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Following the changes of elastic/x-pack-elasticsearch#2975 the hard limit on the number of ML jobs per node is no longer the only limiting factor. Additionally there is now a limit based on the estimated memory usage of the jobs, and this is expected to provide a more sensible limit that accounts for differing resource requirements per job. As a result, it makes sense to raise the default for the hard limit on the number of jobs, on the assumption that the memory limit will prevent the node becoming overloaded if an attempt is made to run many large jobs. Increasing the hard limit will allow more small jobs to be run than was previously the case by default. Of course, this change to the default will have no effect for customers who have already overridden the default in their config files. Original commit: elastic/x-pack-elasticsearch@9fed1d1237
59 lines
2.6 KiB
Plaintext
59 lines
2.6 KiB
Plaintext
[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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You do not need to configure any settings to use {ml}. It is enabled by default.
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[float]
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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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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` in `elasticsearch.yml`, the {ml} APIs are disabled on the node.
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Therefore the node cannot open jobs, start {dfeeds}, or receive transport (internal)
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communication requests related to {ml} APIs. It also affects all {kib} instances
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that connect to this {es} instance; you do not need to disable {ml} in those
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`kibana.yml` files. For more information about disabling {ml} in specific {kib}
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instances, see
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{kibana-ref}/ml-settings-kb.html[{kib} Machine Learning Settings].
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+
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IMPORTANT: If you want to use {ml} features in your cluster, you must have
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`xpack.ml.enabled` set to `true` on all master-eligible nodes. This is the
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default behavior.
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`xpack.ml.max_open_jobs`::
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The maximum number of jobs that can run on a node. Defaults to `20`.
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The maximum number of jobs is also constrained by memory usage, so fewer
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jobs than specified by this setting will run on a node if the estimated
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memory use of the jobs would be higher than allowed.
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`xpack.ml.max_machine_memory_percent`::
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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`::
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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 <<ml-apilimits>>.
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