To deploy Haystack on Docker, start by containerizing your application. Create a Dockerfile that specifies a Python base image, installs Haystack and its dependencies, and copies your application code. For example, your Dockerfile might include commands like FROM python:3.9, RUN pip install farm-haystack[all], and COPY . /app to set up the environment. Use docker build -t haystack-app . to build the image, then run it with docker run -p 8000:8000 haystack-app to expose the API port. If your Haystack setup relies on external services like Elasticsearch or a database, use Docker Compose to define multi-container dependencies. A docker-compose.yml file can spin up Haystack alongside Elasticsearch, ensuring they communicate via network links and environment variables like ELASTICSEARCH_HOST.
For Kubernetes, create YAML manifests to manage deployments, services, and configurations. Start by defining a Deployment for your Haystack app, specifying the container image, resource limits, and environment variables (e.g., ELASTICSEARCH_URL). Use a Service to expose the deployment internally or externally, depending on your setup. If Haystack requires a document store like Elasticsearch, deploy it as a separate StatefulSet with persistent storage to retain data across pod restarts. Use ConfigMap and Secret objects to manage settings like API keys or model paths. For example, a Helm chart can simplify templating these resources. To scale Haystack horizontally, configure the deployment’s replicas count or use a HorizontalPodAutoscaler based on CPU/memory metrics. Deploy to a cloud-managed Kubernetes cluster (e.g., EKS, GKE) for easier infrastructure management.
Key considerations include persistent storage for models or indexes, security, and observability. Mount volumes to containers to cache large ML models and avoid re-downloading them on every restart. Use Kubernetes PersistentVolumeClaims or cloud storage solutions like AWS EBS. Secure sensitive data like API keys with Kubernetes Secret resources instead of hardcoding them. Implement health checks (liveness/readiness probes) to ensure pods restart if the app becomes unresponsive. For monitoring, integrate logging tools like Fluentd and metrics collectors like Prometheus. If using Docker Compose locally, test the setup with docker-compose up before moving to Kubernetes. Both approaches require careful networking configuration—ensure containers or pods can communicate with dependencies (e.g., Elasticsearch on port 9200) via correct service names and ports.