Merge pull request #16 from aisingapore/0.2.1-fix

0.2.1 fixes

.github/ISSUE_TEMPLATE/bug.yml

@@ -6,27 +6,28 @@ assignees: ["syakyr", "deonchia"]
 body:
   - type: markdown
     attributes:
-      value: |
+      value:
         Please fill this form if you have any bugs you wish to resolve 
         when using Kapitan Hull.
   - type: checkboxes
     attributes:
       label: Problem Domain
-      description: \
+      description:
         Let us know which service(s) this problem has impacted.  
         You can choose more than one domain depending on your issue.
       options:
         - label: Docker
         - label: Run:ai
         - label: MLFlow
         - label: Kubernetes 
+        - label: Gitlab
         - label: Other issue
     validations:
       required: true
   - type: checkboxes
     attributes:
       label: OS/Platform(s) Used
-      description: \
+      description:
         What OS or platform(s) did you encounter this issue? Specify
         exact version or distro in the Problem Brief if needed.
       options:
@@ -37,19 +38,20 @@ body:
         - label: Coder
         - label: Run:ai VSCode
         - label: Run:ai JupyterLab
+        - label: Other platforms/None
     validations:
       required: true
   - type: textarea
     attributes:
       label: Problem Brief
-      description: |
+      description:
         Briefly explain what the problem is about in 1 to 3 sentences.
     validations:
       required: true
   - type: textarea
     attributes:
       label: Steps to Reproduce
-      description: |
+      description:
         This may be the most grueling step to write about as you may 
         need to trace back your steps on how you encounter it. If it's 
         a random chance that you may get the issue, estimate the chance 
@@ -59,7 +61,7 @@ body:
   - type: textarea
     attributes:
       label: Expected Result
-      description: |
+      description:
         What is the expected result when reproducing the steps?
     validations:
       required: true

.github/ISSUE_TEMPLATE/query.yml

@@ -5,27 +5,28 @@ labels: ["triage"]
 body:
   - type: markdown
     attributes:
-      value: |
+      value: 
         Please fill this form if you have any queries/issues with 
         Kapitan Hull.
   - type: checkboxes
     attributes:
       label: Query Domain
-      description: \
+      description:
         Let us know which service(s) this query is about.  
         You can choose more than one domain depending on your query.
       options:
         - label: Docker
         - label: Run:ai
         - label: MLFlow
         - label: Kubernetes 
+        - label: Gitlab
         - label: Other issue
     validations:
       required: true
   - type: textarea
     attributes:
       label: Query Brief
-      description: |
+      description: 
         Briefly explain what the query is about in 1 to 3 sentences.
     validations:
       required: true

{{cookiecutter.repo_name}}/.gitlab-ci.yml

@@ -18,6 +18,7 @@ test:conda-build:
       changes:
         - {{cookiecutter.repo_name}}-conda-env.yaml
     - if: $CI_PIPELINE_SOURCE == "push"
+    - if: $CI_PIPELINE_SOURCE == "web"
     - if: $CI_COMMIT_TAG
       when: never
   artifacts:
@@ -41,6 +42,7 @@ test:pylint-pytest:
         - src/**/*
         - conf/**/*
     - if: $CI_PIPELINE_SOURCE == "push"
+    - if: $CI_PIPELINE_SOURCE == "web"
     - if: $CI_COMMIT_TAG
       when: never
   needs:

{{cookiecutter.repo_name}}/aisg-context/guide-site/docs/guide-for-user/04-dev-wksp.md

@@ -4,19 +4,20 @@ An advantage presented by orchestration platforms is that you can
 utilise the Kubernetes cluster's resources for your development and
 engineering works instead of your own resources.
 
-We can make use of
-[Run:ai workspaces](https://docs.run.ai/v2.13/Researcher/user-interface/workspaces/overview/)
-to spin up VSCode or JupyterLab servers with which cluster resources
-can be dedicated.
+We can make use of either [Coder][coder] or 
+[Run:ai workspaces][runai-wksp] to spin up VSCode or JupyterLab servers 
+with which cluster resources can be dedicated.
 
-While there exist the option for engineers to set up either a VSCode
-or JupyterLab service (or both), the former would suffice. Reason
+While there exist the option for engineers to set up either a VSCode or
+JupyterLab service (or both), the former would suffice. Reason being
 being VSCode is an excellent code editor with integrated terminal
-capabilities and it can also work with Jupyter notebooks.
-JupyterLab on the other hand, while being the best interface for
-Jupyter notebooks, has subpar UX for its terminal and code editor.
-That is to be expected however as it is dedicated to the
-Jupyter ecosystem.
+capabilities and it can also work with Jupyter notebooks. JupyterLab
+on the other hand, while being the best interface for Jupyter 
+notebooks, has subpar UX for its terminal and code editor. That is to 
+be expected however as it is dedicated to the Jupyter ecosystem.
+
+[coder]: https://coder.com/
+[runai-wksp]: https://docs.run.ai/v2.13/Researcher/user-interface/workspaces/overview/
 
 ## Workspace Building Blocks
 
@@ -177,9 +178,9 @@ either method to gain access to a remote VSCode developer workspace.
 ### Persistent Workspaces
 
 !!! warning "Attention"
-    If you have spun up using the Run:ai YAML method, then you can 
-    skip this step since you've already prepared your workspace prior 
-    to spinning it up.
+    If you have spun up using the Run:ai YAML method, then you can skip
+    this step since you've already prepared your workspace prior to
+    spinning it up.
 
 As mentioned, a PVC should be attached to the workspaces to persist
 changes to the filesystems. You can use the following command to search
@@ -194,9 +195,9 @@ for the PVC:
 If there's no result, check with the MLOps team about this.
 
 By default, the PVCs would contain a `workspaces` directory with which
-you can create a subdirectory for yourself treat it as your own personal
-workspace, where all your work and other relevant assets can be
-persisted.
+you can create a subdirectory for yourself treat it as your own 
+personal workspace, where all your work and other relevant assets can 
+be persisted.
 
 === "VSCode Server Terminal"
 
@@ -208,8 +209,8 @@ persisted.
 ### Git from VSCode
 
 To clone or push to Git repositories within the VSCode integrated
-terminal, it is recommended that you first disable VSCode's
-Git authentication handler:
+terminal, it is recommended that you first disable VSCode's Git
+authentication handler:
 
 Git by default is installed in the VSCode server image. One thing to
 take note is that as the persistent storage would be accessible by the
@@ -245,6 +246,16 @@ if you intend to use Jupyter notebooks within the VSCode environment.
 - [`ms-toolsai.jupyter`][vsx-jy]: Official extension by Microsoft 
   for Jupyter support.
 
+!!! warning "Manual Installation"
+    For some clusters, you may need to install the extensions manually
+    due to firewall issues. If that is the case, you can download the
+    extension(s) through your local machine and upload them to the 
+    VSCode terminal. From there, you can make use of the following 
+    command:
+    ```
+    $ code-server --install-extension /path/to/extension.vsix
+    ```
+
 !!! warning "Attention"
     Do head over [here][jy-vscode] on how to enable the usage of 
     virtual `conda` environments within VSCode.
@@ -319,10 +330,12 @@ interface for interacting with or editing notebooks.
 
     !!! warning "Attention"
         Under the `Environment` block, there is an expandable section 
-        called `More settings`. Under this section, you can provide more 
-        arguments for a container that will be spun up for the 
-        workspace. For the JupyterLab interface to be able to access any 
-        PVC mounted to the container, you should include the following argument: `--NotebookApp.notebook_dir="/path/to/pvc"`.
+        called `More settings`. Under this section, you can provide 
+        more arguments for a container that will be spun up for the 
+        workspace. For the JupyterLab interface to be able to access 
+        any PVC mounted to the container, you should include the 
+        following argument: 
+        `--NotebookApp.notebook_dir="/path/to/pvc"`.
 
     Once you have selected the blocks, you can proceed to create the
     workspace and you will be redirected to the workspaces page. On this
@@ -427,10 +440,10 @@ server as well as any associated files can be found under
 ## Using Docker within Kubernetes
 
 !!! caution
-    Since these development environments are essentially pods
-    deployed within a Kubernetes cluster, using Docker within the pods
-    themselves is not feasible by default and while possible,
-    should be avoided.
+    Since these development environments are essentially pods deployed
+    within a Kubernetes cluster, using Docker within the pods
+    themselves is not feasible by default and while possible, should
+    be avoided.
 
 ??? info "Reference Link(s)"
 

{{cookiecutter.repo_name}}/aisg-context/guide-site/docs/guide-for-user/05-virtual-env.md

@@ -22,7 +22,8 @@ workspace directory:
 === "VSCode Server Terminal"
 
     ```bash
-    (base) $ conda env create -f {{cookiecutter.repo_name}}-conda-env.yaml \
+    (base) $ conda env create \
+                -f {{cookiecutter.repo_name}}-conda-env.yaml \
                 -p /<NAME_OF_DATA_SOURCE>/workspaces/<YOUR_HYPHENATED_NAME>/conda_envs/{{cookiecutter.repo_name}}
     ```
 
@@ -66,33 +67,30 @@ server to detect the `conda` environments that you would have created.
   In this case, you should be navigating to the folder
   `/<NAME_OF_DATA_SOURCE>/workspaces/<YOUR_HYPHENATED_NAME>/{{cookiecutter.repo_name}}`.
 
-- Install the VSCode extensions
-  [`ms-python.python`](https://marketplace.visualstudio.com/items?itemName=ms-python.python)
-  and
-  [`ms-toolsai.jupyter`](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter).
-  After installation of these extensions, restart VSCode by using
-  the shortcut `Ctrl + Shift + P`, entering `Developer: Reload Window` in the
-  prompt and pressing `Enter` following that.
-
-- Ensure that you have
-  [`ipykernel`](https://ipython.readthedocs.io/en/stable/install/kernel_install.html)
-  installed in the `conda` environment that you intend to use.
-  This template by default lists the library as a dependency under
+- Install the VSCode extensions [`ms-python.python`][py-ext] and
+  [`ms-toolsai.jupyter`][jy-ext]. After installation of these 
+  extensions, restart VSCode by using the shortcut `Ctrl + Shift + P`, 
+  entering `Developer: Reload Window` in the prompt and pressing 
+  `Enter` following that.
+
+- Ensure that you have [`ipykernel`][ipyk] installed in the `conda` 
+  environment that you intend to use. This template by default lists 
+  the library as a dependency under 
   `{{cookiecutter.repo_name}}-conda-env.yaml`. You can check for the
   library like so:
 
 === "VSCode Server Terminal"
 
     ```bash
-    $ conda activate /<NAME_OF_DATA_SOURCE>/workspaces/<YOUR_HYPHENATED_NAME>/conda_envs/{{cookiecutter.repo_name}}-conda-env
+    $ conda activate /<NAME_OF_DATA_SOURCE>/workspaces/<YOUR_HYPHENATED_NAME>/conda_envs/{{cookiecutter.repo_name}}
     $ conda list | grep "ipykernel"
     ipykernel  X.X.X  pypi_0  pypi
     ```
 
-- Now enter `Ctrl + Shift + P` again and execute `Python: Select Interpreter`.
-  Provide the path to the Python executable within the `conda`
-  environment that you intend to use, something like so:
-  `path/to/conda_env/bin/python`.
+- Now enter `Ctrl + Shift + P` again and execute 
+  `Python: Select Interpreter`. Provide the path to the Python 
+  executable within the `conda` environment that you intend to use, 
+  something like so: `path/to/conda_env/bin/python`.
 
 - Open up any Jupyter notebook and click on the button that says
   `Select Kernel` on the top right hand corner. You will be presented
@@ -102,27 +100,29 @@ server to detect the `conda` environments that you would have created.
 - Test out the kernel by running the cells in the sample notebook
   provided under `notebooks/sample-pytorch-notebook.ipynb`.
 
+[py-ext]: https://marketplace.visualstudio.com/items?itemName=ms-python.python
+[jy-ext]: https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter
+[ipyk]: https://ipython.readthedocs.io/en/stable/install/kernel_install.html
+
 ## Jupyter Kernel for JupyterLab
 
-The same with the VSCode server, the JupyterLab server
-would not by default detect `conda` environments. You would have to
-specify to the JupyterLab installation the `ipython` kernel existing
-within your `conda` environment.
+The same with the VSCode server, the JupyterLab server would not by 
+default detect `conda` environments. You would have to specify to the 
+JupyterLab installation the `ipython` kernel existing within your 
+`conda` environment.
 
-- Open up a
-  [terminal within JupyterLab](https://jupyterlab.readthedocs.io/en/stable/user/terminal.html).
+- Open up a [terminal within JupyterLab][jy-tty].
 
 - Activate the `conda` environment in question and ensure that you have
-  [`ipykernel`](https://ipython.readthedocs.io/en/stable/install/kernel_install.html)
-  installed in the `conda` environment that you intend to use.
-  This template by default lists the library as a dependency under
-  `{{cookiecutter.repo_name}}-conda-env.yaml`. You can check for the
-  library like so:
+  [`ipykernel`][ipyk] installed in the `conda` environment that you 
+  intend to use. This template by default lists the library as a 
+  dependency under `{{cookiecutter.repo_name}}-conda-env.yaml`. You can 
+  check for the library like so:
 
 === "JupyterLab Terminal"
 
     ```bash
-    $ conda activate /<NAME_OF_DATA_SOURCE>/workspaces/<YOUR_HYPHENATED_NAME>/conda_envs/{{cookiecutter.repo_name}}-conda-env
+    $ conda activate /<NAME_OF_DATA_SOURCE>/workspaces/<YOUR_HYPHENATED_NAME>/conda_envs/{{cookiecutter.repo_name}}
     $ conda list | grep "ipykernel"
     ipykernel  6.9.2  pypi_0  pypi
     ```
@@ -132,17 +132,16 @@ within your `conda` environment.
 === "JupyterLab Terminal"
 
     ```bash
-    $ ipython kernel install --name "{{cookiecutter.repo_name}}-conda-env" --user
+    $ ipython kernel install --name "{{cookiecutter.repo_name}}" --user
     ```
 
 - Refresh the page.
 
 - Open up the sample notebook provided under `notebooks/sample-pytorch-notebook.ipynb`.
 
-- Within each Jupyter notebook, you can select the kernel of
-  specific `conda` environments that you intend to use by heading to
-  the toolbar under
-  `Kernel` -> `Change Kernel...`.
+- Within each Jupyter notebook, you can select the kernel of specific
+  `conda` environments that you intend to use by heading to the toolbar 
+  under `Kernel` -> `Change Kernel...`.
 
 ![Run:ai - JupyterLab Server Change Kernel](assets/screenshots/runai-jupyterlab-server-change-kernel.png)
 
@@ -151,3 +150,5 @@ within your `conda` environment.
 ??? info "Reference Link(s)"
 
     - [Jupyter Docs - Kernels (Programming Languages)](https://docs.jupyter.org/en/latest/projects/kernels.html)
+
+[jy-tty]: https://jupyterlab.readthedocs.io/en/stable/user/terminal.html

{{cookiecutter.repo_name}}/aisg-context/guide-site/docs/guide-for-user/06-data-storage-versioning.md

@@ -3,10 +3,9 @@
 ## Sample Data
 
 While you may have your own project data to work with, for the purpose
-of following through with this template guide, let's download
-the sample data for the
-[sample problem statement](./02-preface.md#guides-problem-statement)
-at hand within our VSCode server workspace.
+of following through with this template guide, let's download the 
+sample data for the [sample problem statement][prob] at hand within our 
+VSCode server workspace.
 
 === "VSCode Server Terminal"
 
@@ -31,3 +30,5 @@ at hand within our VSCode server workspace.
 
 In the next section, we will work towards processing this set of raw
 data and eventually training an image classification model.
+
+[prob]: ./02-preface.md#guides-problem-statement