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

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

You do not need to configure any settings to use {ml}. It is enabled by default.

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

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_. +
+
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. +
+
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.
+
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`::
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.ml.inference_model.time_to_live`::
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`.

`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 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` (<<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`.

[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.
+
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`.

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

`breaker.model_inference.limit` (<<cluster-update-settings,Dynamic>>)
Limit for model inference breaker, defaults to 50% of JVM heap.
If the parent circuit breaker is less than 50% of 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 with to determine
a final estimation. Defaults to 1.
See <<circuit-breaker>>.

`breaker.model_inference.type`
The underlying type of the circuit breaker. There are two valid options:
`noop`, meaning the circuit breaker does nothing to prevent too much memory usage,
`memory`, meaning the circuit breaker tracks the memory used by inference models and
could potentially break and prevent OutOfMemory errors.
The default is `memory`.