redhat-ai-dev · johnmcollier · Aug 8, 2024 · Aug 7, 2024 · Aug 7, 2024 · Aug 8, 2024
diff --git a/scripts/envs/audio-to-text b/scripts/envs/audio-to-text
@@ -2,6 +2,7 @@ export APP_NAME="audio-to-text"
 export APP_DISPLAY_NAME="Audio to Text Application"
 export APP_DESC="Audio to Text Application example with AI enabled audio transcription"
 export APP_TAGS='["ai", "whispercpp", "python", "asr"]'
+export APP_RUN_COMMAND="streamlit run whisper_client.py"
 export INIT_CONTAINER="quay.io/redhat-ai-dev/whisper-small:latest"
 export INIT_CONTAINER_COMMAND="['/usr/bin/install', '/model/model.file', '/shared/']"
 export MODEL_PATH="/model/model.file"

diff --git a/scripts/envs/chatbot b/scripts/envs/chatbot
@@ -2,6 +2,7 @@ export APP_NAME="chatbot"
 export APP_DISPLAY_NAME="Chatbot Application"
 export APP_DESC="Chatbot Application example with LLM enabled chat applications"
 export APP_TAGS='["ai", "llamacpp", "vllm", "python"]'
+export APP_RUN_COMMAND="streamlit run chatbot_ui.py"
 export INIT_CONTAINER="quay.io/redhat-ai-dev/granite-7b-lab:latest"
 export INIT_CONTAINER_COMMAND="['/usr/bin/install', '/model/model.file', '/shared/']"
 export MODEL_PATH="/model/model.file"

diff --git a/scripts/envs/codegen b/scripts/envs/codegen
@@ -2,6 +2,7 @@ export APP_NAME="codegen"
 export APP_DISPLAY_NAME="Code Generation Application"
 export APP_DESC="Code Generation Application example that generate code in countless programming languages."
 export APP_TAGS='["ai", "llamacpp", "vllm", "python"]'
+export APP_RUN_COMMAND="streamlit run codegen-app.py"
 export INIT_CONTAINER="quay.io/redhat-ai-dev/mistral-7b-code-16k-qlora:latest"
 export INIT_CONTAINER_COMMAND="['/usr/bin/install', '/model/model.file', '/shared/']"
 export MODEL_PATH="/model/model.file"

diff --git a/scripts/envs/object-detection b/scripts/envs/object-detection
@@ -2,6 +2,7 @@ export APP_NAME="object-detection"
 export APP_DISPLAY_NAME="Object Detection Application"
 export APP_DESC="AI enabled Object Detection Application example using DEtection TRansformer(DETR) model to detect objects in an image"
 export APP_TAGS='["ai", "detr", "python"]'
+export APP_RUN_COMMAND="streamlit run object_detection_client.py"
 export INIT_CONTAINER="quay.io/redhat-ai-dev/detr-resnet-101:latest"
 export INIT_CONTAINER_COMMAND="['cp', '-R', '/model/detr-resnet-101', '/shared/detr-resnet-101']"
 export MODEL_PATH="/model/detr-resnet-101"

diff --git a/skeleton/techdoc/docs/.assets/open-terminal.png b/skeleton/techdoc/docs/.assets/open-terminal.png
diff --git a/skeleton/techdoc/docs/.assets/workbench-name.png b/skeleton/techdoc/docs/.assets/workbench-name.png
diff --git a/skeleton/techdoc/docs/rhoai.md b/skeleton/techdoc/docs/rhoai.md
@@ -0,0 +1,37 @@
+# Running Samples in OpenShift AI
+
+This document will outline how you can run build and run your sample applications within an OpenShift AI workbench.
+
+## Prerequisites
+
+- Red Hat OpenShift AI installed, and `Create workbench for OpenShift AI` selected during component creation.
+- `oc` cli installed
+   - `oc` can be downloaded from https://mirror.openshift.com/pub/openshift-v4/clients/ocp/stable/
+- Permissions to run `oc port-forward` on the cluster, specifically an account with the following roles:
+   - `get`, `create`, and `list` for the `pods/portforward` subresource
+
+## Running the Sample
+
+1) Navigate to the OpenShift AI workbench created for your sample application
+
+2) Go to `File->Open` and select `Terminal`
+![image](./.assets/open-terminal.png)
+
+3) In the terminal, run `cd ${{ values.name }}` to navigate to your sample app's directory
+
+4) Run `pip install --upgrade -r requirements.txt` to install the dependencies for your application
+
+5) Run `${{ values.appRunCommand }}` to run the sample in the workbench.
+
+## Accessing the Sample
+
+With the sample app now running, the following steps will allow you to access the sample app in your browser:
+
+1) Navigate back to the OpenShift AI dashboard, and find the name of your workbench.
+![image](./.assets/workbench-name.png)
+
+2) In a terminal window on your machine, run `oc get pods -l app=<workbench-name>`. This will retrieve the name of the pod where the workbench is running.
+
+3) Run `oc port-forward <pod-name> ${{ values.appPort }}` to port forward the sample application's port to your local machine.
+
+4) Finally, visit `http://localhost:${{ values.appPort }}` in your browser to access the application.
diff --git a/skeleton/techdoc/mkdocs.yml b/skeleton/techdoc/mkdocs.yml
@@ -5,6 +5,7 @@ nav:
   - Source Component: source-component.md
   - Pipelines: pipelines.md
   - GitOps Application: gitops-application.md
+  - OpenShift AI: rhoai.md
 
 plugins:
   - techdocs-core
diff --git a/skeleton/template.yaml b/skeleton/template.yaml
@@ -205,6 +205,7 @@ spec:
           srcRepoURL: https://${{ parameters.githubServer if parameters.hostType === 'GitHub' else parameters.gitlabServer }}/${{ parameters.repoOwner }}/${{ parameters.repoName }}
           appContainer: ${{ 'quay.io/redhat-ai-dev/ai-template-bootstrap-app:latest' if parameters.hostType === 'GitHub' else '${APP_INTERFACE_CONTAINER}' }}
           appPort: ${APP_PORT}
+          appRunCommand: "${APP_RUN_COMMAND}"
           modelServiceContainer: ${MODEL_SERVICE_CONTAINER}
           modelServicePort: ${MODEL_SERVICE_PORT}          
     # Renders all the template variables into the files and directory names and content, and places the result in the workspace.
@@ -225,7 +226,7 @@ spec:
           tags: 'sed.edit.APPTAGS'
           owner: ${{ parameters.owner }} 
           repoSlug: '${{ parameters.imageOrg }}/${{ parameters.imageName }}'
-          defaultBranch: ${{ parameters.branch }} 
+          defaultBranch: ${{ parameters.branch }}
     - id: fetch-github-action
       name: Fetch GitHub Action
       action: fetch:plain

diff --git a/templates/audio-to-text/content/docs/.assets/open-terminal.png b/templates/audio-to-text/content/docs/.assets/open-terminal.png
diff --git a/templates/audio-to-text/content/docs/.assets/workbench-name.png b/templates/audio-to-text/content/docs/.assets/workbench-name.png
diff --git a/templates/audio-to-text/content/docs/rhoai.md b/templates/audio-to-text/content/docs/rhoai.md
@@ -0,0 +1,37 @@
+# Running Samples in OpenShift AI
+
+This document will outline how you can run build and run your sample applications within an OpenShift AI workbench.
+
+## Prerequisites
+
+- Red Hat OpenShift AI installed, and `Create workbench for OpenShift AI` selected during component creation.
+- `oc` cli installed
+   - `oc` can be downloaded from https://mirror.openshift.com/pub/openshift-v4/clients/ocp/stable/
+- Permissions to run `oc port-forward` on the cluster, specifically an account with the following roles:
+   - `get`, `create`, and `list` for the `pods/portforward` subresource
+
+## Running the Sample
+
+1) Navigate to the OpenShift AI workbench created for your sample application
+
+2) Go to `File->Open` and select `Terminal`
+![image](./.assets/open-terminal.png)
+
+3) In the terminal, run `cd ${{ values.name }}` to navigate to your sample app's directory
+
+4) Run `pip install --upgrade -r requirements.txt` to install the dependencies for your application
+
+5) Run `${{ values.appRunCommand }}` to run the sample in the workbench.
+
+## Accessing the Sample
+
+With the sample app now running, the following steps will allow you to access the sample app in your browser:
+
+1) Navigate back to the OpenShift AI dashboard, and find the name of your workbench.
+![image](./.assets/workbench-name.png)
+
+2) In a terminal window on your machine, run `oc get pods -l app=<workbench-name>`. This will retrieve the name of the pod where the workbench is running.
+
+3) Run `oc port-forward <pod-name> ${{ values.appPort }}` to port forward the sample application's port to your local machine.
+
+4) Finally, visit `http://localhost:${{ values.appPort }}` in your browser to access the application.
diff --git a/templates/audio-to-text/content/mkdocs.yml b/templates/audio-to-text/content/mkdocs.yml
@@ -5,6 +5,7 @@ nav:
   - Source Component: source-component.md
   - Pipelines: pipelines.md
   - GitOps Application: gitops-application.md
+  - OpenShift AI: rhoai.md
 
 plugins:
   - techdocs-core
diff --git a/templates/audio-to-text/template.yaml b/templates/audio-to-text/template.yaml
@@ -165,6 +165,7 @@ spec:
           srcRepoURL: https://${{ parameters.githubServer if parameters.hostType === 'GitHub' else parameters.gitlabServer }}/${{ parameters.repoOwner }}/${{ parameters.repoName }}
           appContainer: ${{ 'quay.io/redhat-ai-dev/ai-template-bootstrap-app:latest' if parameters.hostType === 'GitHub' else 'quay.io/redhat-ai-dev/audio-to-text:latest' }}
           appPort: 8501
+          appRunCommand: "streamlit run whisper_client.py"
           modelServiceContainer: quay.io/redhat-ai-dev/whispercpp:latest
           modelServicePort: 8001          
     # Renders all the template variables into the files and directory names and content, and places the result in the workspace.
@@ -185,7 +186,7 @@ spec:
           tags: '["ai", "whispercpp", "python", "asr"]'
           owner: ${{ parameters.owner }} 
           repoSlug: '${{ parameters.imageOrg }}/${{ parameters.imageName }}'
-          defaultBranch: ${{ parameters.branch }} 
+          defaultBranch: ${{ parameters.branch }}
     - id: fetch-github-action
       name: Fetch GitHub Action
       action: fetch:plain

diff --git a/templates/chatbot/content/docs/.assets/open-terminal.png b/templates/chatbot/content/docs/.assets/open-terminal.png
diff --git a/templates/chatbot/content/docs/.assets/workbench-name.png b/templates/chatbot/content/docs/.assets/workbench-name.png
diff --git a/templates/chatbot/content/docs/rhoai.md b/templates/chatbot/content/docs/rhoai.md
@@ -0,0 +1,37 @@
+# Running Samples in OpenShift AI
+
+This document will outline how you can run build and run your sample applications within an OpenShift AI workbench.
+
+## Prerequisites
+
+- Red Hat OpenShift AI installed, and `Create workbench for OpenShift AI` selected during component creation.
+- `oc` cli installed
+   - `oc` can be downloaded from https://mirror.openshift.com/pub/openshift-v4/clients/ocp/stable/
+- Permissions to run `oc port-forward` on the cluster, specifically an account with the following roles:
+   - `get`, `create`, and `list` for the `pods/portforward` subresource
+
+## Running the Sample
+
+1) Navigate to the OpenShift AI workbench created for your sample application
+
+2) Go to `File->Open` and select `Terminal`
+![image](./.assets/open-terminal.png)
+
+3) In the terminal, run `cd ${{ values.name }}` to navigate to your sample app's directory
+
+4) Run `pip install --upgrade -r requirements.txt` to install the dependencies for your application
+
+5) Run `${{ values.appRunCommand }}` to run the sample in the workbench.
+
+## Accessing the Sample
+
+With the sample app now running, the following steps will allow you to access the sample app in your browser:
+
+1) Navigate back to the OpenShift AI dashboard, and find the name of your workbench.
+![image](./.assets/workbench-name.png)
+
+2) In a terminal window on your machine, run `oc get pods -l app=<workbench-name>`. This will retrieve the name of the pod where the workbench is running.
+
+3) Run `oc port-forward <pod-name> ${{ values.appPort }}` to port forward the sample application's port to your local machine.
+
+4) Finally, visit `http://localhost:${{ values.appPort }}` in your browser to access the application.
diff --git a/templates/chatbot/content/mkdocs.yml b/templates/chatbot/content/mkdocs.yml
@@ -5,6 +5,7 @@ nav:
   - Source Component: source-component.md
   - Pipelines: pipelines.md
   - GitOps Application: gitops-application.md
+  - OpenShift AI: rhoai.md
 
 plugins:
   - techdocs-core
diff --git a/templates/chatbot/template.yaml b/templates/chatbot/template.yaml
@@ -179,6 +179,7 @@ spec:
           srcRepoURL: https://${{ parameters.githubServer if parameters.hostType === 'GitHub' else parameters.gitlabServer }}/${{ parameters.repoOwner }}/${{ parameters.repoName }}
           appContainer: ${{ 'quay.io/redhat-ai-dev/ai-template-bootstrap-app:latest' if parameters.hostType === 'GitHub' else 'quay.io/redhat-ai-dev/chatbot:latest' }}
           appPort: 8501
+          appRunCommand: "streamlit run chatbot_ui.py"
           modelServiceContainer: quay.io/ai-lab/llamacpp_python:latest
           modelServicePort: 8001          
     # Renders all the template variables into the files and directory names and content, and places the result in the workspace.
@@ -199,7 +200,7 @@ spec:
           tags: '["ai", "llamacpp", "vllm", "python"]'
           owner: ${{ parameters.owner }} 
           repoSlug: '${{ parameters.imageOrg }}/${{ parameters.imageName }}'
-          defaultBranch: ${{ parameters.branch }} 
+          defaultBranch: ${{ parameters.branch }}
     - id: fetch-github-action
       name: Fetch GitHub Action
       action: fetch:plain

diff --git a/templates/codegen/content/docs/.assets/open-terminal.png b/templates/codegen/content/docs/.assets/open-terminal.png
diff --git a/templates/codegen/content/docs/.assets/workbench-name.png b/templates/codegen/content/docs/.assets/workbench-name.png
diff --git a/templates/codegen/content/docs/rhoai.md b/templates/codegen/content/docs/rhoai.md
@@ -0,0 +1,37 @@
+# Running Samples in OpenShift AI
+
+This document will outline how you can run build and run your sample applications within an OpenShift AI workbench.
+
+## Prerequisites
+
+- Red Hat OpenShift AI installed, and `Create workbench for OpenShift AI` selected during component creation.
+- `oc` cli installed
+   - `oc` can be downloaded from https://mirror.openshift.com/pub/openshift-v4/clients/ocp/stable/
+- Permissions to run `oc port-forward` on the cluster, specifically an account with the following roles:
+   - `get`, `create`, and `list` for the `pods/portforward` subresource
+
+## Running the Sample
+
+1) Navigate to the OpenShift AI workbench created for your sample application
+
+2) Go to `File->Open` and select `Terminal`
+![image](./.assets/open-terminal.png)
+
+3) In the terminal, run `cd ${{ values.name }}` to navigate to your sample app's directory
+
+4) Run `pip install --upgrade -r requirements.txt` to install the dependencies for your application
+
+5) Run `${{ values.appRunCommand }}` to run the sample in the workbench.
+
+## Accessing the Sample
+
+With the sample app now running, the following steps will allow you to access the sample app in your browser:
+
+1) Navigate back to the OpenShift AI dashboard, and find the name of your workbench.
+![image](./.assets/workbench-name.png)
+
+2) In a terminal window on your machine, run `oc get pods -l app=<workbench-name>`. This will retrieve the name of the pod where the workbench is running.
+
+3) Run `oc port-forward <pod-name> ${{ values.appPort }}` to port forward the sample application's port to your local machine.
+
+4) Finally, visit `http://localhost:${{ values.appPort }}` in your browser to access the application.
diff --git a/templates/codegen/content/mkdocs.yml b/templates/codegen/content/mkdocs.yml
@@ -5,6 +5,7 @@ nav:
   - Source Component: source-component.md
   - Pipelines: pipelines.md
   - GitOps Application: gitops-application.md
+  - OpenShift AI: rhoai.md
 
 plugins:
   - techdocs-core
diff --git a/templates/codegen/template.yaml b/templates/codegen/template.yaml
@@ -179,6 +179,7 @@ spec:
           srcRepoURL: https://${{ parameters.githubServer if parameters.hostType === 'GitHub' else parameters.gitlabServer }}/${{ parameters.repoOwner }}/${{ parameters.repoName }}
           appContainer: ${{ 'quay.io/redhat-ai-dev/ai-template-bootstrap-app:latest' if parameters.hostType === 'GitHub' else 'quay.io/redhat-ai-dev/codegen:latest' }}
           appPort: 8501
+          appRunCommand: "streamlit run codegen-app.py"
           modelServiceContainer: quay.io/ai-lab/llamacpp_python:latest
           modelServicePort: 8001          
     # Renders all the template variables into the files and directory names and content, and places the result in the workspace.
@@ -199,7 +200,7 @@ spec:
           tags: '["ai", "llamacpp", "vllm", "python"]'
           owner: ${{ parameters.owner }} 
           repoSlug: '${{ parameters.imageOrg }}/${{ parameters.imageName }}'
-          defaultBranch: ${{ parameters.branch }} 
+          defaultBranch: ${{ parameters.branch }}
     - id: fetch-github-action
       name: Fetch GitHub Action
       action: fetch:plain

diff --git a/templates/object-detection/content/docs/.assets/open-terminal.png b/templates/object-detection/content/docs/.assets/open-terminal.png
diff --git a/templates/object-detection/content/docs/.assets/workbench-name.png b/templates/object-detection/content/docs/.assets/workbench-name.png
diff --git a/templates/object-detection/content/docs/rhoai.md b/templates/object-detection/content/docs/rhoai.md
@@ -0,0 +1,37 @@
+# Running Samples in OpenShift AI
+
+This document will outline how you can run build and run your sample applications within an OpenShift AI workbench.
+
+## Prerequisites
+
+- Red Hat OpenShift AI installed, and `Create workbench for OpenShift AI` selected during component creation.
+- `oc` cli installed
+   - `oc` can be downloaded from https://mirror.openshift.com/pub/openshift-v4/clients/ocp/stable/
+- Permissions to run `oc port-forward` on the cluster, specifically an account with the following roles:
+   - `get`, `create`, and `list` for the `pods/portforward` subresource
+
+## Running the Sample
+
+1) Navigate to the OpenShift AI workbench created for your sample application
+
+2) Go to `File->Open` and select `Terminal`
+![image](./.assets/open-terminal.png)
+
+3) In the terminal, run `cd ${{ values.name }}` to navigate to your sample app's directory
+
+4) Run `pip install --upgrade -r requirements.txt` to install the dependencies for your application
+
+5) Run `${{ values.appRunCommand }}` to run the sample in the workbench.
+
+## Accessing the Sample
+
+With the sample app now running, the following steps will allow you to access the sample app in your browser:
+
+1) Navigate back to the OpenShift AI dashboard, and find the name of your workbench.
+![image](./.assets/workbench-name.png)
+
+2) In a terminal window on your machine, run `oc get pods -l app=<workbench-name>`. This will retrieve the name of the pod where the workbench is running.
+
+3) Run `oc port-forward <pod-name> ${{ values.appPort }}` to port forward the sample application's port to your local machine.
+
+4) Finally, visit `http://localhost:${{ values.appPort }}` in your browser to access the application.
diff --git a/templates/object-detection/content/mkdocs.yml b/templates/object-detection/content/mkdocs.yml
@@ -5,6 +5,7 @@ nav:
   - Source Component: source-component.md
   - Pipelines: pipelines.md
   - GitOps Application: gitops-application.md
+  - OpenShift AI: rhoai.md
 
 plugins:
   - techdocs-core
diff --git a/templates/object-detection/template.yaml b/templates/object-detection/template.yaml
@@ -165,6 +165,7 @@ spec:
           srcRepoURL: https://${{ parameters.githubServer if parameters.hostType === 'GitHub' else parameters.gitlabServer }}/${{ parameters.repoOwner }}/${{ parameters.repoName }}
           appContainer: ${{ 'quay.io/redhat-ai-dev/ai-template-bootstrap-app:latest' if parameters.hostType === 'GitHub' else 'quay.io/redhat-ai-dev/object_detection:latest' }}
           appPort: 8501
+          appRunCommand: "streamlit run object_detection_client.py"
           modelServiceContainer: quay.io/redhat-ai-dev/object_detection_python:latest
           modelServicePort: 8000          
     # Renders all the template variables into the files and directory names and content, and places the result in the workspace.
@@ -185,7 +186,7 @@ spec:
           tags: '["ai", "detr", "python"]'
           owner: ${{ parameters.owner }} 
           repoSlug: '${{ parameters.imageOrg }}/${{ parameters.imageName }}'
-          defaultBranch: ${{ parameters.branch }} 
+          defaultBranch: ${{ parameters.branch }}
     - id: fetch-github-action
       name: Fetch GitHub Action
       action: fetch:plain