New model images

applications/compound_explorer/requirements.txt

@@ -8,7 +8,7 @@ sagemaker >= 2.143
 cryptography>=42.0.5
 ipython>=8.17.2
 xgboost>=2.0.3
-scikit-learn >=1.4.2, <= 1.5.2
+scikit-learn >=1.5.2
 joblib>=1.3.2
 requests>=2.32.0
 plotly >= 5.18.0
@@ -18,7 +18,7 @@ dash-bootstrap-templates >= 1.3.0
 dash_ag_grid
 tabulate >= 0.9.0
 shap>=0.43.0
-rdkit>=2024.3.2
+rdkit>=2024.9.5
 mordredcommunity>=2.0.6
 networkx>=3.2
 matplotlib>=3.9.2

model_docker_images/inference/Dockerfile

@@ -0,0 +1,27 @@
+FROM python:3.12-slim
+
+# Install Vim
+RUN apt-get update && apt-get install -y vim
+
+# Copy requirements file
+COPY requirements.txt /tmp/
+
+# Install dependencies
+RUN pip install --no-cache-dir -r /tmp/requirements.txt
+
+# Add the serve script
+COPY serve /usr/local/bin/
+RUN chmod +x /usr/local/bin/serve
+
+# Copy the main.py/entrypoint script
+COPY main.py /opt/program/
+WORKDIR /opt/program
+
+# Make port 8080 available for the web server
+EXPOSE 8080
+
+# Define environment variable
+ENV PYTHONUNBUFFERED=TRUE
+
+# SageMaker will look for this
+CMD ["serve"]

model_docker_images/inference/main.py

@@ -0,0 +1,150 @@
+from fastapi import FastAPI, Request, Response
+from contextlib import asynccontextmanager
+import os
+import sys
+import json
+import importlib.util
+import logging
+import subprocess
+import site
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Global variables
+model = None
+inference_module = None
+
+
+def get_inference_script(model_dir: str) -> str:
+    """Retrieve the inference script name
+
+    Args:
+        model_dir (str): The directory containing the model artifacts
+
+    Returns:
+        str: The name of the inference script
+    """
+
+    # Get the path to the inference-metadata.json file
+    inference_meta_path = os.path.join(model_dir, "inference-metadata.json")
+    with open(inference_meta_path, "r") as f:
+        config = json.load(f)
+        return config["inference_script"]
+
+
+def install_requirements(requirements_path):
+    """Install Python dependencies from requirements file.
+    Uses a persistent cache to speed up container cold starts.
+    Note: Inference containers don't have root access, so we
+          use the --user flag and add the user package path manually.
+    """
+    if os.path.exists(requirements_path):
+        logger.info(f"Installing dependencies from {requirements_path}...")
+
+        # Define a persistent cache location
+        pip_cache_dir = "/opt/ml/model/.cache/pip"
+        os.environ["PIP_CACHE_DIR"] = pip_cache_dir
+
+        try:
+            subprocess.check_call(
+                [
+                    sys.executable,
+                    "-m",
+                    "pip",
+                    "install",
+                    "--cache-dir",
+                    pip_cache_dir,  # Enable caching
+                    "--disable-pip-version-check",
+                    "--no-warn-script-location",
+                    "--user",
+                    "-r",
+                    requirements_path,
+                ]
+            )
+            # Ensure Python can find user-installed packages
+            sys.path.append(site.getusersitepackages())
+            logger.info("Requirements installed successfully.")
+        except subprocess.CalledProcessError as e:
+            logger.error(f"Error installing requirements: {e}")
+            sys.exit(1)
+    else:
+        logger.info(f"No requirements file found at {requirements_path}")
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """Handle model loading on startup and cleanup on shutdown."""
+    global model, inference_module
+
+    # Note: SageMaker will put model.tar.gz in /opt/ml/model
+    #       which includes the model artifacts and inference code
+    model_dir = os.environ.get("SM_MODEL_DIR", "/opt/ml/model")
+    inference_script = get_inference_script(model_dir)
+
+    # List directory contents for debugging
+    logger.info(f"Contents of {model_dir}: {os.listdir(model_dir)}")
+
+    try:
+        # Load the inference script from source_dir
+        inference_script_path = os.path.join(model_dir, inference_script)
+        if not os.path.exists(inference_script_path):
+            raise FileNotFoundError(f"Inference script not found: {inference_script_path}")
+
+        # Install requirements if present
+        install_requirements(os.path.join(model_dir, "requirements.txt"))
+
+        # Ensure the model directory is in the Python path
+        sys.path.insert(0, model_dir)
+
+        # Import the inference module
+        logger.info(f"Importing inference module from {inference_script_path}")
+        spec = importlib.util.spec_from_file_location("inference_module", inference_script_path)
+        inference_module = importlib.util.module_from_spec(spec)
+        sys.modules["inference_module"] = inference_module
+        spec.loader.exec_module(inference_module)
+
+        # Check if model_fn is defined
+        if not hasattr(inference_module, "model_fn"):
+            raise ImportError(f"Inference module {inference_script_path} does not define model_fn")
+
+        # Load the model using model_fn
+        logger.info("Calling model_fn to load the model")
+        model = inference_module.model_fn(model_dir)
+        logger.info(f"Model loaded successfully: {type(model)}")
+
+    except Exception as e:
+        logger.error(f"Error initializing model: {e}", exc_info=True)
+        raise
+
+    yield
+
+    logger.info("Shutting down model server")
+
+
+app = FastAPI(lifespan=lifespan)
+
+
+@app.get("/ping")
+def ping():
+    """Health check endpoint for SageMaker."""
+    # Check if the inference module is loaded
+    return Response(status_code=200 if inference_module else 500)
+
+
+@app.post("/invocations")
+async def invoke(request: Request):
+    """Inference endpoint for SageMaker."""
+    content_type = request.headers.get("Content-Type", "")
+    accept_type = request.headers.get("Accept", "")
+
+    try:
+        body = await request.body()
+        data = inference_module.input_fn(body, content_type)
+        result = inference_module.predict_fn(data, model)
+        output_data, output_content_type = inference_module.output_fn(result, accept_type)
+        return Response(content=output_data, media_type=output_content_type)
+    except Exception as e:
+        logger.error(f"Error during inference: {e}", exc_info=True)
+        return Response(content=json.dumps({"error": str(e)}), status_code=500, media_type="application/json")

model_docker_images/inference/requirements.txt

@@ -0,0 +1,7 @@
+fastapi==0.115.10
+uvicorn==0.34.0
+scikit-learn==1.6.1
+xgboost-cpu==2.1.4
+pandas==2.2.3
+awswrangler==3.11.0
+joblib==1.4.2

model_docker_images/inference/serve

@@ -0,0 +1,6 @@
+#!/bin/bash
+
+# SageMaker expect a 'serve' script to be found in the container which starts the model server.
+
+# Start the FastAPI server using Uvicorn
+exec uvicorn main:app --host 0.0.0.0 --port 8080

model_docker_images/scripts/build_deploy.sh

@@ -0,0 +1,152 @@
+#!/bin/bash
+set -e
+
+# Get the directory of this script
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" &> /dev/null && pwd)"
+# Get the parent directory (project root)
+PROJECT_ROOT="$(dirname "$SCRIPT_DIR")"
+
+# AWS Account ID
+AWS_ACCOUNT_ID="507740646243"
+
+# Define repository names - used for both local and ECR images
+TRAINING_REPO="aws-ml-images/py312-sklearn-xgb-training"
+INFERENCE_REPO="aws-ml-images/py312-sklearn-xgb-inference"
+
+# Local directories
+TRAINING_DIR="$PROJECT_ROOT/training"
+INFERENCE_DIR="$PROJECT_ROOT/inference"
+
+# Image version
+IMAGE_VERSION=${1:-"0.1"}
+
+# Expect AWS_PROFILE to be set in the environment when deploying
+if [ "$2" == "--deploy" ]; then
+    : "${AWS_PROFILE:?AWS_PROFILE environment variable is not set.}"
+fi
+
+# Define the regions to deploy to.
+REGION_LIST=("us-east-1" "us-west-2")
+
+# Colors for output
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+NC='\033[0m' # No Color
+
+# Parse arguments
+DEPLOY=false
+LATEST=false
+for arg in "$@"; do
+    case $arg in
+        --deploy)
+            DEPLOY=true
+            ;;
+        --latest)
+            LATEST=true
+            ;;
+        *)
+            ;;
+    esac
+done
+
+# Function to build a Docker image
+build_image() {
+    local dir=$1
+    local repo_name=$2
+    local tag=$3
+    local full_name="${repo_name}:${tag}"
+
+    echo -e "${YELLOW}Building image: ${full_name}${NC}"
+
+    # Check if Dockerfile exists
+    if [ ! -f "$dir/Dockerfile" ]; then
+        echo "❌ Error: Dockerfile not found in $dir"
+        return 1
+    fi
+
+    # Build the image for AMD64 architecture
+    echo "Building local Docker image ${full_name} for linux/amd64..."
+    docker build --platform linux/amd64 -t $full_name $dir
+
+    echo -e "${GREEN}✅ Successfully built: ${full_name}${NC}"
+    return 0
+}
+
+# Function to deploy an image to ECR
+deploy_image() {
+    local repo_name=$1
+    local tag=$2
+    local use_latest=$3
+    local full_name="${repo_name}:${tag}"
+
+    for REGION in "${REGION_LIST[@]}"; do
+        echo "Processing region: ${REGION}"
+        # Construct the ECR repository URL
+        ECR_REPO="${AWS_ACCOUNT_ID}.dkr.ecr.${REGION}.amazonaws.com/${repo_name}"
+        AWS_ECR_IMAGE="${ECR_REPO}:${tag}"
+
+        echo "Logging in to AWS ECR in ${REGION}..."
+        aws ecr get-login-password --region ${REGION} --profile ${AWS_PROFILE} | \
+            docker login --username AWS --password-stdin "${AWS_ACCOUNT_ID}.dkr.ecr.${REGION}.amazonaws.com"
+
+        echo "Tagging image for AWS ECR as ${AWS_ECR_IMAGE}..."
+        docker tag ${full_name} ${AWS_ECR_IMAGE}
+
+        echo "Pushing Docker image to AWS ECR: ${AWS_ECR_IMAGE}..."
+        docker push ${AWS_ECR_IMAGE}
+
+        if [ "$use_latest" = true ]; then
+            AWS_ECR_LATEST="${ECR_REPO}:latest"
+            echo "Tagging AWS ECR image as latest: ${AWS_ECR_LATEST}..."
+            docker tag ${full_name} ${AWS_ECR_LATEST}
+            echo "Pushing Docker image to AWS ECR: ${AWS_ECR_LATEST}..."
+            docker push ${AWS_ECR_LATEST}
+        fi
+    done
+}
+
+# Build training image
+echo "======================================"
+echo "🏗️  Building training container"
+echo "======================================"
+build_image "$TRAINING_DIR" "$TRAINING_REPO" "$IMAGE_VERSION"
+
+# Build inference image
+echo "======================================"
+echo "🏗️  Building inference container"
+echo "======================================"
+build_image "$INFERENCE_DIR" "$INFERENCE_REPO" "$IMAGE_VERSION"
+
+echo "======================================"
+echo -e "${GREEN}✅ All builds completed successfully!${NC}"
+echo "======================================"
+
+if [ "$DEPLOY" = true ]; then
+    echo "======================================"
+    echo "🚀 Deploying containers to ECR"
+    echo "======================================"
+
+    # Deploy training image
+    echo "Deploying training image..."
+    deploy_image "$TRAINING_REPO" "$IMAGE_VERSION" "$LATEST"
+
+    # Deploy inference image
+    echo "Deploying inference image..."
+    deploy_image "$INFERENCE_REPO" "$IMAGE_VERSION" "$LATEST"
+
+    echo "======================================"
+    echo -e "${GREEN}✅ Deployment complete!${NC}"
+    echo "======================================"
+else
+    echo "Local build complete. Use --deploy to push the images to AWS ECR in regions: ${REGION_LIST[*]}."
+
+    # Print information about the built images
+    echo "======================================"
+    echo "📋 Image information:"
+    echo "Training image: ${TRAINING_REPO}:${IMAGE_VERSION}"
+    echo "Inference image: ${INFERENCE_REPO}:${IMAGE_VERSION}"
+    echo "======================================"
+
+    # Inform about testing option
+    echo "To test these containers, run: $PROJECT_ROOT/tests/run_tests.sh ${IMAGE_VERSION}"
+fi