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update samples from Release-75 as a part of SDK release

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amlrelsa-ms
2020-11-03 19:07:12 +00:00
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commit a6c65f00ec
2 changed files with 3 additions and 3 deletions
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README.md
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@@ -1,7 +1,5 @@
# Azure Machine Learning service example notebooks
> a community-driven repository of examples using mlflow for tracking can be found at https://github.com/Azure/azureml-examples
This repository contains example notebooks demonstrating the [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning-service/) Python SDK which allows you to build, train, deploy and manage machine learning solutions using Azure. The AML SDK allows you the choice of using local or cloud compute resources, while managing and maintaining the complete data science workflow from the cloud.
![Azure ML Workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/media/concept-azure-machine-learning-architecture/workflow.png)

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how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb
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@@ -429,7 +429,8 @@
"dependencies:\n",
"- python=3.6.2\n",
"- pip:\n",
" - azureml-defaults==1.13.0\n",
" - h5py<=2.10.0\n",
" - azureml-defaults\n",
" - tensorflow-gpu==2.0.0\n",
" - keras<=2.3.1\n",
" - matplotlib"
@@ -981,6 +982,7 @@
"\n",
"cd = CondaDependencies.create()\n",
"cd.add_tensorflow_conda_package()\n",
"cd.add_conda_package('h5py<=2.10.0')\n",
"cd.add_conda_package('keras<=2.3.1')\n",
"cd.add_pip_package(\"azureml-defaults\")\n",
"cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')\n",