Merge pull request #219606 from rjaincc/rjaincc/WI-2056968-updating-doc-max-concurrent-trials

Updating documentation for max_concurrent_trials

Author: prmerger-automator[bot]

articles/machine-learning/how-to-auto-train-image-models.md

@@ -369,8 +369,8 @@ You can control the resources spent on your AutoML Image training job by specify
 
 Parameter | Detail
 -----|----
-`max_trials` |  Parameter for maximum number of configurations to sweep. Must be an integer between 1 and 1000. When exploring just the default hyperparameters for a given model algorithm, set this parameter to 1. default value is 1.
-`max_concurrent_trials`| Maximum number of runs that can run concurrently. If not specified, all runs launch in parallel. If specified, must be an integer between 1 and 100.  <br><br> **NOTE:** The number of concurrent runs is gated on the resources available in the specified compute target. Ensure that the compute target has the available resources for the desired concurrency. default value is 1.
+`max_trials` |  Parameter for maximum number of configurations to sweep. Must be an integer between 1 and 1000. When exploring just the default hyperparameters for a given model algorithm, set this parameter to 1. The default value is 1.
+`max_concurrent_trials`| Maximum number of runs that can run concurrently. If specified, must be an integer between 1 and 100.  The default value is 1. <br><br> **NOTE:** <li> The number of concurrent runs is gated on the resources available in the specified compute target. Ensure that the compute target has the available resources for the desired concurrency.  <li> `max_concurrent_trials` is capped at `max_trials` internally. For example, if user sets `max_concurrent_trials=4`, `max_trials=2`, values would be internally updated as `max_concurrent_trials=2`, `max_trials=2`.
 `timeout_minutes`| The amount of time in minutes before the experiment terminates. If none specified, default experiment timeout_minutes is seven days (maximum 60 days)
 
 # [Azure CLI](#tab/cli)

articles/machine-learning/how-to-auto-train-nlp-models.md

@@ -505,8 +505,8 @@ You can control the resources spent on your hyperparameter sweep by specifying t
 
 Parameter | Detail
 -----|----
-`max_trials` |  Required parameter for maximum number of configurations to sweep. Must be an integer between 1 and 1000. When exploring just the default hyperparameters for a given model algorithm, set this parameter to 1.
-`max_concurrent_trials`| Maximum number of runs that can run concurrently. If not specified, all runs launch in parallel. If specified, must be an integer between 1 and 100.  <br><br> **NOTE:** The number of concurrent runs is gated on the resources available in the specified compute target. Ensure that the compute target has the available resources for the desired concurrency.
+`max_trials` |  Parameter for maximum number of configurations to sweep. Must be an integer between 1 and 1000. When exploring just the default hyperparameters for a given model algorithm, set this parameter to 1. The default value is 1.
+`max_concurrent_trials`| Maximum number of runs that can run concurrently. If specified, must be an integer between 1 and 100.  The default value is 1. <br><br> **NOTE:** <li> The number of concurrent runs is gated on the resources available in the specified compute target. Ensure that the compute target has the available resources for the desired concurrency.  <li> `max_concurrent_trials` is capped at `max_trials` internally. For example, if user sets `max_concurrent_trials=4`, `max_trials=2`, values would be internally updated as `max_concurrent_trials=2`, `max_trials=2`.
 
 You can configure all the sweep related parameters as shown in the example below.