Demand forecasting data prep and TCN (Azure#2101)

* initial commit after deleting previous branch due to rebase issues

* data prep notebook

* generated yml files and modified README.md

* changed the name of dataset name for data from public blob. This is necessary because the data-prep notebook uploads the data with the same name and causes version mismatch. Hence, the test and train data have different grains

* added create new version option to dataset registration

* addressed all PR comments

* removed variable that was referenced before declaration

* changed compute target creation cell from markdown to code

.github/workflows/python-sdk-tutorial-auto-ml-forecasting-data-preparation.yml

@@ -0,0 +1,64 @@
+name: auto-ml-forecasting-data-preparation
+# This file is generated by v1/python-sdk/tutorials/automl-with-azureml/generate_workflows.py
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: "0 10 * * 2"
+  pull_request:
+    branches:
+      - main
+    paths:
+      - v1/python-sdk/tutorials/automl-with-azureml/forecasting-data-preparation/**
+      - v1/python-sdk/tutorials/automl-with-azureml/automl_env_linux.yml
+      - .github/workflows/python-sdk-tutorial-auto-ml-forecasting-data-preparation.yml
+jobs:
+  build:
+    runs-on: ${{vars.V1_UBUNTU_RUNNER}}
+    defaults:
+      run:
+        shell: bash -l {0}
+    strategy:
+      fail-fast: false
+    steps:
+    - name: check out repo
+      uses: actions/checkout@v2
+    - name: setup python
+      uses: actions/setup-python@v2
+      with:
+        python-version: "3.8"
+    - name: Run Install packages
+      run: |
+         chmod +x ./v1/scripts/install-packages.sh
+         ./v1/scripts/install-packages.sh
+      shell: bash
+    - name: create automl conda environment
+      uses: conda-incubator/setup-miniconda@v2
+      with:
+          activate-environment: azure_automl
+          environment-file: v1/python-sdk/tutorials/automl-with-azureml/automl_env_linux.yml
+          auto-activate-base: false
+    - name: install papermill and set up the IPython kernel
+      run: |
+        pip install papermill==2.4.0
+        python -m ipykernel install --user --name azure_automl --display-name "Python (azure_automl)"
+        pip list
+    - name: azure login
+      uses: azure/login@v1
+      with:
+        creds: ${{secrets.AZ_CREDS}}
+    - name: Run update-azure-extensions
+      run: |
+         chmod +x ./v1/scripts/update-azure-extensions.sh
+         ./v1/scripts/update-azure-extensions.sh
+      shell: bash
+    - name: attach to workspace
+      run: az ml folder attach -w main -g azureml-examples
+    - name: run auto-ml-forecasting-data-preparation.ipynb
+      run: papermill -k python auto-ml-forecasting-data-preparation.ipynb auto-ml-forecasting-data-preparation.output.ipynb
+      working-directory: v1/python-sdk/tutorials/automl-with-azureml/forecasting-data-preparation
+    - name: upload notebook's working folder as an artifact
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v2
+      with:
+        name: auto-ml-forecasting-data-preparation
+        path: v1/python-sdk/tutorials/automl-with-azureml/forecasting-data-preparation

.github/workflows/python-sdk-tutorial-auto-ml-forecasting-demand-tcn.yml

@@ -0,0 +1,64 @@
+name: auto-ml-forecasting-demand-tcn
+# This file is generated by v1/python-sdk/tutorials/automl-with-azureml/generate_workflows.py
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: "0 11 * * 2"
+  pull_request:
+    branches:
+      - main
+    paths:
+      - v1/python-sdk/tutorials/automl-with-azureml/forecasting-demand-forecasting-tcn/**
+      - v1/python-sdk/tutorials/automl-with-azureml/automl_env_linux.yml
+      - .github/workflows/python-sdk-tutorial-auto-ml-forecasting-demand-tcn.yml
+jobs:
+  build:
+    runs-on: ${{vars.V1_UBUNTU_RUNNER}}
+    defaults:
+      run:
+        shell: bash -l {0}
+    strategy:
+      fail-fast: false
+    steps:
+    - name: check out repo
+      uses: actions/checkout@v2
+    - name: setup python
+      uses: actions/setup-python@v2
+      with:
+        python-version: "3.8"
+    - name: Run Install packages
+      run: |
+         chmod +x ./v1/scripts/install-packages.sh
+         ./v1/scripts/install-packages.sh
+      shell: bash
+    - name: create automl conda environment
+      uses: conda-incubator/setup-miniconda@v2
+      with:
+          activate-environment: azure_automl
+          environment-file: v1/python-sdk/tutorials/automl-with-azureml/automl_env_linux.yml
+          auto-activate-base: false
+    - name: install papermill and set up the IPython kernel
+      run: |
+        pip install papermill==2.4.0
+        python -m ipykernel install --user --name azure_automl --display-name "Python (azure_automl)"
+        pip list
+    - name: azure login
+      uses: azure/login@v1
+      with:
+        creds: ${{secrets.AZ_CREDS}}
+    - name: Run update-azure-extensions
+      run: |
+         chmod +x ./v1/scripts/update-azure-extensions.sh
+         ./v1/scripts/update-azure-extensions.sh
+      shell: bash
+    - name: attach to workspace
+      run: az ml folder attach -w main -g azureml-examples
+    - name: run auto-ml-forecasting-demand-tcn.ipynb
+      run: papermill -k python auto-ml-forecasting-demand-tcn.ipynb auto-ml-forecasting-demand-tcn.output.ipynb
+      working-directory: v1/python-sdk/tutorials/automl-with-azureml/forecasting-demand-forecasting-tcn
+    - name: upload notebook's working folder as an artifact
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v2
+      with:
+        name: auto-ml-forecasting-demand-tcn
+        path: v1/python-sdk/tutorials/automl-with-azureml/forecasting-demand-forecasting-tcn