Merge pull request #3445 from sdgilley/sdg-freshness

freshness pass - how-to-manage-compute-sessions.md & how-to-use-labeled-datasets

Author: prmerger-automator[bot]

articles/machine-learning/how-to-manage-compute-sessions.md

@@ -9,15 +9,15 @@ ms.reviewer: lebaro
 ms.service: azure-machine-learning
 ms.subservice: compute
 ms.topic: how-to
-ms.date: 1/18/2023
+ms.date: 03/10/2025
 # Customer intent: As a data scientist, I want to manage the notebook and terminal sessions on my compute instance for optimal performance.
 ---
 
 # Manage notebook and terminal sessions
 
 Notebook and terminal sessions run on the compute and maintain your current working state.
 
-When you reopen a notebook, or reconnect to a terminal session, you can reconnect to the previous session state (including command history, execution history, and defined variables). However, too many active sessions may slow down the performance of your compute. With too many active sessions, you may find your terminal or notebook cell typing lags, or terminal or notebook command execution may feel slower than expected.
+When you reopen a notebook, or reconnect to a terminal session, you can reconnect to the previous session state (including command history, execution history, and defined variables). However, too many active sessions might slow down the performance of your compute. With too many active sessions, you may find your terminal or notebook cell typing lags, or terminal or notebook command execution may feel slower than expected.
 
 Use the session management panel in Azure Machine Learning studio to help you manage your active sessions and optimize the performance of your compute instance. Navigate to this session management panel from the compute toolbar of either a terminal tab or a notebook tab.
 
@@ -30,7 +30,7 @@ Use the session management panel in Azure Machine Learning studio to help you ma
 
 In the session management panel, select a linked notebook name in the notebook sessions section to reopen a notebook with its previous state.
 
-Notebook sessions are kept active when you close a notebook tab in the Azure Machine Learning studio. So, when you reopen a notebook you'll have access to previously defined variables and execution state - in this case, you're benefitting from the active notebook session.
+Notebook sessions are kept active when you close a notebook tab in the Azure Machine Learning studio. So, when you reopen a notebook you have access to previously defined variables and execution state - in this case, you're benefitting from the active notebook session.
 
 However, keeping too many active notebook sessions can slow down the performance of your compute. So, you should use the session management panel to shut down any notebook sessions you no longer need.
 

articles/machine-learning/v1/how-to-use-labeled-dataset.md

@@ -9,13 +9,13 @@ ms.service: azure-machine-learning
 ms.subservice: mldata
 ms.topic: how-to
 ms.custom: UpdateFrequency5, data4ml, sdkv1
-ms.date: 08/17/2022
+ms.date: 03/10/2025
 #Customer intent: As an experienced Python developer, I need to export my data labels and use them for machine learning tasks.
 ---
 
 # Create and explore Azure Machine Learning dataset with labels
 
-In this article, you'll learn how to export the data labels from an Azure Machine Learning data labeling project and load them into popular formats such as, a pandas dataframe for data exploration. 
+In this article, you learn how to export the data labels from an Azure Machine Learning data labeling project and load them into popular formats, such as a pandas dataframe for data exploration. 
 
 ## What are datasets with labels 
 
@@ -41,7 +41,7 @@ Use the **Export** button on the **Project details** page of your labeling proje
  The COCO file is created in the default blob store of the Azure Machine Learning workspace in a folder within *export/coco*. 
 
 >[!NOTE]
->In object detection projects, the exported "bbox": [x,y,width,height]" values in COCO file are normalized. They are scaled to 1. Example : a bounding box at (10, 10) location, with 30 pixels width , 60 pixels height, in a 640x480 pixel image will be annotated as (0.015625. 0.02083, 0.046875, 0.125). Since the coordintes are normalized, it will show as '0.0' as "width" and "height" for all images. The actual width and height can be obtained using Python library like OpenCV  or Pillow(PIL).
+>In object detection projects, the exported `bbox: [x,y,width,height]` values in COCO file are normalized. They're scaled to 1. For example, a bounding box at (10, 10) location, with 30 pixels width, 60 pixels height, in a 640x480 pixel image is annotated as (0.015625. 0.02083, 0.046875, 0.125). Since the coordinates are normalized, it shows as '0.0' as "width" and "height" for all images. The actual width and height can be obtained using Python library like OpenCV  or Pillow(PIL).
 
 ### Azure Machine Learning dataset
 
@@ -50,11 +50,11 @@ You can access the exported Azure Machine Learning dataset in the **Datasets** s
 ![Exported dataset](../media/how-to-create-labeling-projects/exported-dataset.png)
 
 > [!TIP]
-> Once you have exported your labeled data to an Azure Machine Learning dataset, you can use AutoML to build computer vision models trained on your labeled data. Learn more at [Set up AutoML to train computer vision models with Python](../how-to-auto-train-image-models.md)
+> Once you export your labeled data to an Azure Machine Learning dataset, you can use AutoML to build computer vision models trained on your labeled data. Learn more at [Set up AutoML to train computer vision models with Python](../how-to-auto-train-image-models.md)
 
 ## Explore labeled datasets via pandas dataframe
 
-Load your labeled datasets into a pandas dataframe to leverage popular open-source libraries for data exploration with the [`to_pandas_dataframe()`](/python/api/azureml-core/azureml.data.tabulardataset#to-pandas-dataframe-on-error--null---out-of-range-datetime--null--) method from the `azureml-dataprep` class. 
+Load your labeled datasets into a pandas dataframe to use popular open-source libraries for data exploration with the [`to_pandas_dataframe()`](/python/api/azureml-core/azureml.data.tabulardataset#to-pandas-dataframe-on-error--null---out-of-range-datetime--null--) method from the `azureml-dataprep` class. 
 
 Install the class with the following shell command: 
 
@@ -75,12 +75,6 @@ from azureml.core import Dataset, Workspace
 animal_labels = Dataset.get_by_name(workspace, 'animal_labels')
 animal_pd = animal_labels.to_pandas_dataframe()
 
-import matplotlib.pyplot as plt
-import matplotlib.image as mpimg
-
-#read images from dataset
-img = mpimg.imread(animal_pd['image_url'].iloc(0).open())
-imgplot = plt.imshow(img)
 ```
 
 ## Next steps