add autoencoder and dockerize

Dockerfile.mlflow-server

@@ -1,20 +1,17 @@
-# Dockerfile.mlflow-server
-# Runs a local MLflow tracking server with the repository's model registry and artifacts.
+FROM python:3.11-slim
 
-FROM python:3.13-slim
-
-ENV PYTHONUNBUFFERED=1
+# Install uv
+COPY --from=ghcr.io/astral-sh/uv:latest /uv /bin/uv
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y --no-install-recommends \
-        curl \
-    && rm -rf /var/lib/apt/lists/*
+# Create a virtual environment and install mlflow inside it
+RUN uv venv /app/.venv && \
+    uv pip install --python /app/.venv mlflow
 
-RUN pip install --no-cache-dir mlflow==3.14.0 sqlalchemy
+# Add the virtual environment to the PATH so the mlflow command is recognized
+ENV PATH="/app/.venv/bin:$PATH"
 
-COPY mlflow.db /app/mlflow.db
-COPY mlartifacts /app/mlartifacts
+EXPOSE 8000
 
-EXPOSE 8080
-
-CMD ["mlflow", "server", "--backend-store-uri", "sqlite:///app/mlflow.db", "--default-artifact-root", "file:///app/mlartifacts", "--host", "0.0.0.0", "--port", "8080", "--workers", "1"]
+# Start the server
+CMD ["mlflow", "server", "--host", "0.0.0.0", "--port", "8000", "--backend-store-uri", "sqlite:///mlflow.db", "--default-artifact-root", "./mlartifacts"]

Dockerfile.model-serve

@@ -1,22 +1,29 @@
-# Dockerfile.model-serve
-# Builds a container that serves an MLflow registry model by name and version.
-
-FROM python:3.13-slim
-
-ENV PYTHONUNBUFFERED=1
+FROM astral/uv:python3.12-bookworm-slim
+# Install uv
+COPY --from=ghcr.io/astral-sh/uv:latest /uv /bin/uv
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y --no-install-recommends \
-        curl \
-    && rm -rf /var/lib/apt/lists/*
+# Copy dependency files first to leverage Docker layer caching
+COPY pyproject.toml uv.lock ./
 
-RUN pip install --no-cache-dir mlflow==3.14.0
+# Create a virtual environment and sync dependencies
+# --no-dev ensures testing/formatting tools aren't installed in production
+RUN uv venv && uv sync --frozen --no-dev
 
-ARG MODEL_NAME=IsolationForest_Anomaly_Detector
-ARG MODEL_VERSION=6
-ENV MODEL_NAME=${MODEL_NAME}
-ENV MODEL_VERSION=${MODEL_VERSION}
+# Put the venv in the system PATH so we don't need to manually activate it
+ENV PATH="/app/.venv/bin:$PATH"
+
+# Copy the rest of the project
+COPY . .
+
+# Set the environment variable for the model as requested
+ENV MODEL_URI="models:/Autoencoder_Anomaly_Detector/2"
+
+# The tracking URI where this container will fetch the model. 
+# (Set this to the MLflow server's hostname/IP at runtime, e.g., via Docker Compose)
+ENV MLFLOW_TRACKING_URI="http://localhost:8000"
 
 EXPOSE 5001
 
-CMD ["sh", "-c", "mlflow models serve -m \"models:/$MODEL_NAME/$MODEL_VERSION\" --host 0.0.0.0 --port 5001 --env-manager local"]
+# Serve the model using the environment variable
+CMD mlflow models serve -m "$MODEL_URI" --host 0.0.0.0 --port 5001 --env-manager local

docker-compose.yml

@@ -0,0 +1,32 @@
+services:
+  mlflow-server:
+    build:
+      context: .
+      dockerfile: Dockerfile.mlflow-server
+    ports:
+      - "8000:8000"
+    volumes:
+      # Mount the database file and artifacts directory to persist them
+      - ./mlflow.db:/app/mlflow.db
+      - ./mlartifacts:/app/mlartifacts
+    restart: unless-stopped
+
+  model-serve:
+    build:
+      context: .
+      dockerfile: Dockerfile.model-serve
+    ports:
+      - "5001:5001"
+    environment:
+      # Points to the service name defined above, not localhost
+      - MLFLOW_TRACKING_URI=http://mlflow-server:8000
+      # You can override this at runtime: docker compose run -e MODEL_URI="..." model-serve
+      - MODEL_URI=${MODEL_URI:-models:/Autoencoder_Anomaly_Detector/2}
+    volumes:
+      # Crucial: Since MLflow is using a local disk artifact store, 
+      # the server passes local file paths back to the client. 
+      # The serving container must have access to the exact same artifact paths.
+      - ./mlartifacts:/app/mlartifacts
+    depends_on:
+      - mlflow-server
+    restart: unless-stopped