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update samples from Release-158 as a part of 1.0.74 SDK release
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29 files changed

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‎configuration.ipynb

+1-1
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@@ -103,7 +103,7 @@
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"source": [
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"import azureml.core\n",
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"\n",
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"print(\"This notebook was created using version 1.0.72.1 of the Azure ML SDK\")\n",
106+
"print(\"This notebook was created using version 1.0.74 of the Azure ML SDK\")\n",
107107
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},

‎how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb

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@@ -334,8 +334,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
338-
"In this example, we specify `show_output = True` to print currently running iterations to the console."
337+
"Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while."
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]
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},
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{

‎how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb

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@@ -230,8 +230,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Call the `submit` method on the experiment object and pass the run configuration. Depending on the data and the number of iterations this can run for a while.\n",
234-
"In this example, we specify `show_output = True` to print currently running iterations to the console."
233+
"Call the `submit` method on the experiment object and pass the run configuration. Depending on the data and the number of iterations this can run for a while."
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]
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},
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{

‎how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb

+2-11
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@@ -308,7 +308,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"automl_run = experiment.submit(automl_config, show_output=False)"
311+
"automl_run = experiment.submit(automl_config, show_output=True)"
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]
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},
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{
@@ -320,15 +320,6 @@
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"automl_run"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"automl_run.wait_for_completion()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
@@ -357,7 +348,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"#best_run, fitted_model = local_run.get_output()"
351+
"#best_run, fitted_model = automl_run.get_output()"
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]
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},
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{

‎how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb

+1-1
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@@ -376,7 +376,7 @@
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"hidePrompt": false
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},
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"source": [
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"We will now run the experiment, starting with 10 iterations of model search. The experiment can be continued for more iterations if more accurate results are required. You will see the currently running iterations printing to the console."
379+
"We will now run the experiment, starting with 10 iterations of model search. The experiment can be continued for more iterations if more accurate results are required."
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]
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},
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{

‎how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.ipynb

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@@ -345,7 +345,11 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": [
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"sample-akscompute-provision"
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]
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},
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"outputs": [],
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"source": [
351355
"from azureml.core.compute import AksCompute, ComputeTarget\n",

‎how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.ipynb

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@@ -682,7 +682,11 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": [
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"sample-akswebservice-deploy-from-image"
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]
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},
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"outputs": [],
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"source": [
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"%%time\n",

‎how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb

+5-1
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@@ -166,7 +166,11 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": [
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"sample-localwebservice-deploy"
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]
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},
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"outputs": [],
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"source": [
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"from azureml.core.webservice import LocalWebservice\n",

‎how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb

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@@ -316,9 +316,6 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from azureml.core.webservice import Webservice\n",
320-
"from random import randint\n",
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"\n",
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"aci_service_name = 'my-aci-service-15ad'\n",
323320
"print(\"Service\", aci_service_name)\n",
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"aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
@@ -386,6 +383,22 @@
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"name": "viswamy"
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}
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],
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"category": "deployment",
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"compute": [
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"local"
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],
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"datasets": [
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"PASCAL VOC"
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],
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"deployment": [
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"Azure Container Instance"
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],
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"exclude_from_index": false,
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"framework": [
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"ONNX"
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],
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"friendly_name": "Convert and deploy TinyYolo with ONNX Runtime",
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"index_order": 5,
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"kernelspec": {
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"display_name": "Python 3.6",
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"language": "python",
@@ -402,7 +415,14 @@
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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}
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},
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"star_tag": [
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"featured"
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],
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"tags": [
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"ONNX Converter"
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],
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"task": "Object Detection"
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},
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"nbformat": 4,
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"nbformat_minor": 2

‎how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.yml

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@@ -2,5 +2,6 @@ name: onnx-convert-aml-deploy-tinyyolo
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dependencies:
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- pip:
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- azureml-sdk
5+
- numpy
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- git+https://github.com/apple/[email protected]
67
- onnxmltools==1.3.1

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

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@@ -391,8 +391,6 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from azureml.core.webservice import Webservice\n",
395-
"\n",
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"aci_service_name = 'onnx-demo-emotion'\n",
397395
"print(\"Service\", aci_service_name)\n",
398396
"aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
@@ -755,6 +753,22 @@
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"name": "viswamy"
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}
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],
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"category": "deployment",
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"compute": [
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"local"
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],
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"datasets": [
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"Emotion FER"
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],
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"deployment": [
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"Azure Container Instance"
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],
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"exclude_from_index": false,
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"framework": [
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"ONNX"
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],
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"friendly_name": "Deploy Facial Expression Recognition (FER+) with ONNX Runtime",
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"index_order": 2,
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"kernelspec": {
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"display_name": "Python 3.6",
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"language": "python",
@@ -772,7 +786,12 @@
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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},
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"msauthor": "vinitra.swamy"
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"msauthor": "vinitra.swamy",
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"star_tag": [],
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"tags": [
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"ONNX Model Zoo"
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],
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"task": "Facial Expression Recognition"
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},
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"nbformat": 4,
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"nbformat_minor": 2

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

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@@ -378,8 +378,6 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from azureml.core.webservice import Webservice\n",
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"\n",
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"aci_service_name = 'onnx-demo-mnist'\n",
384382
"print(\"Service\", aci_service_name)\n",
385383
"aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
@@ -763,6 +761,22 @@
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"name": "viswamy"
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}
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],
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"category": "deployment",
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"compute": [
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"local"
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],
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"datasets": [
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"MNIST"
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],
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"deployment": [
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"Azure Container Instance"
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],
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"exclude_from_index": false,
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"framework": [
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"ONNX"
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],
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"friendly_name": "Deploy MNIST digit recognition with ONNX Runtime",
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"index_order": 1,
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"kernelspec": {
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"display_name": "Python 3.6",
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"language": "python",
@@ -780,7 +794,12 @@
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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},
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"msauthor": "vinitra.swamy"
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"msauthor": "vinitra.swamy",
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"star_tag": [],
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"tags": [
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"ONNX Model Zoo"
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],
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"task": "Image Classification"
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},
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"nbformat": 4,
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"nbformat_minor": 2

‎how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"from azureml.core.webservice import Webservice\n",
306305
"from random import randint\n",
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"\n",
308307
"aci_service_name = 'onnx-demo-resnet50'+str(randint(0,100))\n",
@@ -372,6 +371,22 @@
372371
"name": "viswamy"
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}
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],
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"category": "deployment",
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"compute": [
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"local"
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],
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"datasets": [
379+
"ImageNet"
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],
381+
"deployment": [
382+
"Azure Container Instance"
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],
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"exclude_from_index": false,
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"framework": [
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"ONNX"
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],
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"friendly_name": "Deploy ResNet50 with ONNX Runtime",
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"index_order": 4,
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"kernelspec": {
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"display_name": "Python 3.6",
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"language": "python",
@@ -388,7 +403,12 @@
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
391-
}
406+
},
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"star_tag": [],
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"tags": [
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"ONNX Model Zoo"
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],
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"task": "Image Classification"
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},
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"nbformat": 4,
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"nbformat_minor": 2

‎how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"from azureml.core.webservice import Webservice\n",
481480
"from azureml.core.model import Model\n",
482481
"from random import randint\n",
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"\n",
@@ -548,6 +547,22 @@
548547
"name": "viswamy"
549548
}
550549
],
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"category": "deployment",
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"compute": [
552+
"AML Compute"
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],
554+
"datasets": [
555+
"MNIST"
556+
],
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"deployment": [
558+
"Azure Container Instance"
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],
560+
"exclude_from_index": false,
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"framework": [
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"ONNX"
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],
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"friendly_name": "Train MNIST in PyTorch, convert, and deploy with ONNX Runtime",
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"index_order": 3,
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"kernelspec": {
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"display_name": "Python 3.6",
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"language": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.6"
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},
583+
"star_tag": [],
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"tags": [
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"ONNX Converter"
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],
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"task": "Image Classification",
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"widgets": {
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"application/vnd.jupyter.widget-state+json": {
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"state": {

‎how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb

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"execution_count": null,
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"metadata": {
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"tags": [
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"create image"
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"create image",
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"sample-image-create"
239240
]
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},
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"outputs": [],
@@ -330,7 +331,8 @@
330331
"metadata": {
331332
"tags": [
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"deploy service",
333-
"aci"
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"aci",
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"sample-aciwebservice-deploy-config"
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]
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},
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"outputs": [],
@@ -349,7 +351,8 @@
349351
"metadata": {
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"tags": [
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"deploy service",
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"aci"
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"aci",
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"sample-aciwebservice-deploy-from-image"
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]
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},
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"outputs": [],

‎how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb

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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
114+
"tags": [
115+
"sample-batchcompute-attach"
116+
]
117+
},
114118
"outputs": [],
115119
"source": [
116120
"batch_compute_name = 'mybatchcompute' # Name to associate with new compute in workspace\n",

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