Merge pull request #125691 from changeworld/patch-103

Fix typo

Author: ttorble

articles/synapse-analytics/machine-learning/tutorial-horovod-pytorch.md

@@ -30,7 +30,7 @@ Within Azure Synapse Analytics, users can quickly get started with Horovod using
 
 ## Configure the Apache Spark session
 
-At the start of the session, we need to configure a few Apache Spark settings. In most cases, we only need to set the numExecutors and spark.rapids.memory.gpu.reserve. For large models, users may also need to configure the  ```spark.kryoserializer.buffer.max``` setting. For Tensorflow models, users need to set the ```spark.executorEnv.TF_FORCE_GPU_ALLOW_GROWTH``` to be true.
+At the start of the session, we need to configure a few Apache Spark settings. In most cases, we only need to set the numExecutors and spark.rapids.memory.gpu.reserve. For large models, users may also need to configure the  ```spark.kryoserializer.buffer.max``` setting. For TensorFlow models, users need to set the ```spark.executorEnv.TF_FORCE_GPU_ALLOW_GROWTH``` to be true.
 
 In the example, you can see how the Spark configurations can be passed with the ```%%configure``` command. The detailed meaning of each parameter is explained in the [Apache Spark configuration documentation](https://spark.apache.org/docs/latest/configuration.html). The values provided are the suggested, best practice values for Azure Synapse GPU-large pools.