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Merge pull request #13 from nv-gpriest/main
Re-added Tutorial App through PC->Apps. This fixes the issue where the Tutorial App is removed from the Onboarding project when Python Base is updated in AI Workbench.
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.project/spec.yaml

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@@ -23,8 +23,8 @@ layout:
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environment:
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base:
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registry: nvcr.io
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image: nvidia/ai-workbench/python-basic:1.0.2
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build_timestamp: "20241001182612"
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image: nvidia/ai-workbench/python-basic:1.0.6
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build_timestamp: "20250205043314"
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name: Python Basic
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supported_architectures: []
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cuda_version: ""
@@ -35,26 +35,6 @@ environment:
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- python3
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- jupyterlab
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apps:
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- name: Tutorial
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type: custom
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class: webapp
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start_command: |
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cd /project/code/tutorial_app && \
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export PROXY_PREFIX && \
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streamlit run streamlit_app.py --server.baseUrlPath=$PROXY_PREFIX
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health_check_command: curl -I http://localhost:8501
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stop_command: |
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pkill -f 'streamlit run streamlit_app.py'
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user_msg: ""
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logfile_path: ""
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timeout_seconds: 30
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icon_url: ""
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webapp_options:
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autolaunch: true
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port: "8501"
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proxy:
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trim_prefix: false
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url: http://localhost:8501
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- name: jupyterlab
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type: jupyterlab
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class: webapp
@@ -74,7 +54,7 @@ environment:
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programming_languages:
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- python3
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icon_url: https://workbench.download.nvidia.com/static/img/ai-workbench-icon-rectangle.jpg
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image_version: 1.0.5
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image_version: 1.0.6
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os: linux
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os_distro: ubuntu
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os_distro_release: "22.04"
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- name: pip
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binary_path: /usr/bin/pip
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installed_packages:
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- jupyterlab==4.2.4
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- jupyterlab==4.2.5
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package_manager_environment:
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name: ""
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target: ""
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compose_file_path: ""
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execution:
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apps: []
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apps:
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- name: Tutorial
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type: custom
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class: webapp
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start_command: |
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cd /project/code/tutorial_app && \
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export PROXY_PREFIX && \
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streamlit run streamlit_app.py --server.baseUrlPath=$PROXY_PREFIX
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health_check_command: curl -I http://localhost:8501
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stop_command: pkill -f 'streamlit run streamlit_app.py'
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user_msg: ""
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logfile_path: ""
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timeout_seconds: 30
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icon_url: ""
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webapp_options:
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autolaunch: true
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port: "8501"
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proxy:
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trim_prefix: false
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url: http://localhost:8501
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resources:
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gpu:
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requested: 0

code/tutorial_app/pages/basic_01.en_US.yaml

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@@ -39,7 +39,7 @@ tasks:
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msg: |
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##### The Project Tab has a file browser to access, manage and edit files.
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* Click **File Browser** in the LHS menu.
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* Click **File Browser** in the main menu bar on the left-side of **AI Workbench**.
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* Click **Create Folder**, and name it `my-first-folder`.
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response: |
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> **Note:** On your local system, you could have done this in multiple ways without Workbench.
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However, if the project were on a remote location, you would have had to transfer
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the file via something like `scp`, or you could have used JupyterLab's file browser to upload the files.
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the file through something like `scp`, or you could have used JupyterLab's file browser to upload the files.
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test: check_file_in_folder
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* The `tutorial_app` folder. It has the code for this app.
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* The `example-file.txt` file.
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* The `delete-me.txt` file.
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* Click the option dots on the RHS of the `delete-me.txt` file and select `Delete`.
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* Click the option dots on the right-side of the `delete-me.txt` file and select `Delete`.
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response: |
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##### Great. You deleted the `delete-me.txt` file.
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##### Awesome! You deleted the `delete-me.txt` file.
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test: check_file_deleted
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For example, **Edit** lets you view and edit a text file without a full editor. It's not a
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full-featured editor, but it's good for small edits.
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* Click the option dots on the RHS of the `example-file.txt` file and select `Edit`.
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* Click the option dots on the right-side of the `example-file.txt` file and select `Edit`.
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* A bare bones file editor will open. Add some text like `Hello, World!` and click **Save**.
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response: |
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##### Nice. You edited the file.
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##### Congratulations!. You edited the file.
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> **Note:** Again, you can do this multiple ways locally, but if the file were a remote location you would
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need to open up JupyterLab or jump through some hoops in a terminal.
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##### Workbench lets you manage packages without using the command line.
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* Click **Environment > Project Container**
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* Click **Packages** on the RHS to see installed packages.
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* Click **Packages** on the right-side menu to see installed packages.
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* Click *Add* and select `apt`
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* Enter `jq` in the bar, click **Add** and then **Submit**
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Seeing these differences on a file-by-file basis helps you decide whether to commit changes or stick with the current version of a file.
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* Go to **Git > Changes** to see the changes you made in this exercise.
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* The left pane lists the files and the change type: `A` is *added*, `M` is *modified*, and `D` is *deleted*.
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* The left pane lists the files and the change type:
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* `A` is *added*
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* `M` is *modified*
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* `D` is *deleted*
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* Clicking an individual file shows the line by line changes in the right pane.
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* You can resize the left and right panes by grabbing the divider between them and dragging it.
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code/tutorial_app/pages/basic_02.en_US.yaml

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@@ -68,7 +68,7 @@ tasks:
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##### The most obvious way is the big green button that says **JupyterLab**.
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However, let's do it the hard way to give you a more experience with the Status Bar.
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* Click the `0 apps running` in the bottom RHS of the status bar.
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* Click the `0 apps running` in the far right-side of the bottom status bar.
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* Click the toggle to start JupyterLab. It may take a second but will open in your browser.
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> **Note**: The Desktop App should be in the Project Tab for `my-first-project`, **not** for `nvidia-ai-workbench-onboarding`.

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