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.
What’s New In 1.1.0¶
Client libraries and examples now build with a separate Makefile (a Dockerfile is also included for convenience).
Input or output tensors with variable-size dimensions (indicated by -1 in the model configuration) can now represent tensors where the variable dimension has value 0 (zero).
Zero-sized input and output tensors are now supported for batching models. This enables the inference server to support models that require inputs and outputs that have shape [ batch-size ].
TensorFlow custom operations (C++) can now be built into the inference server. An example and documentation are included in this release.
Features¶
Multiple framework 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. Variable-size input and output tensors are allowed if supported by the framework. See Capabilities for detailed support information for each framework.
Concurrent model execution 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 inference server also supports multiple scheduling and batching algorithms that combine individual inference requests together to improve inference throughput. These scheduling and batching decisions are transparent to the client requesting inference.
Custom backend support. The inference server allows individual models to be implemented with custom backends instead of by a deep-learning framework. With a custom backend a model can implement any logic desired, while still benefiting from the GPU support, concurrent execution, dynamic batching and other features provided by the server.
Multi-GPU support. The server can distribute inferencing across all system GPUs.
The inference server monitors the model repository for any change and dynamically reloads the model(s) when necessary, without requiring a server restart. Models and model versions can be added and removed, and model configurations can be modified while the server is running.
Model repositories may reside on a locally accessible file system (e.g. NFS) or in Google Cloud Storage.
Readiness and liveness health endpoints suitable for any orchestration or deployment framework, such as Kubernetes.
Metrics indicating GPU utiliization, server throughput, and server latency.
User Guide
Developer Guide