diff --git a/how-to-use-azureml/machine-learning-pipelines/parallel-run/Code/digit_identification.py b/how-to-use-azureml/machine-learning-pipelines/parallel-run/Code/digit_identification.py
index ff187551a..280f77cc1 100644
--- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/Code/digit_identification.py
+++ b/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))
diff --git a/how-to-use-azureml/machine-learning-pipelines/parallel-run/Code/iris_score.py b/how-to-use-azureml/machine-learning-pipelines/parallel-run/Code/iris_score.py
index 5b1b89c05..6dc0c7cad 100644
--- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/Code/iris_score.py
+++ b/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
diff --git a/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.ipynb b/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.ipynb
index 267d97268..42142b165 100644
--- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.ipynb
+++ b/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.
\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.
\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",
diff --git a/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-partition-per-folder.ipynb b/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-partition-per-folder.ipynb
index 98922dc82..cc793a0d8 100644
--- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-partition-per-folder.ipynb
+++ b/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,
diff --git a/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.ipynb b/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.ipynb
index 103fd61a8..bafa19e52 100644
--- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.ipynb
+++ b/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.
\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.
\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
-}
\ No newline at end of file
+}
diff --git a/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-partition-per-column.ipynb b/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-partition-per-column.ipynb
index 79e355122..224f3d812 100644
--- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-partition-per-column.ipynb
+++ b/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
-}
\ No newline at end of file
+}