[ML] Increase the default timeout for start trained model deployment (#92328)

A 30 second timeout is inline with the default value used in most ML APIs.

docs/changelog/92328.yaml

@@ -0,0 +1,5 @@
+pr: 92328
+summary: Increase the default timeout for the start trained model deployment API
+area: Machine Learning
+type: enhancement
+issues: []

docs/reference/ml/trained-models/apis/start-trained-model-deployment.asciidoc

@@ -28,19 +28,19 @@ in an ingest pipeline or directly in the <<infer-trained-model>> API.
 Scaling inference performance can be achieved by setting the parameters
 `number_of_allocations` and `threads_per_allocation`.
 
-Increasing `threads_per_allocation` means more threads are used when an 
-inference request is processed on a node. This can improve inference speed for 
+Increasing `threads_per_allocation` means more threads are used when an
+inference request is processed on a node. This can improve inference speed for
 certain models. It may also result in improvement to throughput.
 
-Increasing `number_of_allocations` means more threads are used to process 
-multiple inference requests in parallel resulting in throughput improvement. 
-Each model allocation uses a number of threads defined by 
+Increasing `number_of_allocations` means more threads are used to process
+multiple inference requests in parallel resulting in throughput improvement.
+Each model allocation uses a number of threads defined by
 `threads_per_allocation`.
 
-Model allocations are distributed across {ml} nodes. All allocations assigned to 
-a node share the same copy of the model in memory. To avoid thread 
-oversubscription which is detrimental to performance, model allocations are 
-distributed in such a way that the total number of used threads does not surpass 
+Model allocations are distributed across {ml} nodes. All allocations assigned to
+a node share the same copy of the model in memory. To avoid thread
+oversubscription which is detrimental to performance, model allocations are
+distributed in such a way that the total number of used threads does not surpass
 the node's allocated processors.
 
 [[start-trained-model-deployment-path-params]]
@@ -55,9 +55,9 @@ include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=model-id]
 
 `cache_size`::
 (Optional, <<byte-units,byte value>>)
-The inference cache size (in memory outside the JVM heap) per node for the 
-model. The default value is the size of the model as reported by the 
-`model_size_bytes` field in the <<get-trained-models-stats>>. To disable the 
+The inference cache size (in memory outside the JVM heap) per node for the
+model. The default value is the size of the model as reported by the
+`model_size_bytes` field in the <<get-trained-models-stats>>. To disable the
 cache, `0b` can be provided.
 
 `number_of_allocations`::
@@ -71,17 +71,17 @@ The priority of the deployment. The default value is `normal`.
 There are two priority settings:
 +
 --
-* `normal`: Use this for deployments in production. The deployment allocations 
+* `normal`: Use this for deployments in production. The deployment allocations
 are distributed so that node processors are not oversubscribed.
-* `low`: Use this for testing model functionality. The intention is that these 
-deployments are not sent a high volume of input. The deployment is required to 
-have a single allocation with just one thread. Low priority deployments may be 
-assigned on nodes that already utilize all their processors but will be given a 
-lower CPU priority than normal deployments. Low priority deployments may be 
+* `low`: Use this for testing model functionality. The intention is that these
+deployments are not sent a high volume of input. The deployment is required to
+have a single allocation with just one thread. Low priority deployments may be
+assigned on nodes that already utilize all their processors but will be given a
+lower CPU priority than normal deployments. Low priority deployments may be
 unassigned in order to satisfy more allocations of normal priority deployments.
 --
 
-WARNING: Heavy usage of low priority deployments may impact performance of 
+WARNING: Heavy usage of low priority deployments may impact performance of
 normal priority deployments.
 
 `queue_capacity`::
@@ -89,20 +89,20 @@ normal priority deployments.
 Controls how many inference requests are allowed in the queue at a time.
 Every machine learning node in the cluster where the model can be allocated
 has a queue of this size; when the number of requests exceeds the total value,
-new requests are rejected with a 429 error. Defaults to 1024. Max allowed value 
+new requests are rejected with a 429 error. Defaults to 1024. Max allowed value
 is 1000000.
 
 `threads_per_allocation`::
 (Optional, integer)
-Sets the number of threads used by each model allocation during inference. This 
-generally increases the speed per inference request. The inference process is a 
-compute-bound process; `threads_per_allocations` must not exceed the number of 
-available allocated processors per node. Defaults to 1. Must be a power of 2. 
+Sets the number of threads used by each model allocation during inference. This
+generally increases the speed per inference request. The inference process is a
+compute-bound process; `threads_per_allocations` must not exceed the number of
+available allocated processors per node. Defaults to 1. Must be a power of 2.
 Max allowed value is 32.
 
 `timeout`::
 (Optional, time)
-Controls the amount of time to wait for the model to deploy. Defaults to 20 
+Controls the amount of time to wait for the model to deploy. Defaults to 30
 seconds.
 
 `wait_for`::

x-pack/plugin/core/src/main/java/org/elasticsearch/xpack/core/ml/action/StartTrainedModelDeploymentAction.java

@@ -47,7 +47,7 @@ public class StartTrainedModelDeploymentAction extends ActionType<CreateTrainedM
     public static final StartTrainedModelDeploymentAction INSTANCE = new StartTrainedModelDeploymentAction();
     public static final String NAME = "cluster:admin/xpack/ml/trained_models/deployment/start";
 
-    public static final TimeValue DEFAULT_TIMEOUT = new TimeValue(20, TimeUnit.SECONDS);
+    public static final TimeValue DEFAULT_TIMEOUT = new TimeValue(30, TimeUnit.SECONDS);
 
     /**
      * This has been found to be approximately 300MB on linux by manual testing.

x-pack/plugin/core/src/test/java/org/elasticsearch/xpack/core/ml/action/StartTrainedModelDeploymentRequestTests.java

@@ -221,7 +221,7 @@ public class StartTrainedModelDeploymentRequestTests extends AbstractXContentSer
 
     public void testDefaults() {
         Request request = new Request(randomAlphaOfLength(10));
-        assertThat(request.getTimeout(), equalTo(TimeValue.timeValueSeconds(20)));
+        assertThat(request.getTimeout(), equalTo(TimeValue.timeValueSeconds(30)));
         assertThat(request.getWaitForState(), equalTo(AllocationStatus.State.STARTED));
         assertThat(request.getNumberOfAllocations(), equalTo(1));
         assertThat(request.getThreadsPerAllocation(), equalTo(1));