Merge a06727a44d into 9379a18c8a

.github/workflows/run-k8s-tests-on-nvidia-gpu.yaml

@@ -37,6 +37,7 @@ jobs:
         k8s:
           - kubeadm
     runs-on: amd64-nvidia-a100
+    environment: nvidia
     env:
       DOCKER_REGISTRY: ${{ inputs.registry }}
       DOCKER_REPO: ${{ inputs.repo }}

tests/integration/kubernetes/k8s-nvidia-nim.bats

@@ -10,10 +10,13 @@ load "${BATS_TEST_DIRNAME}/../../common.bash"
 # shellcheck disable=SC1091
 load "${BATS_TEST_DIRNAME}/tests_common.sh"
 
+RUNTIME_CLASS_NAME=${RUNTIME_CLASS_NAME:-kata-qemu-nvidia-gpu}
+export RUNTIME_CLASS_NAME
+
 export POD_NAME_INSTRUCT="nvidia-nim-llama-3-1-8b-instruct"
 export POD_NAME_EMBEDQA="nvidia-nim-llama-3-2-nv-embedqa-1b-v2"
 
-export POD_SECRET_INSTRUCT="ngc-secret-instruct"
+export LOCAL_NIM_CACHE="/opt/nim/.cache"
 
 DOCKER_CONFIG_JSON=$(
     echo -n "{\"auths\":{\"nvcr.io\":{\"username\":\"\$oauthtoken\",\"password\":\"${NGC_API_KEY}\",\"auth\":\"$(echo -n "\$oauthtoken:${NGC_API_KEY}" | base64 -w0)\"}}}" |
@@ -28,6 +31,7 @@ setup_file() {
     [[ -d ${PYENV_ROOT}/bin ]] && export PATH="${PYENV_ROOT}/bin:${PATH}"
     eval "$(pyenv init - bash)"
 
+    # shellcheck disable=SC1091  # Virtual environment will be created during test execution
     python3 -m venv "${HOME}"/.cicd/venv
 
     get_pod_config_dir
@@ -35,20 +39,36 @@ setup_file() {
     pod_instruct_yaml_in="${pod_config_dir}/${POD_NAME_INSTRUCT}.yaml.in"
     pod_instruct_yaml="${pod_config_dir}/${POD_NAME_INSTRUCT}.yaml"
 
+    pod_embedqa_yaml_in="${pod_config_dir}/${POD_NAME_EMBEDQA}.yaml.in"
+    pod_embedqa_yaml="${pod_config_dir}/${POD_NAME_EMBEDQA}.yaml"
+
     envsubst <"${pod_instruct_yaml_in}" >"${pod_instruct_yaml}"
+    envsubst <"${pod_embedqa_yaml_in}" >"${pod_embedqa_yaml}"
 
     export POD_INSTRUCT_YAML="${pod_instruct_yaml}"
+    export POD_EMBEDQA_YAML="${pod_embedqa_yaml}"
 }
 
-@test "NVIDIA NIM Llama 3.1-8b Instruct" {
+@test "NVIDIA NIM Llama 3.1-8b Instruct & NVIDIA NIM Llama 3.2 EmbedQA-1b-v2" {
     kubectl apply -f "${POD_INSTRUCT_YAML}"
+    kubectl apply -f "${POD_EMBEDQA_YAML}"
+
     kubectl wait --for=condition=Ready --timeout=500s pod "${POD_NAME_INSTRUCT}"
+    kubectl wait --for=condition=Ready --timeout=500s pod "${POD_NAME_EMBEDQA}"
+
     # shellcheck disable=SC2030  # Variable is shared via file between BATS tests
     POD_IP_INSTRUCT=$(kubectl get pod "${POD_NAME_INSTRUCT}" -o jsonpath='{.status.podIP}')
     [[ -n "${POD_IP_INSTRUCT}" ]]
 
+    # shellcheck disable=SC2030  # Variable is shared via file between BATS tests
+    POD_IP_EMBEDQA=$(kubectl get pod "${POD_NAME_EMBEDQA}" -o jsonpath='{.status.podIP}')
+    [[ -n "${POD_IP_EMBEDQA}" ]]
+
     echo "POD_IP_INSTRUCT=${POD_IP_INSTRUCT}" >"${BATS_SUITE_TMPDIR}/env"
     echo "# POD_IP_INSTRUCT=${POD_IP_INSTRUCT}" >&3
+
+    echo "POD_IP_EMBEDQA=${POD_IP_EMBEDQA}" >>"${BATS_SUITE_TMPDIR}/env"
+    echo "# POD_IP_EMBEDQA=${POD_IP_EMBEDQA}" >&3
 }
 
 @test "List of models available for inference" {
@@ -94,6 +114,209 @@ setup_file() {
     echo "# ANSWER: ${ANSWER}" >&3
 }
 
+@test "Setup the LangChain flow" {
+    # shellcheck disable=SC1091  # Sourcing virtual environment activation script
+    source "${HOME}"/.cicd/venv/bin/activate
+
+    pip install --upgrade pip
+    [[ "$(pip show langchain 2>/dev/null | awk '/^Version:/{print $2}')" = "0.2.5" ]] || pip install langchain==0.2.5
+    [[ "$(pip show langchain-nvidia-ai-endpoints 2>/dev/null | awk '/^Version:/{print $2}')" = "0.1.2" ]] || pip install langchain-nvidia-ai-endpoints==0.1.2
+    [[ "$(pip show faiss-gpu 2>/dev/null | awk '/^Version:/{print $2}')" = "1.7.2" ]] || pip install faiss-gpu==1.7.2
+    [[ "$(pip show langchain-community 2>/dev/null | awk '/^Version:/{print $2}')" = "0.2.5" ]] || pip install langchain-community==0.2.5
+    [[ "$(pip show beautifulsoup4 2>/dev/null | awk '/^Version:/{print $2}')" = "4.13.4" ]] || pip install beautifulsoup4==4.13.4
+}
+
+@test "LangChain NVIDIA AI Endpoints" {
+    # shellcheck disable=SC1091  # File is created by previous test
+    source "${BATS_SUITE_TMPDIR}/env"
+    # shellcheck disable=SC2031  # Variables are shared via file between BATS tests
+    [[ -n "${POD_IP_INSTRUCT}" ]]
+    # shellcheck disable=SC2031  # Variables are shared via file between BATS tests
+    [[ -n "${MODEL_NAME}" ]]
+
+    QUESTION="What is the capital of France?"
+    ANSWER="The capital of France is Paris."
+
+    # shellcheck disable=SC1091  # Sourcing virtual environment activation script
+    source "${HOME}"/.cicd/venv/bin/activate
+    # shellcheck disable=SC2031  # Variables are used in heredoc, not subshell
+    cat <<EOF >"${HOME}"/.cicd/venv/langchain_nim.py
+from langchain_nvidia_ai_endpoints import ChatNVIDIA
+
+llm = ChatNVIDIA(base_url="http://${POD_IP_INSTRUCT}:8000/v1", model="${MODEL_NAME}", temperature=0.1, max_tokens=1000, top_p=1.0)
+
+result = llm.invoke("${QUESTION}")
+print(result.content)
+EOF
+
+    run python3 "${HOME}"/.cicd/venv/langchain_nim.py
+
+    [[ "${status}" -eq 0 ]]
+    [[ "${output}" = "${ANSWER}" ]]
+
+    echo "# QUESTION: ${QUESTION}" >&3
+    echo "# ANSWER: ${ANSWER}" >&3
+}
+
+@test "Kata Documentation RAG" {
+    # shellcheck disable=SC1091  # File is created by previous test
+    source "${BATS_SUITE_TMPDIR}/env"
+    # shellcheck disable=SC2031  # Variables are shared via file between BATS tests
+    [[ -n "${POD_IP_EMBEDQA}" ]]
+    # shellcheck disable=SC2031  # Variables are shared via file between BATS tests
+    [[ -n "${POD_IP_INSTRUCT}" ]]
+
+    # shellcheck disable=SC1091  # Sourcing virtual environment activation script
+    source "${HOME}"/.cicd/venv/bin/activate
+    cat <<EOF >"${HOME}"/.cicd/venv/langchain_nim_kata_rag.py
+import os
+from langchain.chains import ConversationalRetrievalChain, LLMChain
+from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT, QA_PROMPT
+from langchain.chains.question_answering import load_qa_chain
+from langchain.memory import ConversationBufferMemory
+from langchain_community.vectorstores import FAISS
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_nvidia_ai_endpoints import ChatNVIDIA
+from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings
+EOF
+
+    # shellcheck disable=SC2129  # Multiple heredocs are intentional for building the Python script
+    cat <<EOF >>"${HOME}"/.cicd/venv/langchain_nim_kata_rag.py
+import re
+from typing import List, Union
+
+import requests
+from bs4 import BeautifulSoup
+
+def html_document_loader(url: Union[str, bytes]) -> str:
+    try:
+        response = requests.get(url)
+        html_content = response.text
+    except Exception as e:
+        print(f"Failed to load {url} due to exception {e}")
+        return ""
+
+    try:
+        # Create a Beautiful Soup object to parse html
+        soup = BeautifulSoup(html_content, "html.parser")
+
+        # Remove script and style tags
+        for script in soup(["script", "style"]):
+            script.extract()
+
+        # Get the plain text from the HTML document
+        text = soup.get_text()
+
+        # Remove excess whitespace and newlines
+        text = re.sub("\s+", " ", text).strip()
+
+        return text
+    except Exception as e:
+        print(f"Exception {e} while loading document")
+        return ""
+
+EOF
+
+    cat <<EOF >>"${HOME}"/.cicd/venv/langchain_nim_kata_rag.py
+def create_embeddings(embedding_path: str = "./data/nv_embedding"):
+
+    embedding_path = "./data/nv_embedding"
+    print(f"Storing embeddings to {embedding_path}")
+
+    # List of web pages containing Kata technical documentation
+    urls = [
+        "https://github.com/kata-containers/kata-containers/releases",
+    ]
+
+    documents = []
+    for url in urls:
+        document = html_document_loader(url)
+        documents.append(document)
+
+
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=0,
+        length_function=len,
+    )
+    texts = text_splitter.create_documents(documents)
+    index_docs(url, text_splitter, texts, embedding_path)
+    print("Generated embedding successfully")
+EOF
+
+    # shellcheck disable=SC2031  # POD_IP_EMBEDQA is shared via file between BATS tests
+    cat <<EOF >>"${HOME}"/.cicd/venv/langchain_nim_kata_rag.py
+def index_docs(url: Union[str, bytes], splitter, documents: List[str], dest_embed_dir) -> None:
+    embeddings = NVIDIAEmbeddings(base_url="http://${POD_IP_EMBEDQA}:8000/v1", model="nvidia/llama-3.2-nv-embedqa-1b-v2")
+
+    for document in documents:
+        texts = splitter.split_text(document.page_content)
+
+        # metadata to attach to document
+        metadatas = [document.metadata]
+
+        # create embeddings and add to vector store
+        if os.path.exists(dest_embed_dir):
+            update = FAISS.load_local(folder_path=dest_embed_dir, embeddings=embeddings, allow_dangerous_deserialization=True)
+            update.add_texts(texts, metadatas=metadatas)
+            update.save_local(folder_path=dest_embed_dir)
+        else:
+            docsearch = FAISS.from_texts(texts, embedding=embeddings, metadatas=metadatas)
+            docsearch.save_local(folder_path=dest_embed_dir)
+EOF
+
+    # shellcheck disable=SC2031  # POD_IP_EMBEDQA is shared via file between BATS tests
+    cat <<EOF >>"${HOME}"/.cicd/venv/langchain_nim_kata_rag.py
+create_embeddings()
+
+embedding_model = NVIDIAEmbeddings(base_url="http://${POD_IP_EMBEDQA}:8000/v1", model="nvidia/llama-3.2-nv-embedqa-1b-v2")
+EOF
+
+    cat <<EOF >>"${HOME}"/.cicd/venv/langchain_nim_kata_rag.py
+# Embed documents
+embedding_path = "./data/nv_embedding"
+docsearch = FAISS.load_local(folder_path=embedding_path, embeddings=embedding_model, allow_dangerous_deserialization=True)
+EOF
+
+    # shellcheck disable=SC2031  # Variables are used in heredoc, not subshell  
+    cat <<EOF >>"${HOME}"/.cicd/venv/langchain_nim_kata_rag.py
+llm = ChatNVIDIA(base_url="http://${POD_IP_INSTRUCT}:8000/v1", model="meta/llama3-8b-instruct", temperature=0.1, max_tokens=1000, top_p=1.0)
+
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+
+qa_prompt=QA_PROMPT
+
+doc_chain = load_qa_chain(llm, chain_type="stuff", prompt=QA_PROMPT)
+
+qa = ConversationalRetrievalChain.from_llm(
+    llm=llm,
+    retriever=docsearch.as_retriever(),
+    chain_type="stuff",
+    memory=memory,
+    combine_docs_chain_kwargs={'prompt': qa_prompt},
+)
+
+EOF
+
+    QUESTION="What is the latest Kata Containers release?"
+
+    cat <<EOF >>"${HOME}"/.cicd/venv/langchain_nim_kata_rag.py
+query = "${QUESTION}"
+result = qa.invoke({"question": query})
+print("#"+ result.get("answer"))
+
+EOF
+
+    run python3 "${HOME}"/.cicd/venv/langchain_nim_kata_rag.py
+    [[ "${status}" -eq 0 ]]
+
+    ANSWER=$(echo "${output}" | cut -d '#' -f2)
+    [[ -n "${ANSWER}" ]]
+
+    echo "# QUESTION: ${QUESTION}" >&3
+    echo "# ANSWER: ${ANSWER}" >&3
+}
+
 teardown_file() {
         kubectl delete -f "${POD_INSTRUCT_YAML}"
 }

tests/integration/kubernetes/runtimeclass_workloads/nvidia-nim-llama-3-1-8b-instruct.yaml.in

@@ -1,4 +1,3 @@
-
 # Copyright (c) 2025 NVIDIA Corporation
 #
 # SPDX-License-Identifier: Apache-2.0
@@ -20,13 +19,13 @@ metadata:
     app: ${POD_NAME_INSTRUCT}
 spec:
   restartPolicy: Never
-  runtimeClassName: kata-qemu-nvidia-gpu
+  runtimeClassName: "${RUNTIME_CLASS_NAME}"
   imagePullSecrets:
     - name: ngc-secret-instruct
   securityContext:
-    runAsUser: 0
-    runAsGroup: 0
-    fsGroup: 0
+    runAsUser: 1000
+    runAsGroup: 1000
+    fsGroup: 1000
   containers:
   - name: ${POD_NAME_INSTRUCT}
     image: nvcr.io/nim/meta/llama3-8b-instruct:1.0.0
@@ -84,6 +83,6 @@ spec:
   volumes:
   - name: nim-cache
     hostPath:
-      path: "/opr/nim/.cache"
+      path: "${LOCAL_NIM_CACHE}"
       type: DirectoryOrCreate
 

tests/integration/kubernetes/runtimeclass_workloads/nvidia-nim-llama-3-2-nv-embedqa-1b-v2.yaml.in

@@ -7,7 +7,6 @@ apiVersion: v1
 kind: Secret
 metadata:
   name: ngc-secret-embedqa
-  namespace: nim-embedqa
 type: kubernetes.io/dockerconfigjson
 data:
   .dockerconfigjson: ${DOCKER_CONFIG_JSON}
@@ -16,7 +15,6 @@ apiVersion: v1
 kind: Pod
 metadata:
   name: nvidia-nim-llama-3-2-nv-embedqa-1b-v2
-  namespace: nim-embedqa
   labels:
     app: nvidia-nim-llama-3-2-nv-embedqa-1b-v2
 spec:
@@ -26,9 +24,9 @@ spec:
   imagePullSecrets:
     - name: ngc-secret-embedqa
   securityContext:
-    fsGroup: 0
-    runAsGroup: 0
-    runAsUser: 0
+    fsGroup: 1000
+    runAsGroup: 1000
+    runAsUser: 1000
   containers:
   - name: nvidia-nim-llama-3-2-nv-embedqa-1b-v2
     image: nvcr.io/nim/nvidia/llama-3.2-nv-embedqa-1b-v2:1.5.0
@@ -91,5 +89,5 @@ spec:
   volumes:
   - name: nim-cache
     hostPath:
-      path: "/opr/nim/.cache"
+      path: "${LOCAL_NIM_CACHE}"
       type: DirectoryOrCreate