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helm-charts/codegen-openshift-rhoai/Chart.yaml

@@ -1,3 +1,6 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
 apiVersion: v2
 name: codegen
 description: A Helm chart for deploying codegen on Red Hat OpenShift with Red Hat OpenShift AI

helm-charts/codegen-openshift-rhoai/README.md

@@ -2,18 +2,19 @@
 
 Helm chart for deploying CodeGen service on Red Hat OpenShift with Red Hat OpenShift AI.
 
-Serving runtime template in this example uses model *ise-uiuc/Magicoder-S-DS-6.7B* for Xeon and *meta-llama/CodeLlama-7b-hf* for Gaudi.
+Serving runtime template in this example uses model _ise-uiuc/Magicoder-S-DS-6.7B_ for Xeon and _meta-llama/CodeLlama-7b-hf_ for Gaudi.
 
-## Prerequisites 
+## Prerequisites
 
-1. **Red Hat OpenShift Cluster** with dynamic *StorageClass* to provision *PersistentVolumes* e.g. **OpenShift Data Foundation**) and installed Operators: **Red Hat - Authorino (Technical Preview)**, **Red Hat OpenShift Service Mesh**, **Red Hat OpenShift Serverless** and **Red Hat Openshift AI**.
+1. **Red Hat OpenShift Cluster** with dynamic _StorageClass_ to provision _PersistentVolumes_ e.g. **OpenShift Data Foundation**) and installed Operators: **Red Hat - Authorino (Technical Preview)**, **Red Hat OpenShift Service Mesh**, **Red Hat OpenShift Serverless** and **Red Hat Openshift AI**.
 2. Image registry to push there docker images (https://docs.openshift.com/container-platform/4.16/registry/securing-exposing-registry.html).
 3. Access to S3-compatible object storage bucket (e.g. **OpenShift Data Foundation**, **AWS S3**) and values of access and secret access keys and S3 endpoint (https://docs.redhat.com/en/documentation/red_hat_openshift_data_foundation/4.16/html/managing_hybrid_and_multicloud_resources/accessing-the-multicloud-object-gateway-with-your-applications_rhodf#accessing-the-multicloud-object-gateway-with-your-applications_rhodf).
-4. Account on https://huggingface.co/, access to model *ise-uiuc/Magicoder-S-DS-6.7B* (for Xeon) or *meta-llama/CodeLlama-7b-hf* (for Gaudi) and token with Read permissions.
+4. Account on https://huggingface.co/, access to model _ise-uiuc/Magicoder-S-DS-6.7B_ (for Xeon) or _meta-llama/CodeLlama-7b-hf_ (for Gaudi) and token with Read permissions.
 
 ## Deploy model in Red Hat Openshift AI
 
-1. Login to OpenShift CLI and run following commands to create new serving runtime and *hf-token* secret.
+1. Login to OpenShift CLI and run following commands to create new serving runtime and _hf-token_ secret.
+
 ```
 cd GenAIInfra/helm-charts/codegen-openshift-rhoai/
 export HFTOKEN="insert-your-huggingface-token-here"
@@ -25,24 +26,29 @@ On Gaudi:
 helm install servingruntime tgi --set global.huggingfacehubApiToken=${HFTOKEN} --values tgi/gaudi-values.yaml
 ```
 
-Verify if template has been created with ```oc get template -n redhat-ods-applications``` command.
+Verify if template has been created with `oc get template -n redhat-ods-applications` command.
 
 2. Find the route for **Red Hat OpenShift AI** dashboard with below command and open it in the browser:
+
 ```
 oc get routes -A | grep rhods-dashboard
 ```
-3. Go to **Data Science Project** and clik **Create data science project**. Fill the **Name** and click **Create**.
-4. Go to **Workbenches** tab and clik **Create workbench**. Fill the **Name**, under **Notebook image** choose *Standard Data Science*, under **Cluster storage** choose *Create new persistent storage* and change **Persistent storage size** to 40 GB. Click **Create workbench**.
+
+3. Go to **Data Science Project** and click **Create data science project**. Fill the **Name** and click **Create**.
+4. Go to **Workbenches** tab and click **Create workbench**. Fill the **Name**, under **Notebook image** choose _Standard Data Science_, under **Cluster storage** choose _Create new persistent storage_ and change **Persistent storage size** to 40 GB. Click **Create workbench**.
 5. Open newly created Jupiter notebook and run following commands to download the model and upload it on s3:
+
 ```
 %env S3_ENDPOINT=<S3_RGW_ROUTE>
 %env S3_ACCESS_KEY=<AWS_ACCESS_KEY_ID>
 %env S3_SECRET_KEY=<AWS_SECRET_ACCESS_KEY>
 %env HF_TOKEN=<PASTE_HUGGINGFACE_TOKEN>
 ```
+
 ```
 !pip install huggingface-hub
 ```
+
 ```
 import os
 import boto3
@@ -63,15 +69,21 @@ s3_resource = session.resource('s3',
                                aws_secret_access_key=s3_secretkey)
 bucket = s3_resource.Bucket(bucket_name)
 ```
-For Xeon download *ise-uiuc/Magicoder-S-DS-6.7B*:
+
+For Xeon download _ise-uiuc/Magicoder-S-DS-6.7B_:
+
 ```
 snapshot_download("ise-uiuc/Magicoder-S-DS-6.7B", cache_dir=f'./models', token=hf_token)
 ```
-For Gaudi download *meta-llama/CodeLlama-7b-hf*:
+
+For Gaudi download _meta-llama/CodeLlama-7b-hf_:
+
 ```
 snapshot_download("meta-llama/CodeLlama-7b-hf", cache_dir=f'./models', token=hf_token)
 ```
+
 Upload the downloaded model to S3:
+
 ```
 files = (file for file in glob.glob(f'{path}/**/*', recursive=True) if os.path.isfile(file) and "snapshots" in file)
 for filename in files:
@@ -80,8 +92,8 @@ for filename in files:
     bucket.upload_file(filename, f'{path}{s3_name}')
 ```
 
-6. Go to your project in **Red Hat OpenShift AI** dashboard, then "Models" tab and click **Deploy model** under *Single-model serving platform*. Fill the **Name**, choose newly created **Serving runtime**: *Text Generation Inference Magicoder-S-DS-6.7B on CPU* (for Xeon) or *Text Generation Inference CodeLlama-7b-hf on Gaudi* (for Gaudi), **Model framework**: *llm* and change **Model server size** to *Custom*: 16 CPUs and 64 Gi memory. For deployment with Gaudi select proper **Accelerator**. Click the checkbox to create external route in **Model route** section and uncheck the **Token authentication**. Under **Model location** choose *New data connection* and fill all required fields for s3 access, **Bucket** *first.bucket* and **Path**: *models*. Click **Deploy**. It takes about 10 minutes to get *Loaded* status.\
-If it's not going to *Loaded* status and revision changed status to "ProgressDeadlineExceeded" (``oc get revision``), scale model deployment to 0 and than to 1 with command ``oc scale deployment.apps/<model_deployment_name> --replicas=1`` and wait about 10 minutes for deployment.
+6. Go to your project in **Red Hat OpenShift AI** dashboard, then "Models" tab and click **Deploy model** under _Single-model serving platform_. Fill the **Name**, choose newly created **Serving runtime**: _Text Generation Inference Magicoder-S-DS-6.7B on CPU_ (for Xeon) or _Text Generation Inference CodeLlama-7b-hf on Gaudi_ (for Gaudi), **Model framework**: _llm_ and change **Model server size** to _Custom_: 16 CPUs and 64 Gi memory. For deployment with Gaudi select proper **Accelerator**. Click the checkbox to create external route in **Model route** section and uncheck the **Token authentication**. Under **Model location** choose _New data connection_ and fill all required fields for s3 access, **Bucket** _first.bucket_ and **Path**: _models_. Click **Deploy**. It takes about 10 minutes to get _Loaded_ status.\
+   If it's not going to _Loaded_ status and revision changed status to "ProgressDeadlineExceeded" (`oc get revision`), scale model deployment to 0 and than to 1 with command `oc scale deployment.apps/<model_deployment_name> --replicas=1` and wait about 10 minutes for deployment.
 
 ## Install the Chart
 
@@ -101,11 +113,12 @@ sed -i "s/insert-your-namespace-here/${NAMESPACE}/g" codegen-openshift-rhoai/llm
 helm dependency update codegen-openshift-rhoai
 
 helm install codegen codegen-openshift-rhoai --set image.repository=image-registry.openshift-image-registry.svc:5000/${NAMESPACE}/codegen --set llm-uservice.image.repository=image-registry.openshift-image-registry.svc:5000/${NAMESPACE}/llm-tgi --set react-ui.image.repository=image-registry.openshift-image-registry.svc:5000/${NAMESPACE}/react-ui --set global.clusterDomain=${CLUSTERDOMAIN} --set global.huggingfacehubApiToken=${HFTOKEN} --set llm-uservice.servingRuntime.name=${MODELNAME} --set llm-uservice.servingRuntime.namespace=${PROJECT}
-``` 
+```
 
 ## Verify
 
-To verify the installation, run the command `oc get pods` to make sure all pods are running. Wait about 5 minutes for building images. When 4 pods achieve *Completed* status, the rest with services should go to *Running*.
+To verify the installation, run the command `oc get pods` to make sure all pods are running. Wait about 5 minutes for building images. When 4 pods achieve _Completed_ status, the rest with services should go to _Running_.
 
 ## Launch the UI
-To access the frontend, find the route for *react-ui* with command `oc get routes` and open it in the browser.
+
+To access the frontend, find the route for _react-ui_ with command `oc get routes` and open it in the browser.

helm-charts/codegen-openshift-rhoai/llm-uservice/Chart.yaml

@@ -1,3 +1,6 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
 apiVersion: v2
 name: llm-uservice
 description: A Helm chart for deploying llm-uservice on Red Hat OpenShift with Red Hat OpenShift AI

helm-charts/codegen-openshift-rhoai/llm-uservice/templates/buildconfig.yaml

@@ -1,3 +1,6 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
 kind: BuildConfig
 apiVersion: build.openshift.io/v1
 metadata:

helm-charts/codegen-openshift-rhoai/llm-uservice/templates/configmap.yaml

@@ -1,3 +1,6 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
 apiVersion: v1
 kind: ConfigMap
 metadata:
@@ -7,7 +10,7 @@ data:
     #!/bin/bash
     EXISTS=$(oc get secret --ignore-not-found rhoai-ca-bundle)
 
-    if [[ -z "${EXISTS}" ]]; then    
+    if [[ -z "${EXISTS}" ]]; then
       oc create secret generic -n {{ .Release.Namespace }} rhoai-ca-bundle --from-literal=tls.crt="$(oc extract secret/knative-serving-cert -n istio-system --to=- --keys=tls.crt)"
     else
       echo "oc get secret --ignore-not-found rhoai-ca-bundle returned non-empty string, not creating a secret"

helm-charts/codegen-openshift-rhoai/llm-uservice/templates/deployment.yaml

@@ -1,3 +1,6 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
 apiVersion: apps/v1
 kind: Deployment
 metadata:
@@ -14,7 +17,7 @@ spec:
   template:
     metadata:
       labels:
-        {{- include "llm-uservice.selectorLabels" . | nindent 8 }}      
+        {{- include "llm-uservice.selectorLabels" . | nindent 8 }}
     spec:
       securityContext: {}
       containers:

helm-charts/codegen-openshift-rhoai/llm-uservice/templates/imagestream.yaml

@@ -1,3 +1,6 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
 ---
 apiVersion: image.openshift.io/v1
 kind: ImageStream

helm-charts/codegen-openshift-rhoai/llm-uservice/templates/job.yaml

@@ -1,3 +1,6 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
 apiVersion: batch/v1
 kind: Job
 metadata:

helm-charts/codegen-openshift-rhoai/llm-uservice/templates/rbac/crb-rhoai.yaml

@@ -1,3 +1,6 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
 apiVersion: rbac.authorization.k8s.io/v1
 kind: ClusterRoleBinding
 metadata:

helm-charts/codegen-openshift-rhoai/llm-uservice/templates/rbac/role.yaml

@@ -1,3 +1,6 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
 {{- range $key, $value := .Values.rbac.roles }}
 {{- if $value.createRole }}
 ---