Added Inference Spec & CI

.github/workflows/deploy_inference_apps.yaml

@@ -0,0 +1,84 @@
+name: Deploy Inference Apps
+
+on:
+  push:
+    branches:
+      - main
+  workflow_dispatch:
+
+permissions:
+  id-token: write  
+  contents: read    
+
+jobs:
+  deploy_dev:
+    name: Deploy Inference Apps (Development)
+    runs-on: ubuntu-latest
+    env:
+      ENV: dev
+      AWS_REGION: us-east-1
+      AWS_CI_ROLE: ${{ secrets.AWS_INFER_CI_ROLE__DEV }}
+    steps: 
+
+      - name: Configure AWS credentials
+        uses: aws-actions/configure-aws-credentials@v4
+        with:
+          aws-region: ${{ env.AWS_REGION }}
+          role-to-assume: ${{ env.AWS_CI_ROLE}}
+
+      - name: Deploy Ray Serve
+        shell: bash
+        run: |
+          RAY_CLUSTER_NAME=ray-cluster-$ENV
+          RAY_PRIVATE_IP=$(aws ec2 describe-instances --region $AWS_DEFAULT_REGION --filters "Name=tag:Name,Values=ray-cluster-$ENV-head" --query "Reservations[*].Instances[*].PrivateIpAddress" --output text)
+          RAY_BASTION_PUBLIC_IP=$(aws ec2 describe-instances --region $AWS_DEFAULT_REGION --filters "Name=tag:Name,Values=ray-cluster-$ENV-bastion" --query "Reservations[*].Instances[*].PublicIpAddress" --output text)
+          RAY_CLUSTER_KEY_PAIR_FILE=$RAY_CLUSTER_NAME
+          RAY_CLUSTER_SECRET_KEY_PAIR_NAME=$RAY_CLUSTER_NAME-key-pair-secret
+
+          aws secretsmanager get-secret-value --region $AWS_REGION --secret-id $RAY_CLUSTER_SECRET_KEY_PAIR_NAME --query SecretString --output text > ./${RAY_CLUSTER_KEY_PAIR_FILE}.pem
+          chmod 400 ./${RAY_CLUSTER_KEY_PAIR_FILE}.pem
+
+          echo "Deploying Ray Serve on $RAY_CLUSTER_NAME..."
+          if ssh -o StrictHostKeyChecking=no -i ./${RAY_CLUSTER_KEY_PAIR_FILE}.pem ubuntu@$RAY_BASTION_PUBLIC_IP "source ~/.profile && bash bastion-ray-serve-deploy.sh $ENV" >/dev/null 2>&1; then
+            echo "Deployment succeeded."
+          else
+            echo "Deployment failed."
+            exit 1
+          fi
+
+  deploy_prod:
+    name: Deploy Inference Apps (Production)
+    runs-on: ubuntu-latest
+    depends-on: deploy_dev
+    env:
+      ENV: prod
+      AWS_REGION: us-east-1
+      AWS_CI_ROLE: ${{ secrets.AWS_INFER_CI_ROLE__PROD }}
+    steps: 
+
+      - name: Configure AWS credentials
+        uses: aws-actions/configure-aws-credentials@v4
+        with:
+          aws-region: ${{ env.AWS_REGION }}
+          role-to-assume: ${{ env.AWS_CI_ROLE}}
+
+      - name: Deploy Ray Serve
+        shell: bash
+        run: |
+          RAY_CLUSTER_NAME=ray-cluster-$ENV
+          RAY_PRIVATE_IP=$(aws ec2 describe-instances --region $AWS_DEFAULT_REGION --filters "Name=tag:Name,Values=ray-cluster-$ENV-head" --query "Reservations[*].Instances[*].PrivateIpAddress" --output text)
+          RAY_BASTION_PUBLIC_IP=$(aws ec2 describe-instances --region $AWS_DEFAULT_REGION --filters "Name=tag:Name,Values=ray-cluster-$ENV-bastion" --query "Reservations[*].Instances[*].PublicIpAddress" --output text)
+          RAY_CLUSTER_KEY_PAIR_FILE=$RAY_CLUSTER_NAME
+          RAY_CLUSTER_SECRET_KEY_PAIR_NAME=$RAY_CLUSTER_NAME-key-pair-secret
+
+          aws secretsmanager get-secret-value --region $AWS_REGION --secret-id $RAY_CLUSTER_SECRET_KEY_PAIR_NAME --query SecretString --output text > ./${RAY_CLUSTER_KEY_PAIR_FILE}.pem
+          chmod 400 ./${RAY_CLUSTER_KEY_PAIR_FILE}.pem
+
+          echo "Deploying Ray Serve on $RAY_CLUSTER_NAME..."
+          if ssh -o StrictHostKeyChecking=no -i ./${RAY_CLUSTER_KEY_PAIR_FILE}.pem ubuntu@$RAY_BASTION_PUBLIC_IP "source ~/.profile && bash bastion-ray-serve-deploy.sh $ENV" >/dev/null 2>&1; then
+            echo "Deployment succeeded."
+          else
+            echo "Deployment failed."
+            exit 1
+          fi
+

.github/workflows/ecr_sagemaker_publish.yml

@@ -0,0 +1,81 @@
+name: Sagemaker ECR Publish (RC)
+
+on:
+  push:
+    branches:
+      - main
+  workflow_dispatch:
+    inputs:
+      is_release_candidate:
+        description: 'Is this a release candidate?'
+        required: true
+        default: 'true'
+
+# Needed for OIDC / assume role
+permissions:
+  id-token: write  
+  contents: read    
+
+jobs:
+  publish_image:
+    name: Publish Sagemaker Image (Release Candidate)
+    runs-on: ubuntu-latest
+    env:
+      VALIDATOR_TAG_NAME: competitorcheck
+      AWS_REGION: us-east-1
+      WORKING_DIR: "./"
+      AWS_CI_ROLE__PROD: ${{ secrets.AWS_CI_ROLE__PROD }}
+      AWS_ECR_RELEASE_CANDIDATE: ${{ inputs.is_release_candidate || 'true' }}
+    steps: 
+
+      - name: Check out head
+        uses: actions/checkout@v3
+        with:
+          persist-credentials: false
+
+      - name: Set ECR Tag
+        id: set-ecr-tag
+        run: |
+          if [ ${{ env.AWS_ECR_RELEASE_CANDIDATE }} == 'true' ]; then
+            echo "This is a release candidate."
+            echo "Setting tag to -rc"
+            ECR_TAG=$VALIDATOR_TAG_NAME-rc
+          else
+            echo "This is a production image."
+            ECR_TAG=$VALIDATOR_TAG_NAME
+          fi
+          echo "Setting ECR tag to $ECR_TAG"
+          echo "ECR_TAG=$ECR_TAG" >> "$GITHUB_OUTPUT"
+
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@master
+        with:
+          platforms: linux/amd64
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@master
+        with:
+          platforms: linux/amd64
+
+      - name: Configure AWS credentials
+        uses: aws-actions/configure-aws-credentials@v4
+        with:
+          aws-region: ${{ env.AWS_REGION }}
+          role-to-assume: ${{ env.AWS_CI_ROLE__PROD}}
+
+      - name: Login to Amazon ECR
+        id: login-ecr
+        uses: aws-actions/amazon-ecr-login@v2
+        with:
+          mask-password: 'true'
+
+      - name: Build & Push ECR Image
+        uses: docker/build-push-action@v2
+        with:
+          builder: ${{ steps.buildx.outputs.name }}
+          context: ${{ env.WORKING_DIR }}
+          platforms: linux/amd64
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
+          push: true
+          tags: 064852979926.dkr.ecr.us-east-1.amazonaws.com/gr-sagemaker-validator-images-prod:${{ steps.set-ecr-tag.outputs.ECR_TAG }}

Dockerfile

@@ -0,0 +1,35 @@
+# Use an official PyTorch image with CUDA support
+FROM pytorch/pytorch:1.13.1-cuda11.6-cudnn8-runtime
+
+# Set the working directory
+WORKDIR /app
+
+# Copy the pyproject.toml and any other necessary files (e.g., README, LICENSE)
+COPY pyproject.toml .
+COPY README.md .
+COPY LICENSE .
+
+# Install dependencies from the pyproject.toml file
+RUN pip install --upgrade pip setuptools wheel
+RUN pip install .
+
+# Install the necessary packages for the FastAPI app, including CuPy with CUDA 11.6
+RUN pip install fastapi "uvicorn[standard]" gunicorn "spacy[cuda116]"
+
+# Copy the entire project code into the container
+COPY . /app
+
+# Copy the serve script into the container
+COPY serve /usr/local/bin/serve
+
+# Make the serve script executable
+RUN chmod +x /usr/local/bin/serve
+
+# Set environment variable to determine the device (cuda or cpu)
+ENV env=prod
+
+# Expose the port that the FastAPI app will run on
+EXPOSE 8080
+
+# Set the entrypoint for SageMaker to the serve script
+ENTRYPOINT ["serve"]

app.py

@@ -57,7 +57,7 @@ def infer(cls, text_vals, competitors):
         )
 
         return output_data.model_dump()
-    
+
 @app.post("/validate", response_model=OutputResponse)
 async def competitor_check(input_request: InputRequest):
     competitors = []
@@ -71,3 +71,12 @@ async def competitor_check(input_request: InputRequest):
         raise HTTPException(status_code=400, detail="Invalid input format")
 
     return CompetitorCheck.infer(text_vals, competitors)
+
+# Sagemaker specific endpoints
+@app.get("/ping")
+async def healtchcheck():
+    return {"status": "ok"}
+
+@app.post("/invocations", response_model=OutputResponse)
+async def competitor_check_sagemaker(input_request: InputRequest):
+    return await competitor_check(input_request)

app_inference_spec.py

@@ -0,0 +1,75 @@
+from fastapi import HTTPException
+from pydantic import BaseModel
+from typing import List, Tuple
+import spacy
+from models_host.base_inference_spec import BaseInferenceSpec
+
+class InferenceData(BaseModel):
+    name: str
+    shape: List[int]
+    data: List
+    datatype: str
+
+
+class InputRequest(BaseModel):
+    inputs: List[InferenceData]
+
+class OutputResponse(BaseModel):
+    modelname: str
+    modelversion: str
+    outputs: List[InferenceData]
+
+class InferenceSpec(BaseInferenceSpec):
+    model_name = "en_core_web_trf"
+    model = None
+
+    def load(self):
+        model_name = self.model_name
+        print(f"Loading model {model_name}...")
+        if not spacy.util.is_package(model_name):
+            print(
+                f"Spacy model {model_name} not installed. "
+                "Download should start now and take a few minutes."
+            )
+            spacy.cli.download(model_name)  # type: ignore
+        self.model = spacy.load(model_name)
+
+    def process_request(self, input_request: InputRequest) -> Tuple[Tuple, dict]:
+        competitors = []
+        for inp in input_request.inputs:
+            if inp.name == "text":
+                text_vals = inp.data
+            elif inp.name == "competitors":
+                competitors = inp.data
+
+        if text_vals is None or competitors is None:
+            raise HTTPException(status_code=400, detail="Invalid input format")
+
+        args = (text_vals, competitors)
+        kwargs = {}
+        return args, kwargs
+
+    def infer(self, text_vals, competitors) -> OutputResponse:
+        outputs = []
+        for idx, text in enumerate(text_vals):
+            doc = self.model(text) # type: ignore
+
+            located_competitors = []
+            for ent in doc.ents:
+                if ent.text in competitors:
+                    located_competitors.append(ent.text)
+
+            outputs.append(
+                InferenceData(
+                    name=f"result{idx}",
+                    datatype="BYTES",
+                    shape=[1],
+                    data=[located_competitors],
+                )
+            )
+
+        output_data = OutputResponse(
+            modelname=self.model_name, modelversion="1", outputs=outputs
+        )
+
+        return output_data

example_inference.json

@@ -0,0 +1,16 @@
+{
+    "inputs": [
+        {
+            "name": "text",
+            "shape": [1],
+            "data": ["Apple made the iphone"],
+            "datatype": "BYTES"
+        },
+        {
+            "name": "competitors",
+            "shape": [1],
+            "data": ["Apple"],
+            "datatype": "BYTES"
+        }
+    ]
+}

serve

@@ -0,0 +1,57 @@
+#!/usr/bin/env python
+
+import multiprocessing
+import os
+import signal
+import subprocess
+import sys
+import spacy
+
+cpu_count = multiprocessing.cpu_count()
+default_worker_count = max(cpu_count // 2,1)
+
+model_server_timeout = os.environ.get('MODEL_SERVER_TIMEOUT', 60)
+model_server_workers = int(os.environ.get('MODEL_SERVER_WORKERS', default_worker_count))
+
+def sigterm_handler(gunicorn_pid):
+    try:
+        os.kill(gunicorn_pid, signal.SIGTERM)
+    except OSError:
+        pass
+
+    sys.exit(0)
+
+def save_and_load_model():
+    model = "en_core_web_trf"
+    if not spacy.util.is_package(model):
+        print(
+            f"Spacy model {model} not installed. "
+            "Download should start now and take a few minutes."
+        )
+        spacy.require_gpu()
+        spacy.cli.download(model)  # type: ignore
+
+def start_server():
+    print(f'Starting the inference server with {model_server_workers} workers.')
+
+    save_and_load_model()
+
+    # Start Gunicorn to serve the FastAPI app
+    gunicorn = subprocess.Popen(['gunicorn',
+                                 '--timeout', str(model_server_timeout),
+                                 '-k', 'uvicorn.workers.UvicornWorker',
+                                 '-b', '0.0.0.0:8080',
+                                 '-w', str(model_server_workers),
+                                 'app:app'])
+
+    signal.signal(signal.SIGTERM, lambda a, b: sigterm_handler(gunicorn.pid))
+
+    # Wait for the Gunicorn process to exit
+    gunicorn.wait()
+
+    print('Inference server exiting')
+
+# The main routine just invokes the start function.
+
+if __name__ == '__main__':
+    start_server()