NVIDIA TensorRT Inference Server¶
The NVIDIA TensorRT Inference Server provides a cloud inferencing solution optimized for NVIDIA GPUs. The server provides an inference service via an HTTP or gRPC endpoint, allowing remote clients to request inferencing for any model being managed by the server. The NVIDIA Inference Server provides the following features:
- Multiple model support. The server can manage any number and mix of models (limited by system disk and memory resources). Supports TensorRT, TensorFlow GraphDef, TensorFlow SavedModel and Caffe2 NetDef model formats. Also supports TensorFlow-TensorRT integrated models.
- Multi-GPU support. The server can distribute inferencing across all system GPUs.
- Multi-tenancy support. Multiple models (or multiple instances of the same model) can run simultaneously on the same GPU.
- Batching support. For models that support batching, the server can accept requests for a batch of inputs and respond with the corresponding batch of outputs. The server also supports dynamic batching where individual inference requests are dynamically combined together to improve inference throughput. Dynamic batching is transparent to the client requesting inference.
- Model repositories may reside on a locally accessible file system or in Google Cloud Storage.
- Readiness and liveness health endpoints suitable for Kubernetes-style orchestration.
- Metrics indicating GPU utiliization, server throughput, and server latency.
- Installing the Server
- Running the Server
- Client Libraries and Examples
- Model Repository
- Model Configuration
- Inference Server API