated master nodes, do not set
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the `ml` role.
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* It is strongly recommended that dedicated {ml} nodes also have the
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`remote_cluster_client` role; otherwise, {ccs} fails when used in {ml} jobs or
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{dfeeds}. See <<remote-node>>.
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====
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`xpack.ml.enabled`::
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(<<static-cluster-setting,Static>>) The default value (`true`) enables {ml} APIs
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on the node.
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IMPORTANT: If you want to use {ml-features} in your cluster, it is recommended
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that you use the default value for this setting on all nodes.
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If set to `false`, the {ml} APIs are disabled on the node. For example, the node
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cannot open jobs, start {dfeeds}, receive transport (internal) communication
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requests, or requests from clients (including {kib}) related to {ml} APIs.
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`xpack.ml.inference_model.cache_size`::
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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 (such as `2gb`) or a percentage of total allocated
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heap. Defaults to `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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(<<static-cluster-setting,Static>>) The time to live (TTL) for trained models in
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the inference model cache. The TTL is calculated from last access. Users of the
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cache (such as the inference processor or inference aggregator) cache a model on
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its first use and reset the TTL on every use. If a cached model is not accessed
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for the duration of the TTL, it is flagged for eviction from the cache. If a
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document is processed later, the model is again loaded into the cache. To update
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this setting in {ess}, see
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{cloud}/ec-add-user-settings.html[Add {es} user settings]. Defaults to `5m`.
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// end::interference-model-ttl-tag[]
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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`::
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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. The limit is based on the total memory of the machine,
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not current free memory. Jobs are not allocated to a node if doing so would
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cause the estimated memory use of {ml} jobs to exceed the limit. When the
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{operator-feature} is enabled, this setting can be updated only by operator
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users. The minimum value is `5`; the maximum value is `200`. Defaults to `30`.
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+
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--
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TIP: Do not configure this setting to a value higher than the amount of memory
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left over after running the {es} JVM unless you have enough swap space to
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accommodate it and have determined this is an appropriate configuration for a
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specialist use case. The maximum setting value is for the special case where it
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has been determined that using swap space for {ml} jobs is acceptable. The
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general best practice is to not use swap on {es} nodes.
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--
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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 {ml} jobs in this cluster. If you try to create a
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job with a `model_memory_limit` property value that is greater than this setting
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value, an error occurs. Existing jobs are not affected when you update this
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setting. If this setting is `0` or unset, there is no maximum
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`model_memory_limit` value. If there are no nodes that meet the memory
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requirements for a job, this lack of a maximum memory limit means it's possible
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to create jobs that cannot be assigned to any available nodes. For more
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information about the `model_memory_limit` property, see
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<<ml-put-job,Create {anomaly-jobs}>> or <<put-dfanalytics>>. Defaults to `0` if
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`xpack.ml.use_auto_machine_memory_percent` is `false`. If
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`xpack.ml.use_auto_machine_memory_percent` is `true` and
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`xpack.ml.max_model_memory_limit` is not explicitly set then it will default to
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the largest `model_memory_limit` that could be assigned in the cluster.
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[[xpack.ml.max_open_jobs]]
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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. In this context, jobs include both {anomaly-jobs} and
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{dfanalytics-jobs}. The maximum number of jobs is also constrained by memory
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usage. Thus if the estimated memory usage of the jobs would be higher than
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allowed, fewer jobs will run on a node. Prior to version 7.1, this setting was a
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per-node non-dynamic setting. It became a cluster-wide dynamic setting in
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version 7.1. As a result, changes to its value after node startup are used only
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after every node in the cluster is running version 7.1 or higher. The minimum
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value is `1`; the maximum value is `512`. Defaults to `512`.
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`xpack.ml.nightly_maintenance_requests_per_second`::
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(<<cluster-update-settings,Dynamic>>) The rate at which the nightly maintenance
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task deletes expired model snapshots and results. The setting is a proxy to the
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<<docs-delete-by-query-throttle,`requests_per_second`>> parameter used in the
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delete by query requests and controls throttling. When the {operator-feature} is
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enabled, this setting can be updated only by operator users. Valid values must
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be greater than `0.0` or equal to `-1.0`, where `-1.0` means a default value is
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used. Defaults to `-1.0`
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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. When the {operator-feature} is enabled, this setting can be updated only
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by operator users. 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`::
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(<<cluster-update-settings,Dynamic>>) Reserved. When the {operator-feature} is
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enabled, this setting can be updated only by operator users.
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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. Defaults to `500`.
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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. If the current number of {ml} nodes is greater than or equal to this
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setting, it is assumed that there are no more lazy nodes available as the
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desired number of nodes have already been provisioned. If a job is opened and
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this setting has a value greater than zero and there are no nodes that can
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accept the job, the job stays in the `OPENING` state until a new {ml} node is
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added to the cluster and the job is assigned to run on that node. When the
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{operator-feature} is enabled, this setting can be updated only by operator
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users. Defaults to `0`.
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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.max_ml_node_size`::
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(<<cluster-update-settings,Dynamic>>)
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The maximum node size for {ml} nodes in a deployment that supports automatic
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cluster scaling. If you set it to the maximum possible size of future {ml} nodes,
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when a {ml} job is assigned to a lazy node it can check (and fail quickly) when
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scaling cannot support the size of the job. When the {operator-feature} is
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enabled, this setting can be updated only by operator users. Defaults to `0b`,
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which means it will be assumed that automatic cluster scaling can add arbitrarily large nodes to the cluster.
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`xpack.ml.persist_results_max_retries`::
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(<<cluster-update-settings,Dynamic>>) The maximum number of times to retry bulk
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indexing requests that fail while processing {ml} results. If the limit is
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reached, the {ml} job stops processing data and its status is `failed`. When the
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{operator-feature} is enabled, this setting can be updated only by operator
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users. The minimum value is `0`; the maximum value is `50`. Defaults to `20`.
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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. When such processes are started they must
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connect to the {es} JVM. If the process does not connect within the time period
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specified by this setting then the process is assumed to have failed. When the
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{operator-feature} is enabled, this setting can be updated only by operator
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users. The minimum value is `5s`. Defaults to `10s`.
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`xpack.ml.use_auto_machine_memory_percent`::
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(<<cluster-update-settings,Dynamic>>) If this setting is `true`, the
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`xpack.ml.max_machine_memory_percent` setting is ignored. Instead, the maximum
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percentage of the machine's memory that can be used for running {ml} analytics
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processes is calculated automatically and takes into account the total node size
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and the size of the JVM on the node. When the {operator-feature} is enabled, this
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setting can be updated only by operator users. The default value is `false`.
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--
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[IMPORTANT]
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====
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* If you do not have dedicated {ml} nodes (that is to say, the node has
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multiple roles), do not enable this setting. Its calculations assume that {ml}
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analytics are the main purpose of the node.
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* The calculation assumes that dedicated {ml} nodes have at least
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`256MB` memory reserved outside of the JVM. If you have tiny {ml}
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nodes in your cluster, you shouldn't use this setting.
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====
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--
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If this setting is `true` it also affects the default value for
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`xpack.ml.max_model_memory_limit`. In this case `xpack.ml.max_model_memory_limit`
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defaults to the largest size that could be assigned in the current cluster.
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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`::
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(<<cluster-update-settings,Dynamic>>) The limit for the trained model circuit
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breaker. This value is defined as a percentage of the JVM heap. Defaults to
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`50%`. If the <<parent-circuit-breaker,parent circuit breaker>> is set to a
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value less than `50%`, this setting uses that value as its default instead.
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`breaker.model_inference.overhead`::
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(<<cluster-update-settings,Dynamic>>) A constant that all trained model
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estimations are multiplied by to determine a final estimation. See
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<<circuit-breaker>>. Defaults to `1`.
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`breaker.model_inference.type`::
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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 trained models and can potentially
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break and prevent `OutOfMemory` errors. The default value is `memory`.
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