Acrolinx

Author: whhender

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

@@ -21,7 +21,7 @@ Within Azure Synapse Analytics, users can quickly get started with Horovod using
 - Create a GPU-enabled Apache Spark pool in your Azure Synapse Analytics workspace. For details, see [Create a GPU-enabled Apache Spark pool in Azure Synapse](../spark/apache-spark-gpu-concept.md). For this tutorial, we suggest using the GPU-Large cluster size with 3 nodes.
 
 > [!NOTE]
-> The Preview for Azure Synapse GPU-enabled pools has now been deprecated.
+> The Preview for Azure Synapse GPU-enabled pools is deprecated.
 
 ## Configure the Apache Spark session
 
@@ -46,7 +46,7 @@ In the example, you can see how the Spark configurations can be passed with the
 }
 ```
 
-For this tutorial, we will use the following configurations:
+For this tutorial, we'll use the following configurations:
 
 ```python
 
@@ -97,7 +97,7 @@ from azure.synapse.ml.horovodutils import AdlsStore
 
 ## Connect to alternative storage account
 
-We need the Azure Data Lake Storage (ADLS) account for storing intermediate and model data. If you are using an alternative storage account, be sure to set up the [linked service](../../data-factory/concepts-linked-services.md) to automatically authenticate and read from the account. In addition, you need to modify the following properties: ```remote_url```, ```account_name```, and ```linked_service_name```.
+We need the Azure Data Lake Storage (ADLS) account for storing intermediate and model data. If you're using an alternative storage account, be sure to set up the [linked service](../../data-factory/concepts-linked-services.md) to automatically authenticate and read from the account. In addition, you need to modify the following properties: ```remote_url```, ```account_name```, and ```linked_service_name```.
 
 ```python
 num_proc = 3  # equal to numExecutors
@@ -127,7 +127,7 @@ print(adls_store_path)
 
 ## Prepare dataset
 
-Next, we will prepare the dataset for training. In this tutorial, we will use the MNIST dataset from [Azure Open Datasets](/azure/open-datasets/dataset-mnist?tabs=azureml-opendatasets).
+Next, we'll prepare the dataset for training. In this tutorial, we'll use the MNIST dataset from [Azure Open Datasets](/azure/open-datasets/dataset-mnist?tabs=azureml-opendatasets).
 
 ```python
 # Initialize SparkSession
@@ -148,7 +148,7 @@ mnist_df.head()
 
 ## Process data with Apache Spark
 
-Now, we will create an Apache Spark dataframe. This dataframe will be used with the ```HorovodEstimator``` for training.
+Now, we'll create an Apache Spark dataframe. This dataframe will be used with the ```HorovodEstimator``` for training.
 
 ```python
 # Create Spark DataFrame for training
@@ -167,7 +167,7 @@ train_df.count()
 
 ## Define DNN model
 
-Once we are finished processing our dataset, we can now define our PyTorch model. The same code could also be used to train a single-node PyTorch model.
+Once we're finished processing our dataset, we can now define our PyTorch model. The same code could also be used to train a single-node PyTorch model.
 
 ```python
 # Define the PyTorch model without any Horovod-specific parameters