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

[role="xpack"]
[[ml-settings]]
=== Machine learning settings in Elasticsearch
++++
<titleabbrev>Machine learning settings</titleabbrev>
++++

[[ml-settings-description]]
// tag::ml-settings-description-tag[]
You do not need to configure any settings to use {ml}. It is enabled by default.

IMPORTANT: {ml-cap} uses SSE4.2 instructions, so it works only on machines whose
CPUs {wikipedia}/SSE4#Supporting_CPUs[support] SSE4.2. If you run {es} on older
hardware, you must disable {ml} (by setting `xpack.ml.enabled` to `false`).

// end::ml-settings-description-tag[]

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

`node.roles: [ ml ]`::
(<<static-cluster-setting,Static>>) Set `node.roles` to contain `ml` to identify
the node as a _{ml} node_ that is capable of running jobs. Every node is a {ml}
node by default.
+
If you use the `node.roles` setting, then all required roles must be explicitly  
set. Consult <<modules-node>> to learn more.
+
IMPORTANT: On dedicated coordinating nodes or dedicated master nodes, do not set
the `ml` role.


`xpack.ml.enabled`::
(<<static-cluster-setting,Static>>) Set to `true` (default) to enable {ml} APIs
on the node.
+
If set to `false`, 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. If the node is a coordinating node, {ml} requests
from clients (including {kib}) also fail. For more information about disabling
{ml} in specific {kib} instances, see
{kibana-ref}/ml-settings-kb.html[{kib} {ml} settings].
+
IMPORTANT: If you want to use {ml-features} in your cluster, it is recommended
that you set `xpack.ml.enabled` to `true` on all nodes. This is the default 
behavior. At a minimum, it must be enabled on all master-eligible nodes. If you 
want to use {ml-features} in clients or {kib}, it must also be enabled on all 
coordinating nodes.

`xpack.ml.inference_model.cache_size`::
(<<static-cluster-setting,Static>>) The maximum inference cache size allowed.
The inference cache exists in the JVM heap on each ingest node. The cache
affords faster processing times for the `inference` processor. The value can be
a static byte sized value (i.e. "2gb") or a percentage of total allocated heap.
The default is "40%". See also <<model-inference-circuit-breaker>>.

[[xpack-interference-model-ttl]]
// tag::interference-model-ttl-tag[]
`xpack.ml.inference_model.time_to_live` {ess-icon}::
(<<static-cluster-setting,Static>>) The time to live (TTL) for models in the
inference model cache. The TTL is calculated from last access. The `inference`
processor attempts to load the model from cache. If the `inference` processor
does not receive any documents for the duration of the TTL, the referenced model
is flagged for eviction from the cache. If a document is processed later, the
model is again loaded into the cache. Defaults to `5m`.
// end::interference-model-ttl-tag[]

`xpack.ml.max_inference_processors`::
(<<cluster-update-settings,Dynamic>>) The total number of `inference` type
processors allowed across all ingest pipelines. Once the limit is reached,
adding an `inference` processor to a pipeline is disallowed. Defaults to `50`.

`xpack.ml.max_machine_memory_percent`::
(<<cluster-update-settings,Dynamic>>) 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 are not 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`::
(<<cluster-update-settings,Dynamic>>) 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
<<put-analysislimits>>.

[[xpack.ml.max_open_jobs]]
`xpack.ml.max_open_jobs`::
(<<cluster-update-settings,Dynamic>>) The maximum number of jobs that can run 
simultaneously on a node. Defaults to `20`. In this context, jobs include both
{anomaly-jobs} and {dfanalytics-jobs}. The maximum number of jobs is also
constrained by memory usage. Thus if the estimated memory usage of the jobs
would be higher than allowed, fewer jobs will run on a node. Prior to version
7.1, this setting was a per-node non-dynamic setting. It became a cluster-wide
dynamic setting in version 7.1. As a result, changes to its value after node
startup are used only after every node in the cluster is running version 7.1 or
higher. The maximum permitted value is `512`.

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

[discrete]
[[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 {ml} nodes.
Useful in situations where {ml} nodes are not desired until the first {ml} job
opens. It defaults to `0` and has a maximum acceptable value of `3`. If the
current number of {ml} nodes is greater than or equal to this setting, it is
assumed that there are no more lazy nodes available as the desired number
of nodes have already been provisioned. If a job is opened and this setting has
a value greater than zero and there are no nodes that can accept the job, the
job stays in the `OPENING` state until a new {ml} node is added to the cluster
and the job is assigned to run on that node.
+
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.

`xpack.ml.process_connect_timeout`::
(<<cluster-update-settings,Dynamic>>) The connection timeout for {ml} processes
that run separately from the {es} JVM. Defaults to `10s`. Some {ml} processing
is done by processes that run separately to the {es} JVM. When such processes
are started they must connect to the {es} JVM. If such a process does not
connect within the time period specified by this setting then the process is
assumed to have failed. Defaults to `10s`. The minimum value for this setting is
`5s`.

[discrete]
[[model-inference-circuit-breaker]]
==== {ml-cap} circuit breaker settings

`breaker.model_inference.limit`::
(<<cluster-update-settings,Dynamic>>) Limit for the model inference breaker,
which defaults to 50% of the JVM heap. If the parent circuit breaker is less
than 50% of the JVM heap, it is bound to that limit instead. See
<<circuit-breaker>>.

`breaker.model_inference.overhead`::
(<<cluster-update-settings,Dynamic>>) A constant that all accounting estimations
are multiplied by to determine a final estimation. Defaults to 1. See
<<circuit-breaker>>.

`breaker.model_inference.type`::
(<<static-cluster-setting,Static>>) The underlying type of the circuit breaker.
There are two valid options: `noop` and `memory`. `noop` means the circuit
breaker does nothing to prevent too much memory usage. `memory` means the
circuit breaker tracks the memory used by inference models and can potentially
break and prevent `OutOfMemory` errors. The default is `memory`.