You 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
General machine learning settingsedit
node.roles: [ ml ]
mlto identify the node as a machine learning node. If you want to run machine learning jobs, there must be at least one machine learning node in your cluster.
If you set
node.roles, you must explicitly specify all the required roles for the node. To learn more, refer to Node.
On dedicated coordinating nodes or dedicated master nodes, do not set
It is strongly recommended that dedicated machine learning nodes also have the
remote_cluster_clientrole; otherwise, cross-cluster search fails when used in machine learning jobs or datafeeds. See Remote-eligible node.
- On dedicated coordinating nodes or dedicated master nodes, do not set the
(Static) The default value (
true) enables machine learning APIs on the node.
If you want to use machine learning features in your cluster, it is recommended that you use the default value for this setting on all nodes.
If set to
false, the machine learning APIs are disabled on the node. For example, the node cannot open jobs, start datafeeds, receive transport (internal) communication requests, or requests from clients (including Kibana) related to machine learning APIs.
(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
inferenceprocessor. The value can be a static byte sized value (such as
2gb) or a percentage of total allocated heap. Defaults to
40%. See also Machine learning circuit breaker settings.
(Static) The time to live (TTL) for trained models in
the inference model cache. The TTL is calculated from last access. Users of the
cache (such as the inference processor or inference aggregator) cache a model on
its first use and reset the TTL on every use. If a cached model is not accessed
for the duration of the TTL, it is flagged for eviction from the cache. If a
document is processed later, the model is again loaded into the cache. To update
this setting in Elasticsearch Service, see
Add Elasticsearch user settings. Defaults to
(Dynamic) The total number of
inferencetype processors allowed across all ingest pipelines. Once the limit is reached, adding an
inferenceprocessor to a pipeline is disallowed. Defaults to
(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. 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. When the operator privileges feature is enabled, this setting can be updated only by operator users. The minimum value is
5; the maximum value is
200. Defaults to
Do not configure this setting to a value higher than the amount of memory left over after running the Elasticsearch JVM unless you have enough swap space to accommodate it and have determined this is an appropriate configuration for a specialist use case. The maximum setting value is for the special case where it has been determined that using swap space for machine learning jobs is acceptable. The general best practice is to not use swap on Elasticsearch nodes.
(Dynamic) The maximum
model_memory_limitproperty value that can be set for any machine learning jobs in this cluster. If you try to create a job with a
model_memory_limitproperty value that is greater than this setting value, an error occurs. Existing jobs are not affected when you update this setting. If this setting is
0or unset, there is no maximum
model_memory_limitvalue. If there are no nodes that meet the memory requirements for a job, this lack of a maximum memory limit means it’s possible to create jobs that cannot be assigned to any available nodes. For more information about the
model_memory_limitproperty, see Create anomaly detection jobs or Create data frame analytics jobs. Defaults to
xpack.ml.max_model_memory_limitis not explicitly set then it will default to the largest
model_memory_limitthat could be assigned in the cluster.
(Dynamic) The maximum number of jobs that can run
simultaneously on a node. 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 minimum
1; the maximum value is
512. Defaults to
(Dynamic) The rate at which the nightly maintenance
task deletes expired model snapshots and results. The setting is a proxy to the
requests_per_secondparameter used in the delete by query requests and controls throttling. When the operator privileges feature is enabled, this setting can be updated only by operator users. Valid values must be greater than
0.0or equal to
-1.0means a default value is used. Defaults to
(Dynamic) The maximum number of jobs that can
concurrently be in the
openingstate on each node. Typically, jobs spend a small amount of time in this state before they move to
openstate. Jobs that must restore large models when they are opening spend more time in the
openingstate. When the operator privileges feature is enabled, this setting can be updated only by operator users. Defaults to
Advanced machine learning settingsedit
These settings are for advanced use cases; the default values are generally sufficient:
- (Dynamic) Reserved. When the operator privileges feature is enabled, this setting can be updated only by operator users.
(Dynamic) The maximum number of records that are
output per bucket. Defaults to
(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. 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 the
OPENINGstate until a new machine learning node is added to the cluster and the job is assigned to run on that node. When the operator privileges feature is enabled, this setting can be updated only by operator users. Defaults to
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.
The maximum node size for machine learning nodes in a deployment that supports automatic
cluster scaling. If you set it to the maximum possible size of future machine learning nodes,
when a machine learning job is assigned to a lazy node it can check (and fail quickly) when
scaling cannot support the size of the job. When the operator privileges feature is
enabled, this setting can be updated only by operator users. Defaults to
0b, which means it will be assumed that automatic cluster scaling can add arbitrarily large nodes to the cluster.
(Dynamic) The location of the machine learning model repository where the model artifact files are available in case of a model installation in a restricted or closed network.
xpack.ml.model_repositorycan be a string of a file location or an HTTP/HTTPS server. Example values are:
xpack.ml.model_repositoryis a file location, it must point to a subdirectory of the
configdirectory of Elasticsearch.
(Dynamic) The maximum number of times to retry bulk
indexing requests that fail while processing machine learning results. If the limit is
reached, the machine learning job stops processing data and its status is
failed. When the operator privileges feature is enabled, this setting can be updated only by operator users. The minimum value is
0; the maximum value is
50. Defaults to
(Dynamic) The connection timeout for machine learning processes
that run separately from the Elasticsearch JVM. When such processes are started they must
connect to the Elasticsearch JVM. If the process does not connect within the time period
specified by this setting then the process is assumed to have failed. When the
operator privileges feature is enabled, this setting can be updated only by operator
users. The minimum value is
5s. Defaults to
(Dynamic) If this setting is
xpack.ml.max_machine_memory_percentsetting is ignored. Instead, the maximum percentage of the machine’s memory that can be used for running machine learning analytics processes is calculated automatically and takes into account the total node size and the size of the JVM on the node. When the operator privileges feature is enabled, this setting can be updated only by operator users. The default value is
- If you do not have dedicated machine learning nodes (that is to say, the node has multiple roles), do not enable this setting. Its calculations assume that machine learning analytics are the main purpose of the node.
The calculation assumes that dedicated machine learning nodes have at least
256MBmemory reserved outside of the JVM. If you have tiny machine learning nodes in your cluster, you shouldn’t use this setting.
If this setting is
trueit also affects the default value for
xpack.ml.max_model_memory_limit. In this case
xpack.ml.max_model_memory_limitdefaults to the largest size that could be assigned in the current cluster.
Machine learning circuit breaker settingsedit
The relevant circuit breaker settings can be found in the Circuit Breakers page.
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