update samples from Release-62 as a part of SDK release
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amlrelsa-ms
@@ -65,7 +65,7 @@ Visit following repos to see projects contributed by Azure ML users:
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- [UMass Amherst Student Samples](https://github.com/katiehouse3/microsoft-azure-ml-notebooks) - A number of end-to-end machine learning notebooks, including machine translation, image classification, and customer churn, created by students in the 696DS course at UMass Amherst.
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## Data/Telemetry
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This repository collects usage data and sends it to Microsoft to help improve our products and services. Read Microsoft's [privacy statement to learn more](https://privacy.microsoft.com/en-US/privacystatement)
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This repository collects usage data and sends it to Mircosoft to help improve our products and services. Read Microsoft's [privacy statement to learn more](https://privacy.microsoft.com/en-US/privacystatement)
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To opt out of tracking, please go to the raw markdown or .ipynb files and remove the following line of code:
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@@ -4,7 +4,7 @@ Learn how to use Azure Machine Learning services for experimentation and model m
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As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) notebook first to set up your Azure ML Workspace. Then, run the notebooks in following recommended order.
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* [train-within-notebook](./training/train-within-notebook): Train a model while tracking run history, and learn how to deploy the model as web service to Azure Container Instance.
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* [train-within-notebook](./training/train-within-notebook): Train a model hile tracking run history, and learn how to deploy the model as web service to Azure Container Instance.
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* [train-on-local](./training/train-on-local): Learn how to submit a run to local computer and use Azure ML managed run configuration.
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* [train-on-amlcompute](./training/train-on-amlcompute): Use a 1-n node Azure ML managed compute cluster for remote runs on Azure CPU or GPU infrastructure.
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* [train-on-remote-vm](./training/train-on-remote-vm): Use Data Science Virtual Machine as a target for remote runs.
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@@ -287,8 +287,8 @@
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"# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n",
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"# environment, otherwise if a model is trained or deployed in a different environment this can\n",
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"# cause errors. Please take extra care when specifying your dependencies in a production environment.\n",
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"run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[sklearn_dep, pandas_dep],\n",
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" pip_packages=azureml_pip_packages)\n",
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"azureml_pip_packages.extend([sklearn_dep, pandas_dep])\n",
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"run_config.environment.python.conda_dependencies = CondaDependencies.create(pip_packages=azureml_pip_packages)\n",
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"\n",
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"from azureml.core import Run\n",
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"from azureml.core import ScriptRunConfig\n",
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@@ -427,8 +427,8 @@
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"# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n",
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"# environment, otherwise if a model is trained or deployed in a different environment this can\n",
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"# cause errors. Please take extra care when specifying your dependencies in a production environment.\n",
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"run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[sklearn_dep, pandas_dep],\n",
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" pip_packages=azureml_pip_packages)\n",
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"azureml_pip_packages.extend([sklearn_dep, pandas_dep])\n",
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"run_config.environment.python.conda_dependencies = CondaDependencies.create(pip_packages=azureml_pip_packages)\n",
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"\n",
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"from azureml.core import Run\n",
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"from azureml.core import ScriptRunConfig\n",
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@@ -350,8 +350,7 @@
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"# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n",
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"# environment, otherwise if a model is trained or deployed in a different environment this can\n",
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"# cause errors. Please take extra care when specifying your dependencies in a production environment.\n",
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"myenv = CondaDependencies.create(conda_packages=[sklearn_dep, pandas_dep],\n",
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" pip_packages=['sklearn-pandas', 'pyyaml'] + azureml_pip_packages,\n",
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"myenv = CondaDependencies.create(pip_packages=['sklearn-pandas', 'pyyaml', sklearn_dep, pandas_dep] + azureml_pip_packages,\n",
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" pin_sdk_version=False)\n",
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"\n",
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"with open(\"myenv.yml\",\"w\") as f:\n",
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@@ -294,8 +294,8 @@
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"# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n",
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"# environment, otherwise if a model is trained or deployed in a different environment this can\n",
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"# cause errors. Please take extra care when specifying your dependencies in a production environment.\n",
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"run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[sklearn_dep, pandas_dep],\n",
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" pip_packages=['sklearn_pandas', 'pyyaml'] + azureml_pip_packages,\n",
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"azureml_pip_packages.extend(['sklearn-pandas', 'pyyaml', sklearn_dep, pandas_dep])\n",
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"run_config.environment.python.conda_dependencies = CondaDependencies.create(pip_packages=azureml_pip_packages,\n",
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" pin_sdk_version=False)\n",
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"# Now submit a run on AmlCompute\n",
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"from azureml.core.script_run_config import ScriptRunConfig\n",
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@@ -459,8 +459,8 @@
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"# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n",
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"# environment, otherwise if a model is trained or deployed in a different environment this can\n",
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"# cause errors. Please take extra care when specifying your dependencies in a production environment.\n",
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"myenv = CondaDependencies.create(conda_packages=[sklearn_dep, pandas_dep],\n",
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" pip_packages=['sklearn-pandas', 'pyyaml'] + azureml_pip_packages,\n",
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"azureml_pip_packages.extend(['sklearn-pandas', 'pyyaml', sklearn_dep, pandas_dep])\n",
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"myenv = CondaDependencies.create(pip_packages=azureml_pip_packages,\n",
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" pin_sdk_version=False)\n",
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"\n",
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"with open(\"myenv.yml\",\"w\") as f:\n",
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@@ -17,7 +17,7 @@ These notebooks below are designed to go in sequence.
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12. [aml-pipelines-setup-versioned-pipeline-endpoints.ipynb](https://aka.ms/pl-ver-endpoint): This notebook shows how you can setup PipelineEndpoint and submit a Pipeline using the PipelineEndpoint.
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13. [aml-pipelines-showcasing-datapath-and-pipelineparameter.ipynb](https://aka.ms/pl-datapath): This notebook showcases how to use DataPath and PipelineParameter in AML Pipeline.
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14. [aml-pipelines-how-to-use-pipeline-drafts.ipynb](http://aka.ms/pl-pl-draft): This notebook shows how to use Pipeline Drafts. Pipeline Drafts are mutable pipelines which can be used to submit runs and create Published Pipelines.
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15. [aml-pipelines-how-to-use-modulestep.ipynb](https://aka.ms/pl-modulestep): This notebook shows how to define Module, ModuleVersion and how to use them in an AML Pipeline using ModuleStep.
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15. [aml-pipelines-hot-to-use-modulestep.ipynb](https://aka.ms/pl-modulestep): This notebook shows how to define Module, ModuleVersion and how to use them in an AML Pipeline using ModuleStep.
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16. [aml-pipelines-with-notebook-runner-step.ipynb](https://aka.ms/pl-nbrstep): This notebook shows how you can run another notebook as a step in Azure Machine Learning Pipeline.
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@@ -164,6 +164,7 @@
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" print('db_compute_name {}'.format(db_compute_name))\n",
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" print('db_resource_group {}'.format(db_resource_group))\n",
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" print('db_workspace_name {}'.format(db_workspace_name))\n",
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" print('db_access_token {}'.format(db_access_token))\n",
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" \n",
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" config = DatabricksCompute.attach_configuration(\n",
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" resource_group = db_resource_group,\n",
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@@ -755,4 +756,4 @@
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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}
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@@ -57,13 +57,8 @@
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"source": [
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"Import the `Workspace` class, and load your subscription information from the file `config.json` using the function `from_config().` This looks for the JSON file in the current directory by default, but you can also specify a path parameter to point to the file using `from_config(path=\"your/file/path\")`. If you are running this notebook in a cloud notebook server in your workspace, the file is automatically in the root directory.\n",
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"\n",
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"If the following code asks for additional authentication, simply paste the link in a browser and enter the authentication token. In addition, if you have more than one tenant linked to your user, you will need to add the following lines:\n",
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"```\n",
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"from azureml.core.authentication import InteractiveLoginAuthentication\n",
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"interactive_auth = InteractiveLoginAuthentication(tenant_id=\"your-tenant-id\")\n",
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"Additional details on authentication can be found here: https://aka.ms/aml-notebook-auth \n",
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"```\n"
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]
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"If the following code asks for additional authentication, simply paste the link in a browser and enter the authentication token."
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]
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},
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{
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"cell_type": "code",
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@@ -391,4 +386,4 @@
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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}
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