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Get machine learning memory stats API

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Get machine learning memory stats API

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Returns information on how machine learning is using memory.

Request

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GET _ml/memory/_stats
GET _ml/memory/<node_id>/_stats

Prerequisites

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Requires the monitor_ml cluster privilege. This privilege is included in the machine_learning_user built-in role.

Description

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Get information about how machine learning jobs and trained models are using memory, on each node, both within the JVM heap, and natively, outside of the JVM.

Path parameters

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<node_id>: (Optional, string) The names of particular nodes in the cluster to target. For example, nodeId1,nodeId2 or ml:true. For node selection options, see Node specification.

Query parameters

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human: Specify this query parameter to include the fields with units in the response. Otherwise only the _in_bytes sizes are returned in the response.
master_timeout: (Optional, time units) Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error. Defaults to 30s.
timeout: (Optional, time units) Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error. Defaults to 30s.

Response body

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_nodes

(object) Contains statistics about the number of nodes selected by the request.

Properties of _nodes

failed: (integer) Number of nodes that rejected the request or failed to respond. If this value is not 0, a reason for the rejection or failure is included in the response.
successful: (integer) Number of nodes that responded successfully to the request.
total: (integer) Total number of nodes selected by the request.

cluster_name

(string) Name of the cluster. Based on the cluster.name setting.

nodes

(object) Contains statistics for the nodes selected by the request.

Properties of nodes

<node_id>

(object) Contains statistics for the node.

Properties of <node_id>

attributes

(object) Lists node attributes such as ml.machine_memory or ml.max_open_jobs settings.

ephemeral_id

(string) The ephemeral ID of the node.

jvm

(object) Contains Java Virtual Machine (JVM) statistics for the node.

Properties of jvm

heap_max: (byte value) Maximum amount of memory available for use by the heap.
heap_max_in_bytes: (integer) Maximum amount of memory, in bytes, available for use by the heap.
java_inference: (byte value) Amount of Java heap currently being used for caching inference models.
java_inference_in_bytes: (integer) Amount of Java heap, in bytes, currently being used for caching inference models.
java_inference_max: (byte value) Maximum amount of Java heap to be used for caching inference models.
java_inference_max_in_bytes: (integer) Maximum amount of Java heap, in bytes, to be used for caching inference models.

mem

(object) Contains statistics about memory usage for the node.

Properties of mem

adjusted_total

(byte value) If the amount of physical memory has been overridden using the es.total_memory_bytes system property then this reports the overridden value. Otherwise it reports the same value as total.

adjusted_total_in_bytes

(integer) If the amount of physical memory has been overridden using the es.total_memory_bytes system property then this reports the overridden value in bytes. Otherwise it reports the same value as total_in_bytes.

ml

(object) Contains statistics about machine learning use of native memory on the node.

Properties of ml

anomaly_detectors: (byte value) Amount of native memory set aside for anomaly detection jobs.
anomaly_detectors_in_bytes: (integer) Amount of native memory, in bytes, set aside for anomaly detection jobs.
data_frame_analytics: (byte value) Amount of native memory set aside for data frame analytics jobs.
data_frame_analytics_in_bytes: (integer) Amount of native memory, in bytes, set aside for data frame analytics jobs.
max: (byte value) Maximum amount of native memory (separate to the JVM heap) that may be used by machine learning native processes.
max_in_bytes: (integer) Maximum amount of native memory (separate to the JVM heap), in bytes, that may be used by machine learning native processes.
native_code_overhead: (byte value) Amount of native memory set aside for loading machine learning native code shared libraries.
native_code_overhead_in_bytes: (integer) Amount of native memory, in bytes, set aside for loading machine learning native code shared libraries.
native_inference: (byte value) Amount of native memory set aside for trained models that have a PyTorch model_type.
native_inference_in_bytes: (integer) Amount of native memory, in bytes, set aside for trained models that have a PyTorch model_type.

total

(byte value) Total amount of physical memory.

total_in_bytes

(integer) Total amount of physical memory in bytes.

name

(string) Human-readable identifier for the node. Based on the Node name setting setting.

roles

(array of strings) Roles assigned to the node. See Node.

transport_address

(string) The host and port where transport HTTP connections are accepted.

Examples

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response = client.ml.get_memory_stats(
  human: true
)
puts response

GET _ml/memory/_stats?human

This is a possible response:

{
  "_nodes": {
    "total": 1,
    "successful": 1,
    "failed": 0
  },
  "cluster_name": "my_cluster",
  "nodes": {
    "pQHNt5rXTTWNvUgOrdynKg": {
      "name": "node-0",
      "ephemeral_id": "ITZ6WGZnSqqeT_unfit2SQ",
      "transport_address": "127.0.0.1:9300",
      "attributes": {
        "ml.machine_memory": "68719476736",
        "ml.max_jvm_size": "536870912"
      },
      "roles": [
        "data",
        "data_cold",
        "data_content",
        "data_frozen",
        "data_hot",
        "data_warm",
        "ingest",
        "master",
        "ml",
        "remote_cluster_client",
        "transform"
      ],
      "mem": {
        "total": "64gb",
        "total_in_bytes": 68719476736,
        "adjusted_total": "64gb",
        "adjusted_total_in_bytes": 68719476736,
        "ml": {
          "max": "19.1gb",
          "max_in_bytes": 20615843020,
          "native_code_overhead": "0b",
          "native_code_overhead_in_bytes": 0,
          "anomaly_detectors": "0b",
          "anomaly_detectors_in_bytes": 0,
          "data_frame_analytics": "0b",
          "data_frame_analytics_in_bytes": 0,
          "native_inference": "0b",
          "native_inference_in_bytes": 0
        }
      },
      "jvm": {
        "heap_max": "512mb",
        "heap_max_in_bytes": 536870912,
        "java_inference_max": "204.7mb",
        "java_inference_max_in_bytes": 214748364,
        "java_inference": "0b",
        "java_inference_in_bytes": 0
      }
    }
  }
}

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