Triton Model Navigator
Model conversion plays a crucial role in unlocking the maximum performance capabilities of the underlying hardware. By applying these transformation techniques, models can be optimized to fully utilize the specific features and optimizations offered by the hardware architecture. Furthermore, conversions allow for serialization of models, separating them from the source code. This serialization process enhances portability, allowing the models to be seamlessly deployed in production environments. The decoupling of models from the source code also facilitates maintenance, updates, and collaboration among developers.
However, this process comprises multiple steps and offers various potential paths, making manual execution complicated and time-consuming. The Triton Model Navigator offers a user-friendly and automatic solution for converting, optimizing and deploying machine learning models. It offers a single entry point for various supported frameworks, allowing users to start the process of searching for the best deployment option with a single call to the dedicated optimize function. Model Navigator handles model export, conversion, correctness testing, and performance profiling, saving all generated artifacts.
Throughout the optimize process, the Model Navigator considers multiple factors, including different precisions, runtimes, and data shapes. This careful consideration allows for the adjustment of the model's configuration to achieve improved performance, effectively minimizing the costs associated with serving the model.
Converted models can be easily deployed on the PyTriton or Triton Inference Server.
The Model Navigator generates multiple optimized and production-ready models. The table below illustrates the model formats that can be obtained by using the Model Navigator with various frameworks.
Table: Supported conversion target formats per each supported Python framework or file.
| PyTorch | TensorFlow 2 | JAX | ONNX |
|---|---|---|---|
| Torch 2 Compile | SavedModel | SavedModel | TensorRT |
| TorchScript Trace | TensorFlowTensorRT | TensorFlowTensorRT | |
| TorchScript Script | ONNX | ONNX | |
| TorchTensorRT | TensorRT | TensorRT | |
| ONNX | |||
| TensorRT |
Note: The Model Navigator has the capability to support any Python function as input. However, in this particular case, its role is limited to profiling the function without generating any serialized models.
The Model Navigator stores all artifacts within the navigator_workspace. Additionally, it provides the option to save a portable and transferable Navigator Package that includes only the models with minimal latency and maximal throughput. This package also includes base formats that can be used to regenerate the TensorRT plan on the target hardware.
Table: Model formats that can be generated from saved Navigator Package and from model sources.
| From model source | From Navigator Package |
|---|---|
| SavedModel | TorchTensorRT |
| TensorFlowTensorRT | TensorFlowTensorRT |
| TorchScript Trace | ONNX |
| TorchScript Script | TensorRT |
| Torch 2 Compile | |
| TorchTensorRT | |
| ONNX | |
| TensoRT |