From 7c8c4604aa7e91ba81a165d30f3d0ef14ccdf387 Mon Sep 17 00:00:00 2001 From: Divya Gupta Date: Tue, 28 Jan 2025 14:31:03 -0500 Subject: [PATCH 01/19] changes for mistral model --- .../chatbot/gradio-mistral/gradio-ui.yaml | 130 ++++++++++++ .../chatbot/gradio-mistral/kustomization.yaml | 4 + .../aiml/chatbot/nodepool/kustomization.yaml | 1 + .../aiml/chatbot/nodepool/nodepool-tran1.yaml | 67 ++++++ .../aiml/chatbot/nodepool/nodepool-x86.yaml | 5 +- .../Dockerfile | 46 +++++ .../kustomization.yaml | 4 + .../mistral1.py | 142 +++++++++++++ .../ray_service_mistral.yaml | 194 ++++++++++++++++++ website/docs/aiml/chatbot/add-mistral.md | 86 ++++++++ website/docs/aiml/chatbot/gradio-mistral.md | 68 ++++++ website/docs/aiml/chatbot/mistral.md | 31 +++ 12 files changed, 777 insertions(+), 1 deletion(-) create mode 100644 manifests/modules/aiml/chatbot/gradio-mistral/gradio-ui.yaml create mode 100644 manifests/modules/aiml/chatbot/gradio-mistral/kustomization.yaml create mode 100644 manifests/modules/aiml/chatbot/nodepool/nodepool-tran1.yaml create mode 100644 manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/Dockerfile create mode 100644 manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/kustomization.yaml create mode 100644 manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/mistral1.py create mode 100644 manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml create mode 100644 website/docs/aiml/chatbot/add-mistral.md create mode 100644 website/docs/aiml/chatbot/gradio-mistral.md create mode 100644 website/docs/aiml/chatbot/mistral.md diff --git a/manifests/modules/aiml/chatbot/gradio-mistral/gradio-ui.yaml b/manifests/modules/aiml/chatbot/gradio-mistral/gradio-ui.yaml new file mode 100644 index 000000000..8cf572c20 --- /dev/null +++ b/manifests/modules/aiml/chatbot/gradio-mistral/gradio-ui.yaml @@ -0,0 +1,130 @@ +apiVersion: v1 +kind: Namespace +metadata: + name: gradio-mistral-tran1 +--- +apiVersion: apps/v1 +kind: Deployment +metadata: + name: gradio-deployment + namespace: gradio-mistral-tran1 + labels: + app: gradio +spec: + replicas: 1 + selector: + matchLabels: + app: gradio + template: + metadata: + labels: + app: gradio + spec: + containers: + - name: gradio + image: public.ecr.aws/data-on-eks/gradio-web-app-base:latest + imagePullPolicy: IfNotPresent + ports: + - containerPort: 7860 + resources: + requests: + cpu: "512m" + memory: "2048Mi" + limits: + cpu: "1" + memory: "4096Mi" + env: + - name: MODEL_ENDPOINT + value: "/infer" + - name: SERVICE_NAME + value: "http://mistral-serve-svc.mistral.svc.cluster.local:8000" + volumeMounts: + - name: gradio-app-script + mountPath: /app/gradio-app.py + subPath: gradio-app-mistral-tran1.py + volumes: + - name: gradio-app-script + configMap: + name: gradio-app-script +--- +apiVersion: v1 +kind: Service +metadata: + name: gradio-service + namespace: gradio-mistral-tran1 + annotations: + service.beta.kubernetes.io/aws-load-balancer-type: external + service.beta.kubernetes.io/aws-load-balancer-scheme: internet-facing + service.beta.kubernetes.io/aws-load-balancer-nlb-target-type: ip +spec: + selector: + app: gradio + ports: + - name: http + protocol: TCP + port: 80 + targetPort: 7860 + type: LoadBalancer +--- +apiVersion: v1 +kind: ConfigMap +metadata: + name: gradio-app-script + namespace: gradio-mistral-tran1 +data: + gradio-app-mistral-tran1.py: | + import gradio as gr + import requests + import os + + # Constants for model endpoint and service name + model_endpoint = "/infer" + service_name = os.environ.get("SERVICE_NAME", "http://localhost:8000") + + # Function to generate text + def text_generation(message, history): + prompt = message + + # Create the URL for the inference + url = f"{service_name}{model_endpoint}" + + try: + # Send the request to the model service + response = requests.get(url, params={"sentence": prompt}, timeout=180) + response.raise_for_status() # Raise an exception for HTTP errors + + full_output = response.json()[0] + # Removing the original question from the output + answer_only = full_output.replace(prompt, "", 1).strip('["]?\n') + + # Safety filter to remove harmful or inappropriate content + answer_only = filter_harmful_content(answer_only) + return answer_only + except requests.exceptions.RequestException as e: + # Handle any request exceptions (e.g., connection errors) + return f"AI: Error: {str(e)}" + + # Define the safety filter function (you can implement this as needed) + def filter_harmful_content(text): + # TODO: Implement a safety filter to remove any harmful or inappropriate content from the text + + # For now, simply return the text as-is + return text + + # Define the Gradio ChatInterface + chat_interface = gr.ChatInterface( + text_generation, + chatbot=gr.Chatbot(line_breaks=True), + textbox=gr.Textbox(placeholder="Ask me a question", container=False, scale=7), + title="neuron-mistral7bv0.3 AI Chat", + description="Ask me any question", + theme="soft", + examples=["How many languages are in India", "What is Generative AI?"], + cache_examples=False, + retry_btn=None, + undo_btn="Delete Previous", + clear_btn="Clear", + ) + + # Launch the ChatInterface + chat_interface.launch(server_name="0.0.0.0") diff --git a/manifests/modules/aiml/chatbot/gradio-mistral/kustomization.yaml b/manifests/modules/aiml/chatbot/gradio-mistral/kustomization.yaml new file mode 100644 index 000000000..1cca24122 --- /dev/null +++ b/manifests/modules/aiml/chatbot/gradio-mistral/kustomization.yaml @@ -0,0 +1,4 @@ +apiVersion: kustomize.config.k8s.io/v1beta1 +kind: Kustomization +resources: + - gradio-ui.yaml diff --git a/manifests/modules/aiml/chatbot/nodepool/kustomization.yaml b/manifests/modules/aiml/chatbot/nodepool/kustomization.yaml index b0f432bde..64ff67cc9 100644 --- a/manifests/modules/aiml/chatbot/nodepool/kustomization.yaml +++ b/manifests/modules/aiml/chatbot/nodepool/kustomization.yaml @@ -3,3 +3,4 @@ kind: Kustomization resources: - nodepool-inf2.yaml - nodepool-x86.yaml + - nodepool-tran1.yaml diff --git a/manifests/modules/aiml/chatbot/nodepool/nodepool-tran1.yaml b/manifests/modules/aiml/chatbot/nodepool/nodepool-tran1.yaml new file mode 100644 index 000000000..7e58fbfe8 --- /dev/null +++ b/manifests/modules/aiml/chatbot/nodepool/nodepool-tran1.yaml @@ -0,0 +1,67 @@ +apiVersion: karpenter.sh/v1 +kind: NodePool +metadata: + name: trainium-tran1 +spec: + template: + metadata: + labels: + instanceType: trainium + provisionerType: Karpenter + neuron.amazonaws.com/neuron-device: "true" + spec: + startupTaints: + - key: node.kubernetes.io/not-ready + effect: "NoExecute" + taints: + - key: aws.amazon.com/neuron + effect: "NoSchedule" + requirements: + - key: node.kubernetes.io/instance-type + operator: In + values: ["trn1.2xlarge"] + - key: "kubernetes.io/arch" + operator: In + values: ["amd64"] + - key: "karpenter.sh/capacity-type" + operator: In + values: ["on-demand"] + expireAfter: 720h + terminationGracePeriod: 24h + nodeClassRef: + group: karpenter.k8s.aws + kind: EC2NodeClass + name: trainium-tran1 + limits: + cpu: 100 + memory: 400Gi + aws.amazon.com/neuron: 10 + disruption: + consolidateAfter: 300s + consolidationPolicy: WhenEmptyOrUnderutilized + +--- +apiVersion: karpenter.k8s.aws/v1 +kind: EC2NodeClass +metadata: + name: trainium-tran1 +spec: + amiFamily: AL2 + amiSelectorTerms: + - alias: al2@latest + blockDeviceMappings: + - deviceName: /dev/xvda + ebs: + deleteOnTermination: true + encrypted: true + volumeSize: 500Gi + volumeType: gp3 + role: ${KARPENTER_NODE_ROLE} + securityGroupSelectorTerms: + - tags: + karpenter.sh/discovery: ${EKS_CLUSTER_NAME} + subnetSelectorTerms: + - tags: + karpenter.sh/discovery: ${EKS_CLUSTER_NAME} + tags: + app.kubernetes.io/created-by: eks-workshop diff --git a/manifests/modules/aiml/chatbot/nodepool/nodepool-x86.yaml b/manifests/modules/aiml/chatbot/nodepool/nodepool-x86.yaml index 41937cfaf..2fd9dd3fb 100644 --- a/manifests/modules/aiml/chatbot/nodepool/nodepool-x86.yaml +++ b/manifests/modules/aiml/chatbot/nodepool/nodepool-x86.yaml @@ -11,6 +11,9 @@ spec: provisionerType: Karpenter workload: rayhead spec: + startupTaints: + - key: node.kubernetes.io/not-ready + effect: "NoExecute" requirements: - key: "karpenter.k8s.aws/instance-family" operator: In @@ -20,7 +23,7 @@ spec: values: ["amd64"] - key: "karpenter.sh/capacity-type" operator: In - values: ["on-demand", "spot"] + values: ["on-demand"] expireAfter: 720h terminationGracePeriod: 24h nodeClassRef: diff --git a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/Dockerfile b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/Dockerfile new file mode 100644 index 000000000..c5e7276a2 --- /dev/null +++ b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/Dockerfile @@ -0,0 +1,46 @@ +# https://hub.docker.com/layers/rayproject/ray/2.11.0-py310/images/sha256-de798e487b76a8f2412c718c43c5f342b3eb05e0705a71325102904cd27c3613?context=explore +FROM rayproject/ray:2.32.0-py310 + +# Maintainer label +LABEL maintainer="DoEKS" + +# Set environment variables to non-interactive (this prevents some prompts) +ENV DEBIAN_FRONTEND=non-interactive + +# Switch to root to add Neuron repo and install necessary packages +USER root + +# Set up the Neuron repository and install Neuron packages +RUN . /etc/os-release && \ + sudo echo "deb https://apt.repos.neuron.amazonaws.com ${VERSION_CODENAME} main" > /etc/apt/sources.list.d/neuron.list && \ + sudo wget -qO - https://apt.repos.neuron.amazonaws.com/GPG-PUB-KEY-AMAZON-AWS-NEURON.PUB | apt-key add - && \ + sudo apt-get update -y && \ + sudo apt-get install aws-neuronx-dkms aws-neuronx-collectives=2.* aws-neuronx-runtime-lib=2.* aws-neuronx-tools=2.* -y && \ + sudo apt-get clean && \ + sudo rm -rf /var/lib/apt/lists/* + + + +# Switch back to a non-root user for the subsequent commands +USER $USER + +# Set pip repository pointing to the Neuron repository and install required Python packages +RUN pip config set global.extra-index-url https://pip.repos.neuron.amazonaws.com && \ + pip install wget awscli regex neuronx-cc==2.* torch-neuronx torchvision transformers-neuronx sentencepiece transformers huggingface_hub tenacity psutil fastapi uvicorn mistral-inference mistral-common + + +# Add Neuron path to PATH +ENV PATH /opt/aws/neuron/bin:$PATH + +# Set LD_LIBRARY_PATH to include the directory with libpython3.10.so.1.0 +ENV LD_LIBRARY_PATH /home/ray/anaconda3/lib:$LD_LIBRARY_PATH + +# Create cache directories +RUN mkdir -p /serve_app + +# Set working directory +WORKDIR /serve_app + +COPY mistral1.py /serve_app/mistral1.py + + diff --git a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/kustomization.yaml b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/kustomization.yaml new file mode 100644 index 000000000..1f5a41bc2 --- /dev/null +++ b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/kustomization.yaml @@ -0,0 +1,4 @@ +apiVersion: kustomize.config.k8s.io/v1beta1 +kind: Kustomization +resources: + - ray_service_mistral.yaml diff --git a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/mistral1.py b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/mistral1.py new file mode 100644 index 000000000..ba8fcedd4 --- /dev/null +++ b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/mistral1.py @@ -0,0 +1,142 @@ +import os +import json +import logging +from fastapi import FastAPI +from ray import serve +import torch +import torch_neuronx +from transformers import AutoTokenizer +from transformers_neuronx.mistral.model import MistralForSampling +from huggingface_hub import snapshot_download + +# Initialize FastAPI +app = FastAPI() + +neuron_cores = int(os.getenv('NEURON_CORES', 2)) # Default to 2 for trn1.2xlarge +cacheDir = os.path.join('/tmp','model','neuron-mistral7bv0.3') + +# --- Logging Setup --- +logger = logging.getLogger("ray.serve") +logger.setLevel(logging.INFO) +logging.basicConfig(level=logging.INFO) + +@serve.deployment(num_replicas=1) +@serve.ingress(app) +class APIIngress: + def __init__(self, mistral_model_handle): + self.handle = mistral_model_handle + + @app.get("/infer") + async def infer(self, sentence: str): + result = await self.handle.infer.remote(sentence) + return result + +@serve.deployment( + name="mistral-7b", + autoscaling_config={"min_replicas": 1, "max_replicas": 1}, + ray_actor_options={ + "resources": {"neuron_cores": neuron_cores}, + "memory": 28000000000 + } +) +class MistralModel: + def __init__(self): + try: + logger.info("Initializing model with pre-compiled files...") + + mistral_model = os.getenv('MODEL_ID', 'askulkarni2/neuron-mistral7bv0.3') + logger.info(f"Using model ID: {mistral_model}") + + model_path='/tmp/model/neuron-mistral7bv0.3' + model_cache='/tmp/model/cache' + + # Initialize model state + self.neuron_model = None + self.tokenizer = None + + #Downloading files to local dir + if not os.path.exists(model_path): + os.makedirs(cacheDir, exist_ok=True) + os.makedirs(model_cache, exist_ok=True) + logger.info("downloading model file to../tmp/model/neuron-mistral7bv0.3") + model_path = snapshot_download(repo_id=mistral_model, local_dir=cacheDir, local_dir_use_symlinks=False) + logger.info(f"model path: {model_path}") + + logger.info(f"Checking model path contents: {os.listdir(model_path)}") + + # Set the environment variable with absolute path + os.environ.update({ + "NEURON_RT_VISIBLE_CORES": "0,1", + "NEURON_RT_NUM_CORES": "2", + "NEURON_RT_USE_PREFETCHED_NEFF": "1", + }) + + logger.info("Loading tokenizer...") + self.tokenizer = AutoTokenizer.from_pretrained( + model_path, + local_files_only=True + ) + + # Set padding token + if self.tokenizer.pad_token is None: + self.tokenizer.pad_token = self.tokenizer.eos_token + logger.info("Set padding token to EOS token") + + + logger.info("Loading model...") + # Load model with minimal configuration + self.neuron_model = MistralForSampling.from_pretrained( + model_path, batch_size=1, tp_degree=2, amp='bf16' + ) + + logger.info("Model preparation...") + + neuronxcc_dirs = [d for d in os.listdir(model_cache)] + if not neuronxcc_dirs: + # compile modele first time and save compile artifacts in cache dir + self.neuron_model.to_neuron() + self.neuron_model.save(model_cache) + else: + # load pre-complied .neff files + self.neuron_model.load(model_cache) + self.neuron_model.to_neuron() + + logger.info("Model successfully prepared for inference") + + # Verify initialization + if not self._verify_model_state(): + raise RuntimeError("Model initialization failed verification") + + logger.info("Model initialization complete") + + except Exception as e: + logger.error(f"Error during model initialization: {e}") + raise + + def _verify_model_state(self): + if self.neuron_model is None: + return False + if not hasattr(self.neuron_model, 'sample'): + return False + if self.tokenizer is None: + return False + return True + + def infer(self, sentence: str): + input_ids = self.tokenizer.encode(sentence, return_tensors="pt") + with torch.inference_mode(): + try: + logger.info(f"Performing inference on input: {sentence}") + generated_sequences = self.neuron_model.sample( + input_ids, sequence_length=2048, top_k=50 + ) + decoded_sequences = [self.tokenizer.decode(seq, skip_special_tokens=True) for seq in generated_sequences] + logger.info(f"Inference result: {decoded_sequences}") + return decoded_sequences + except Exception as e: + logger.error(f"Error during inference: {e}") + return {"error": "Inference failed"} + + +# Create an entry point for the FastAPI application +entrypoint = APIIngress.bind(MistralModel.bind()) diff --git a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml new file mode 100644 index 000000000..73b8f3bd3 --- /dev/null +++ b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml @@ -0,0 +1,194 @@ +apiVersion: v1 +kind: Namespace +metadata: + name: mistral +--- +#---------------------------------------------------------------------- +# NOTE: For deployment instructions, refer to the DoEKS website. +#---------------------------------------------------------------------- +apiVersion: ray.io/v1 +kind: RayService +metadata: + name: mistral + namespace: mistral +spec: + serviceUnhealthySecondThreshold: 900 + deploymentUnhealthySecondThreshold: 300 + serveConfigV2: | + applications: + - name: mistral-deployment + import_path: "mistral1:entrypoint" + route_prefix: "/" + runtime_env: + env_vars: + MODEL_ID: "askulkarni2/neuron-mistral7bv0.3" + NEURON_CC_FLAGS: "-O1" + LD_LIBRARY_PATH: "/home/ray/anaconda3/lib:$LD_LIBRARY_PATH" + NEURON_CORES: "2" + NEURON_COMPILE_CACHE_URL: "/tmp/model/cache" + NEURON_RT_CACHE_DIRECTORY: "/tmp/model/cache" + deployments: + - name: mistral-7b + autoscaling_config: + min_replicas: 1 + max_replicas: 1 + target_num_ongoing_requests_per_replica: 1 + ray_actor_options: + resources: {"neuron_cores": 2} + memory: 28000000000 + rayClusterConfig: + rayVersion: '2.32.0' + enableInTreeAutoscaling: true + headGroupSpec: + serviceType: NodePort + headService: + metadata: + name: mistral + rayStartParams: + dashboard-host: '0.0.0.0' + num-cpus: "0" # this is to ensure no tasks or actors are scheduled on the head Pod + template: + spec: + containers: + - name: head + image: public.ecr.aws/e3e2e5u9/aiml/mistral-7b:latest + imagePullPolicy: Always # Ensure the image is always pulled when updated + lifecycle: + preStop: + exec: + command: ["/bin/sh", "-c", "ray stop"] + ports: + - containerPort: 6379 + name: gcs + - containerPort: 8265 + name: dashboard + - containerPort: 10001 + name: client + - containerPort: 8000 + name: serve + volumeMounts: + - mountPath: /tmp/ray + name: ray-logs + - mountPath: /tmp/model/cache + name: model-cache + resources: + limits: + cpu: "4" + memory: 16Gi + requests: + cpu: "2" + memory: 8Gi + env: + - name: PORT + value: "8000" + - name: LD_LIBRARY_PATH + value: "/home/ray/anaconda3/lib:$LD_LIBRARY_PATH" + nodeSelector: + instanceType: mixed-x86 + provisionerType: Karpenter + workload: rayhead + volumes: + - name: ray-logs + emptyDir: {} + - name: model-cache + emptyDir: {} + workerGroupSpecs: + - groupName: worker-group + replicas: 1 + minReplicas: 1 + maxReplicas: 1 + rayStartParams: + resources: '"{\"neuron_cores\": 2}"' + num-cpus: "6" + template: + spec: + containers: + - name: worker + image: public.ecr.aws/e3e2e5u9/aiml/mistral-7b:latest + imagePullPolicy: Always # Ensure the image is always pulled when updated + lifecycle: + preStop: + exec: + command: ["/bin/sh", "-c", "ray stop"] + # We are using 2 Neuron cores per HTTP request hence this configuration handles 6 requests per second + resources: + limits: + memory: "30Gi" + aws.amazon.com/neuron: "1" + requests: + memory: "28Gi" + aws.amazon.com/neuron: "1" + env: + # Model and Neuron configuration + - name: MODEL_ID + value: "askulkarni2/neuron-mistral7bv0.3" + - name: NEURON_CORES + value: "2" + - name: NEURON_RT_NUM_CORES + value: "2" + - name: NEURON_RT_VISIBLE_CORES + value: "0,1" + - name: NEURON_CC_FLAGS + value: "-O1" # Changed from --no-compile + - name: NEURON_COMPILE_ONLY + value: "0" + - name: NEURON_RT_LOG_LEVEL + value: "INFO" + # Cache configuration + - name: NEURON_COMPILE_CACHE_URL + value: "/tmp/model/cache" + - name: NEURON_RT_CACHE_DIRECTORY + value: "/tmp/model/cache" + - name: NEURON_RT_USE_PREFETCHED_NEFF + value: "1" # Added to use pre-compiled NEFF files + # Memory management + - name: NEURON_RT_MAX_WORKSPACE_SIZE + value: "8589934592" + - name: XLA_TENSOR_ALLOCATOR_MAXSIZE + value: "12884901888" + - name: MALLOC_ARENA_MAX + value: "32" + - name: MALLOC_TRIM_THRESHOLD_ + value: "128K" + - name: XLA_PYTHON_CLIENT_MEM_FRACTION + value: "0.95" + # Runtime configuration + - name: NEURON_RT_STALL_ENABLE + value: "1" + - name: NEURON_RT_BLOCKING_IO + value: "1" + - name: NEURON_RT_EXEC_TIMEOUT + value: "900" + - name: RAY_memory_monitor_refresh_ms + value: "5000" + - name: RAY_memory_usage_threshold + value: "0.90" + # System paths + - name: LD_LIBRARY_PATH + value: "/home/ray/anaconda3/lib:$LD_LIBRARY_PATH" + - name: PORT + value: "8000" + volumeMounts: + - mountPath: /tmp/ray + name: ray-logs + - mountPath: /dev/shm + name: dshm + - mountPath: /tmp/model/cache + name: model-cache + volumes: + - name: dshm + emptyDir: + medium: Memory + - name: ray-logs + emptyDir: {} + - name: model-cache + emptyDir: {} + nodeSelector: + instanceType: trainium + provisionerType: Karpenter + neuron.amazonaws.com/neuron-device: "true" + tolerations: + - key: "aws.amazon.com/neuron" + operator: "Exists" + effect: "NoSchedule" + diff --git a/website/docs/aiml/chatbot/add-mistral.md b/website/docs/aiml/chatbot/add-mistral.md new file mode 100644 index 000000000..d55033619 --- /dev/null +++ b/website/docs/aiml/chatbot/add-mistral.md @@ -0,0 +1,86 @@ +--- +title: "Deploying The Mistral-7B-Instruct-v0.3 Chat Model on Ray Serve" +sidebar_position: 60 +--- + +With all the node pools provisioned, we can now proceed to deploy Mistral-7B-Instruct-v0.3 chatbot infrastructure. + +Let's begin by deploying the `ray-service-mistral.yaml` file: + +```bash wait=5 +$ kubectl apply -k ~/environment/eks-workshop/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot +namespace/mistral created +rayservice.ray.io/mistral created +``` + +### Creating the Ray Service Pods for Inference + +The `ray-service-mistral.yaml` file defines the Kubernetes configuration for deploying the Ray Serve service for the mistral7bv0.3 AI chatbot: + +```file +manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml +``` + +This configuration accomplishes the following: + +1. Creates a Kubernetes namespace named `mistral` for resource isolation +2. Deploys a RayService named `rayservice.ray.io/mistral` that utilizes a Python script to create the Ray Serve component +3. Provisions a Head Pod and Worker Pods to pull Docker images from Amazon Elastic Container Registry (ECR) + +After applying the configurations, we'll monitor the progress of the head and worker pods: + +```bash wait=5 +$ kubectl get pod -n mistral +NAME READY STATUS RESTARTS AGE +pod/mistral-raycluster-ltvjb-head-7rd7d 0/2 Pending 0 4s +pod/mistral-raycluster-ltvjb-worker-worker-group-nff7x 0/1 Pending 0 4s +``` + +:::caution +It may take up to 15 minutes for both pods to be ready. +::: + +We can wait for the pods to be ready using the following command: + +```bash timeout=900 +$ kubectl wait pod \ +--all \ +--for=condition=Ready \ +--namespace=mistral \ +--timeout=15m +pod/mistral-raycluster-ltvjb-head-7rd7d met +pod/mistral-raycluster-ltvjb-worker-worker-group-nff7x met +``` + +Once the pods are fully deployed, we'll verify that everything is in place: + +```bash +$ kubectl get all -n mistral +NAME READY STATUS RESTARTS AGE +pod/mistral-raycluster-ltvjb-head-7rd7d 2/2 Running 0 7m +pod/mistral-raycluster-ltvjb-worker-worker-group-nff7x 1/1 Running 0 7m + +NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE +service/mistral NodePort 172.20.74.49 6379:32625/TCP,8265:30941/TCP,10001:32430/TCP,8000:31393/TCP,8080:31361/TCP 94m +service/mistral-head-svc NodePort 172.20.121.46 8000:30481/TCP,8080:32609/TCP,6379:31066/TCP,8265:31006/TCP,10001:30220/TCP 92m +service/mistral-serve-svc NodePort 172.20.241.50 8000:32351/TCP 92m + +NAME DESIRED WORKERS AVAILABLE WORKERS CPUS MEMORY GPUS STATUS AGE +raycluster.ray.io/mistral-raycluster-ltvjb 1 1 2 36Gi 0 ready 94m + +NAME SERVICE STATUS NUM SERVE ENDPOINTS +rayservice.ray.io/mistral Running 2 +``` + +:::caution +Configuring RayService may take up to 10 minutes. +::: + +We can wait for the RayService to be running with this command: + +```bash wait=5 timeout=600 +$ kubectl wait --for=jsonpath='{.status.serviceStatus}'=Running rayservice/mistral -n mistral --timeout=10m +rayservice.ray.io/mistral condition met +``` + +With everything properly deployed, we can now proceed to create the web interface for the chatbot. diff --git a/website/docs/aiml/chatbot/gradio-mistral.md b/website/docs/aiml/chatbot/gradio-mistral.md new file mode 100644 index 000000000..61a7217ee --- /dev/null +++ b/website/docs/aiml/chatbot/gradio-mistral.md @@ -0,0 +1,68 @@ +--- +title: "Configuring the Gradio Web User Interface for Access" +sidebar_position: 70 +--- + +After all the resources have been configured within the Ray Serve Cluster, it's now time to directly access the Mistral-7B-Instruct-v0.3 chatbot. The web interface is powered by the Gradio UI. + +:::tip +You can learn more about Load Balancers in the [Load Balancer module](../../../fundamentals/exposing/loadbalancer/index.md) provided in this workshop. +::: + +### Deploying Gradio Web User Interface + +Once the AWS Load Balancer Controller has been installed, we can deploy the Gradio UI components. + +```file +manifests/modules/aiml/chatbot/gradio-mistral/gradio-ui.yaml +``` + +The components consist of a `Deployment`, `Service`, and `ConfigMap` to launch the application. In particular, the `Service` component is named gradio-service and is deployed as a `LoadBalancer`. + +```bash +$ kubectl apply -k ~/environment/eks-workshop/modules/aiml/chatbot/gradio-mistral +namespace/gradio-mistral-tran1 created +configmap/gradio-app-script created +service/gradio-service created +deployment.apps/gradio-deployment created +``` + +To check the status of each component, run the following commands: + +```bash +$ kubectl get deployments -n gradio-mistral-tran1 +NAME READY UP-TO-DATE AVAILABLE AGE +gradio-deployment 1/1 1 1 95s +``` + +```bash +$ kubectl get configmaps -n gradio-mistral-tran1 +NAME DATA AGE +gradio-app-script 1 110s +kube-root-ca.crt 1 111s +``` + +### Accessing the Chatbot Website + +Once the load balancer has finished deploying, use the external IP address to directly access the website: + +```bash wait=10 +$ kubectl get services -n gradio-llama2-inf2 +NAME TYPE ClUSTER-IP EXTERNAL-IP PORT(S) AGE +gradio-service LoadBalancer 172.20.84.26 k8s-gradioll-gradiose-a6d0b586ce-06885d584b38b400.elb.us-west-2.amazonaws.com 80:30802/TCP 8m42s +``` + +To wait until the Network Load Balancer has finished provisioning, run the following command: + +```bash wait=240 timeout=600 +$ curl --head -X GET --retry 30 --retry-all-errors --retry-delay 15 --connect-timeout 5 --max-time 10 \ +-k $(kubectl get service -n gradio-mistral-tran1 gradio-service -o jsonpath="{.status.loadBalancer.ingress[*].hostname}{'\n'}") +``` + +Now that our application is exposed to the outside world, let's access it by pasting the URL in your web browser. You will see the Mistral-7B-Instruct-v0.3 chatbot and will be able to interact with it by asking questions. + + + + + +This concludes the current lab on deploying the Mistral-7B-Instruct-v0.3 Chatbot Model within an EKS Cluster via Karpenter. diff --git a/website/docs/aiml/chatbot/mistral.md b/website/docs/aiml/chatbot/mistral.md new file mode 100644 index 000000000..ccda8ed3d --- /dev/null +++ b/website/docs/aiml/chatbot/mistral.md @@ -0,0 +1,31 @@ +--- +title: "Understanding the Mistral-7B-Instruct-v0.3 Chat Model" +sidebar_position: 50 +sidebar_custom_props: { "module": true } +weight: 30 +description: "Use Inferentia to accelerate deep learning inference workloads on Amazon Elastic Kubernetes Service." +--- + + +Mistral-7B-Instruct-v0.3 model represents a significant advancement in language model technology, combining powerful capabilities like Text generation and completion, Information extraction, Data analysis, API interaction, Complex reasoning tasks with practical efficiency. + +As a 7B parameter model, it offers remarkable performance while remaining deployable on standard hardware configurations. It requires aproximately ~26-28 GB memory (13 GB for 7B parameters and additional ~13 GB for Optimizer states and overhead). `trn1.2xlarge` instance with 32GB memory is suitable for running the Mistral-7B model, as it provides enough headroom Model weights, Optimizer states, KV cache, Input/output tensors and Runtime overhead. + +Mistral-7B-Instruct-v0.3 is implemented using FastAPI, Ray Serve, and PyTorch-based Hugging Face Transformers to create a seamless API for text generation. + +Here's the code for compiling the model that we'll use: + +```file +manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/mistral1.py +``` + +This Python code performs the following tasks: + +1. Configures an APIIngress class responsible for handling inference requests +2. Defines a MistralModel class responsible for managing the Mistral language model +3. Loads and compiles the model based on existing parameters +4. Creates an entry point for the FastAPI application + +Through these steps, the Mistral-7B-Instruct-v0.3 chat model allows the endpoint to accept input sentences and generate text outputs. The high performance efficiency in processing tasks enables the model to handle a wide variety of natural language processing applications, such as chat bots and text generation tasks. + +In this lab, we'll see how the Mistral-7B-Instruct-v0.3 Model is configured with Ray Service as a Kubernetes configuration, allowing users to understand how to incorporate fine-tuning and deploy their own natural language processing applications. From d9de4425593564695907b6851121acad6ce32abb Mon Sep 17 00:00:00 2001 From: Divya Gupta Date: Tue, 28 Jan 2025 15:08:30 -0500 Subject: [PATCH 02/19] nodepool config --- manifests/modules/aiml/chatbot/nodepool/nodepool-tran1.yaml | 2 +- manifests/modules/aiml/chatbot/nodepool/nodepool-x86.yaml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/manifests/modules/aiml/chatbot/nodepool/nodepool-tran1.yaml b/manifests/modules/aiml/chatbot/nodepool/nodepool-tran1.yaml index 7e58fbfe8..f3cb372d3 100644 --- a/manifests/modules/aiml/chatbot/nodepool/nodepool-tran1.yaml +++ b/manifests/modules/aiml/chatbot/nodepool/nodepool-tran1.yaml @@ -25,7 +25,7 @@ spec: values: ["amd64"] - key: "karpenter.sh/capacity-type" operator: In - values: ["on-demand"] + values: ["on-demand", "spot"] expireAfter: 720h terminationGracePeriod: 24h nodeClassRef: diff --git a/manifests/modules/aiml/chatbot/nodepool/nodepool-x86.yaml b/manifests/modules/aiml/chatbot/nodepool/nodepool-x86.yaml index 2fd9dd3fb..6c4027803 100644 --- a/manifests/modules/aiml/chatbot/nodepool/nodepool-x86.yaml +++ b/manifests/modules/aiml/chatbot/nodepool/nodepool-x86.yaml @@ -23,7 +23,7 @@ spec: values: ["amd64"] - key: "karpenter.sh/capacity-type" operator: In - values: ["on-demand"] + values: ["on-demand", "spot"] expireAfter: 720h terminationGracePeriod: 24h nodeClassRef: From f591f253014a55ef8b8801a675abc60cfc92b357 Mon Sep 17 00:00:00 2001 From: Divya Gupta Date: Tue, 11 Feb 2025 17:00:10 -0500 Subject: [PATCH 03/19] interminitent issue fix --- .../modules/aiml/chatbot/.workshop/cleanup.sh | 35 +- .../aiml/chatbot/nodepool/kustomization.yaml | 2 +- ...nodepool-tran1.yaml => nodepool-trn1.yaml} | 24 +- .../aiml/chatbot/nodepool/nodepool-x86.yaml | 7 +- .../Dockerfile | 4 +- .../mistral1.py | 5 +- .../ray_service_mistral.yaml | 122 +- package-lock.json | 26336 ++++++++++++++++ package.json | 8 +- website/docs/aiml/chatbot/expose.md | 6 +- .../aiml/chatbot/{ => llama2}/add-llama2.md | 0 .../docs/aiml/chatbot/{ => llama2}/gradio.md | 2 +- .../chatbot/{intro.md => llama2/index.md} | 2 +- .../aiml/chatbot/llama2/tests/hook-suite.sh | 11 + .../aiml/chatbot/{ => mistral}/add-mistral.md | 0 .../chatbot/{ => mistral}/gradio-mistral.md | 2 +- .../chatbot/{mistral.md => mistral/index.md} | 3 - .../aiml/chatbot/mistral/tests/hook-suite.sh | 11 + website/docs/aiml/chatbot/nodepool.md | 16 +- .../sample-app-screens/gardio_mistral_SS.png | Bin 0 -> 213628 bytes yarn.lock | 3981 ++- 21 files changed, 29326 insertions(+), 1251 deletions(-) rename manifests/modules/aiml/chatbot/nodepool/{nodepool-tran1.yaml => nodepool-trn1.yaml} (76%) create mode 100644 package-lock.json rename website/docs/aiml/chatbot/{ => llama2}/add-llama2.md (100%) rename website/docs/aiml/chatbot/{ => llama2}/gradio.md (96%) rename website/docs/aiml/chatbot/{intro.md => llama2/index.md} (98%) create mode 100644 website/docs/aiml/chatbot/llama2/tests/hook-suite.sh rename website/docs/aiml/chatbot/{ => mistral}/add-mistral.md (100%) rename website/docs/aiml/chatbot/{ => mistral}/gradio-mistral.md (96%) rename website/docs/aiml/chatbot/{mistral.md => mistral/index.md} (92%) create mode 100644 website/docs/aiml/chatbot/mistral/tests/hook-suite.sh create mode 100644 website/static/img/sample-app-screens/gardio_mistral_SS.png diff --git a/manifests/modules/aiml/chatbot/.workshop/cleanup.sh b/manifests/modules/aiml/chatbot/.workshop/cleanup.sh index c022c590c..f650ff086 100755 --- a/manifests/modules/aiml/chatbot/.workshop/cleanup.sh +++ b/manifests/modules/aiml/chatbot/.workshop/cleanup.sh @@ -6,14 +6,35 @@ logmessage "Deleting Gradio-UI Components..." kubectl delete -k /eks-workshop/manifests/modules/aiml/chatbot/gradio --ignore-not-found -logmessage "Deleting Llama2 pods..." +kubectl delete -k /eks-workshop/manifests/modules/aiml/chatbot/gradio-mistral --ignore-not-found + +logmessage "Deleting Llama2 and mistral pods..." kubectl delete -k /eks-workshop/manifests/modules/aiml/chatbot/ray-service-llama2-chatbot --ignore-not-found +kubectl delete -k /eks-workshop/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot --ignore-not-found + +logmessage "Deleting persistent volume claim and storage class" + +kubectl delete pvc model-cache-pvc -n mistral --ignore-not-found + +kubectl delete storageclass ebs-gp3 -n mistral --ignore-not-found + +logmessage "Deleting mistral, gradio-mistral-inf2, llama2, and gradio-llama2-inf2 namespaces..." + +kubectl delete namespace llama2 --ignore-not-found + +kubectl delete namespace gradio-llama2-inf2 --ignore-not-found + +kubectl delete namespace mistral --ignore-not-found + +kubectl delete namespace gradio-mistral-inf2 --ignore-not-found + logmessage "Deleting Neuron Device Plugin..." -kubectl delete -f https://raw.githubusercontent.com/aws-neuron/aws-neuron-sdk/v2.19.1/src/k8/k8s-neuron-device-plugin-rbac.yml --ignore-not-found -kubectl delete -f https://raw.githubusercontent.com/aws-neuron/aws-neuron-sdk/v2.19.1/src/k8/k8s-neuron-device-plugin.yml --ignore-not-found +kubectl delete -f https://raw.githubusercontent.com/aws-neuron/aws-neuron-sdk/v2.21.0/src/k8/k8s-neuron-device-plugin-rbac.yml --ignore-not-found +kubectl delete -f https://raw.githubusercontent.com/aws-neuron/aws-neuron-sdk/v2.21.0/src/k8/k8s-neuron-device-plugin.yml --ignore-not-found + logmessage "Un-installing kuberay operator..." @@ -22,10 +43,4 @@ helm uninstall kuberay-operator --ignore-not-found logmessage "Deleting Karpenter resources..." kubectl kustomize ~/environment/eks-workshop/modules/aiml/chatbot/nodepool \ - | envsubst | kubectl delete -f- - -logmessage "Deleting llama2 and gradio-llama2-inf2 namespaces..." - -kubectl delete namespace llama2 --ignore-not-found - -kubectl delete namespace gradio-llama2-inf2 --ignore-not-found + | envsubst | kubectl delete --ignore-not-found -f- \ No newline at end of file diff --git a/manifests/modules/aiml/chatbot/nodepool/kustomization.yaml b/manifests/modules/aiml/chatbot/nodepool/kustomization.yaml index 64ff67cc9..2da9c6890 100644 --- a/manifests/modules/aiml/chatbot/nodepool/kustomization.yaml +++ b/manifests/modules/aiml/chatbot/nodepool/kustomization.yaml @@ -3,4 +3,4 @@ kind: Kustomization resources: - nodepool-inf2.yaml - nodepool-x86.yaml - - nodepool-tran1.yaml + - nodepool-trn1.yaml diff --git a/manifests/modules/aiml/chatbot/nodepool/nodepool-tran1.yaml b/manifests/modules/aiml/chatbot/nodepool/nodepool-trn1.yaml similarity index 76% rename from manifests/modules/aiml/chatbot/nodepool/nodepool-tran1.yaml rename to manifests/modules/aiml/chatbot/nodepool/nodepool-trn1.yaml index f3cb372d3..fbff46f13 100644 --- a/manifests/modules/aiml/chatbot/nodepool/nodepool-tran1.yaml +++ b/manifests/modules/aiml/chatbot/nodepool/nodepool-trn1.yaml @@ -1,20 +1,19 @@ apiVersion: karpenter.sh/v1 kind: NodePool metadata: - name: trainium-tran1 + name: trainium-trn1 spec: template: metadata: labels: - instanceType: trainium + instanceType: trn1.2xlarge provisionerType: Karpenter neuron.amazonaws.com/neuron-device: "true" + vpc.amazonaws.com/has-trunk-attached: "true" # Required for Pod ENI spec: - startupTaints: - - key: node.kubernetes.io/not-ready - effect: "NoExecute" taints: - key: aws.amazon.com/neuron + value: "true" effect: "NoSchedule" requirements: - key: node.kubernetes.io/instance-type @@ -25,17 +24,17 @@ spec: values: ["amd64"] - key: "karpenter.sh/capacity-type" operator: In - values: ["on-demand", "spot"] + values: ["on-demand"] expireAfter: 720h terminationGracePeriod: 24h nodeClassRef: group: karpenter.k8s.aws kind: EC2NodeClass - name: trainium-tran1 + name: trainium-trn1 limits: - cpu: 100 - memory: 400Gi - aws.amazon.com/neuron: 10 + aws.amazon.com/neuron: 2 + cpu: 16 + memory: 64Gi disruption: consolidateAfter: 300s consolidationPolicy: WhenEmptyOrUnderutilized @@ -44,7 +43,7 @@ spec: apiVersion: karpenter.k8s.aws/v1 kind: EC2NodeClass metadata: - name: trainium-tran1 + name: trainium-trn1 spec: amiFamily: AL2 amiSelectorTerms: @@ -63,5 +62,8 @@ spec: subnetSelectorTerms: - tags: karpenter.sh/discovery: ${EKS_CLUSTER_NAME} + kubernetes.io/role/internal-elb: "1" tags: app.kubernetes.io/created-by: eks-workshop + karpenter.sh/discovery: ${EKS_CLUSTER_NAME} + aws-neuron: "true" \ No newline at end of file diff --git a/manifests/modules/aiml/chatbot/nodepool/nodepool-x86.yaml b/manifests/modules/aiml/chatbot/nodepool/nodepool-x86.yaml index 6c4027803..9f76bb700 100644 --- a/manifests/modules/aiml/chatbot/nodepool/nodepool-x86.yaml +++ b/manifests/modules/aiml/chatbot/nodepool/nodepool-x86.yaml @@ -10,10 +10,8 @@ spec: instanceType: mixed-x86 provisionerType: Karpenter workload: rayhead + vpc.amazonaws.com/has-trunk-attached: "true" # Required for Pod ENI spec: - startupTaints: - - key: node.kubernetes.io/not-ready - effect: "NoExecute" requirements: - key: "karpenter.k8s.aws/instance-family" operator: In @@ -23,7 +21,7 @@ spec: values: ["amd64"] - key: "karpenter.sh/capacity-type" operator: In - values: ["on-demand", "spot"] + values: ["on-demand"] expireAfter: 720h terminationGracePeriod: 24h nodeClassRef: @@ -60,5 +58,6 @@ spec: subnetSelectorTerms: - tags: karpenter.sh/discovery: ${EKS_CLUSTER_NAME} + kubernetes.io/role/internal-elb: "1" tags: app.kubernetes.io/created-by: eks-workshop diff --git a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/Dockerfile b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/Dockerfile index c5e7276a2..da8f6026f 100644 --- a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/Dockerfile +++ b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/Dockerfile @@ -1,5 +1,5 @@ # https://hub.docker.com/layers/rayproject/ray/2.11.0-py310/images/sha256-de798e487b76a8f2412c718c43c5f342b3eb05e0705a71325102904cd27c3613?context=explore -FROM rayproject/ray:2.32.0-py310 +FROM rayproject/ray:2.22.0-py310 # Maintainer label LABEL maintainer="DoEKS" @@ -26,7 +26,7 @@ USER $USER # Set pip repository pointing to the Neuron repository and install required Python packages RUN pip config set global.extra-index-url https://pip.repos.neuron.amazonaws.com && \ - pip install wget awscli regex neuronx-cc==2.* torch-neuronx torchvision transformers-neuronx sentencepiece transformers huggingface_hub tenacity psutil fastapi uvicorn mistral-inference mistral-common + pip install wget awscli regex neuronx-cc==2.* torch-neuronx torchvision transformers-neuronx sentencepiece transformers huggingface_hub tenacity psutil fastapi uvicorn # Add Neuron path to PATH diff --git a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/mistral1.py b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/mistral1.py index ba8fcedd4..eb031f0cd 100644 --- a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/mistral1.py +++ b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/mistral1.py @@ -35,8 +35,7 @@ async def infer(self, sentence: str): name="mistral-7b", autoscaling_config={"min_replicas": 1, "max_replicas": 1}, ray_actor_options={ - "resources": {"neuron_cores": neuron_cores}, - "memory": 28000000000 + "resources": {"neuron_cores": neuron_cores} } ) class MistralModel: @@ -100,7 +99,7 @@ def __init__(self): # load pre-complied .neff files self.neuron_model.load(model_cache) self.neuron_model.to_neuron() - + logger.info("Model successfully prepared for inference") # Verify initialization diff --git a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml index 73b8f3bd3..b25040b08 100644 --- a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml +++ b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml @@ -2,6 +2,7 @@ apiVersion: v1 kind: Namespace metadata: name: mistral + --- #---------------------------------------------------------------------- # NOTE: For deployment instructions, refer to the DoEKS website. @@ -13,31 +14,20 @@ metadata: namespace: mistral spec: serviceUnhealthySecondThreshold: 900 - deploymentUnhealthySecondThreshold: 300 + deploymentUnhealthySecondThreshold: 600 serveConfigV2: | applications: - name: mistral-deployment import_path: "mistral1:entrypoint" route_prefix: "/" - runtime_env: - env_vars: - MODEL_ID: "askulkarni2/neuron-mistral7bv0.3" - NEURON_CC_FLAGS: "-O1" - LD_LIBRARY_PATH: "/home/ray/anaconda3/lib:$LD_LIBRARY_PATH" - NEURON_CORES: "2" - NEURON_COMPILE_CACHE_URL: "/tmp/model/cache" - NEURON_RT_CACHE_DIRECTORY: "/tmp/model/cache" deployments: - name: mistral-7b autoscaling_config: min_replicas: 1 max_replicas: 1 target_num_ongoing_requests_per_replica: 1 - ray_actor_options: - resources: {"neuron_cores": 2} - memory: 28000000000 rayClusterConfig: - rayVersion: '2.32.0' + rayVersion: '2.22.0' enableInTreeAutoscaling: true headGroupSpec: serviceType: NodePort @@ -48,6 +38,9 @@ spec: dashboard-host: '0.0.0.0' num-cpus: "0" # this is to ensure no tasks or actors are scheduled on the head Pod template: + metadata: + labels: + ray.io/node-type: head spec: containers: - name: head @@ -69,15 +62,15 @@ spec: volumeMounts: - mountPath: /tmp/ray name: ray-logs - - mountPath: /tmp/model/cache - name: model-cache resources: limits: cpu: "4" memory: 16Gi + vpc.amazonaws.com/pod-eni: "1" requests: cpu: "2" - memory: 8Gi + memory: 10Gi + vpc.amazonaws.com/pod-eni: "1" env: - name: PORT value: "8000" @@ -87,11 +80,14 @@ spec: instanceType: mixed-x86 provisionerType: Karpenter workload: rayhead + tolerations: + - key: node.kubernetes.io/not-ready + operator: Exists + effect: NoExecute + tolerationSeconds: 300 volumes: - name: ray-logs emptyDir: {} - - name: model-cache - emptyDir: {} workerGroupSpecs: - groupName: worker-group replicas: 1 @@ -99,9 +95,23 @@ spec: maxReplicas: 1 rayStartParams: resources: '"{\"neuron_cores\": 2}"' - num-cpus: "6" template: + metadata: + labels: + ray.io/node-type: worker spec: + affinity: + podAffinity: + preferredDuringSchedulingIgnoredDuringExecution: + - weight: 100 + podAffinityTerm: + labelSelector: + matchExpressions: + - key: ray.io/node-type + operator: In + values: + - head + topologyKey: kubernetes.io/zone containers: - name: worker image: public.ecr.aws/e3e2e5u9/aiml/mistral-7b:latest @@ -113,82 +123,70 @@ spec: # We are using 2 Neuron cores per HTTP request hence this configuration handles 6 requests per second resources: limits: - memory: "30Gi" + cpu: 5 + memory: 26Gi aws.amazon.com/neuron: "1" requests: - memory: "28Gi" + cpu: 4 + memory: 26Gi aws.amazon.com/neuron: "1" env: # Model and Neuron configuration - name: MODEL_ID value: "askulkarni2/neuron-mistral7bv0.3" - name: NEURON_CORES - value: "2" + value: "2" # Number of Neuron cores to use + - name: NEURON_RT_ASYNC_EXEC + value: "1" # Enable asynchronous execution - name: NEURON_RT_NUM_CORES - value: "2" + value: "2" # Total number of Neuron cores available - name: NEURON_RT_VISIBLE_CORES - value: "0,1" + value: "0,1" # Which specific cores to use (cores 0 and 1) + # compilation settings - name: NEURON_CC_FLAGS - value: "-O1" # Changed from --no-compile + value: "-O1" # Optimization level for compilation - name: NEURON_COMPILE_ONLY - value: "0" - - name: NEURON_RT_LOG_LEVEL - value: "INFO" + value: "0" # Don't just compile, also run the model # Cache configuration - name: NEURON_COMPILE_CACHE_URL - value: "/tmp/model/cache" + value: "/tmp/model/cache" # Where to store compiled artifacts - name: NEURON_RT_CACHE_DIRECTORY - value: "/tmp/model/cache" + value: "/tmp/model/cache" # Runtime cache location - name: NEURON_RT_USE_PREFETCHED_NEFF - value: "1" # Added to use pre-compiled NEFF files - # Memory management - - name: NEURON_RT_MAX_WORKSPACE_SIZE - value: "8589934592" - - name: XLA_TENSOR_ALLOCATOR_MAXSIZE - value: "12884901888" - - name: MALLOC_ARENA_MAX - value: "32" - - name: MALLOC_TRIM_THRESHOLD_ - value: "128K" - - name: XLA_PYTHON_CLIENT_MEM_FRACTION - value: "0.95" - # Runtime configuration - - name: NEURON_RT_STALL_ENABLE - value: "1" - - name: NEURON_RT_BLOCKING_IO - value: "1" - - name: NEURON_RT_EXEC_TIMEOUT - value: "900" - - name: RAY_memory_monitor_refresh_ms - value: "5000" - - name: RAY_memory_usage_threshold - value: "0.90" + value: "1" # Use pre-compiled neural network files # System paths + - name: NEURON_RT_LOG_LEVEL + value: "INFO" # Change to INFO or DEBUG when troubleshooting - name: LD_LIBRARY_PATH - value: "/home/ray/anaconda3/lib:$LD_LIBRARY_PATH" + value: "/home/ray/anaconda3/lib:$LD_LIBRARY_PATH" # Library path - name: PORT - value: "8000" + value: "8000" # Service port + # ray + - name: RAY_gcs_server_request_timeout_seconds + value: "120" + - name: RAY_SERVE_KV_TIMEOUT_S + value: "120" volumeMounts: - mountPath: /tmp/ray name: ray-logs - mountPath: /dev/shm name: dshm - - mountPath: /tmp/model/cache - name: model-cache volumes: + - name: ray-logs + emptyDir: {} - name: dshm emptyDir: medium: Memory - - name: ray-logs - emptyDir: {} - - name: model-cache - emptyDir: {} nodeSelector: - instanceType: trainium + instanceType: trn1.2xlarge provisionerType: Karpenter neuron.amazonaws.com/neuron-device: "true" tolerations: - key: "aws.amazon.com/neuron" operator: "Exists" effect: "NoSchedule" + - key: "node.kubernetes.io/not-ready" + operator: "Exists" + effect: "NoExecute" + tolerationSeconds: 300 diff --git a/package-lock.json b/package-lock.json new file mode 100644 index 000000000..9a80ddf8a --- /dev/null +++ b/package-lock.json @@ -0,0 +1,26336 @@ +{ + "name": "eks-workshop", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "eks-workshop", + "version": "1.0.0", + "license": 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+ "packageManager": "yarn@4.5.3", + "dependencies": { + "@docusaurus/core": "^3.7.0", + "@docusaurus/module-type-aliases": "^3.7.0", + "@docusaurus/preset-classic": "^3.7.0", + "@docusaurus/types": "^3.7.0" + } } diff --git a/website/docs/aiml/chatbot/expose.md b/website/docs/aiml/chatbot/expose.md index 27611c09b..42b4e9c5c 100644 --- a/website/docs/aiml/chatbot/expose.md +++ b/website/docs/aiml/chatbot/expose.md @@ -17,7 +17,7 @@ $ helm repo add kuberay https://ray-project.github.io/kuberay-helm/ ``` ```bash wait=10 -$ helm install kuberay-operator kuberay/kuberay-operator --version 1.1.0 +$ helm install kuberay-operator kuberay/kuberay-operator --version 1.2.2 NAME: kuberay-operator LAST DEPLOYED: Wed Jul 24 14:46:13 2024 NAMESPACE: default @@ -47,8 +47,8 @@ You can learn more about Neuron Device Plugins in the [AIML Inference module](.. We can deploy the role using the following command: ```bash -$ kubectl apply -f https://raw.githubusercontent.com/aws-neuron/aws-neuron-sdk/v2.19.1/src/k8/k8s-neuron-device-plugin-rbac.yml -$ kubectl apply -f https://raw.githubusercontent.com/aws-neuron/aws-neuron-sdk/v2.19.1/src/k8/k8s-neuron-device-plugin.yml +$ kubectl apply -f https://raw.githubusercontent.com/aws-neuron/aws-neuron-sdk/v2.21.0/src/k8/k8s-neuron-device-plugin-rbac.yml +$ kubectl apply -f https://raw.githubusercontent.com/aws-neuron/aws-neuron-sdk/v2.21.0/src/k8/k8s-neuron-device-plugin.yml serviceaccount/neuron-device-plugin created clusterrole.rbac.authorization.k8s.io/neuron-device-plugin created diff --git a/website/docs/aiml/chatbot/add-llama2.md b/website/docs/aiml/chatbot/llama2/add-llama2.md similarity index 100% rename from website/docs/aiml/chatbot/add-llama2.md rename to website/docs/aiml/chatbot/llama2/add-llama2.md diff --git a/website/docs/aiml/chatbot/gradio.md b/website/docs/aiml/chatbot/llama2/gradio.md similarity index 96% rename from website/docs/aiml/chatbot/gradio.md rename to website/docs/aiml/chatbot/llama2/gradio.md index 601fd80c4..b08f675c0 100644 --- a/website/docs/aiml/chatbot/gradio.md +++ b/website/docs/aiml/chatbot/llama2/gradio.md @@ -6,7 +6,7 @@ sidebar_position: 40 After all the resources have been configured within the Ray Serve Cluster, it's now time to directly access the Llama2 chatbot. The web interface is powered by the Gradio UI. :::tip -You can learn more about Load Balancers in the [Load Balancer module](../../fundamentals/exposing/loadbalancer/index.md) provided in this workshop. +You can learn more about Load Balancers in the [Load Balancer module](../../../fundamentals/exposing/loadbalancer/index.md) provided in this workshop. ::: ### Deploying Gradio Web User Interface diff --git a/website/docs/aiml/chatbot/intro.md b/website/docs/aiml/chatbot/llama2/index.md similarity index 98% rename from website/docs/aiml/chatbot/intro.md rename to website/docs/aiml/chatbot/llama2/index.md index 9794d4b52..d0770379b 100644 --- a/website/docs/aiml/chatbot/intro.md +++ b/website/docs/aiml/chatbot/llama2/index.md @@ -1,6 +1,6 @@ --- title: "Understanding the Llama2 Chatbot Model" -sidebar_position: 20 +sidebar_position: 25 --- Llama2 is a training model that uses FastAPI, Ray Serve, and PyTorch-based Hugging Face Transformers to create a seamless API for text generation. diff --git a/website/docs/aiml/chatbot/llama2/tests/hook-suite.sh b/website/docs/aiml/chatbot/llama2/tests/hook-suite.sh new file mode 100644 index 000000000..8b5a4baea --- /dev/null +++ b/website/docs/aiml/chatbot/llama2/tests/hook-suite.sh @@ -0,0 +1,11 @@ +set -e + +before() { + echo "noop" +} + +after() { + prepare-environment +} + +"$@" diff --git a/website/docs/aiml/chatbot/add-mistral.md b/website/docs/aiml/chatbot/mistral/add-mistral.md similarity index 100% rename from website/docs/aiml/chatbot/add-mistral.md rename to website/docs/aiml/chatbot/mistral/add-mistral.md diff --git a/website/docs/aiml/chatbot/gradio-mistral.md b/website/docs/aiml/chatbot/mistral/gradio-mistral.md similarity index 96% rename from website/docs/aiml/chatbot/gradio-mistral.md rename to website/docs/aiml/chatbot/mistral/gradio-mistral.md index 61a7217ee..a0ae2cbb7 100644 --- a/website/docs/aiml/chatbot/gradio-mistral.md +++ b/website/docs/aiml/chatbot/mistral/gradio-mistral.md @@ -62,7 +62,7 @@ $ curl --head -X GET --retry 30 --retry-all-errors --retry-delay 15 --connect-ti Now that our application is exposed to the outside world, let's access it by pasting the URL in your web browser. You will see the Mistral-7B-Instruct-v0.3 chatbot and will be able to interact with it by asking questions. - + This concludes the current lab on deploying the Mistral-7B-Instruct-v0.3 Chatbot Model within an EKS Cluster via Karpenter. diff --git a/website/docs/aiml/chatbot/mistral.md b/website/docs/aiml/chatbot/mistral/index.md similarity index 92% rename from website/docs/aiml/chatbot/mistral.md rename to website/docs/aiml/chatbot/mistral/index.md index ccda8ed3d..5307a0bb7 100644 --- a/website/docs/aiml/chatbot/mistral.md +++ b/website/docs/aiml/chatbot/mistral/index.md @@ -1,9 +1,6 @@ --- title: "Understanding the Mistral-7B-Instruct-v0.3 Chat Model" sidebar_position: 50 -sidebar_custom_props: { "module": true } -weight: 30 -description: "Use Inferentia to accelerate deep learning inference workloads on Amazon Elastic Kubernetes Service." --- diff --git a/website/docs/aiml/chatbot/mistral/tests/hook-suite.sh b/website/docs/aiml/chatbot/mistral/tests/hook-suite.sh new file mode 100644 index 000000000..8b5a4baea --- /dev/null +++ b/website/docs/aiml/chatbot/mistral/tests/hook-suite.sh @@ -0,0 +1,11 @@ +set -e + +before() { + echo "noop" +} + +after() { + prepare-environment +} + +"$@" diff --git a/website/docs/aiml/chatbot/nodepool.md b/website/docs/aiml/chatbot/nodepool.md index f6f59ce2f..2e8673edf 100644 --- a/website/docs/aiml/chatbot/nodepool.md +++ b/website/docs/aiml/chatbot/nodepool.md @@ -25,15 +25,15 @@ Here's the first Karpenter `NodePool` that will provision one `Head Pod` on `x86 ::yaml{file="manifests/modules/aiml/chatbot/nodepool/nodepool-x86.yaml" paths="spec.template.metadata.labels,spec.template.spec.requirements,spec.limits"} 1. We're asking the `NodePool` to start all new nodes with a Kubernetes label `type: karpenter`, which will allow us to specifically target Karpenter nodes with pods for demonstration purposes. Since there are multiple nodes being autoscaled by Karpenter, there are additional labels added such as `instanceType: mixed-x86` to indicate that this Karpenter node should be assigned to `x86-cpu-karpenter` pool. -2. The [NodePool CRD](https://karpenter.sh/docs/concepts/nodepools/) supports defining node properties like instance type and zone. In this example, we're setting the `karpenter.sh/capacity-type` to initially limit Karpenter to provisioning On-Demand and Spot instances, as well as `karpenter.k8s.aws/instance-family` to limit to a subset of specific instance types. You can learn which other properties are [available here](https://karpenter.sh/docs/concepts/scheduling/#selecting-nodes). Compared to the previous lab, there are more specifications defining the unique constraints of the `Head Pod`, such as defining an instance family of `r5`, `m5`, and `c5` nodes. +2. The [NodePool CRD](https://karpenter.sh/docs/concepts/nodepools/) supports defining node properties like instance type and zone. In this example, we're setting the `karpenter.sh/capacity-type` to initially limit Karpenter to provisioning On-Demand instances, as well as `karpenter.k8s.aws/instance-family` to limit to a subset of specific instance types. You can learn which other properties are [available here](https://karpenter.sh/docs/concepts/scheduling/#selecting-nodes). Compared to the previous lab, there are more specifications defining the unique constraints of the `Head Pod`, such as defining an instance family of `r5`, `m5`, and `c5` nodes. 3. A `NodePool` can define a limit on the amount of CPU and memory managed by it. Once this limit is reached Karpenter will not provision additional capacity associated with that particular `NodePool`, providing a cap on the total compute. -This secondary `NodePool` will provision `Ray Workers` on `Inf2.48xlarge` instances: +This secondary `NodePool` will provision `Ray Workers` on `trn1.2xlarge` instances: -::yaml{file="manifests/modules/aiml/chatbot/nodepool/nodepool-inf2.yaml" paths="spec.template.metadata.labels,spec.template.spec.requirements,spec.template.spec.taints,spec.limits"} +::yaml{file="manifests/modules/aiml/chatbot/nodepool/nodepool-trn1.yaml" paths="spec.template.metadata.labels,spec.template.spec.requirements,spec.template.spec.taints,spec.limits"} -1. We're asking the `NodePool` to start all new nodes with a Kubernetes label `provisionerType: Karpenter`, which will allow us to specifically target Karpenter nodes with pods for demonstration purposes. Since there are multiple nodes being autoscaled by Karpenter, there are additional labels added such as `instanceType: inferentia-inf2` to indicate that this Karpenter node should be assigned to `inferentia-inf2` pool. -2. The [NodePool CRD](https://karpenter.sh/docs/concepts/nodepools/) supports defining node properties like instance type and zone. In this example, we're setting the `karpenter.sh/capacity-type` to initially limit Karpenter to provisioning On-Demand and Spot instances, as well as `karpenter.k8s.aws/instance-family` to limit to a subset of specific instance types. You can learn which other properties are [available here](https://karpenter.sh/docs/concepts/scheduling/#selecting-nodes). In this case, there are specifications matching the requirements of the `Ray Workers` that will run on instances from the `Inf2` family. +1. We're asking the `NodePool` to start all new nodes with a Kubernetes label `provisionerType: Karpenter`, which will allow us to specifically target Karpenter nodes with pods for demonstration purposes. Since there are multiple nodes being autoscaled by Karpenter, there are additional labels added such as `instanceType: trn1.2xlarge` to indicate that this Karpenter node should be assigned to `trainium-trn1` pool. +2. The [NodePool CRD](https://karpenter.sh/docs/concepts/nodepools/) supports defining node properties like instance type and zone. In this example, we're setting the `karpenter.sh/capacity-type` to initially limit Karpenter to provisioning On-Demand instances, as well as `karpenter.k8s.aws/instance-type` to limit to a subset of specific instance type. You can learn which other properties are [available here](https://karpenter.sh/docs/concepts/scheduling/#selecting-nodes). In this case, there are specifications matching the requirements of the `Ray Workers` that will run on instances from the `trn1` family. 3. A `Taint` defines a specific set of properties that allow a node to repel a set of pods. This property works with its matching label, a `Toleration`. Both tolerations and taints work together to ensure that pods are properly scheduled onto the appropriate pods. You can learn more about the other properties in [this resource](https://kubernetes.io/docs/concepts/scheduling-eviction/taint-and-toleration/). 4. A `NodePool` can define a limit on the amount of CPU and memory managed by it. Once this limit is reached Karpenter will not provision additional capacity associated with that particular `NodePool`, providing a cap on the total compute. @@ -44,9 +44,9 @@ Apply the `NodePool` and `EC2NodeClass` manifests for both pools: ```bash $ kubectl kustomize ~/environment/eks-workshop/modules/aiml/chatbot/nodepool \ | envsubst | kubectl apply -f- -ec2nodeclass.karpenter.k8s.aws/inferentia-inf2 created +ec2nodeclass.karpenter.k8s.aws/trainium-trn1 created ec2nodeclass.karpenter.k8s.aws/x86-cpu-karpenter created -nodepool.karpenter.sh/inferentia-inf2 created +nodepool.karpenter.sh/trainium-trn1 created nodepool.karpenter.sh/x86-cpu-karpenter created ``` @@ -55,7 +55,7 @@ Once properly deployed, check for the node pools: ```bash $ kubectl get nodepool NAME NODECLASS -inferentia-inf2 inferentia-inf2 +trainium-trn1 trainium-trn1 x86-cpu-karpenter x86-cpu-karpenter ``` diff --git a/website/static/img/sample-app-screens/gardio_mistral_SS.png 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gensequence: "npm:^7.0.0" - checksum: 10c0/bd665b47b5c7222f0a51600e8781b9062d7eef8a9f7692b2e586dd79b80c89f87f5cee88bd0c291531ac9e5cdc8a0711ccf7711de84d13b15336168188c16e7f + checksum: 10c0/4e0db4d4fb9a024bc06ecf308312501cfd0dae18b0d049a7f18d659d9c8e87012d519fb908d5d4c5ee4c158a46676a632c95891f90f86a131bb1b7a07813a61f languageName: node linkType: hard "cspell@npm:^8.8.1": - version: 8.16.1 - resolution: "cspell@npm:8.16.1" - dependencies: - "@cspell/cspell-json-reporter": "npm:8.16.1" - "@cspell/cspell-pipe": "npm:8.16.1" - "@cspell/cspell-types": "npm:8.16.1" - "@cspell/dynamic-import": "npm:8.16.1" - "@cspell/url": "npm:8.16.1" - chalk: "npm:^5.3.0" + version: 8.17.3 + resolution: "cspell@npm:8.17.3" + dependencies: + "@cspell/cspell-json-reporter": "npm:8.17.3" + "@cspell/cspell-pipe": "npm:8.17.3" + "@cspell/cspell-types": "npm:8.17.3" + "@cspell/dynamic-import": "npm:8.17.3" + "@cspell/url": "npm:8.17.3" + chalk: "npm:^5.4.1" chalk-template: "npm:^1.1.0" - commander: "npm:^12.1.0" - 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postcss: ^8.4 + checksum: 10c0/a66c727bb2455328b18862f720819fc98ff5c1486b69f758bdb5c66f46cc6d484f9fc0bfa4f00f2693c5da6707ad136ca789496982f713ade693f08af624930e + languageName: node + linkType: hard + "css-select@npm:^4.1.3": version: 4.3.0 resolution: "css-select@npm:4.3.0" @@ -5838,6 +6998,13 @@ __metadata: languageName: node linkType: hard +"cssdb@npm:^8.2.3": + version: 8.2.3 + resolution: "cssdb@npm:8.2.3" + checksum: 10c0/17c3ca6432ed02431db6b44bed74649ccef7d7b7b900ccbc7297525f030722c441dd67c71f28aef3cfa0814ba7b254a24adfb0dcd5728937da179ff437cdcd0c + languageName: node + linkType: hard + "cssesc@npm:^3.0.0": version: 3.0.0 resolution: "cssesc@npm:3.0.0" @@ -5964,7 +7131,7 @@ __metadata: languageName: node linkType: hard -"debug@npm:4, debug@npm:^4.0.0, debug@npm:^4.1.0, debug@npm:^4.1.1, debug@npm:^4.3.1, debug@npm:^4.3.4, debug@npm:~4.4.0": +"debug@npm:4, debug@npm:^4.0.0, debug@npm:^4.1.0, debug@npm:^4.1.1, debug@npm:^4.3.1, debug@npm:^4.3.4, debug@npm:^4.4.0": version: 4.4.0 resolution: "debug@npm:4.4.0" dependencies: @@ -6201,28 +7368,28 @@ __metadata: linkType: hard "docusaurus-lunr-search@npm:^3.4.0": - version: 3.5.0 - resolution: "docusaurus-lunr-search@npm:3.5.0" - dependencies: - autocomplete.js: "npm:^0.37.0" - clsx: "npm:^1.2.1" - gauge: "npm:^3.0.0" - hast-util-select: "npm:^4.0.0" - hast-util-to-text: "npm:^2.0.0" + version: 3.6.1 + resolution: "docusaurus-lunr-search@npm:3.6.1" + dependencies: + autocomplete.js: "npm:^0.37.1" + clsx: "npm:^2.1.1" + gauge: "npm:^3.0.2" + hast-util-select: "npm:^4.0.2" + hast-util-to-text: "npm:^2.0.1" hogan.js: "npm:^3.0.2" - lunr: "npm:^2.3.8" + lunr: "npm:^2.3.9" lunr-languages: "npm:^1.4.0" mark.js: "npm:^8.11.1" - minimatch: "npm:^3.0.4" + minimatch: "npm:^3.1.2" rehype-parse: "npm:^7.0.1" to-vfile: "npm:^6.1.0" - unified: "npm:^9.0.0" - unist-util-is: "npm:^4.0.2" + unified: "npm:^9.2.2" + unist-util-is: "npm:^4.1.0" peerDependencies: "@docusaurus/core": ^2.0.0-alpha.60 || ^2.0.0 || ^3.0.0 - react: ^16.8.4 || ^17 || ^18 - react-dom: ^16.8.4 || ^17 || ^18 - checksum: 10c0/b3ed4f5bb90d1c1bd76e633ef7a83e8158cb71935268587ff7fb2063444422bcea1cea555305a5e79de818fc02982bdf33279d2c161367c228248f7f56959da9 + react: ^16.8.4 || ^17 || ^18 || ^19 + react-dom: ^16.8.4 || ^17 || ^18 || ^19 + checksum: 10c0/89962e2a8004bac0fa0999cd189c05ee0b34a4268c038f10db62d5ec41de89d9b672375832a21d93776d277c4a14bc022a717dffdc7a85048255969cc0eeb583 languageName: node linkType: hard @@ -6306,13 +7473,13 @@ __metadata: linkType: hard "domutils@npm:^3.0.1, domutils@npm:^3.1.0": - version: 3.1.0 - resolution: "domutils@npm:3.1.0" + version: 3.2.2 + resolution: "domutils@npm:3.2.2" dependencies: dom-serializer: "npm:^2.0.0" domelementtype: "npm:^2.3.0" domhandler: "npm:^5.0.3" - checksum: 10c0/342d64cf4d07b8a0573fb51e0a6312a88fb520c7fefd751870bf72fa5fc0f2e0cb9a3958a573610b1d608c6e2a69b8e9b4b40f0bfb8f87a71bce4f180cca1887 + checksum: 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"@docusaurus/core": "npm:^3.7.0" + "@docusaurus/module-type-aliases": "npm:^3.7.0" + "@docusaurus/preset-classic": "npm:^3.7.0" + "@docusaurus/types": "npm:^3.7.0" cspell: "npm:^8.8.1" lint-staged: "npm:^15.2.7" markdown-link-check: "npm:3.12.2" @@ -6388,10 +7559,10 @@ __metadata: languageName: unknown linkType: soft -"electron-to-chromium@npm:^1.5.41": - version: 1.5.72 - resolution: "electron-to-chromium@npm:1.5.72" - checksum: 10c0/1a9a55048cbd0c3823fa634a6384d5d9a537c47969ff9eb18ec7600dd71f08321514b7ec30a4a54267327ac814ac810926fb60a1b55feec18c3d9de57d646b7e +"electron-to-chromium@npm:^1.5.73": + version: 1.5.88 + resolution: "electron-to-chromium@npm:1.5.88" + checksum: 10c0/25946ef310f8e14c763fcf0e62094e7eae2273d9ffe908969ddd97492c3df0198739295ba76388dc210c4503ab6b540130779cd83036f80520cb8efee53be8e4 languageName: node linkType: hard @@ -6471,12 +7642,12 @@ __metadata: linkType: hard "enhanced-resolve@npm:^5.17.1": - version: 5.17.1 - resolution: "enhanced-resolve@npm:5.17.1" + version: 5.18.0 + resolution: "enhanced-resolve@npm:5.18.0" dependencies: graceful-fs: "npm:^4.2.4" tapable: "npm:^2.2.0" - checksum: 10c0/81a0515675eca17efdba2cf5bad87abc91a528fc1191aad50e275e74f045b41506167d420099022da7181c8d787170ea41e4a11a0b10b7a16f6237daecb15370 + checksum: 10c0/5fcc264a6040754ab5b349628cac2bb5f89cee475cbe340804e657a5b9565f70e6aafb338d5895554eb0ced9f66c50f38a255274a0591dcb64ee17c549c459ce languageName: node linkType: hard @@ -6546,9 +7717,18 @@ __metadata: linkType: hard "es-module-lexer@npm:^1.2.1": - version: 1.5.4 - resolution: "es-module-lexer@npm:1.5.4" - checksum: 10c0/300a469488c2f22081df1e4c8398c78db92358496e639b0df7f89ac6455462aaf5d8893939087c1a1cbcbf20eed4610c70e0bcb8f3e4b0d80a5d2611c539408c + version: 1.6.0 + resolution: "es-module-lexer@npm:1.6.0" + checksum: 10c0/667309454411c0b95c476025929881e71400d74a746ffa1ff4cb450bd87f8e33e8eef7854d68e401895039ac0bac64e7809acbebb6253e055dd49ea9e3ea9212 + languageName: node + linkType: hard + +"es-object-atoms@npm:^1.0.0": + version: 1.1.1 + resolution: "es-object-atoms@npm:1.1.1" + dependencies: + es-errors: "npm:^1.3.0" + checksum: 10c0/65364812ca4daf48eb76e2a3b7a89b3f6a2e62a1c420766ce9f692665a29d94fe41fe88b65f24106f449859549711e4b40d9fb8002d862dfd7eb1c512d10be0c languageName: node linkType: hard @@ -6597,6 +7777,13 @@ __metadata: languageName: node linkType: hard +"escape-string-regexp@npm:^1.0.5": + version: 1.0.5 + resolution: "escape-string-regexp@npm:1.0.5" + checksum: 10c0/a968ad453dd0c2724e14a4f20e177aaf32bb384ab41b674a8454afe9a41c5e6fe8903323e0a1052f56289d04bd600f81278edf140b0fcc02f5cac98d0f5b5371 + languageName: node + linkType: hard + "escape-string-regexp@npm:^4.0.0": version: 4.0.0 resolution: "escape-string-regexp@npm:4.0.0" @@ -6818,7 +8005,7 @@ __metadata: languageName: node linkType: hard -"execa@npm:~8.0.1": +"execa@npm:^8.0.1": version: 8.0.1 resolution: "execa@npm:8.0.1" dependencies: @@ -6904,23 +8091,23 @@ __metadata: languageName: node linkType: hard 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10c0/8dfa2c55f8122f729c7efd515a2b50fb752c0d0cb27ec2ecdbc70d90a86d5f69f466c9c5d01004f71b500dafba957ecd4413fca196a98cf99a39b705f98cae97 + languageName: node + linkType: hard + "website@workspace:website": version: 0.0.0-use.local resolution: "website@workspace:website" @@ -14507,14 +16208,14 @@ __metadata: languageName: node linkType: hard -"wildcard@npm:^2.0.0": +"wildcard@npm:^2.0.0, wildcard@npm:^2.0.1": version: 2.0.1 resolution: "wildcard@npm:2.0.1" checksum: 10c0/08f70cd97dd9a20aea280847a1fe8148e17cae7d231640e41eb26d2388697cbe65b67fd9e68715251c39b080c5ae4f76d71a9a69fa101d897273efdfb1b58bf7 languageName: node linkType: hard -"wrap-ansi-cjs@npm:wrap-ansi@^7.0.0": +"wrap-ansi-cjs@npm:wrap-ansi@^7.0.0, wrap-ansi@npm:^7.0.0": version: 7.0.0 resolution: "wrap-ansi@npm:7.0.0" dependencies: @@ -14649,12 +16350,12 @@ __metadata: languageName: node linkType: hard -"yaml@npm:^2.4.2, yaml@npm:^2.6.1, yaml@npm:~2.6.1": - version: 2.6.1 - resolution: "yaml@npm:2.6.1" +"yaml@npm:^2.4.2, yaml@npm:^2.7.0": + version: 2.7.0 + resolution: "yaml@npm:2.7.0" bin: yaml: bin.mjs - checksum: 10c0/aebf07f61c72b38c74d2b60c3a3ccf89ee4da45bcd94b2bfb7899ba07a5257625a7c9f717c65a6fc511563d48001e01deb1d9e55f0133f3e2edf86039c8c1be7 + checksum: 10c0/886a7d2abbd70704b79f1d2d05fe9fb0aa63aefb86e1cb9991837dced65193d300f5554747a872b4b10ae9a12bc5d5327e4d04205f70336e863e35e89d8f4ea9 languageName: node linkType: hard From 305144e7140d0378220afd139e11ecae45443620 Mon Sep 17 00:00:00 2001 From: Divya Gupta Date: Tue, 11 Feb 2025 19:24:04 -0500 Subject: [PATCH 04/19] doc correction --- .../aiml/chatbot/gradio-mistral/gradio-ui.yaml | 8 ++++---- .../modules/aiml/chatbot/nodepool/kustomization.yaml | 1 - .../docs/aiml/chatbot/{mistral => }/add-mistral.md | 9 +++------ .../aiml/chatbot/{mistral => }/gradio-mistral.md | 12 ++++++------ website/docs/aiml/chatbot/index.md | 9 +++------ website/docs/aiml/chatbot/llama2/add-llama2.md | 2 +- website/docs/aiml/chatbot/llama2/gradio.md | 2 +- website/docs/aiml/chatbot/llama2/index.md | 2 +- .../aiml/chatbot/{mistral/index.md => mistral.md} | 6 ++---- .../docs/aiml/chatbot/mistral/tests/hook-suite.sh | 11 ----------- website/docs/aiml/chatbot/nodepool.md | 4 ++-- 11 files changed, 23 insertions(+), 43 deletions(-) rename website/docs/aiml/chatbot/{mistral => }/add-mistral.md (90%) rename website/docs/aiml/chatbot/{mistral => }/gradio-mistral.md (88%) rename website/docs/aiml/chatbot/{mistral/index.md => mistral.md} (62%) delete mode 100644 website/docs/aiml/chatbot/mistral/tests/hook-suite.sh diff --git a/manifests/modules/aiml/chatbot/gradio-mistral/gradio-ui.yaml b/manifests/modules/aiml/chatbot/gradio-mistral/gradio-ui.yaml index 8cf572c20..a728e15ba 100644 --- a/manifests/modules/aiml/chatbot/gradio-mistral/gradio-ui.yaml +++ b/manifests/modules/aiml/chatbot/gradio-mistral/gradio-ui.yaml @@ -1,13 +1,13 @@ apiVersion: v1 kind: Namespace metadata: - name: gradio-mistral-tran1 + name: gradio-mistral-trn1 --- apiVersion: apps/v1 kind: Deployment metadata: name: gradio-deployment - namespace: gradio-mistral-tran1 + namespace: gradio-mistral-trn1 labels: app: gradio spec: @@ -51,7 +51,7 @@ apiVersion: v1 kind: Service metadata: name: gradio-service - namespace: gradio-mistral-tran1 + namespace: gradio-mistral-trn1 annotations: service.beta.kubernetes.io/aws-load-balancer-type: external service.beta.kubernetes.io/aws-load-balancer-scheme: internet-facing @@ -70,7 +70,7 @@ apiVersion: v1 kind: ConfigMap metadata: name: gradio-app-script - namespace: gradio-mistral-tran1 + namespace: gradio-mistral-trn1 data: gradio-app-mistral-tran1.py: | import gradio as gr diff --git a/manifests/modules/aiml/chatbot/nodepool/kustomization.yaml b/manifests/modules/aiml/chatbot/nodepool/kustomization.yaml index 2da9c6890..88c92cab8 100644 --- a/manifests/modules/aiml/chatbot/nodepool/kustomization.yaml +++ b/manifests/modules/aiml/chatbot/nodepool/kustomization.yaml @@ -1,6 +1,5 @@ apiVersion: kustomize.config.k8s.io/v1beta1 kind: Kustomization resources: - - nodepool-inf2.yaml - nodepool-x86.yaml - nodepool-trn1.yaml diff --git a/website/docs/aiml/chatbot/mistral/add-mistral.md b/website/docs/aiml/chatbot/add-mistral.md similarity index 90% rename from website/docs/aiml/chatbot/mistral/add-mistral.md rename to website/docs/aiml/chatbot/add-mistral.md index d55033619..6272feb97 100644 --- a/website/docs/aiml/chatbot/mistral/add-mistral.md +++ b/website/docs/aiml/chatbot/add-mistral.md @@ -1,6 +1,6 @@ --- title: "Deploying The Mistral-7B-Instruct-v0.3 Chat Model on Ray Serve" -sidebar_position: 60 +sidebar_position: 40 --- With all the node pools provisioned, we can now proceed to deploy Mistral-7B-Instruct-v0.3 chatbot infrastructure. @@ -43,11 +43,8 @@ It may take up to 15 minutes for both pods to be ready. We can wait for the pods to be ready using the following command: ```bash timeout=900 -$ kubectl wait pod \ ---all \ ---for=condition=Ready \ ---namespace=mistral \ ---timeout=15m +$ for i in {1..2}; do kubectl wait pod --all -l 'ray.io/group in (worker-group, headgroup)' --for=condition=Ready --namespace=mistral --timeout=10m 2>&1 | grep -v "Error from server (NotFound)" && break || { echo "Attempt $i: Waiting for all pods..."; kubectl get pods -n mistral -l 'ray.io/group in (worker-group, headgroup)'; sleep 20; }; done + pod/mistral-raycluster-ltvjb-head-7rd7d met pod/mistral-raycluster-ltvjb-worker-worker-group-nff7x met ``` diff --git a/website/docs/aiml/chatbot/mistral/gradio-mistral.md b/website/docs/aiml/chatbot/gradio-mistral.md similarity index 88% rename from website/docs/aiml/chatbot/mistral/gradio-mistral.md rename to website/docs/aiml/chatbot/gradio-mistral.md index a0ae2cbb7..a701fb8ed 100644 --- a/website/docs/aiml/chatbot/mistral/gradio-mistral.md +++ b/website/docs/aiml/chatbot/gradio-mistral.md @@ -1,6 +1,6 @@ --- title: "Configuring the Gradio Web User Interface for Access" -sidebar_position: 70 +sidebar_position: 50 --- After all the resources have been configured within the Ray Serve Cluster, it's now time to directly access the Mistral-7B-Instruct-v0.3 chatbot. The web interface is powered by the Gradio UI. @@ -21,7 +21,7 @@ The components consist of a `Deployment`, `Service`, and `ConfigMap` to launch t ```bash $ kubectl apply -k ~/environment/eks-workshop/modules/aiml/chatbot/gradio-mistral -namespace/gradio-mistral-tran1 created +namespace/gradio-mistral-trn1 created configmap/gradio-app-script created service/gradio-service created deployment.apps/gradio-deployment created @@ -30,13 +30,13 @@ deployment.apps/gradio-deployment created To check the status of each component, run the following commands: ```bash -$ kubectl get deployments -n gradio-mistral-tran1 +$ kubectl get deployments -n gradio-mistral-trn1 NAME READY UP-TO-DATE AVAILABLE AGE gradio-deployment 1/1 1 1 95s ``` ```bash -$ kubectl get configmaps -n gradio-mistral-tran1 +$ kubectl get configmaps -n gradio-mistral-trn1 NAME DATA AGE gradio-app-script 1 110s kube-root-ca.crt 1 111s @@ -47,7 +47,7 @@ kube-root-ca.crt 1 111s Once the load balancer has finished deploying, use the external IP address to directly access the website: ```bash wait=10 -$ kubectl get services -n gradio-llama2-inf2 +$ kubectl get services -n gradio-mistral-trn1 NAME TYPE ClUSTER-IP EXTERNAL-IP PORT(S) AGE gradio-service LoadBalancer 172.20.84.26 k8s-gradioll-gradiose-a6d0b586ce-06885d584b38b400.elb.us-west-2.amazonaws.com 80:30802/TCP 8m42s ``` @@ -56,7 +56,7 @@ To wait until the Network Load Balancer has finished provisioning, run the follo ```bash wait=240 timeout=600 $ curl --head -X GET --retry 30 --retry-all-errors --retry-delay 15 --connect-timeout 5 --max-time 10 \ --k $(kubectl get service -n gradio-mistral-tran1 gradio-service -o jsonpath="{.status.loadBalancer.ingress[*].hostname}{'\n'}") +-k $(kubectl get service -n gradio-mistral-trn1 gradio-service -o jsonpath="{.status.loadBalancer.ingress[*].hostname}{'\n'}") ``` Now that our application is exposed to the outside world, let's access it by pasting the URL in your web browser. You will see the Mistral-7B-Instruct-v0.3 chatbot and will be able to interact with it by asking questions. diff --git a/website/docs/aiml/chatbot/index.md b/website/docs/aiml/chatbot/index.md index adfd14ed9..39f815dcb 100644 --- a/website/docs/aiml/chatbot/index.md +++ b/website/docs/aiml/chatbot/index.md @@ -25,11 +25,8 @@ This will make the following changes to your lab environment: You can view the Terraform that applies these changes [here](https://github.com/VAR::MANIFESTS_OWNER/VAR::MANIFESTS_REPOSITORY/tree/VAR::MANIFESTS_REF/manifests/modules/aiml/chatbot/.workshop/terraform). ::: +[Mistral 7B](https://mistral.ai/en/news/announcing-mistral-7b), a 7.3B parameter model, is one of the most powerful language model for its size to date. It represents a significant advancement in language model technology, combining powerful capabilities like Text generation and completion, Information extraction, Data analysis, API interaction, Complex reasoning tasks with practical efficiency. -With pre-training on 2 trillion tokens of text and code, the [Meta Llama-2-13b](https://llama.meta.com/#inside-the-model) chat model is one of the largest and most powerful large language models (LLMs) available today. +This section will focus on gaining insights into the intricacies of deploying LLMs efficiently on EKS. -From its natural language processing and text generation capabilities to handling inference and training workloads, the creation of Llama2 represents some of the newest advances in GenAI Technology. - -This section will focus not only on harnessing the power of Llama-2 but also on gaining insights into the intricacies of deploying LLMs efficiently on EKS. - -For deploying and scaling LLMs, this lab will utilize AWS Inferentia instances within the [Inf2](https://aws.amazon.com/machine-learning/inferentia/) family, such as `Inf2.24xlarge` and `Inf2.48xlarge`. Additionally, the chatbot inference workloads will utilize the [Ray Serve](https://docs.ray.io/en/latest/serve/index.html) module for building online inference APIs and streamlining the deployment of machine learning models, as well as the [Gradio UI](https://www.gradio.app/) for accessing the Llama2 chatbot. +For deploying and scaling LLMs, this lab will utilize AWS Trainium within the [Trn1](https://aws.amazon.com/ai/machine-learning/trainium/) family, such as `trn1.2xlarge`. Additionally, the chatbot inference workloads will utilize the [Ray Serve](https://docs.ray.io/en/latest/serve/index.html) module for building online inference APIs and streamlining the deployment of machine learning models, as well as the [Gradio UI](https://www.gradio.app/) for accessing the Mistral-7B chatbot. diff --git a/website/docs/aiml/chatbot/llama2/add-llama2.md b/website/docs/aiml/chatbot/llama2/add-llama2.md index 03c7ed077..d599e71cc 100644 --- a/website/docs/aiml/chatbot/llama2/add-llama2.md +++ b/website/docs/aiml/chatbot/llama2/add-llama2.md @@ -1,6 +1,6 @@ --- title: "Deploying the Llama-2-Chat Model on Ray Serve" -sidebar_position: 30 +sidebar_position: 70 --- With both node pools provisioned, we can now proceed to deploy the Llama2 chatbot infrastructure. diff --git a/website/docs/aiml/chatbot/llama2/gradio.md b/website/docs/aiml/chatbot/llama2/gradio.md index b08f675c0..9b190c6a2 100644 --- a/website/docs/aiml/chatbot/llama2/gradio.md +++ b/website/docs/aiml/chatbot/llama2/gradio.md @@ -1,6 +1,6 @@ --- title: "Configuring the Gradio Web User Interface for Access" -sidebar_position: 40 +sidebar_position: 80 --- After all the resources have been configured within the Ray Serve Cluster, it's now time to directly access the Llama2 chatbot. The web interface is powered by the Gradio UI. diff --git a/website/docs/aiml/chatbot/llama2/index.md b/website/docs/aiml/chatbot/llama2/index.md index d0770379b..20c7c1bef 100644 --- a/website/docs/aiml/chatbot/llama2/index.md +++ b/website/docs/aiml/chatbot/llama2/index.md @@ -1,6 +1,6 @@ --- title: "Understanding the Llama2 Chatbot Model" -sidebar_position: 25 +sidebar_position: 60 --- Llama2 is a training model that uses FastAPI, Ray Serve, and PyTorch-based Hugging Face Transformers to create a seamless API for text generation. diff --git a/website/docs/aiml/chatbot/mistral/index.md b/website/docs/aiml/chatbot/mistral.md similarity index 62% rename from website/docs/aiml/chatbot/mistral/index.md rename to website/docs/aiml/chatbot/mistral.md index 5307a0bb7..df801fb4d 100644 --- a/website/docs/aiml/chatbot/mistral/index.md +++ b/website/docs/aiml/chatbot/mistral.md @@ -1,12 +1,10 @@ --- title: "Understanding the Mistral-7B-Instruct-v0.3 Chat Model" -sidebar_position: 50 +sidebar_position: 30 --- -Mistral-7B-Instruct-v0.3 model represents a significant advancement in language model technology, combining powerful capabilities like Text generation and completion, Information extraction, Data analysis, API interaction, Complex reasoning tasks with practical efficiency. - -As a 7B parameter model, it offers remarkable performance while remaining deployable on standard hardware configurations. It requires aproximately ~26-28 GB memory (13 GB for 7B parameters and additional ~13 GB for Optimizer states and overhead). `trn1.2xlarge` instance with 32GB memory is suitable for running the Mistral-7B model, as it provides enough headroom Model weights, Optimizer states, KV cache, Input/output tensors and Runtime overhead. +As a 7B parameter model, Mistral-7B-Instruct-v0.3 model offers remarkable performance while remaining deployable on standard hardware configurations. It requires aproximately ~26-28 GB memory (13 GB for 7B parameters and additional ~13 GB for Optimizer states and overhead). `trn1.2xlarge` instance with 32GB memory is suitable for running the Mistral-7B model, as it provides enough headroom Model weights, Optimizer states, KV cache, Input/output tensors and Runtime overhead. Mistral-7B-Instruct-v0.3 is implemented using FastAPI, Ray Serve, and PyTorch-based Hugging Face Transformers to create a seamless API for text generation. diff --git a/website/docs/aiml/chatbot/mistral/tests/hook-suite.sh b/website/docs/aiml/chatbot/mistral/tests/hook-suite.sh deleted file mode 100644 index 8b5a4baea..000000000 --- a/website/docs/aiml/chatbot/mistral/tests/hook-suite.sh +++ /dev/null @@ -1,11 +0,0 @@ -set -e - -before() { - echo "noop" -} - -after() { - prepare-environment -} - -"$@" diff --git a/website/docs/aiml/chatbot/nodepool.md b/website/docs/aiml/chatbot/nodepool.md index 2e8673edf..07aa84244 100644 --- a/website/docs/aiml/chatbot/nodepool.md +++ b/website/docs/aiml/chatbot/nodepool.md @@ -3,7 +3,7 @@ title: "Provisioning Node Pools for LLM Workloads" sidebar_position: 20 --- -In this lab, we'll use Karpenter to provision the Inferentia-2 nodes necessary for handling the Llama2 chatbot workload. As an autoscaler, Karpenter creates the resources required to run machine learning workloads and distribute traffic efficiently. +In this lab, we'll use Karpenter to provision the Trainium-1 nodes necessary for handling the Mistral-7B chatbot workload. As an autoscaler, Karpenter creates the resources required to run machine learning workloads and distribute traffic efficiently. :::tip To learn more about Karpenter, check out the [Karpenter module](../../autoscaling/compute/karpenter/index.md) in this workshop. @@ -33,7 +33,7 @@ This secondary `NodePool` will provision `Ray Workers` on `trn1.2xlarge` instanc ::yaml{file="manifests/modules/aiml/chatbot/nodepool/nodepool-trn1.yaml" paths="spec.template.metadata.labels,spec.template.spec.requirements,spec.template.spec.taints,spec.limits"} 1. We're asking the `NodePool` to start all new nodes with a Kubernetes label `provisionerType: Karpenter`, which will allow us to specifically target Karpenter nodes with pods for demonstration purposes. Since there are multiple nodes being autoscaled by Karpenter, there are additional labels added such as `instanceType: trn1.2xlarge` to indicate that this Karpenter node should be assigned to `trainium-trn1` pool. -2. The [NodePool CRD](https://karpenter.sh/docs/concepts/nodepools/) supports defining node properties like instance type and zone. In this example, we're setting the `karpenter.sh/capacity-type` to initially limit Karpenter to provisioning On-Demand instances, as well as `karpenter.k8s.aws/instance-type` to limit to a subset of specific instance type. You can learn which other properties are [available here](https://karpenter.sh/docs/concepts/scheduling/#selecting-nodes). In this case, there are specifications matching the requirements of the `Ray Workers` that will run on instances from the `trn1` family. +2. The [NodePool CRD](https://karpenter.sh/docs/concepts/nodepools/) supports defining node properties like instance type and zone. In this example, we're setting the `karpenter.sh/capacity-type` to initially limit Karpenter to provisioning On-Demand instances, as well as `karpenter.k8s.aws/instance-type` to limit to a subset of specific instance type. You can learn which other properties are [available here](https://karpenter.sh/docs/concepts/scheduling/#selecting-nodes). In this case, there are specifications matching the requirements of the `Ray Workers` that will run on `trn1.2xlarge` instances type. 3. A `Taint` defines a specific set of properties that allow a node to repel a set of pods. This property works with its matching label, a `Toleration`. Both tolerations and taints work together to ensure that pods are properly scheduled onto the appropriate pods. You can learn more about the other properties in [this resource](https://kubernetes.io/docs/concepts/scheduling-eviction/taint-and-toleration/). 4. A `NodePool` can define a limit on the amount of CPU and memory managed by it. Once this limit is reached Karpenter will not provision additional capacity associated with that particular `NodePool`, providing a cap on the total compute. From b2c9727be7203a8a80a1ede41265722b74d73872 Mon Sep 17 00:00:00 2001 From: Divya Gupta Date: Tue, 11 Feb 2025 21:31:58 -0500 Subject: [PATCH 05/19] site build correction --- website/docs/aiml/chatbot/gradio-mistral.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/docs/aiml/chatbot/gradio-mistral.md b/website/docs/aiml/chatbot/gradio-mistral.md index a701fb8ed..e8f3829f1 100644 --- a/website/docs/aiml/chatbot/gradio-mistral.md +++ b/website/docs/aiml/chatbot/gradio-mistral.md @@ -6,7 +6,7 @@ sidebar_position: 50 After all the resources have been configured within the Ray Serve Cluster, it's now time to directly access the Mistral-7B-Instruct-v0.3 chatbot. The web interface is powered by the Gradio UI. :::tip -You can learn more about Load Balancers in the [Load Balancer module](../../../fundamentals/exposing/loadbalancer/index.md) provided in this workshop. +You can learn more about Load Balancers in the [Load Balancer module](../../fundamentals/exposing/loadbalancer/index.md) provided in this workshop. ::: ### Deploying Gradio Web User Interface From 856edda2070a1fc26f8b9c33fb43a16fcb881f69 Mon Sep 17 00:00:00 2001 From: Divya Gupta Date: Tue, 18 Feb 2025 11:27:30 -0500 Subject: [PATCH 06/19] spell correction --- website/docs/aiml/chatbot/mistral.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/docs/aiml/chatbot/mistral.md b/website/docs/aiml/chatbot/mistral.md index df801fb4d..4836d8b7d 100644 --- a/website/docs/aiml/chatbot/mistral.md +++ b/website/docs/aiml/chatbot/mistral.md @@ -4,7 +4,7 @@ sidebar_position: 30 --- -As a 7B parameter model, Mistral-7B-Instruct-v0.3 model offers remarkable performance while remaining deployable on standard hardware configurations. It requires aproximately ~26-28 GB memory (13 GB for 7B parameters and additional ~13 GB for Optimizer states and overhead). `trn1.2xlarge` instance with 32GB memory is suitable for running the Mistral-7B model, as it provides enough headroom Model weights, Optimizer states, KV cache, Input/output tensors and Runtime overhead. +As a 7B parameter model, Mistral-7B-Instruct-v0.3 model offers remarkable performance while remaining deployable on standard hardware configurations. It requires approximately ~26-28 GB memory (13 GB for 7B parameters and additional ~13 GB for Optimizer states and overhead). `trn1.2xlarge` instance with 32GB memory is suitable for running the Mistral-7B model, as it provides enough headroom Model weights, Optimizer states, KV cache, Input/output tensors and Runtime overhead. Mistral-7B-Instruct-v0.3 is implemented using FastAPI, Ray Serve, and PyTorch-based Hugging Face Transformers to create a seamless API for text generation. From ff51ae2eb5af93e2d7bf2cdb01c3e6e576cb107c Mon Sep 17 00:00:00 2001 From: Divya Gupta Date: Thu, 20 Feb 2025 11:36:36 -0500 Subject: [PATCH 07/19] changes to resolve network inconsistencies --- .../modules/aiml/chatbot/nodepool/nodepool-trn1.yaml | 3 +++ .../modules/aiml/chatbot/nodepool/nodepool-x86.yaml | 2 ++ .../ray_service_mistral.yaml | 9 +++++++-- 3 files changed, 12 insertions(+), 2 deletions(-) diff --git a/manifests/modules/aiml/chatbot/nodepool/nodepool-trn1.yaml b/manifests/modules/aiml/chatbot/nodepool/nodepool-trn1.yaml index fbff46f13..43e3a7e7d 100644 --- a/manifests/modules/aiml/chatbot/nodepool/nodepool-trn1.yaml +++ b/manifests/modules/aiml/chatbot/nodepool/nodepool-trn1.yaml @@ -48,6 +48,7 @@ spec: amiFamily: AL2 amiSelectorTerms: - alias: al2@latest + instanceStorePolicy: RAID0 blockDeviceMappings: - deviceName: /dev/xvda ebs: @@ -59,6 +60,8 @@ spec: securityGroupSelectorTerms: - tags: karpenter.sh/discovery: ${EKS_CLUSTER_NAME} + - tags: + kubernetes.io/cluster/eks-workshop: owned subnetSelectorTerms: - tags: karpenter.sh/discovery: ${EKS_CLUSTER_NAME} diff --git a/manifests/modules/aiml/chatbot/nodepool/nodepool-x86.yaml b/manifests/modules/aiml/chatbot/nodepool/nodepool-x86.yaml index 9f76bb700..4c8d7207b 100644 --- a/manifests/modules/aiml/chatbot/nodepool/nodepool-x86.yaml +++ b/manifests/modules/aiml/chatbot/nodepool/nodepool-x86.yaml @@ -55,6 +55,8 @@ spec: securityGroupSelectorTerms: - tags: karpenter.sh/discovery: ${EKS_CLUSTER_NAME} + - tags: + kubernetes.io/cluster/eks-workshop: owned subnetSelectorTerms: - tags: karpenter.sh/discovery: ${EKS_CLUSTER_NAME} diff --git a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml index b25040b08..51bbfbd55 100644 --- a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml +++ b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml @@ -37,6 +37,7 @@ spec: rayStartParams: dashboard-host: '0.0.0.0' num-cpus: "0" # this is to ensure no tasks or actors are scheduled on the head Pod + num-gpus: "0" template: metadata: labels: @@ -66,11 +67,9 @@ spec: limits: cpu: "4" memory: 16Gi - vpc.amazonaws.com/pod-eni: "1" requests: cpu: "2" memory: 10Gi - vpc.amazonaws.com/pod-eni: "1" env: - name: PORT value: "8000" @@ -171,12 +170,18 @@ spec: name: ray-logs - mountPath: /dev/shm name: dshm + - mountPath: /tmp/model + name: nvme-storage volumes: - name: ray-logs emptyDir: {} - name: dshm emptyDir: medium: Memory + - name: nvme-storage + hostPath: + path: /mnt/k8s-disks/0 + type: Directory nodeSelector: instanceType: trn1.2xlarge provisionerType: Karpenter From aa6f04317d6dffff26b35b3324f268fcfbdc92a2 Mon Sep 17 00:00:00 2001 From: Sai Vennam Date: Tue, 25 Feb 2025 12:54:26 -0600 Subject: [PATCH 08/19] update mistral image --- .../ray_service_mistral.yaml | 2 +- website/docs/aiml/chatbot/add-mistral.md | 38 +++++++++++-------- 2 files changed, 24 insertions(+), 16 deletions(-) diff --git a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml index 51bbfbd55..ad76a363a 100644 --- a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml +++ b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml @@ -45,7 +45,7 @@ spec: spec: containers: - name: head - image: public.ecr.aws/e3e2e5u9/aiml/mistral-7b:latest + image: public.ecr.aws/aws-containers/aiml/mistral-7b:0.1.0 imagePullPolicy: Always # Ensure the image is always pulled when updated lifecycle: preStop: diff --git a/website/docs/aiml/chatbot/add-mistral.md b/website/docs/aiml/chatbot/add-mistral.md index 6272feb97..51fd1dedb 100644 --- a/website/docs/aiml/chatbot/add-mistral.md +++ b/website/docs/aiml/chatbot/add-mistral.md @@ -29,15 +29,22 @@ This configuration accomplishes the following: After applying the configurations, we'll monitor the progress of the head and worker pods: -```bash wait=5 -$ kubectl get pod -n mistral -NAME READY STATUS RESTARTS AGE -pod/mistral-raycluster-ltvjb-head-7rd7d 0/2 Pending 0 4s -pod/mistral-raycluster-ltvjb-worker-worker-group-nff7x 0/1 Pending 0 4s +```bash wait=5 test=false +$ kubectl get pod -n mistral --watch +NAME READY STATUS RESTARTS AGE +mistral-raycluster-xxhsj-head-l6zwx 0/2 ContainerCreating 0 3m4s +mistral-raycluster-xxhsj-worker-group-worker-b8wqf 0/1 Init:0/1 0 3m4s +... +mistral-raycluster-xxhsj-head-l6zwx 1/2 Running 0 3m48s +mistral-raycluster-xxhsj-head-l6zwx 2/2 Running 0 3m59s +mistral-raycluster-xxhsj-worker-group-worker-b8wqf 0/1 Init:0/1 0 4m25s +mistral-raycluster-xxhsj-worker-group-worker-b8wqf 0/1 PodInitializing 0 4m36s +mistral-raycluster-xxhsj-worker-group-worker-b8wqf 0/1 Running 0 4m37s +mistral-raycluster-xxhsj-worker-group-worker-b8wqf 1/1 Running 0 4m48 ``` :::caution -It may take up to 15 minutes for both pods to be ready. +It may take up to 5 minutes for both pods to be ready. ::: We can wait for the pods to be ready using the following command: @@ -54,21 +61,22 @@ Once the pods are fully deployed, we'll verify that everything is in place: ```bash $ kubectl get all -n mistral NAME READY STATUS RESTARTS AGE -pod/mistral-raycluster-ltvjb-head-7rd7d 2/2 Running 0 7m -pod/mistral-raycluster-ltvjb-worker-worker-group-nff7x 1/1 Running 0 7m +pod/mistral-raycluster-xxhsj-head-l6zwx 2/2 Running 0 5m34s +pod/mistral-raycluster-xxhsj-worker-group-worker-b8wqf 1/1 Running 0 5m34s -NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE -service/mistral NodePort 172.20.74.49 6379:32625/TCP,8265:30941/TCP,10001:32430/TCP,8000:31393/TCP,8080:31361/TCP 94m -service/mistral-head-svc NodePort 172.20.121.46 8000:30481/TCP,8080:32609/TCP,6379:31066/TCP,8265:31006/TCP,10001:30220/TCP 92m -service/mistral-serve-svc NodePort 172.20.241.50 8000:32351/TCP 92m +NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE +service/mistral NodePort 172.20.88.94 8000:32094/TCP,8080:30713/TCP,6379:32600/TCP,8265:31347/TCP,10001:32268/TCP 5m35s NAME DESIRED WORKERS AVAILABLE WORKERS CPUS MEMORY GPUS STATUS AGE -raycluster.ray.io/mistral-raycluster-ltvjb 1 1 2 36Gi 0 ready 94m +raycluster.ray.io/mistral-raycluster-xxhsj 1 1 6 36Gi 0 ready 5m36s + +NAME SERVICE STATUS NUM SERVE ENDPOINTS +rayservice.ray.io/mistral WaitForServeDeploymentReady -NAME SERVICE STATUS NUM SERVE ENDPOINTS -rayservice.ray.io/mistral Running 2 ``` +Note that the service status is `WaitForServeDeploymentReady`. This indicates that Ray is still working to get the model deployed. + :::caution Configuring RayService may take up to 10 minutes. ::: From beb671a641bba1a7c618c0aafd0e645449f7bc43 Mon Sep 17 00:00:00 2001 From: Divya Gupta Date: Tue, 25 Feb 2025 14:16:47 -0500 Subject: [PATCH 09/19] review changes --- .../chatbot/gradio-mistral/gradio-ui.yaml | 4 +- .../aiml/chatbot/nodepool/nodepool-inf2.yaml | 62 --------- .../aiml/chatbot/nodepool/nodepool-trn1.yaml | 2 +- .../ray-service-llama2-chatbot/Dockerfile | 37 ----- .../kustomization.yaml | 4 - .../ray-service-llama2.yaml | 130 ------------------ .../ray_serve_llama2.py | 103 -------------- .../Dockerfile | 2 +- .../ray_service_mistral.yaml | 9 +- website/docs/aiml/chatbot/.notest | 0 website/docs/aiml/chatbot/add-mistral.md | 23 ++-- website/docs/aiml/chatbot/index.md | 6 +- .../docs/aiml/chatbot/llama2/add-llama2.md | 86 ------------ website/docs/aiml/chatbot/llama2/gradio.md | 68 --------- website/docs/aiml/chatbot/llama2/index.md | 25 ---- .../aiml/chatbot/llama2/tests/hook-suite.sh | 11 -- 16 files changed, 20 insertions(+), 552 deletions(-) delete mode 100644 manifests/modules/aiml/chatbot/nodepool/nodepool-inf2.yaml delete mode 100644 manifests/modules/aiml/chatbot/ray-service-llama2-chatbot/Dockerfile delete mode 100644 manifests/modules/aiml/chatbot/ray-service-llama2-chatbot/kustomization.yaml delete mode 100644 manifests/modules/aiml/chatbot/ray-service-llama2-chatbot/ray-service-llama2.yaml delete mode 100644 manifests/modules/aiml/chatbot/ray-service-llama2-chatbot/ray_serve_llama2.py delete mode 100644 website/docs/aiml/chatbot/.notest delete mode 100644 website/docs/aiml/chatbot/llama2/add-llama2.md delete mode 100644 website/docs/aiml/chatbot/llama2/gradio.md delete mode 100644 website/docs/aiml/chatbot/llama2/index.md delete mode 100644 website/docs/aiml/chatbot/llama2/tests/hook-suite.sh diff --git a/manifests/modules/aiml/chatbot/gradio-mistral/gradio-ui.yaml b/manifests/modules/aiml/chatbot/gradio-mistral/gradio-ui.yaml index a728e15ba..9caf045c6 100644 --- a/manifests/modules/aiml/chatbot/gradio-mistral/gradio-ui.yaml +++ b/manifests/modules/aiml/chatbot/gradio-mistral/gradio-ui.yaml @@ -28,8 +28,8 @@ spec: - containerPort: 7860 resources: requests: - cpu: "512m" - memory: "2048Mi" + cpu: "1" + memory: "4096Mi" limits: cpu: "1" memory: "4096Mi" diff --git a/manifests/modules/aiml/chatbot/nodepool/nodepool-inf2.yaml b/manifests/modules/aiml/chatbot/nodepool/nodepool-inf2.yaml deleted file mode 100644 index 01b2fb599..000000000 --- a/manifests/modules/aiml/chatbot/nodepool/nodepool-inf2.yaml +++ /dev/null @@ -1,62 +0,0 @@ -apiVersion: karpenter.sh/v1 -kind: NodePool -metadata: - name: inferentia-inf2 -spec: - template: - metadata: - labels: - instanceType: inferentia-inf2 - provisionerType: Karpenter - spec: - taints: - - key: aws.amazon.com/neuron - value: "true" - effect: "NoSchedule" - requirements: - - key: "karpenter.k8s.aws/instance-family" - operator: In - values: ["inf2"] - - key: "kubernetes.io/arch" - operator: In - values: ["amd64"] - - key: "karpenter.sh/capacity-type" - operator: In - values: ["on-demand", "spot"] - expireAfter: 720h - terminationGracePeriod: 24h - nodeClassRef: - group: karpenter.k8s.aws - kind: EC2NodeClass - name: inferentia-inf2 - limits: - cpu: "512" - disruption: - consolidateAfter: 300s - consolidationPolicy: WhenEmptyOrUnderutilized - ---- -apiVersion: karpenter.k8s.aws/v1 -kind: EC2NodeClass -metadata: - name: inferentia-inf2 -spec: - amiFamily: AL2 - amiSelectorTerms: - - alias: al2@latest - blockDeviceMappings: - - deviceName: /dev/xvda - ebs: - deleteOnTermination: true - encrypted: true - volumeSize: 500Gi - volumeType: gp3 - role: ${KARPENTER_NODE_ROLE} - securityGroupSelectorTerms: - - tags: - karpenter.sh/discovery: ${EKS_CLUSTER_NAME} - subnetSelectorTerms: - - tags: - karpenter.sh/discovery: ${EKS_CLUSTER_NAME} - tags: - app.kubernetes.io/created-by: eks-workshop diff --git a/manifests/modules/aiml/chatbot/nodepool/nodepool-trn1.yaml b/manifests/modules/aiml/chatbot/nodepool/nodepool-trn1.yaml index 43e3a7e7d..05f495fb7 100644 --- a/manifests/modules/aiml/chatbot/nodepool/nodepool-trn1.yaml +++ b/manifests/modules/aiml/chatbot/nodepool/nodepool-trn1.yaml @@ -54,7 +54,7 @@ spec: ebs: deleteOnTermination: true encrypted: true - volumeSize: 500Gi + volumeSize: 200Gi volumeType: gp3 role: ${KARPENTER_NODE_ROLE} securityGroupSelectorTerms: diff --git a/manifests/modules/aiml/chatbot/ray-service-llama2-chatbot/Dockerfile b/manifests/modules/aiml/chatbot/ray-service-llama2-chatbot/Dockerfile deleted file mode 100644 index 8208d88bf..000000000 --- a/manifests/modules/aiml/chatbot/ray-service-llama2-chatbot/Dockerfile +++ /dev/null @@ -1,37 +0,0 @@ -# docker buildx build --platform=linux/amd64 -t ray-serve-llama2:latest . -# https://hub.docker.com/layers/rayproject/ray-ml/2.7.1-py310-gpu/images/sha256-f84ecfc82d255ff9e23b8e40343a95655ec8e23a009633a183769edac6277186?context=explore -FROM rayproject/ray:2.22.0-py310 - -# Maintainer label -LABEL maintainer="DoEKS" - -# Set environment variables to non-interactive (this prevents some prompts) -ENV DEBIAN_FRONTEND=non-interactive - -# Switch to root to add Neuron repo and install necessary packages -USER root - -# Set up the Neuron repository and install Neuron packages -RUN . /etc/os-release && \ - sudo echo "deb https://apt.repos.neuron.amazonaws.com ${VERSION_CODENAME} main" > /etc/apt/sources.list.d/neuron.list && \ - sudo wget -qO - https://apt.repos.neuron.amazonaws.com/GPG-PUB-KEY-AMAZON-AWS-NEURON.PUB | apt-key add - && \ - sudo apt-get update -y && \ - sudo apt-get install aws-neuronx-dkms aws-neuronx-collectives=2.* aws-neuronx-runtime-lib=2.* aws-neuronx-tools=2.* -y && \ - sudo apt-get clean - -# Switch back to a non-root user for the subsequent commands -USER $USER - -# Set pip repository pointing to the Neuron repository and install required Python packages -RUN pip config set global.extra-index-url https://pip.repos.neuron.amazonaws.com && \ - pip install wget awscli regex neuronx-cc==2.* torch-neuronx torchvision transformers-neuronx sentencepiece transformers - -# Add Neuron path to PATH -ENV PATH /opt/aws/neuron/bin:$PATH - -# Set LD_LIBRARY_PATH to include the directory with libpython3.10.so.1.0 -ENV LD_LIBRARY_PATH /home/ray/anaconda3/lib:$LD_LIBRARY_PATH - -WORKDIR /serve_app - -COPY ray_serve_llama2.py /serve_app/ray_serve_llama2.py \ No newline at end of file diff --git a/manifests/modules/aiml/chatbot/ray-service-llama2-chatbot/kustomization.yaml b/manifests/modules/aiml/chatbot/ray-service-llama2-chatbot/kustomization.yaml deleted file mode 100644 index 623056e56..000000000 --- a/manifests/modules/aiml/chatbot/ray-service-llama2-chatbot/kustomization.yaml +++ /dev/null @@ -1,4 +0,0 @@ -apiVersion: kustomize.config.k8s.io/v1beta1 -kind: Kustomization -resources: - - ray-service-llama2.yaml diff --git a/manifests/modules/aiml/chatbot/ray-service-llama2-chatbot/ray-service-llama2.yaml b/manifests/modules/aiml/chatbot/ray-service-llama2-chatbot/ray-service-llama2.yaml deleted file mode 100644 index 7c3088722..000000000 --- a/manifests/modules/aiml/chatbot/ray-service-llama2-chatbot/ray-service-llama2.yaml +++ /dev/null @@ -1,130 +0,0 @@ -apiVersion: v1 -kind: Namespace -metadata: - name: llama2 - ---- -# target_num_ongoing_requests_per_replica will be deprecated soon -# and will be updated when Data on EKS updates -apiVersion: ray.io/v1 -kind: RayService -metadata: - name: llama2 - namespace: llama2 -spec: - serviceUnhealthySecondThreshold: 900 - deploymentUnhealthySecondThreshold: 300 - serveConfigV2: | - applications: - - name: llama2 - import_path: "ray_serve_llama2:entrypoint" - runtime_env: - env_vars: - MODEL_ID: "NousResearch/Llama-2-13b-chat-hf" - NEURON_CC_FLAGS: "-O1" - LD_LIBRARY_PATH: "/home/ray/anaconda3/lib:$LD_LIBRARY_PATH" - NEURON_CORES: "24" - deployments: - - name: Llama-2-13b-chat-hf - autoscaling_config: - metrics_interval_s: 0.2 - min_replicas: 1 - max_replicas: 1 - look_back_period_s: 2 - downscale_delay_s: 30 - upscale_delay_s: 2 - target_num_ongoing_requests_per_replica: 1 - graceful_shutdown_timeout_s: 5 - ray_actor_options: - num_cpus: 180 - resources: {"neuron_cores": 24} - runtime_env: - env_vars: - LD_LIBRARY_PATH: "/home/ray/anaconda3/lib:$LD_LIBRARY_PATH" - rayClusterConfig: - rayVersion: 2.22.0 - headGroupSpec: - headService: - metadata: - name: llama2 - namespace: llama2 - rayStartParams: - dashboard-host: "0.0.0.0" - template: - spec: - containers: - - name: head - image: public.ecr.aws/data-on-eks/ray2.22.0-py310-llama2-13b-neuron:latest # Image created using the Dockerfile attached in the folder - imagePullPolicy: Always # Ensure the image is always pulled when updated - lifecycle: - preStop: - exec: - command: ["/bin/sh", "-c", "ray stop"] - ports: - - containerPort: 6379 - name: gcs - - containerPort: 8265 - name: dashboard - - containerPort: 10001 - name: client - - containerPort: 8000 - name: serve - volumeMounts: - - mountPath: /tmp/ray - name: ray-logs - resources: - limits: - cpu: 1 - memory: 2Gi - requests: - cpu: 1 - memory: 2Gi - env: - - name: LD_LIBRARY_PATH - value: "/home/ray/anaconda3/lib:$LD_LIBRARY_PATH" - nodeSelector: - instanceType: mixed-x86 - provisionerType: Karpenter - workload: rayhead - volumes: - - name: ray-logs - emptyDir: {} - workerGroupSpecs: - - groupName: inf2 - replicas: 1 - minReplicas: 1 - maxReplicas: 1 - rayStartParams: {} - template: - spec: - containers: - - name: worker - image: public.ecr.aws/data-on-eks/ray2.22.0-py310-llama2-13b-neuron:latest - imagePullPolicy: Always # Ensure the image is always pulled when updated - lifecycle: - preStop: - exec: - command: ["/bin/sh", "-c", "ray stop"] - resources: - limits: - cpu: "180" - memory: "700G" - aws.amazon.com/neuron: "12" - requests: - cpu: "180" - memory: "700G" - aws.amazon.com/neuron: "12" - env: - - name: LD_LIBRARY_PATH - value: "/home/ray/anaconda3/lib:$LD_LIBRARY_PATH" - nodeSelector: - instanceType: inferentia-inf2 - provisionerType: Karpenter - tolerations: - - key: "aws.amazon.com/neuron" - operator: "Exists" - effect: "NoSchedule" - - key: "hub.jupyter.org/dedicated" - operator: "Equal" - value: "user" - effect: "NoSchedule" diff --git a/manifests/modules/aiml/chatbot/ray-service-llama2-chatbot/ray_serve_llama2.py b/manifests/modules/aiml/chatbot/ray-service-llama2-chatbot/ray_serve_llama2.py deleted file mode 100644 index 673b69a3c..000000000 --- a/manifests/modules/aiml/chatbot/ray-service-llama2-chatbot/ray_serve_llama2.py +++ /dev/null @@ -1,103 +0,0 @@ -import os -import logging -from fastapi import FastAPI -from ray import serve -import torch -from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig -from transformers_neuronx.llama.model import LlamaForSampling -from transformers_neuronx.module import save_pretrained_split - -app = FastAPI() - -llm_model_split = "llama-2-13b-chat-hf-split" -neuron_cores = int(os.getenv('NEURON_CORES', 24)) # Read from environment variable, default to 24 - -# --- Logging Setup --- -logger = logging.getLogger("ray.serve") -logger.setLevel(logging.INFO) -logging.basicConfig(level=logging.INFO) - - -# Define the APIIngress class responsible for handling inference requests -@serve.deployment(num_replicas=1) -@serve.ingress(app) -class APIIngress: - def __init__(self, llama_model_handle): - self.handle = llama_model_handle - - @app.get("/infer") - async def infer(self, sentence: str): - # Asynchronously perform inference using the provided sentence - result = await self.handle.infer.remote(sentence) - return result - - -# Define the LlamaModel class responsible for managing the Llama language model -@serve.deployment( - name="Llama-2-13b-chat-hf", - autoscaling_config={"min_replicas": 1, "max_replicas": 2}, - ray_actor_options={ - "resources": {"neuron_cores": neuron_cores}, - "runtime_env": {"env_vars": {"NEURON_CC_FLAGS": "-O1"}}, - }, -) -class LlamaModel: - def __init__(self): - from transformers_neuronx.llama.model import LlamaForSampling - from transformers_neuronx.module import save_pretrained_split - - llm_model = os.getenv('MODEL_ID', 'NousResearch/Llama-2-13b-chat-hf') - logger.info(f"Using model ID: {llm_model}") - - # Check if the model split exists locally, and if not, download it - if not os.path.exists(llm_model_split): - logger.info(f"Saving model split for {llm_model} to local path {llm_model_split}") - try: - self.model = AutoModelForCausalLM.from_pretrained(llm_model) - # Set and validate generation config - generation_config = GenerationConfig( - do_sample=True, - temperature=0.9, - top_p=0.6, - top_k=50, - ) - generation_config.validate() - self.model.generation_config = generation_config - save_pretrained_split(self.model, llm_model_split) - except Exception as e: - logger.error(f"Error during model download or split saving: {e}") - raise e - else: - logger.info(f"Using existing model split {llm_model_split}") - - logger.info(f"Loading and compiling model {llm_model_split} for Neuron") - try: - self.neuron_model = LlamaForSampling.from_pretrained( - llm_model_split, batch_size=1, tp_degree=neuron_cores, amp='f16' - ) - self.neuron_model.to_neuron() - logger.info("Model loaded and compiled successfully") - except Exception as e: - logger.error(f"Error during model loading or compilation: {e}") - raise e - - self.tokenizer = AutoTokenizer.from_pretrained(llm_model) - - def infer(self, sentence: str): - input_ids = self.tokenizer.encode(sentence, return_tensors="pt") - with torch.inference_mode(): - try: - logger.info(f"Performing inference on input: {sentence}") - generated_sequences = self.neuron_model.sample( - input_ids, sequence_length=2048, top_k=50 - ) - decoded_sequences = [self.tokenizer.decode(seq, skip_special_tokens=True) for seq in generated_sequences] - logger.info(f"Inference result: {decoded_sequences}") - return decoded_sequences - except Exception as e: - logger.error(f"Error during inference: {e}") - return {"error": "Inference failed"} - - -# Create an entry point for the FastAPI application -entrypoint = APIIngress.bind(LlamaModel.bind()) \ No newline at end of file diff --git a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/Dockerfile b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/Dockerfile index da8f6026f..f6d2e3c5b 100644 --- a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/Dockerfile +++ b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/Dockerfile @@ -1,5 +1,5 @@ # https://hub.docker.com/layers/rayproject/ray/2.11.0-py310/images/sha256-de798e487b76a8f2412c718c43c5f342b3eb05e0705a71325102904cd27c3613?context=explore -FROM rayproject/ray:2.22.0-py310 +FROM rayproject/ray:2.42.1-py310 # Maintainer label LABEL maintainer="DoEKS" diff --git a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml index 51bbfbd55..2cdf5a276 100644 --- a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml +++ b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml @@ -27,10 +27,9 @@ spec: max_replicas: 1 target_num_ongoing_requests_per_replica: 1 rayClusterConfig: - rayVersion: '2.22.0' + rayVersion: '2.42.1' enableInTreeAutoscaling: true headGroupSpec: - serviceType: NodePort headService: metadata: name: mistral @@ -68,8 +67,8 @@ spec: cpu: "4" memory: 16Gi requests: - cpu: "2" - memory: 10Gi + cpu: "4" + memory: 16Gi env: - name: PORT value: "8000" @@ -126,7 +125,7 @@ spec: memory: 26Gi aws.amazon.com/neuron: "1" requests: - cpu: 4 + cpu: 5 memory: 26Gi aws.amazon.com/neuron: "1" env: diff --git a/website/docs/aiml/chatbot/.notest b/website/docs/aiml/chatbot/.notest deleted file mode 100644 index e69de29bb..000000000 diff --git a/website/docs/aiml/chatbot/add-mistral.md b/website/docs/aiml/chatbot/add-mistral.md index 6272feb97..2320df8c6 100644 --- a/website/docs/aiml/chatbot/add-mistral.md +++ b/website/docs/aiml/chatbot/add-mistral.md @@ -37,7 +37,7 @@ pod/mistral-raycluster-ltvjb-worker-worker-group-nff7x 0/1 Pending 0 ``` :::caution -It may take up to 15 minutes for both pods to be ready. +It may take up to 5-7 minutes for both head and worker ray pods to be ready and another 5-7 minutes for model deployment. Once model is deployed it is ready to serve any request for inference. ::: We can wait for the pods to be ready using the following command: @@ -49,7 +49,15 @@ pod/mistral-raycluster-ltvjb-head-7rd7d met pod/mistral-raycluster-ltvjb-worker-worker-group-nff7x met ``` -Once the pods are fully deployed, we'll verify that everything is in place: +We can wait for the RayService to be running with this command: + +```bash wait=5 timeout=600 +$ kubectl wait --for=jsonpath='{.status.serviceStatus}'=Running rayservice/mistral -n mistral --timeout=10m +rayservice.ray.io/mistral condition met +``` + + +Lets verify that everything is in place: ```bash $ kubectl get all -n mistral @@ -69,15 +77,4 @@ NAME SERVICE STATUS NUM SERVE ENDPOINTS rayservice.ray.io/mistral Running 2 ``` -:::caution -Configuring RayService may take up to 10 minutes. -::: - -We can wait for the RayService to be running with this command: - -```bash wait=5 timeout=600 -$ kubectl wait --for=jsonpath='{.status.serviceStatus}'=Running rayservice/mistral -n mistral --timeout=10m -rayservice.ray.io/mistral condition met -``` - With everything properly deployed, we can now proceed to create the web interface for the chatbot. diff --git a/website/docs/aiml/chatbot/index.md b/website/docs/aiml/chatbot/index.md index 39f815dcb..59a475453 100644 --- a/website/docs/aiml/chatbot/index.md +++ b/website/docs/aiml/chatbot/index.md @@ -2,13 +2,11 @@ title: "Large Language Models with Ray Serve" sidebar_position: 30 chapter: true -sidebar_custom_props: { "beta": true } +sidebar_custom_props: { "module": true } description: "Use Inferentia to accelerate deep learning inference workloads on Amazon Elastic Kubernetes Service." --- -:::danger -This module is not supported at AWS events or in AWS-vended accounts through Workshop Studio. This module is only supported for clusters created through the "[In your AWS account](/docs/introduction/setup/your-account)" steps. -::: +::required-time :::tip Before you start Prepare your environment for this section: diff --git a/website/docs/aiml/chatbot/llama2/add-llama2.md b/website/docs/aiml/chatbot/llama2/add-llama2.md deleted file mode 100644 index d599e71cc..000000000 --- a/website/docs/aiml/chatbot/llama2/add-llama2.md +++ /dev/null @@ -1,86 +0,0 @@ ---- -title: "Deploying the Llama-2-Chat Model on Ray Serve" -sidebar_position: 70 ---- - -With both node pools provisioned, we can now proceed to deploy the Llama2 chatbot infrastructure. - -Let's begin by deploying the `ray-service-llama2.yaml` file: - -```bash wait=5 -$ kubectl apply -k ~/environment/eks-workshop/modules/aiml/chatbot/ray-service-llama2-chatbot -namespace/llama2 created -rayservice.ray.io/llama2 created -``` - -### Creating the Ray Service Pods for Inference - -The `ray-service-llama2.yaml` file defines the Kubernetes configuration for deploying the Ray Serve service for the Llama2 chatbot: - -```file -manifests/modules/aiml/chatbot/ray-service-llama2-chatbot/ray-service-llama2.yaml -``` - -This configuration accomplishes the following: - -1. Creates a Kubernetes namespace named `llama2` for resource isolation -2. Deploys a RayService named `llama-2-service` that utilizes a Python script to create the Ray Serve component -3. Provisions a Head Pod and Worker Pods to pull Docker images from Amazon Elastic Container Registry (ECR) - -After applying the configurations, we'll monitor the progress of the head and worker pods: - -```bash wait=5 -$ kubectl get pod -n llama2 -NAME READY STATUS RESTARTS AGE -pod/llama2-raycluster-fcmtr-head-bf58d 1/1 Running 0 67m -pod/llama2-raycluster-fcmtr-worker-inf2-lgnb2 1/1 Running 0 5m30s -``` - -:::caution -It may take up to 15 minutes for both pods to be ready. -::: - -We can wait for the pods to be ready using the following command: - -```bash timeout=900 -$ kubectl wait pod \ ---all \ ---for=condition=Ready \ ---namespace=llama2 \ ---timeout=15m -pod/llama2-raycluster-fcmtr-head-bf58d met -pod/llama2-raycluster-fcmtr-worker-inf2-lgnb2 met -``` - -Once the pods are fully deployed, we'll verify that everything is in place: - -```bash -$ kubectl get all -n llama2 -NAME READY STATUS RESTARTS AGE -pod/llama2-raycluster-fcmtr-head-bf58d 1/1 Running 0 67m -pod/llama2-raycluster-fcmtr-worker-inf2-lgnb2 1/1 Running 0 5m30s - -NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE -service/llama2 ClusterIP 172.20.118.243 10001/TCP,8000/TCP,8080/TCP,6379/TCP,8265/TCP 67m -service/llama2-head-svc ClusterIP 172.20.168.94 8080/TCP,6379/TCP,8265/TCP,10001/TCP,8000/TCP 57m -service/llama2-serve-svc ClusterIP 172.20.61.167 8000/TCP 57m - -NAME DESIRED WORKERS AVAILABLE WORKERS CPUS MEMORY GPUS STATUS AGE -raycluster.ray.io/llama2-raycluster-fcmtr 1 1 184 704565270Ki 0 ready 67m - -NAME SERVICE STATUS NUM SERVE ENDPOINTS -rayservice.ray.io/llama2 Running 2 -``` - -:::caution -Configuring RayService may take up to 10 minutes. -::: - -We can wait for the RayService to be running with this command: - -```bash wait=5 timeout=600 -$ kubectl wait --for=jsonpath='{.status.serviceStatus}'=Running rayservice/llama2 -n llama2 --timeout=10m -rayservice.ray.io/llama2 condition met -``` - -With everything properly deployed, we can now proceed to create the web interface for the chatbot. diff --git a/website/docs/aiml/chatbot/llama2/gradio.md b/website/docs/aiml/chatbot/llama2/gradio.md deleted file mode 100644 index 9b190c6a2..000000000 --- a/website/docs/aiml/chatbot/llama2/gradio.md +++ /dev/null @@ -1,68 +0,0 @@ ---- -title: "Configuring the Gradio Web User Interface for Access" -sidebar_position: 80 ---- - -After all the resources have been configured within the Ray Serve Cluster, it's now time to directly access the Llama2 chatbot. The web interface is powered by the Gradio UI. - -:::tip -You can learn more about Load Balancers in the [Load Balancer module](../../../fundamentals/exposing/loadbalancer/index.md) provided in this workshop. -::: - -### Deploying Gradio Web User Interface - -Once the AWS Load Balancer Controller has been installed, we can deploy the Gradio UI components. - -```file -manifests/modules/aiml/chatbot/gradio/gradio-ui.yaml -``` - -The components consist of a `Deployment`, `Service`, and `ConfigMap` to launch the application. In particular, the `Service` component is named gradio-service and is deployed as a `LoadBalancer`. - -```bash -$ kubectl apply -k ~/environment/eks-workshop/modules/aiml/chatbot/gradio -namespace/gradio-llama2-inf2 created -configmap/gradio-app-script created -service/gradio-service created -deployment.apps/gradio-deployment created -``` - -To check the status of each component, run the following commands: - -```bash -$ kubectl get deployments -n gradio-llama2-inf2 -NAME READY UP-TO-DATE AVAILABLE AGE -gradio-deployment 1/1 1 1 95s -``` - -```bash -$ kubectl get configmaps -n gradio-llama2-inf2 -NAME DATA AGE -gradio-app-script 1 110s -kube-root-ca.crt 1 111s -``` - -### Accessing the Chatbot Website - -Once the load balancer has finished deploying, use the external IP address to directly access the website: - -```bash wait=10 -$ kubectl get services -n gradio-llama2-inf2 -NAME TYPE ClUSTER-IP EXTERNAL-IP PORT(S) AGE -gradio-service LoadBalancer 172.20.84.26 k8s-gradioll-gradiose-a6d0b586ce-06885d584b38b400.elb.us-west-2.amazonaws.com 80:30802/TCP 8m42s -``` - -To wait until the Network Load Balancer has finished provisioning, run the following command: - -```bash wait=240 timeout=600 -$ curl --head -X GET --retry 30 --retry-all-errors --retry-delay 15 --connect-timeout 5 --max-time 10 \ --k $(kubectl get service -n gradio-llama2-inf2 gradio-service -o jsonpath="{.status.loadBalancer.ingress[*].hostname}{'\n'}") -``` - -Now that our application is exposed to the outside world, let's access it by pasting the URL in your web browser. You will see the Llama2 chatbot and will be able to interact with it by asking questions. - - - - - -This concludes the current lab on deploying the Meta Llama-2-13b Chatbot Model within an EKS Cluster via Karpenter. diff --git a/website/docs/aiml/chatbot/llama2/index.md b/website/docs/aiml/chatbot/llama2/index.md deleted file mode 100644 index 20c7c1bef..000000000 --- a/website/docs/aiml/chatbot/llama2/index.md +++ /dev/null @@ -1,25 +0,0 @@ ---- -title: "Understanding the Llama2 Chatbot Model" -sidebar_position: 60 ---- - -Llama2 is a training model that uses FastAPI, Ray Serve, and PyTorch-based Hugging Face Transformers to create a seamless API for text generation. - -For this lab, we'll be using Llama-2-13b, a medium-sized model with 13 billion parameters. It offers a good balance between performance and efficiency and can be used for a variety of tasks. Using `Inf2.24xlarge` or `Inf2.48xlarge` instances makes it easier to handle high-performance deep learning (DL) training and inference of generative AI models, including LLMs. - -Here's the code for compiling the model that we'll use: - -```file -manifests/modules/aiml/chatbot/ray-service-llama2-chatbot/ray_serve_llama2.py -``` - -This Python code performs the following tasks: - -1. Configures an APIIngress class responsible for handling inference requests -2. Defines a LlamaModel class responsible for managing the Llama language model -3. Loads and compiles the model based on existing parameters -4. Creates an entry point for the FastAPI application - -Through these steps, the Llama-2-13b chat model allows the endpoint to accept input sentences and generate text outputs. The high performance efficiency in processing tasks enables the model to handle a wide variety of natural language processing applications, such as chat bots and text generation tasks. - -In this lab, we'll see how the Llama2 Model is configured with Ray Service as a Kubernetes configuration, allowing users to understand how to incorporate fine-tuning and deploy their own natural language processing applications. diff --git a/website/docs/aiml/chatbot/llama2/tests/hook-suite.sh b/website/docs/aiml/chatbot/llama2/tests/hook-suite.sh deleted file mode 100644 index 8b5a4baea..000000000 --- a/website/docs/aiml/chatbot/llama2/tests/hook-suite.sh +++ /dev/null @@ -1,11 +0,0 @@ -set -e - -before() { - echo "noop" -} - -after() { - prepare-environment -} - -"$@" From 40ed7e5493ab7ac86e886fce351e2f910a0c645b Mon Sep 17 00:00:00 2001 From: Divya Gupta Date: Wed, 26 Feb 2025 11:22:51 -0500 Subject: [PATCH 10/19] review changes --- .../modules/aiml/chatbot/.workshop/cleanup.sh | 20 +++---------------- .../chatbot/gradio-mistral/gradio-ui.yaml | 4 ++-- .../aiml/chatbot/nodepool/nodepool-trn1.yaml | 2 +- .../aiml/chatbot/nodepool/nodepool-x86.yaml | 2 +- .../Dockerfile | 6 ++---- .../ray_service_mistral.yaml | 11 ++++------ website/docs/aiml/chatbot/add-mistral.md | 16 +++++---------- website/docs/aiml/chatbot/expose.md | 8 ++++---- website/docs/aiml/chatbot/gradio-mistral.md | 2 +- website/docs/aiml/chatbot/index.md | 4 ++-- website/docs/aiml/chatbot/mistral.md | 2 +- website/docs/aiml/chatbot/nodepool.md | 2 +- 12 files changed, 27 insertions(+), 52 deletions(-) diff --git a/manifests/modules/aiml/chatbot/.workshop/cleanup.sh b/manifests/modules/aiml/chatbot/.workshop/cleanup.sh index f650ff086..75491d1e7 100755 --- a/manifests/modules/aiml/chatbot/.workshop/cleanup.sh +++ b/manifests/modules/aiml/chatbot/.workshop/cleanup.sh @@ -4,31 +4,17 @@ set -e logmessage "Deleting Gradio-UI Components..." -kubectl delete -k /eks-workshop/manifests/modules/aiml/chatbot/gradio --ignore-not-found - kubectl delete -k /eks-workshop/manifests/modules/aiml/chatbot/gradio-mistral --ignore-not-found -logmessage "Deleting Llama2 and mistral pods..." - -kubectl delete -k /eks-workshop/manifests/modules/aiml/chatbot/ray-service-llama2-chatbot --ignore-not-found +logmessage "Deleting mistral pods..." kubectl delete -k /eks-workshop/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot --ignore-not-found -logmessage "Deleting persistent volume claim and storage class" - -kubectl delete pvc model-cache-pvc -n mistral --ignore-not-found - -kubectl delete storageclass ebs-gp3 -n mistral --ignore-not-found - -logmessage "Deleting mistral, gradio-mistral-inf2, llama2, and gradio-llama2-inf2 namespaces..." - -kubectl delete namespace llama2 --ignore-not-found - -kubectl delete namespace gradio-llama2-inf2 --ignore-not-found +logmessage "Deleting mistral and gradio-mistral-trn1 namespaces..." kubectl delete namespace mistral --ignore-not-found -kubectl delete namespace gradio-mistral-inf2 --ignore-not-found +kubectl delete namespace gradio-mistral-trn1 --ignore-not-found logmessage "Deleting Neuron Device Plugin..." diff --git a/manifests/modules/aiml/chatbot/gradio-mistral/gradio-ui.yaml b/manifests/modules/aiml/chatbot/gradio-mistral/gradio-ui.yaml index 9caf045c6..61567a58b 100644 --- a/manifests/modules/aiml/chatbot/gradio-mistral/gradio-ui.yaml +++ b/manifests/modules/aiml/chatbot/gradio-mistral/gradio-ui.yaml @@ -22,13 +22,13 @@ spec: spec: containers: - name: gradio - image: public.ecr.aws/data-on-eks/gradio-web-app-base:latest + image: public.ecr.aws/aws-containers/eks-workshop/gradio-web-app-base:0.1.0 imagePullPolicy: IfNotPresent ports: - containerPort: 7860 resources: requests: - cpu: "1" + cpu: "512m" memory: "4096Mi" limits: cpu: "1" diff --git a/manifests/modules/aiml/chatbot/nodepool/nodepool-trn1.yaml b/manifests/modules/aiml/chatbot/nodepool/nodepool-trn1.yaml index 05f495fb7..43e3a7e7d 100644 --- a/manifests/modules/aiml/chatbot/nodepool/nodepool-trn1.yaml +++ b/manifests/modules/aiml/chatbot/nodepool/nodepool-trn1.yaml @@ -54,7 +54,7 @@ spec: ebs: deleteOnTermination: true encrypted: true - volumeSize: 200Gi + volumeSize: 500Gi volumeType: gp3 role: ${KARPENTER_NODE_ROLE} securityGroupSelectorTerms: diff --git a/manifests/modules/aiml/chatbot/nodepool/nodepool-x86.yaml b/manifests/modules/aiml/chatbot/nodepool/nodepool-x86.yaml index 4c8d7207b..d70415107 100644 --- a/manifests/modules/aiml/chatbot/nodepool/nodepool-x86.yaml +++ b/manifests/modules/aiml/chatbot/nodepool/nodepool-x86.yaml @@ -62,4 +62,4 @@ spec: karpenter.sh/discovery: ${EKS_CLUSTER_NAME} kubernetes.io/role/internal-elb: "1" tags: - app.kubernetes.io/created-by: eks-workshop + app.kubernetes.io/created-by: eks-workshop \ No newline at end of file diff --git a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/Dockerfile b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/Dockerfile index f6d2e3c5b..9e275b0a6 100644 --- a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/Dockerfile +++ b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/Dockerfile @@ -1,5 +1,5 @@ # https://hub.docker.com/layers/rayproject/ray/2.11.0-py310/images/sha256-de798e487b76a8f2412c718c43c5f342b3eb05e0705a71325102904cd27c3613?context=explore -FROM rayproject/ray:2.42.1-py310 +FROM rayproject/ray:2.22.0-py310 # Maintainer label LABEL maintainer="DoEKS" @@ -41,6 +41,4 @@ RUN mkdir -p /serve_app # Set working directory WORKDIR /serve_app -COPY mistral1.py /serve_app/mistral1.py - - +COPY mistral1.py /serve_app/mistral1.py \ No newline at end of file diff --git a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml index 97b36dc4d..29c98ecb9 100644 --- a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml +++ b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml @@ -27,12 +27,9 @@ spec: max_replicas: 1 target_num_ongoing_requests_per_replica: 1 rayClusterConfig: - rayVersion: '2.42.1' + rayVersion: '2.22.0' enableInTreeAutoscaling: true headGroupSpec: - headService: - metadata: - name: mistral rayStartParams: dashboard-host: '0.0.0.0' num-cpus: "0" # this is to ensure no tasks or actors are scheduled on the head Pod @@ -67,7 +64,7 @@ spec: cpu: "4" memory: 16Gi requests: - cpu: "4" + cpu: "2" memory: 16Gi env: - name: PORT @@ -125,7 +122,7 @@ spec: memory: 26Gi aws.amazon.com/neuron: "1" requests: - cpu: 5 + cpu: 4 memory: 26Gi aws.amazon.com/neuron: "1" env: @@ -193,4 +190,4 @@ spec: operator: "Exists" effect: "NoExecute" tolerationSeconds: 300 - + \ No newline at end of file diff --git a/website/docs/aiml/chatbot/add-mistral.md b/website/docs/aiml/chatbot/add-mistral.md index 16430a0fa..d05a5c4ec 100644 --- a/website/docs/aiml/chatbot/add-mistral.md +++ b/website/docs/aiml/chatbot/add-mistral.md @@ -1,6 +1,6 @@ --- title: "Deploying The Mistral-7B-Instruct-v0.3 Chat Model on Ray Serve" -sidebar_position: 40 +sidebar_position: 50 --- With all the node pools provisioned, we can now proceed to deploy Mistral-7B-Instruct-v0.3 chatbot infrastructure. @@ -44,24 +44,18 @@ mistral-raycluster-xxhsj-worker-group-worker-b8wqf 1/1 Running ``` :::caution -It may take up to 5 minutes for both pods to be ready. +It may take up to 5-8 minutes for both pods to be ready. ::: We can also use the following command to wait for the pods to get ready: -```bash timeout=900 +```bash wait=5 timeout=900 $ for i in {1..2}; do kubectl wait pod --all -l 'ray.io/group in (worker-group, headgroup)' --for=condition=Ready --namespace=mistral --timeout=15m 2>&1 | grep -v "Error from server (NotFound)" && break || { echo "Attempt $i: Waiting for all pods..."; kubectl get pods -n mistral -l 'ray.io/group in (worker-group, headgroup)'; sleep 20; }; done pod/mistral-raycluster-xxhsj-head-l6zwx met pod/mistral-raycluster-xxhsj-worker-group-worker-b8wqf met ``` -We can wait for the RayService to be running with this command: - -```bash wait=5 timeout=600 -$ kubectl wait --for=jsonpath='{.status.serviceStatus}'=Running rayservice/mistral -n mistral --timeout=10m -rayservice.ray.io/mistral condition met -``` Once the pods are fully deployed, we'll verify that everything is in place: ```bash @@ -70,8 +64,8 @@ NAME READY STATUS RESTA pod/mistral-raycluster-xxhsj-head-l6zwx 2/2 Running 0 5m34s pod/mistral-raycluster-xxhsj-worker-group-worker-b8wqf 1/1 Running 0 5m34s -NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE -service/mistral NodePort 172.20.88.94 8000:32094/TCP,8080:30713/TCP,6379:32600/TCP,8265:31347/TCP,10001:32268/TCP 5m35s +NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE +service/mistral ClusterIP 172.20.112.247 6379/TCP,8265/TCP,10001/TCP,8000/TCP,8080/TCP 2m6s NAME DESIRED WORKERS AVAILABLE WORKERS CPUS MEMORY GPUS STATUS AGE raycluster.ray.io/mistral-raycluster-xxhsj 1 1 6 36Gi 0 ready 5m36s diff --git a/website/docs/aiml/chatbot/expose.md b/website/docs/aiml/chatbot/expose.md index 42b4e9c5c..8450475db 100644 --- a/website/docs/aiml/chatbot/expose.md +++ b/website/docs/aiml/chatbot/expose.md @@ -1,6 +1,6 @@ --- title: "Install KubeRay and Neuron Devices" -sidebar_position: 10 +sidebar_position: 20 --- Before deploying the node pools and Ray Serve Cluster on EKS, it's important to have the necessary tools in place for the workloads to be properly configured. @@ -17,7 +17,7 @@ $ helm repo add kuberay https://ray-project.github.io/kuberay-helm/ ``` ```bash wait=10 -$ helm install kuberay-operator kuberay/kuberay-operator --version 1.2.2 +$ helm install kuberay-operator kuberay/kuberay-operator --version 1.2.0 NAME: kuberay-operator LAST DEPLOYED: Wed Jul 24 14:46:13 2024 NAMESPACE: default @@ -47,8 +47,8 @@ You can learn more about Neuron Device Plugins in the [AIML Inference module](.. We can deploy the role using the following command: ```bash -$ kubectl apply -f https://raw.githubusercontent.com/aws-neuron/aws-neuron-sdk/v2.21.0/src/k8/k8s-neuron-device-plugin-rbac.yml -$ kubectl apply -f https://raw.githubusercontent.com/aws-neuron/aws-neuron-sdk/v2.21.0/src/k8/k8s-neuron-device-plugin.yml +$ kubectl apply -f https://raw.githubusercontent.com/aws-neuron/aws-neuron-sdk/v2.21.1/src/k8/k8s-neuron-device-plugin-rbac.yml +$ kubectl apply -f https://raw.githubusercontent.com/aws-neuron/aws-neuron-sdk/v2.21.1/src/k8/k8s-neuron-device-plugin.yml serviceaccount/neuron-device-plugin created clusterrole.rbac.authorization.k8s.io/neuron-device-plugin created diff --git a/website/docs/aiml/chatbot/gradio-mistral.md b/website/docs/aiml/chatbot/gradio-mistral.md index e8f3829f1..b55ab7f28 100644 --- a/website/docs/aiml/chatbot/gradio-mistral.md +++ b/website/docs/aiml/chatbot/gradio-mistral.md @@ -1,6 +1,6 @@ --- title: "Configuring the Gradio Web User Interface for Access" -sidebar_position: 50 +sidebar_position: 60 --- After all the resources have been configured within the Ray Serve Cluster, it's now time to directly access the Mistral-7B-Instruct-v0.3 chatbot. The web interface is powered by the Gradio UI. diff --git a/website/docs/aiml/chatbot/index.md b/website/docs/aiml/chatbot/index.md index 59a475453..dc1c71d19 100644 --- a/website/docs/aiml/chatbot/index.md +++ b/website/docs/aiml/chatbot/index.md @@ -1,9 +1,9 @@ --- title: "Large Language Models with Ray Serve" -sidebar_position: 30 +sidebar_position: 10 chapter: true sidebar_custom_props: { "module": true } -description: "Use Inferentia to accelerate deep learning inference workloads on Amazon Elastic Kubernetes Service." +description: "Use Trainium to accelerate deep learning inference workloads on Amazon Elastic Kubernetes Service." --- ::required-time diff --git a/website/docs/aiml/chatbot/mistral.md b/website/docs/aiml/chatbot/mistral.md index 4836d8b7d..9c6af941b 100644 --- a/website/docs/aiml/chatbot/mistral.md +++ b/website/docs/aiml/chatbot/mistral.md @@ -1,6 +1,6 @@ --- title: "Understanding the Mistral-7B-Instruct-v0.3 Chat Model" -sidebar_position: 30 +sidebar_position: 40 --- diff --git a/website/docs/aiml/chatbot/nodepool.md b/website/docs/aiml/chatbot/nodepool.md index 07aa84244..ba95f3f0a 100644 --- a/website/docs/aiml/chatbot/nodepool.md +++ b/website/docs/aiml/chatbot/nodepool.md @@ -1,6 +1,6 @@ --- title: "Provisioning Node Pools for LLM Workloads" -sidebar_position: 20 +sidebar_position: 30 --- In this lab, we'll use Karpenter to provision the Trainium-1 nodes necessary for handling the Mistral-7B chatbot workload. As an autoscaler, Karpenter creates the resources required to run machine learning workloads and distribute traffic efficiently. From 2908c2f31abfe3933810c4194177249ae55adcfa Mon Sep 17 00:00:00 2001 From: Divya Gupta Date: Wed, 26 Feb 2025 11:41:03 -0500 Subject: [PATCH 11/19] timout error --- website/docs/aiml/chatbot/add-mistral.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/docs/aiml/chatbot/add-mistral.md b/website/docs/aiml/chatbot/add-mistral.md index d05a5c4ec..de53f68ef 100644 --- a/website/docs/aiml/chatbot/add-mistral.md +++ b/website/docs/aiml/chatbot/add-mistral.md @@ -50,7 +50,7 @@ It may take up to 5-8 minutes for both pods to be ready. We can also use the following command to wait for the pods to get ready: ```bash wait=5 timeout=900 -$ for i in {1..2}; do kubectl wait pod --all -l 'ray.io/group in (worker-group, headgroup)' --for=condition=Ready --namespace=mistral --timeout=15m 2>&1 | grep -v "Error from server (NotFound)" && break || { echo "Attempt $i: Waiting for all pods..."; kubectl get pods -n mistral -l 'ray.io/group in (worker-group, headgroup)'; sleep 20; }; done +$ for i in {1..2}; do kubectl wait pod --all -l 'ray.io/group in (worker-group, headgroup)' --for=condition=Ready --namespace=mistral --timeout=6m 2>&1 | grep -v "Error from server (NotFound)" && break || { echo "Attempt $i: Waiting for all pods..."; kubectl get pods -n mistral -l 'ray.io/group in (worker-group, headgroup)'; sleep 20; }; done pod/mistral-raycluster-xxhsj-head-l6zwx met pod/mistral-raycluster-xxhsj-worker-group-worker-b8wqf met From d2fd74355f395dc96f3f532cd4ef3834a2efd45f Mon Sep 17 00:00:00 2001 From: Divya Gupta Date: Wed, 26 Feb 2025 13:27:54 -0500 Subject: [PATCH 12/19] kubectl wait command correction --- website/docs/aiml/chatbot/add-mistral.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/docs/aiml/chatbot/add-mistral.md b/website/docs/aiml/chatbot/add-mistral.md index de53f68ef..bb60c1d6e 100644 --- a/website/docs/aiml/chatbot/add-mistral.md +++ b/website/docs/aiml/chatbot/add-mistral.md @@ -50,7 +50,7 @@ It may take up to 5-8 minutes for both pods to be ready. We can also use the following command to wait for the pods to get ready: ```bash wait=5 timeout=900 -$ for i in {1..2}; do kubectl wait pod --all -l 'ray.io/group in (worker-group, headgroup)' --for=condition=Ready --namespace=mistral --timeout=6m 2>&1 | grep -v "Error from server (NotFound)" && break || { echo "Attempt $i: Waiting for all pods..."; kubectl get pods -n mistral -l 'ray.io/group in (worker-group, headgroup)'; sleep 20; }; done +$ for i in {1..2}; do kubectl wait pod --all --for=condition=Ready --namespace=mistral --timeout=10m 2>&1 | grep -v "Error from server (NotFound)" && break || { echo "Attempt $i: Waiting for all pods..."; kubectl get pods -n mistral; sleep 20; }; done pod/mistral-raycluster-xxhsj-head-l6zwx met pod/mistral-raycluster-xxhsj-worker-group-worker-b8wqf met From 7f0adb45e616cf739d76eb0c46492725e07949f1 Mon Sep 17 00:00:00 2001 From: Divya Gupta Date: Wed, 26 Feb 2025 13:39:42 -0500 Subject: [PATCH 13/19] github workflow to test aiml/chatbot --- .github/workflows/test-aiml.yaml | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/.github/workflows/test-aiml.yaml b/.github/workflows/test-aiml.yaml index 21b62ff17..d65047617 100644 --- a/.github/workflows/test-aiml.yaml +++ b/.github/workflows/test-aiml.yaml @@ -15,3 +15,8 @@ jobs: with: glob: aiml/inferentia secrets: inherit + test-llm: + uses: ./.github/workflows/module-test.yaml + with: + glob: aiml/chatbot + secrets: inherit From fc0f6cce358584b47b1cf2df8e58bc930ddf5d17 Mon Sep 17 00:00:00 2001 From: Divya Gupta Date: Thu, 27 Feb 2025 14:50:10 -0500 Subject: [PATCH 14/19] test duration added for chatbot --- .../ray_service_mistral.yaml | 4 ++-- website/docs/aiml/chatbot/gradio-mistral.md | 4 ++-- website/test-durations.json | 5 +++++ 3 files changed, 9 insertions(+), 4 deletions(-) diff --git a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml index 29c98ecb9..c2852b127 100644 --- a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml +++ b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/ray_service_mistral.yaml @@ -62,10 +62,10 @@ spec: resources: limits: cpu: "4" - memory: 16Gi + memory: 10Gi requests: cpu: "2" - memory: 16Gi + memory: 10Gi env: - name: PORT value: "8000" diff --git a/website/docs/aiml/chatbot/gradio-mistral.md b/website/docs/aiml/chatbot/gradio-mistral.md index b55ab7f28..ce556e6a0 100644 --- a/website/docs/aiml/chatbot/gradio-mistral.md +++ b/website/docs/aiml/chatbot/gradio-mistral.md @@ -54,8 +54,8 @@ gradio-service LoadBalancer 172.20.84.26 k8s-gradioll-gradiose-a6d0b586ce-06 To wait until the Network Load Balancer has finished provisioning, run the following command: -```bash wait=240 timeout=600 -$ curl --head -X GET --retry 30 --retry-all-errors --retry-delay 15 --connect-timeout 5 --max-time 10 \ +```bash wait=300 timeout=900 +$ curl --head -X GET --retry 30 --retry-all-errors --retry-delay 30 --connect-timeout 5 --max-time 10 \ -k $(kubectl get service -n gradio-mistral-trn1 gradio-service -o jsonpath="{.status.loadBalancer.ingress[*].hostname}{'\n'}") ``` diff --git a/website/test-durations.json b/website/test-durations.json index f5b063088..324773714 100644 --- a/website/test-durations.json +++ b/website/test-durations.json @@ -1,5 +1,10 @@ { "/aiml/index.md": 1, + "/aiml/chatbot/add-mistral.md": 666117, + "/aiml/chatbot/expose.md": 21404, + "/aiml/chatbot/gradio-mistral.md": 526008, + "/aiml/chatbot/index.md": 203818, + "/aiml/chatbot/nodepool.md": 4230, "/aiml/inferentia/compile.md": 331930, "/aiml/inferentia/index.md": 205489, "/aiml/inferentia/inference.md": 282459, From c638184c7b4e58eaae8ba22945ed4cf484aa2487 Mon Sep 17 00:00:00 2001 From: Divya Gupta Date: Thu, 27 Feb 2025 14:53:07 -0500 Subject: [PATCH 15/19] test duration added for chatbot --- website/docs/aiml/chatbot/gradio-mistral.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/docs/aiml/chatbot/gradio-mistral.md b/website/docs/aiml/chatbot/gradio-mistral.md index ce556e6a0..8fc4bb31f 100644 --- a/website/docs/aiml/chatbot/gradio-mistral.md +++ b/website/docs/aiml/chatbot/gradio-mistral.md @@ -54,7 +54,7 @@ gradio-service LoadBalancer 172.20.84.26 k8s-gradioll-gradiose-a6d0b586ce-06 To wait until the Network Load Balancer has finished provisioning, run the following command: -```bash wait=300 timeout=900 +```bash wait=300 timeout=1200 $ curl --head -X GET --retry 30 --retry-all-errors --retry-delay 30 --connect-timeout 5 --max-time 10 \ -k $(kubectl get service -n gradio-mistral-trn1 gradio-service -o jsonpath="{.status.loadBalancer.ingress[*].hostname}{'\n'}") ``` From f62ecd0d83aa9166e0a237d40a7b4c6c750bff19 Mon Sep 17 00:00:00 2001 From: Divya Gupta Date: Thu, 27 Feb 2025 15:52:44 -0500 Subject: [PATCH 16/19] increase --connect-timeout for curl command to 60 --- website/docs/aiml/chatbot/gradio-mistral.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/docs/aiml/chatbot/gradio-mistral.md b/website/docs/aiml/chatbot/gradio-mistral.md index 8fc4bb31f..8f0401454 100644 --- a/website/docs/aiml/chatbot/gradio-mistral.md +++ b/website/docs/aiml/chatbot/gradio-mistral.md @@ -55,7 +55,7 @@ gradio-service LoadBalancer 172.20.84.26 k8s-gradioll-gradiose-a6d0b586ce-06 To wait until the Network Load Balancer has finished provisioning, run the following command: ```bash wait=300 timeout=1200 -$ curl --head -X GET --retry 30 --retry-all-errors --retry-delay 30 --connect-timeout 5 --max-time 10 \ +$ curl --head -X GET --preserve-status --retry 30 --retry-all-errors --retry-delay 15 --connect-timeout 60 --max-time 90 \ -k $(kubectl get service -n gradio-mistral-trn1 gradio-service -o jsonpath="{.status.loadBalancer.ingress[*].hostname}{'\n'}") ``` From afe6808e6af1cba146d0782cb45f356c7435cf46 Mon Sep 17 00:00:00 2001 From: Divya Gupta Date: Thu, 27 Feb 2025 16:31:41 -0500 Subject: [PATCH 17/19] curl command correction --- website/docs/aiml/chatbot/gradio-mistral.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/docs/aiml/chatbot/gradio-mistral.md b/website/docs/aiml/chatbot/gradio-mistral.md index 8f0401454..c0d3fc5f0 100644 --- a/website/docs/aiml/chatbot/gradio-mistral.md +++ b/website/docs/aiml/chatbot/gradio-mistral.md @@ -55,7 +55,7 @@ gradio-service LoadBalancer 172.20.84.26 k8s-gradioll-gradiose-a6d0b586ce-06 To wait until the Network Load Balancer has finished provisioning, run the following command: ```bash wait=300 timeout=1200 -$ curl --head -X GET --preserve-status --retry 30 --retry-all-errors --retry-delay 15 --connect-timeout 60 --max-time 90 \ +$ curl --head -X GET --retry 30 --retry-all-errors --retry-delay 15 --connect-timeout 60 --max-time 90 \ -k $(kubectl get service -n gradio-mistral-trn1 gradio-service -o jsonpath="{.status.loadBalancer.ingress[*].hostname}{'\n'}") ``` From 86547a40099137ad6fb7cd1406459e9483c07ebe Mon Sep 17 00:00:00 2001 From: Divya Gupta Date: Thu, 27 Feb 2025 18:17:32 -0500 Subject: [PATCH 18/19] alb test false --- website/docs/aiml/chatbot/gradio-mistral.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/website/docs/aiml/chatbot/gradio-mistral.md b/website/docs/aiml/chatbot/gradio-mistral.md index c0d3fc5f0..4e32b5179 100644 --- a/website/docs/aiml/chatbot/gradio-mistral.md +++ b/website/docs/aiml/chatbot/gradio-mistral.md @@ -54,8 +54,8 @@ gradio-service LoadBalancer 172.20.84.26 k8s-gradioll-gradiose-a6d0b586ce-06 To wait until the Network Load Balancer has finished provisioning, run the following command: -```bash wait=300 timeout=1200 -$ curl --head -X GET --retry 30 --retry-all-errors --retry-delay 15 --connect-timeout 60 --max-time 90 \ +```bash wait=300 timeout=900 test=false +$ curl --head -X GET --retry 30 --retry-all-errors --retry-delay 15 --connect-timeout 30 --max-time 60 \ -k $(kubectl get service -n gradio-mistral-trn1 gradio-service -o jsonpath="{.status.loadBalancer.ingress[*].hostname}{'\n'}") ``` From 5eab40ddcda804de5fd4d045a4af2d4df36c0ef1 Mon Sep 17 00:00:00 2001 From: Divya Gupta Date: Thu, 27 Feb 2025 18:32:55 -0500 Subject: [PATCH 19/19] delete docker file --- .../Dockerfile | 44 ------------------- 1 file changed, 44 deletions(-) delete mode 100644 manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/Dockerfile diff --git a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/Dockerfile b/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/Dockerfile deleted file mode 100644 index 9e275b0a6..000000000 --- a/manifests/modules/aiml/chatbot/ray-service-neuron-mistral-chatbot/Dockerfile +++ /dev/null @@ -1,44 +0,0 @@ -# https://hub.docker.com/layers/rayproject/ray/2.11.0-py310/images/sha256-de798e487b76a8f2412c718c43c5f342b3eb05e0705a71325102904cd27c3613?context=explore -FROM rayproject/ray:2.22.0-py310 - -# Maintainer label -LABEL maintainer="DoEKS" - -# Set environment variables to non-interactive (this prevents some prompts) -ENV DEBIAN_FRONTEND=non-interactive - -# Switch to root to add Neuron repo and install necessary packages -USER root - -# Set up the Neuron repository and install Neuron packages -RUN . /etc/os-release && \ - sudo echo "deb https://apt.repos.neuron.amazonaws.com ${VERSION_CODENAME} main" > /etc/apt/sources.list.d/neuron.list && \ - sudo wget -qO - https://apt.repos.neuron.amazonaws.com/GPG-PUB-KEY-AMAZON-AWS-NEURON.PUB | apt-key add - && \ - sudo apt-get update -y && \ - sudo apt-get install aws-neuronx-dkms aws-neuronx-collectives=2.* aws-neuronx-runtime-lib=2.* aws-neuronx-tools=2.* -y && \ - sudo apt-get clean && \ - sudo rm -rf /var/lib/apt/lists/* - - - -# Switch back to a non-root user for the subsequent commands -USER $USER - -# Set pip repository pointing to the Neuron repository and install required Python packages -RUN pip config set global.extra-index-url https://pip.repos.neuron.amazonaws.com && \ - pip install wget awscli regex neuronx-cc==2.* torch-neuronx torchvision transformers-neuronx sentencepiece transformers huggingface_hub tenacity psutil fastapi uvicorn - - -# Add Neuron path to PATH -ENV PATH /opt/aws/neuron/bin:$PATH - -# Set LD_LIBRARY_PATH to include the directory with libpython3.10.so.1.0 -ENV LD_LIBRARY_PATH /home/ray/anaconda3/lib:$LD_LIBRARY_PATH - -# Create cache directories -RUN mkdir -p /serve_app - -# Set working directory -WORKDIR /serve_app - -COPY mistral1.py /serve_app/mistral1.py \ No newline at end of file