update samples from Release-166 as a part of SDK release

Author: amlrelsa-ms

how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env.yml

@@ -9,7 +9,6 @@ dependencies:
 - PyJWT < 2.0.0
 - numpy==1.22.3
 - pywin32==227
-- cryptography<37.0.0
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.

how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env_mac.yml

@@ -11,7 +11,6 @@ dependencies:
 - urllib3==1.26.7
 - PyJWT < 2.0.0
 - numpy>=1.21.6,<=1.22.3
-- cryptography<37.0.0
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.

how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/pytorch_train.py

@@ -166,7 +166,7 @@ def download_data():
     from zipfile import ZipFile
     # download data
     data_file = './fowl_data.zip'
-    download_url = 'https://azureopendatastorage.blob.core.windows.net/testpublic/temp/fowl_data.zip'
+    download_url = 'https://azuremlexamples.blob.core.windows.net/datasets/fowl_data.zip'
     urllib.request.urlretrieve(download_url, filename=data_file)
 
     # extract files

how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb

@@ -176,7 +176,7 @@
       "metadata": {},
       "source": [
         "### Download training data\n",
-        "The dataset we will use (located on a public blob [here](https://azureopendatastorage.blob.core.windows.net/testpublic/temp/fowl_data.zip) as a zip file) consists of about 120 training images each for turkeys and chickens, with 100 validation images for each class. The images are a subset of the [Open Images v5 Dataset](https://storage.googleapis.com/openimages/web/index.html). We will download and extract the dataset as part of our training script `pytorch_train.py`"
+        "The dataset we will use (located on a public blob [here](https://azuremlexamples.blob.core.windows.net/datasets/fowl_data.zip) as a zip file) consists of about 120 training images each for turkeys and chickens, with 100 validation images for each class. The images are a subset of the [Open Images v5 Dataset](https://storage.googleapis.com/openimages/web/index.html). We will download and extract the dataset as part of our training script `pytorch_train.py`"
       ]
     },
     {
@@ -260,15 +260,14 @@
         "\n",
         "channels:\n",
         "- conda-forge\n",
+        "- pytorch\n",
         "dependencies:\n",
-        "- python=3.6.2\n",
+        "- python=3.8.12\n",
         "- pip=21.3.1\n",
+        "- pytorch::pytorch==1.8.1\n",
+        "- pytorch::torchvision==0.9.1\n",
         "- pip:\n",
-        "  - azureml-defaults==1.43.0\n",
-        "  - torch==1.6.0\n",
-        "  - torchvision==0.7.0\n",
-        "  - future==0.17.1\n",
-        "  - pillow"
+        "  - azureml-defaults"
       ]
     },
     {

how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.ipynb

@@ -160,7 +160,7 @@
       "source": [
         "from azureml.core import Dataset\n",
         "\n",
-        "web_paths = ['https://azureopendatastorage.blob.core.windows.net/testpublic/text8.zip']\n",
+        "web_paths = ['https://azuremlexamples.blob.core.windows.net/datasets/text8.zip']\n",
         "dataset = Dataset.File.from_files(path=web_paths)"
       ]
     },