Update python and TF version to fix notebook

how-to-use-azureml/machine-learning-pipelines/parallel-run/Code/digit_identification.py

@@ -11,14 +11,17 @@
 
 def init():
     global g_tf_sess
+
+    # Disable eager execution
+    tf.compat.v1.disable_eager_execution()
 
     # pull down model from workspace
     model_path = Model.get_model_path("mnist-prs")
 
     # contruct graph to execute
-    tf.reset_default_graph()
-    saver = tf.train.import_meta_graph(os.path.join(model_path, 'mnist-tf.model.meta'))
-    g_tf_sess = tf.Session(config=tf.ConfigProto(device_count={'GPU': 0}))
+    tf.compat.v1.reset_default_graph()
+    saver = tf.compat.v1.train.import_meta_graph(os.path.join(model_path, 'mnist-tf.model.meta'))
+    g_tf_sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(device_count={'GPU': 0}))
     saver.restore(g_tf_sess, os.path.join(model_path, 'mnist-tf.model'))
 
 
@@ -33,7 +36,7 @@ def run(mini_batch):
         data = Image.open(image)
         np_im = np.array(data).reshape((1, 784))
         # perform inference
-        inference_result = output.eval(feed_dict={in_tensor: np_im}, session=g_tf_sess)
+        inference_result = g_tf_sess.run(output, feed_dict={in_tensor: np_im})
         # find best probability, and add to result list
         best_result = np.argmax(inference_result)
         resultList.append("{}: {}".format(os.path.basename(image), best_result))

how-to-use-azureml/machine-learning-pipelines/parallel-run/Code/iris_score.py

@@ -1,10 +1,6 @@
-import io
 import pickle
 import argparse
-import numpy as np
-
 from azureml.core.model import Model
-from sklearn.linear_model import LogisticRegression
 
 from azureml_user.parallel_run import EntryScript
 

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

@@ -306,7 +306,7 @@
         "#### An entry script\n",
         "This script accepts requests, scores the requests by using the model, and returns the results.\n",
         "- __init()__ - Typically this function loads the model into a global object. This function is run only once at the start of batch processing per worker node/process. Init method can make use of following environment variables (ParallelRunStep input):\n",
-        "    1.\tAZUREML_BI_OUTPUT_PATH \u00e2\u20ac\u201c output folder path\n",
+        "    1.\tAZUREML_BI_OUTPUT_PATH - output folder path\n",
         "- __run(mini_batch)__ - The method to be parallelized. Each invocation will have one minibatch.<BR>\n",
         "__mini_batch__: Batch inference will invoke run method and pass either a list or Pandas DataFrame as an argument to the method. Each entry in min_batch will be - a filepath if input is a FileDataset, a Pandas DataFrame if input is a TabularDataset.<BR>\n",
         "__run__ method response: run() method should return a Pandas DataFrame or an array. For append_row output_action, these returned elements are appended into the common output file. For summary_only, the contents of the elements are ignored. For all output actions, each returned output element indicates one successful inference of input element in the input mini-batch.\n",
@@ -359,9 +359,9 @@
         "from azureml.core import Environment\n",
         "from azureml.core.runconfig import CondaDependencies, DEFAULT_CPU_IMAGE\n",
         "\n",
-        "batch_conda_deps = CondaDependencies.create(python_version=\"3.7\",\n",
+        "batch_conda_deps = CondaDependencies.create(python_version=\"3.8\",\n",
         "                                            conda_packages=['pip==20.2.4'],\n",
-        "                                            pip_packages=[\"tensorflow==1.15.2\", \"pillow\", \"protobuf==3.20.1\",\n",
+        "                                            pip_packages=[\"tensorflow==2.13.0\", \"pillow\", \"protobuf==4.23.3\",\n",
         "                                                          \"azureml-core\", \"azureml-dataset-runtime[fuse]\"])\n",
         "batch_env = Environment(name=\"batch_environment\")\n",
         "batch_env.python.conda_dependencies = batch_conda_deps\n",
@@ -615,7 +615,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.9"
+      "version": "3.8.16"
     },
     "tags": [
       "Batch Inferencing",

how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-partition-per-folder.ipynb

@@ -390,7 +390,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.9"
+      "version": "3.8.16"
     }
   },
   "nbformat": 4,

how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.ipynb

@@ -252,7 +252,7 @@
         "#### An entry script\n",
         "This script accepts requests, scores the requests by using the model, and returns the results.\n",
         "- __init()__ - Typically this function loads the model into a global object. This function is run only once at the start of batch processing per worker node/process. init method can make use of following environment variables (ParallelRunStep input):\n",
-        "    1.\tAZUREML_BI_OUTPUT_PATH \u00e2\u20ac\u201c output folder path\n",
+        "    1.\tAZUREML_BI_OUTPUT_PATH - output folder path\n",
         "- __run(mini_batch)__ - The method to be parallelized. Each invocation will have one minibatch.<BR>\n",
         "__mini_batch__: Batch inference will invoke run method and pass either a list or Pandas DataFrame as an argument to the method. Each entry in min_batch will be - a filepath if input is a FileDataset, a Pandas DataFrame if input is a TabularDataset.<BR>\n",
         "__run__ method response: run() method should return a Pandas DataFrame or an array. For append_row output_action, these returned elements are appended into the common output file. For summary_only, the contents of the elements are ignored. For all output actions, each returned output element indicates one successful inference of input element in the input mini-batch.\n",
@@ -308,10 +308,10 @@
         "from azureml.core import Environment\n",
         "from azureml.core.runconfig import CondaDependencies\n",
         "\n",
-        "predict_conda_deps = CondaDependencies.create(python_version=\"3.7\", \n",
+        "predict_conda_deps = CondaDependencies.create(python_version=\"3.8\", \n",
         "                                              conda_packages=['pip==20.2.4'],\n",
-        "                                              pip_packages=[\"scikit-learn==0.20.3\",\n",
-        "                                                            \"azureml-core\", \"azureml-dataset-runtime[pandas,fuse]\"])\n",
+        "                                              pip_packages=[\"numpy==1.19.5\", \"pandas==1.4.4\", \"scikit-learn==0.22.2\",\n",
+        "                                                            \"azureml-core\", \"azureml-dataset-runtime[fuse]\"])\n",
         "\n",
         "predict_env = Environment(name=\"predict_environment\")\n",
         "predict_env.python.conda_dependencies = predict_conda_deps\n",
@@ -531,4 +531,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-partition-per-column.ipynb

@@ -413,9 +413,9 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.13"
+      "version": "3.8.16"
     }
   },
   "nbformat": 4,
   "nbformat_minor": 4
-}
+}