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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

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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",
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"print(\"This notebook was created using version 1.0.74 of the Azure ML SDK\")\n",
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"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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"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",
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"In this example, we specify `show_output = True` to print currently running iterations to the console."
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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."
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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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"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",
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"In this example, we specify `show_output = True` to print currently running iterations to the console."
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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."
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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

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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)"
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"automl_run = experiment.submit(automl_config, show_output=True)"
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]
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},
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{
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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": {},
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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()"
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"#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

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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."
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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."
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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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{
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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": [
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"from azureml.core.compute import AksCompute, ComputeTarget\n",

how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.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": {
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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

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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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"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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"from random import randint\n",
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"\n",
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"aci_service_name = 'my-aci-service-15ad'\n",
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"print(\"Service\", aci_service_name)\n",
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"aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
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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",
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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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dependencies:
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- pip:
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- azureml-sdk
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- numpy
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- git+https://github.com/apple/[email protected]
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- onnxmltools==1.3.1

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