update samples from Release-146 as a part of SDK release

update samples from Release-144 as a part of  SDK release

how-to-use-azureml/automated-machine-learning/automl_env.yml

@@ -17,6 +17,7 @@ dependencies:
 - notebook
 - pywin32==227
 - PySocks==1.7.1
+- jsonschema==4.5.1
 - conda-forge::pyqt==5.12.3
 
 - pip:

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

@@ -9,9 +9,11 @@ dependencies:
 - PyJWT < 2.0.0
 - numpy==1.18.5
 - pywin32==227
+- cryptography<37.0.0
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
+  - azure-mgmt-core==1.3.0
   - azure-core==1.21.1
   - azure-identity==1.7.0
   - azureml-defaults

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

@@ -11,9 +11,11 @@ dependencies:
 - urllib3==1.26.7
 - PyJWT < 2.0.0
 - numpy==1.19.5
+- cryptography<37.0.0
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
+  - azure-mgmt-core==1.3.0
   - azure-core==1.21.1
   - azure-identity==1.7.0
   - azureml-defaults

how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb

@@ -513,13 +513,7 @@
         "conda_run_config.environment.docker.enabled = True\n",
         "\n",
         "# specify CondaDependencies obj\n",
-        "conda_run_config.environment.python.conda_dependencies = (\n",
-        "    automl_run.get_environment().python.conda_dependencies\n",
-        ")\n",
-        "\n",
-        "conda_run_config.environment.python.conda_dependencies.add_pip_package(\n",
-        "    \"dotnetcore2==2.1.23\"\n",
-        ")"
+        "conda_run_config.environment = automl_run.get_environment()"
       ]
     },
     {
@@ -648,28 +642,6 @@
         ")"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create the conda dependencies for setting up the service\n",
-        "We need to create the conda dependencies comprising of the *azureml* packages using the training environment from the *automl_run*."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "conda_dep = automl_run.get_environment().python.conda_dependencies\n",
-        "\n",
-        "with open(\"myenv.yml\", \"w\") as f:\n",
-        "    f.write(conda_dep.serialize_to_string())\n",
-        "with open(\"myenv.yml\", \"r\") as f:\n",
-        "    print(f.read())"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -692,7 +664,7 @@
       "metadata": {},
       "source": [
         "### Deploy the service\n",
-        "In the cell below, we deploy the service using the conda file and the scoring file from the previous steps. "
+        "In the cell below, we deploy the service using the automl training environment and the scoring file from the previous steps. "
       ]
     },
     {
@@ -714,7 +686,7 @@
         "    description=\"Get local explanations for Machine test data\",\n",
         ")\n",
         "\n",
-        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
+        "myenv = automl_run.get_environment()\n",
         "inference_config = InferenceConfig(entry_script=\"score_explain.py\", environment=myenv)\n",
         "\n",
         "# Use configs and models generated above\n",

how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.ipynb

@@ -361,7 +361,7 @@
         "\n",
         "batch_conda_deps = CondaDependencies.create(python_version=\"3.7\",\n",
         "                                            conda_packages=['pip==20.2.4'],\n",
-        "                                            pip_packages=[\"tensorflow==1.15.2\", \"pillow\", \n",
+        "                                            pip_packages=[\"tensorflow==1.15.2\", \"pillow\", \"protobuf==3.20.1\",\n",
         "                                                          \"azureml-core\", \"azureml-dataset-runtime[fuse]\"])\n",
         "batch_env = Environment(name=\"batch_environment\")\n",
         "batch_env.python.conda_dependencies = batch_conda_deps\n",

how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/docker/Dockerfile-cpu

@@ -11,6 +11,8 @@ RUN pip install azureml-core
 RUN pip install ray==0.8.7
 RUN pip install ray[rllib,tune,serve]==0.8.7
 RUN pip install tensorflow==1.14.0
+RUN pip install 'msrest<0.7.0'
+RUN pip install protobuf==3.20.0
 
 RUN apt-get update
 RUN apt-get install -y jq

how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/docker/Dockerfile-gpu

@@ -37,8 +37,7 @@ RUN pip install gym[atari]==0.19.0
 RUN pip install gym[accept-rom-license]==0.19.0
 
 # Install pip dependencies
-RUN HOROVOD_WITH_TENSORFLOW=1 \
-    pip install 'matplotlib>=3.3,<3.4' \
+RUN pip install 'matplotlib>=3.3,<3.4' \
                 'psutil>=5.8,<5.9' \
                 'tqdm>=4.59,<4.60' \
                 'pandas>=1.1,<1.2' \
@@ -70,6 +69,9 @@ RUN pip install --no-cache-dir \
 # This is required for ray 0.8.7
 RUN pip install -U aiohttp==3.7.4
 
+RUN pip install 'msrest<0.7.0'
+RUN pip install protobuf==3.20.0
+
 # This is needed for mpi to locate libpython
 ENV LD_LIBRARY_PATH $AZUREML_CONDA_ENVIRONMENT_PATH/lib:$LD_LIBRARY_PATH
 

how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/pong_rllib.ipynb

@@ -93,7 +93,7 @@
       "source": [
         "%matplotlib inline\n",
         "\n",
-        "# Azure Machine Learning Core imports\n",
+        "# Azure Machine Learning core imports\n",
         "import azureml.core\n",
         "\n",
         "# Check core SDK version number\n",

how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/docker/Dockerfile

@@ -12,6 +12,7 @@ RUN pip install azureml-dataset-runtime
 RUN pip install ray==0.8.7
 RUN pip install ray[rllib,tune,serve]==0.8.7
 RUN pip install tensorflow==1.14.0
+RUN pip install 'msrest<0.7.0'
 
 RUN apt-get update
 RUN apt-get install -y jq

how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/docker/Dockerfile

@@ -30,5 +30,9 @@ RUN pip install ray-on-aml==0.1.6 & \
     conda install -y -c conda-forge x264='1!152.20180717' ffmpeg=4.0.2 && \
     conda install -c anaconda opencv
 
+RUN pip install protobuf==3.20.0
+
 RUN pip install --upgrade ray==0.8.3 \
-    ray[rllib,dashboard,tune]==0.8.3
+    ray[rllib,dashboard,tune]==0.8.3
+
+RUN pip install 'msrest<0.7.0'

how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/docker/cpu/Dockerfile

@@ -28,7 +28,11 @@ RUN cd multiagent-particle-envs && \
 
 RUN pip3 install ray-on-aml==0.1.6
 
+RUN pip install protobuf==3.20.0
+
 RUN pip3 install --upgrade \
     ray==0.8.7 \
     ray[rllib]==0.8.7 \
-    ray[tune]==0.8.7
+    ray[tune]==0.8.7
+
+RUN pip install 'msrest<0.7.0'

tutorials/machine-learning-pipelines-advanced/scripts/batch_scoring.py

@@ -5,17 +5,19 @@ import os
 import argparse
 import datetime
 import time
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 from math import ceil
 import numpy as np
+import sys
 import shutil
-from tensorflow.contrib.slim.python.slim.nets import inception_v3
+import subprocess
+import tf_slim
 
 from azureml.core import Run
 from azureml.core.model import Model
 from azureml.core.dataset import Dataset
 
-slim = tf.contrib.slim
+slim = tf_slim
 
 image_size = 299
 num_channel = 3
@@ -32,16 +34,18 @@ def get_class_label_dict(labels_dir):
 
 def init():
     global g_tf_sess, probabilities, label_dict, input_images
+    subprocess.run(["git", "clone", "https://github.com/tensorflow/models/"])
+    sys.path.append("./models/research/slim")
 
     parser = argparse.ArgumentParser(description="Start a tensorflow model serving")
     parser.add_argument('--model_name', dest="model_name", required=True)
     parser.add_argument('--labels_dir', dest="labels_dir", required=True)
     args, _ = parser.parse_known_args()
-
+    from nets import inception_v3, inception_utils
     label_dict = get_class_label_dict(args.labels_dir)
     classes_num = len(label_dict)
-
-    with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
+    tf.disable_v2_behavior()
+    with slim.arg_scope(inception_utils.inception_arg_scope()):
         input_images = tf.placeholder(tf.float32, [1, image_size, image_size, num_channel])
         logits, _ = inception_v3.inception_v3(input_images,
                                               num_classes=classes_num,

tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb

@@ -247,7 +247,7 @@
         "    config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_NC6\",\n",
         "                                                   vm_priority=\"lowpriority\", \n",
         "                                                   min_nodes=0, \n",
-        "                                                   max_nodes=1)\n",
+        "                                                   max_nodes=2)\n",
         "\n",
         "    compute_target = ComputeTarget.create(workspace=ws, name=compute_name, provisioning_configuration=config)\n",
         "    compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)"
@@ -305,9 +305,10 @@
         "from azureml.core.conda_dependencies import CondaDependencies\n",
         "from azureml.core.runconfig import DEFAULT_GPU_IMAGE\n",
         "\n",
-        "cd = CondaDependencies.create(python_version=\"3.7\",\n",
+        "cd = CondaDependencies.create(python_version=\"3.8\",\n",
         "                              conda_packages=['pip==20.2.4'],\n",
-        "                              pip_packages=[\"tensorflow-gpu==1.15.2\",\n",
+        "                              pip_packages=[\"tensorflow-gpu==2.3.0\",\n",
+        "                                            \"tf_slim==1.1.0\", \"protobuf==3.20.1\",\n",
         "                                            \"azureml-core\", \"azureml-dataset-runtime[fuse]\"])\n",
         "\n",
         "env = Environment(name=\"parallelenv\")\n",