Once you have trained and exported your model, typically using the SavedModel format, the next significant step is making it available to serve predictions. While you could build a custom application (e.g., using Flask or FastAPI) to load the SavedModel and expose an API endpoint, this approach often lacks the robustness, performance, and lifecycle management features required for demanding production environments. This is precisely the problem TensorFlow Serving aims to solve.
TensorFlow Serving is a dedicated, high-performance serving system specifically designed for machine learning models in production. Think of it not just as a library, but as a standalone server application optimized for inference. It takes your trained models (packaged as SavedModels) and makes them accessible over a network via well-defined APIs, typically REST or gRPC.
Why Use a Dedicated Serving System?
Deploying models involves more than just loading a file and running model.predict(). Production systems often face requirements like:
- High Throughput & Low Latency: Serving predictions quickly to many users simultaneously.
- Model Version Management: Deploying updated models without downtime (canary releases, rollbacks).
- Multiple Models: Serving different models or model types from the same infrastructure.
- Resource Management: Efficiently utilizing hardware (CPUs, GPUs, TPUs).
- Lifecycle Management: Loading, unloading, and managing models dynamically.
TensorFlow Serving is engineered to handle these challenges effectively. It provides out-of-the-box solutions for managing the lifecycle of your models, allowing you to deploy new versions, run A/B tests between versions, or serve multiple distinct models concurrently, all while maintaining high performance.
Core Concepts of TensorFlow Serving
While later sections cover practical usage, understanding the basic architecture is helpful. TensorFlow Serving employs several abstractions:
- Servables: These are the fundamental objects that TensorFlow Serving manages. Typically, a Servable represents a specific version of a trained model ready for inference. It could also be a lookup table or other data needed for computation.
- Sources: Plugins that discover and provide Servables. A common source monitors a filesystem path for new SavedModel directories (each representing a model version).
- Loaders: Responsible for loading the data for a Servable, including allocating necessary resources (like GPU memory). They know how to interpret specific model formats (like SavedModel).
- Managers: Handle the full lifecycle of Servables, including loading (via Loaders), serving, and unloading based on policies defined by Sources. They coordinate the transition between model versions.
- Core: The central component that manages the lifecycle and exposes APIs (gRPC, REST) for clients to send inference requests.
Basic architecture of TensorFlow Serving, showing how a client request flows through the API to the manager, which uses loaders and sources to serve predictions from managed model versions (Servables).
Benefits
Using TensorFlow Serving offers several advantages:
- Performance: Highly optimized C++ server designed for low latency and high throughput. Supports hardware accelerators like GPUs. Implements optimizations like request batching automatically.
- Flexibility: Easily manage multiple models or multiple versions of the same model simultaneously. Supports canary deployments and rollbacks.
- Production Readiness: Built for stability and reliability in production environments.
- Extensibility: Designed to be extensible for supporting new model types, filesystems, or deployment scenarios.
- Standardization: Provides standard inference APIs (REST and gRPC), simplifying client integration. Relies on the SavedModel format, integrating well with the TensorFlow ecosystem.
In essence, TensorFlow Serving provides the infrastructure glue between your trained TensorFlow models and the applications that need to consume their predictions at scale. The following sections will demonstrate how to prepare your models and deploy them using this powerful system.
