solution if pipeline not working

docs/get-started/cost-billing/how-pricing-works.md

@@ -323,7 +323,7 @@ environment settings, and persistent volume claims (PVCs) for future use.
 
 1. Go to the [NERC's OpenShift Web Console](https://console.apps.shift.nerc.mghpcc.org).
 
-2. Click on the **Perspective Switcher** drop-down menu and select **Developer**.
+2. In the **Navigation Menu**, navigate to the **Workloads** -> **Topology** menu.
 
 3. Click the pod or application you want to scale to see the _Overview_ panel to
     the right.

docs/migration-moc-to-nerc/Step1.md

@@ -27,7 +27,7 @@ and continue with these steps once you recieve approval.
 
         ![ColdFront_Projects](images/S1_ColdFront_Projects.png)
 
-    2. Under Projects>> Click on the name of your project
+    2. Under Projects >> Click on the name of your project
        (highlighted in yellow above).
 
         ![ColdFront_Projects](images/S1_ColdFront_ManageProject.png)

docs/openshift-ai/data-science-project/testing-model-in-the-rhoai.md

@@ -127,7 +127,8 @@ To deploy the Pre-Post Processing Service service and the Application:
     ![YAML Editor Pre-Post Processing Service Content](images/intelligent_application_deployment-yaml-content.png)
 
 -   If both deployments are successful, you will be able to see both of them grouped
-    under "intelligent-application" on the **Topology View** menu, as shown below:
+    under "intelligent-application" on the **Workloads** -> **Topology** menu,
+    as shown below:
 
     ![Intelligent Application Under Topology](images/intelligent_application-topology.png)
 

docs/openshift-ai/index.md

@@ -56,6 +56,8 @@ the list below.
 
 - [Serving vLLM and Granite Models](other-projects/serving-vLLM-and-Granite-Models.md)
 
+- [Serving Text Generation Inference Service (TGIS) and FLAN-T5 Small Model](other-projects/serving-tgis-and-FLAN-T5-Model.md)
+
 ## LLM Clients
 
 - [LLM Client - AnythingLLM](other-projects/LLM-client-AnythingLLM.md)

docs/openshift-ai/logging-in/access-the-rhoai-dashboard.md

@@ -7,8 +7,9 @@ Make sure you are selecting "**mss-keycloak**" as shown here:
 
 ![OpenShift Login with KeyCloak](images/openshift_login.png)
 
-Once you successfully authenticate, you should see the NERC OpenShift Web Console
-as shown below:
+Once you successfully authenticate, you will see a graphical user interface
+displaying a list of projects on the **Projects** page based on your **ColdFront**
+allocations, as shown below:
 
 ![OpenShift Web Console](images/openshift-web-console.png)
 

docs/openshift-ai/other-projects/LLM-chat.md

@@ -57,13 +57,9 @@ This deployment sets up a ready-to-use container runtime that pulls the
 
 1. Go to the [NERC's OpenShift Web Console](https://console.apps.shift.nerc.mghpcc.org).
 
-2. Click on the **Perspective Switcher** drop-down menu and select **Developer**.
+2. In the **Navigation Menu**, navigate to the **Workloads** -> **Topology** menu.
 
-3. In the **Navigation Menu**, click **Topology**.
-
-    ![Topology View](images/topology-view.png)
-
-4. Click the button to open the llama-cpp-server UI:
+3. Click the button to open the llama-cpp-server UI:
 
     ![llama Deployment](images/llama-deployment.png)
 
@@ -75,12 +71,12 @@ This deployment sets up a ready-to-use container runtime that pulls the
 
     ![llama New Chat UI](images/llama-change-UI-promt-style.png)
 
-5. Test your inferencing by querying the inferencing runtime at the "Say Something"
+4. Test your inferencing by querying the inferencing runtime at the "Say Something"
     box:
 
     ![llama Start Chat](images/llama-start-chat.png)
 
-6. Start Chatting:
+5. Start Chatting:
 
     You can begin interacting with the LLM.
 

docs/openshift-ai/other-projects/fraud-detection-predictive-ai-app.md

@@ -474,8 +474,8 @@ created" message and the following resources listed:
 ![Resources successfully created Importing More YAML](images/yaml-import-success.png)
 
 ii. Once the deployment is successful, you will be able to see all resources
-are created and grouped under "minio" application grouping on the
-**Topology View** menu, as shown below:
+are created and grouped under "minio" application grouping on the **Workloads**
+-> **Topology** menu, as shown below:
 
 ![MinIO Under Topology](images/minio-topology.png)
 
@@ -509,10 +509,10 @@ c. Verify the buckets are created on the MinIO Web Console:
 
 -   Click on any connection from the list that was created and then click
 the action menu (⋮) at the end of the selected connection row. Choose
-"Edit connection" from the dropdown menu. This will open a pop-up
+"Edit" from the dropdown menu. This will open a pop-up
  window as shown below:
 
-    ![Edit Connection Pop up](images/edit-data-connection.png)
+    ![Edit Connection Pop up](images/edit-connection.png)
 
 -   Note both  *Access key* (by clicking eye icon near the end of the textbox) and
 *Secret key*.
@@ -585,6 +585,18 @@ v. Click **Configure pipeline server**.
 vi. Wait until the loading spinner disappears and **Start by importing a pipeline**
 is displayed.
 
+!!! note "Important Note"
+
+    You must wait until the pipeline configuration is complete before you continue
+    and create your workbench. If you continue and [create your workbench](#3-creating-a-workbench-and-a-notebook)
+    **before** the pipeline server is ready, your workbench will not be able to
+    submit pipelines to it.
+
+    If you have waited **more than 5 minutes**, and the pipeline server configuration
+    does not complete, you can delete the pipeline server and create it again.
+
+    ![Delete pipeline server](images/ds-project-delete-pipeline-server.png)
+
 **Verification**:
 
 a. Navigate to the **Pipelines** tab for the project.
@@ -605,73 +617,40 @@ for the pipeline server. as shown below:
 
 **Procedure**:
 
-Prepare your Jupyter notebook server for using a GPU, you need to have:
+Prepare your Jupyter notebook server for using a **GPU**, you need to have:
 
-Select the correct data science project and create workbench, see
-[Populate the data science project](../data-science-project/using-projects-the-rhoai.md#populate-the-data-science-project-with-a-workbench)
-for more information.
+-   Select the correct data science project and create workbench, see
+    [Populate the data science project](../data-science-project/using-projects-the-rhoai.md#populate-the-data-science-project-with-a-workbench)
+    for more information.
 
 Please ensure that you start your Jupyter notebook server with options as
 depicted in the following configuration screen. This screen provides you
 with the opportunity to select a notebook image and configure its options,
 including the Accelerator and Number of accelerators (GPUs).
 
-![Fraud detection Workbench Information](images/fraud-detection-workbench.png)
+Click **Attach existing connections** under the **Connections** section, and attach
+the **"My Storage"** connection that was set up previously to the workbench:
 
-For our example project, let's name it "Fraud detection". We'll select the
-**TensorFlow** image with Recommended Version (selected by default), choose
-a **Deployment size** of **Small**, choose **Accelerator** of
-**NVIDIA V100 GPU**, **Number of accelerators** as **1**, and allocate
-a **Cluster storage** space of **20GB** (Selected By Default).
+![Attach Existing Connection to the Workbench](images/attach-connection-to-workbench.png)
 
-Here, you will use **Environment Variables** to specify the Key/Value pairs related
-to the S3-compatible object storage bucket for storing your model.
+Search and add "My Storage":
 
-To add Environment variables please follow the following steps:
+![Attach Existing Connection to the Workbench](images/attach-existing-connection-2.png)
 
-i. Click on **"Add variable"**.
+Click on "Attach" button:
 
-ii. Select **"Config Map"** from the dropdown for the environment variable type.
-
-iii. Choose **"Key / Value"** and enter the following keys along with their corresponding
-values, which you have retrieved while "Editing connection":
-
-![Edit Connection Pop up](images/edit-data-connection.png)
-
-**Environment Variables**:
+![Attach Connection to the Workbench](images/attach-my-storage.png)
 
-    Key: AWS_ACCESS_KEY_ID
-    Value: <Access key>
+The final workbench setup, before clicking the **Create workbench** button, should
+look like this:
 
-    Key: AWS_SECRET_ACCESS_KEY
-    Value: <Secret key>
-
-    Key: AWS_S3_ENDPOINT
-    Value: <Endpoint>
-
-    Key: AWS_DEFAULT_REGION
-    Value: <Region>
-
-    Key: AWS_S3_BUCKET
-    Value: <Bucket>
-
-!!! note "Alternatively, Running `oc` commands"
-
-    Alternatively, you can run the following `oc` commands:
-
-    i. To get *Access key* run:
-
-    `oc get secret minio-root-user -o template --template '{{.data.MINIO_ROOT_USER}}' | base64 --decode`
-
-    ii. And to get *Secret key* run:
-
-    `oc get secret minio-root-user -o template --template '{{.data.MINIO_ROOT_PASSWORD}}' | base64 --decode`
-
-    iii. And to get *Endpoint* run:
-
-    `oc get route minio-s3 -o template --template '{{.spec.host}}'`
+![Fraud detection Workbench Information](images/fraud-detection-workbench.png)
 
-    You need to add `https://` in the front of the endpoint host url.
+For our example project, let's name it "Fraud detection". We'll select the
+**TensorFlow** image with Recommended Version (selected by default), choose
+a **Deployment size** of **Small**, choose **Accelerator** of
+**NVIDIA V100 GPU**, **Number of accelerators** as **1**, and allocate
+a **Cluster storage** space of **20GB** (Selected By Default).
 
 !!! info "Running Workbench without GPU"
 
@@ -1017,10 +996,10 @@ you test the model API.
 
 !!! question "Important Note"
 
-    If you create your workbench before the pipeline server is ready, your workbench
-    will not be able to submit pipelines to it. If you configured the pipeline server
-    after you created your workbench, you need to stop the workbench and then started
-    your workbench.
+    If you create your workbench before the pipeline server is ready, it won't
+    be able to submit pipelines. If the pipeline server was configured **after**
+    your workbench was created, you'll need to **stop** and then **restart**
+    your workbench. Wait until the workbench status shows as *Running*.
 
 #### Automating workflows with data science pipelines
 
@@ -1040,13 +1019,13 @@ This pipeline will:
 
 -   Save the trained model to **S3 storage** bucket.
 
-Your completed pipeline should resemble the one in the `5_Train_Save.pipeline` file.
+Your completed pipeline should resemble the one in the `6 Train Save.pipeline` file.
 
 !!! note "Note"
 
     To explore the **pipeline editor**, follow the steps in the next procedure to
     create your own pipeline. Alternatively, you can **skip the procedure** and
-    instead feel free to run and use the provided `5_Train_Save.pipeline` file.
+    instead feel free to run and use the provided `6 Train Save.pipeline` file.
 
 ##### Create a pipeline
 
@@ -1240,7 +1219,7 @@ iii. Repeat **Step ii** for each of the following Kubernetes secrets:
 
 -   **Secret Key**: AWS_S3_BUCKET
 
-iv.Select File → Save Pipeline As to save and rename the *.pipeline* file. For
+iv.Select File -> Save Pipeline As to save and rename the *.pipeline* file. For
 example, rename it to **My Train Save.pipeline**.
 
 ##### Run the Pipeline
@@ -1250,15 +1229,19 @@ You can use your own newly created pipeline or the pipeline in the provided
 
 **Procedure**:
 
-i. Click the play button in the toolbar of the pipeline editor.
+i. Click the **play** button in the toolbar of the pipeline editor.
 
 ![Pipeline Run Button](images/wb-pipeline-run-button.png)
 
-ii. Enter a name for your pipeline.
+ii. In the next popup:
+
+![pipeline expanded](images/run-pipeline-ok.png)
 
-iii. Verify that the **Runtime Configuration**: is set to **Data Science Pipeline**.
+-   Enter a name for your pipeline i.e. `My Train Save`.
 
-iv. Click **OK**.
+-   Verify that the **Runtime Configuration**: is set to **Data Science Pipeline**.
+
+-   Click **OK**.
 
 !!! failure "Troubleshooting Help"
 
@@ -1268,16 +1251,16 @@ iv. Click **OK**.
     after you created your workbench, you need to stop the workbench and then started
     your workbench.
 
-v. In the OpenShift AI dashboard, open your data science project and expand the
+iii. In the OpenShift AI dashboard, open your data science project and expand the
 newly created pipeline.
 
 ![New pipeline expanded](images/dsp-pipeline-complete.png)
 
-vi. Click **View runs**.
+iv. Click **View runs**.
 
 ![View runs for selected pipeline](images/dsp-view-run.png)
 
-vii. Click your run and then view the pipeline run in progress.
+v. Click your run and then view the pipeline run in progress.
 
 ![Pipeline run progress](images/pipeline-run-complete.png)
 
@@ -1373,6 +1356,50 @@ the following files:
 
     ![Create Pipeline Run](images/ds-pipeline-run.png)
 
+#### Schedule execution
+
+We can also **schedule** an execution so that the confidence check is executed at
+regular intervals.
+
+To do that:
+
+-   Go back to the OpenShift AI dashboard, open your data science project
+
+-   Find the pipeline you just ran in the Pipelines tab
+
+-   Click the 3 dots at the very end of the pipeline row, and click "Create schedule".
+
+    ![Create schedule](images/create-schedule.png)
+
+-   On the next screen:
+
+    i. keep the **Experiment** to `Default`,
+
+    ii. Set a **Name**,
+
+    iii. select a `Periodic` **Trigger type**,
+
+    iv. run it every **Day** with Maximum concurrent runs of **3**.
+
+    v. keep the `My Train Save` **Pipeline** and **Version** (*The Pipeline's name
+        we set while running the Pipeline for the first time*)
+
+    vi. and click **Create schedule**:
+
+    ![Daily Pipeline Run Schedule](images/dailyrun-3.png)
+
+    vii. This will shows the Graph view of the Scheduled Pipeline Run:
+
+    ![Scheduled Pipeline Run](images/dailyrun-scheduled.png)
+
+    viii. In **Data Science Pipelines** -> **Runs**, click the **Schedules** tab
+    to verify that the **Scheduled** run is visible, as shown below:
+
+    ![Schedule Run](images/schedule-run.png)
+
+    It will run daily **3** runs, and will inform us if anything goes wrong with
+    your training and saving the model process.
+
 ## Deploy the Model Application on NERC OpenShift
 
 The **model application** includes a visual user interface (UI) powered by [Gradio](https://www.gradio.app/),
@@ -1415,11 +1442,18 @@ Console:
 
 ![The NERC OpenShift Web Console Link](images/the-nerc-openshift-web-console-link.png)
 
-Ensure you are in **Developer** view and have selected the correct data science
-project. Then, click on "**+Add**" in the left menu and select "**Import from Git**".
+From your NERC's OpenShift Web Console, navigate to your project corresponding to
+the NERC RHOAI Data Science Project and select the "Import from Git" button,
+represented by the "+" icon in the top navigation bar as shown below:
 
 ![Import from Git](images/Import_from_Git.png)
 
+**Alternatively**, navigate to the **Topology** page under **Workloads**. Right-click
+on the page and select the "Import from Git" option from the **Add to Project**
+menu as shown below:
+
+![Import from Git - Right-click](images/Import_from_Git-right-click.png)
+
 In the "Git Repo URL" enter: `https://github.com/nerc-project/fraud-detection` (this
 is the same repository you [pulled into RHOAI earlier](#importing-the-tutorial-files-into-the-jupyter-environment)).
 Then press "Show advanced Git options" and set "Context dir" to "/application"

docs/openshift-ai/other-projects/how-access-s3-data-then-download-and-analyze-it.md

@@ -16,7 +16,8 @@ and Number of accelerators (GPUs).
 ![Standard Data Science Workbech Information](images/standard-data-science-workbench.png)
 
 For our example project, let's name it "Standard Data Science Workbench". We'll
-select the **Standard Data Science** image, choose a **Deployment size** of **Small**,
+select the **Standard Data Science** image with Recommended Version
+(selected by default), choose a **Deployment size** of **Small**,
 **Accelerator** as **None** (no GPU is needed for this setup), and allocate a
 **Cluster storage** space of **1GB**.