update samples from Release-122 as a part of SDK release

Co-authored-by: amlrelsa-ms <amlrelsa@microsoft.com>

how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb

@@ -70,7 +70,7 @@
         "\n",
         "import urllib.request\n",
         "\n",
-        "onnx_model_url = \"https://github.com/onnx/models/blob/master/vision/body_analysis/emotion_ferplus/model/emotion-ferplus-7.tar.gz?raw=true\"\n",
+        "onnx_model_url = \"https://github.com/onnx/models/blob/main/vision/body_analysis/emotion_ferplus/model/emotion-ferplus-7.tar.gz?raw=true\"\n",
         "\n",
         "urllib.request.urlretrieve(onnx_model_url, filename=\"emotion-ferplus-7.tar.gz\")\n",
         "\n",

how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb

@@ -70,7 +70,7 @@
         "\n",
         "import urllib.request\n",
         "\n",
-        "onnx_model_url = \"https://github.com/onnx/models/blob/master/vision/classification/mnist/model/mnist-7.tar.gz?raw=true\"\n",
+        "onnx_model_url = \"https://github.com/onnx/models/blob/main/vision/classification/mnist/model/mnist-7.tar.gz?raw=true\"\n",
         "\n",
         "urllib.request.urlretrieve(onnx_model_url, filename=\"mnist-7.tar.gz\")"
       ]

how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/scripts/prepdata/cleanse.py

@@ -5,17 +5,6 @@ import argparse
 import os
 from azureml.core import Run
 
-
-def get_dict(dict_str):
-    pairs = dict_str.strip("{}").split(r'\;')
-    new_dict = {}
-    for pair in pairs:
-        key, value = pair.strip().split(":")
-        new_dict[key.strip().strip("'")] = value.strip().strip("'")
-
-    return new_dict
-
-
 print("Cleans the input data")
 
 # Get the input green_taxi_data. To learn more about how to access dataset in your script, please
@@ -23,7 +12,6 @@ print("Cleans the input data")
 run = Run.get_context()
 raw_data = run.input_datasets["raw_data"]
 
-
 parser = argparse.ArgumentParser("cleanse")
 parser.add_argument("--output_cleanse", type=str, help="cleaned taxi data directory")
 parser.add_argument("--useful_columns", type=str, help="useful columns to keep")
@@ -38,8 +26,8 @@ print("Argument 3(output cleansed taxi data path): %s" % args.output_cleanse)
 # These functions ensure that null data is removed from the dataset,
 # which will help increase machine learning model accuracy.
 
-useful_columns = [s.strip().strip("'") for s in args.useful_columns.strip("[]").split(r'\;')]
-columns = get_dict(args.columns)
+useful_columns = eval(args.useful_columns.replace(';', ','))
+columns = eval(args.columns.replace(';', ','))
 
 new_df = (raw_data.to_pandas_dataframe()
           .dropna(how='all')

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

@@ -254,6 +254,7 @@
         "- conda-forge\n",
         "dependencies:\n",
         "- python=3.6.2\n",
+        "- pip=21.3.1\n",
         "- pip:\n",
         "  - azureml-defaults\n",
         "  - azureml-opendatasets\n",

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

@@ -431,6 +431,7 @@
         "- conda-forge\n",
         "dependencies:\n",
         "- python=3.6.2\n",
+        "- pip=21.3.1\n",
         "- pip:\n",
         "  - h5py<=2.10.0\n",
         "  - azureml-defaults\n",

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

@@ -262,6 +262,7 @@
         "- conda-forge\n",
         "dependencies:\n",
         "- python=3.6.2\n",
+        "- pip=21.3.1\n",
         "- pip:\n",
         "  - azureml-defaults\n",
         "  - torch==1.6.0\n",