elasticsearch/docs/reference/settings/ml-settings.asciidoc

[role="xpack"]
[[ml-settings]]
=== Machine learning settings in Elasticsearch
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<titleabbrev>Machine learning settings</titleabbrev>
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You do not need to configure any settings to use {ml}. It is enabled by default.

All of these settings can be added to the `elasticsearch.yml` configuration file. 
The dynamic settings can also be updated across a cluster with the 
<<cluster-update-settings,cluster update settings API>>.

TIP: Dynamic settings take precedence over settings in the `elasticsearch.yml` 
file.

[float]
[[general-ml-settings]]
==== General machine learning settings

`node.ml`::
Set to `true` (default) to identify the node as a _machine learning node_. +
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If set to `false` in `elasticsearch.yml`, the node cannot run jobs. If set to
`true` but `xpack.ml.enabled` is set to `false`, the `node.ml` setting is
ignored and the node cannot run jobs. If you want to run jobs, there must be at
least one machine learning node in your cluster. +
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IMPORTANT: On dedicated coordinating nodes or dedicated master nodes, disable
the `node.ml` role.

`xpack.ml.enabled`::
Set to `true` (default) to enable {ml} on the node. +
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If set to `false` in `elasticsearch.yml`, the {ml} APIs are disabled on the node.
Therefore the node cannot open jobs, start {dfeeds}, or receive transport (internal)
communication requests related to {ml} APIs. It also affects all {kib} instances
that connect to this {es} instance; you do not need to disable {ml} in those
`kibana.yml` files. For more information about disabling {ml} in specific {kib}
instances, see
{kibana-ref}/ml-settings-kb.html[{kib} Machine Learning Settings].
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IMPORTANT: If you want to use {ml} features in your cluster, you must have
`xpack.ml.enabled` set to `true` on all master-eligible nodes. This is the
default behavior.

`xpack.ml.max_machine_memory_percent`::
The maximum percentage of the machine's memory that {ml} may use for running
analytics processes. (These processes are separate to the {es} JVM.) Defaults to
`30` percent. The limit is based on the total memory of the machine, not current
free memory. Jobs will not be allocated to a node if doing so would cause the
estimated memory use of {ml} jobs to exceed the limit.

`xpack.ml.max_model_memory_limit`::
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 <<ml-apilimits>>.

`xpack.ml.max_open_jobs`::
The maximum number of jobs that can run on a node. Defaults to `20`.
The maximum number of jobs is also constrained by memory usage, so fewer
jobs than specified by this setting will run on a node if the estimated
memory use of the jobs would be higher than allowed.

`xpack.ml.node_concurrent_job_allocations`::
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`.

[float]
[[advanced-ml-settings]]
==== Advanced machine learning settings

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

`xpack.ml.enable_config_migration` (<<cluster-update-settings,Dynamic>>)::
Reserved.

`xpack.ml.max_anomaly_records` (<<cluster-update-settings,Dynamic>>)::
The maximum number of records that are output per bucket. The default value is 
`500`.

`xpack.ml.max_lazy_ml_nodes` (<<cluster-update-settings,Dynamic>>)::
The number of lazily spun up Machine Learning nodes. Useful in situations
where ML nodes are not desired until the first Machine Learning Job
is opened. It defaults to `0` and has a maximum acceptable value of `3`.
If the current number of ML nodes is `>=` than this setting, then it is
assumed that there are no more lazy nodes available as the desired number
of nodes have already been provisioned. When a job is opened with this
setting set at `>0` and there are no nodes that can accept the job, then
the job will stay in the `OPENING` state until a new ML node is added to the
cluster and the job is assigned to run on that node.
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IMPORTANT: This setting assumes some external process is capable of adding ML nodes
to the cluster. This setting is only useful when used in conjunction with
such an external process.