Machine learning settings in Elasticsearch
editMachine learning settings in Elasticsearch
editYou do not need to configure any settings to use machine learning. It is enabled by default.
Machine learning uses SSE4.2 instructions, so it works only on machines whose
CPUs support SSE4.2. If you run Elasticsearch on older
hardware, you must disable machine learning (by setting xpack.ml.enabled
to false
).
General machine learning settings
edit-
node.roles: [ ml ]
-
(Static) Set
node.roles
to containml
to identify the node as a machine learning node that is capable of running jobs. Every node is a machine learning node by default.If you use the
node.roles
setting, then all required roles must be explicitly set. Consult Node to learn more.On dedicated coordinating nodes or dedicated master nodes, do not set the
ml
role. -
xpack.ml.enabled
-
(Static) Set to
true
(default) to enable machine learning APIs on the node.If set to
false
, the machine learning APIs are disabled on the node. Therefore the node cannot open jobs, start datafeeds, or receive transport (internal) communication requests related to machine learning APIs. If the node is a coordinating node, machine learning requests from clients (including Kibana) also fail. For more information about disabling machine learning in specific Kibana instances, see Kibana machine learning settings.If you want to use machine learning features in your cluster, it is recommended that you set
xpack.ml.enabled
totrue
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 machine learning features in clients or Kibana, it must also be enabled on all coordinating nodes. -
xpack.ml.inference_model.cache_size
-
(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 Machine learning circuit breaker settings.
-
xpack.ml.inference_model.time_to_live
-
(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 theinference
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 to5m
. -
xpack.ml.max_inference_processors
-
(Dynamic) The total number of
inference
type processors allowed across all ingest pipelines. Once the limit is reached, adding aninference
processor to a pipeline is disallowed. Defaults to50
. -
xpack.ml.max_machine_memory_percent
-
(Dynamic) The maximum percentage of the machine’s
memory that machine learning may use for running analytics processes. (These processes are
separate to the Elasticsearch 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 machine learning jobs to exceed the limit. -
xpack.ml.max_model_memory_limit
-
(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 amodel_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 themodel_memory_limit
property, see `analysis_limits`.
-
xpack.ml.max_open_jobs
-
(Dynamic) The maximum number of jobs that can run
simultaneously on a node. Defaults to
20
. In this context, jobs include both anomaly detection jobs and data frame analytics 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 is512
. -
xpack.ml.nightly_maintenance_requests_per_second
-
(Dynamic) The rate at which the nightly maintenance
task deletes expired model snapshots and results. The setting is a proxy to the
requests_per_second parameter used in the
delete by query requests and controls throttling. Valid values must be greater
than
0.0
or equal to-1.0
where-1.0
means a default value is used. Defaults to-1.0
-
xpack.ml.node_concurrent_job_allocations
-
(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 toopen
state. Jobs that must restore large models when they are opening spend more time in theopening
state. Defaults to2
.
Advanced machine learning settings
editThese settings are for advanced use cases; the default values are generally sufficient:
-
xpack.ml.enable_config_migration
- (Dynamic) Reserved.
-
xpack.ml.max_anomaly_records
-
(Dynamic) The maximum number of records that are
output per bucket. The default value is
500
. -
xpack.ml.max_lazy_ml_nodes
-
(Dynamic) The number of lazily spun up machine learning nodes. Useful in situations where machine learning nodes are not desired until the first machine learning job opens. It defaults to
0
and has a maximum acceptable value of3
. If the current number of machine learning 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 theOPENING
state until a new machine learning node is added to the cluster and the job is assigned to run on that node.This setting assumes some external process is capable of adding machine learning nodes to the cluster. This setting is only useful when used in conjunction with such an external process.
-
xpack.ml.process_connect_timeout
-
(Dynamic) The connection timeout for machine learning processes
that run separately from the Elasticsearch JVM. Defaults to
10s
. Some machine learning processing is done by processes that run separately to the Elasticsearch JVM. When such processes are started they must connect to the Elasticsearch 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 to10s
. The minimum value for this setting is5s
.
Machine learning circuit breaker settings
edit-
breaker.model_inference.limit
- (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 settings.
-
breaker.model_inference.overhead
- (Dynamic) A constant that all accounting estimations are multiplied by to determine a final estimation. Defaults to 1. See Circuit breaker settings.
-
breaker.model_inference.type
-
(Static) The underlying type of the circuit breaker.
There are two valid options:
noop
andmemory
.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 preventOutOfMemory
errors. The default ismemory
.