#STYLIZER 6 ON MULTIPLE COMPUTERS FULL#
#STYLIZER 6 ON MULTIPLE COMPUTERS SERIES#

After these multiplications, the result of the convolution multiplications is summed using FP32. The exponent and therefore the dynamic range of FP32 are preserved. When TF32 is used, the multiplicands of a convolution are rounded to their nearest FP16 equivalent. When the model requires FP32 precision, TensorRT still enables TF32 on NVIDIA Ampere architecture and later by default, but it can be disabled using -noTF32. This is not only true for TensorRT but also for ONNX optimization in general. To get the most performance by using Tensor Cores it is important to have tensor alignment of, for example, eight elements for FP16. Model quality can be affected by reducing the numeric precision, so you can manually label specific layers to be executed at higher precision using the -layerPrecisions option. Lower precision usually results in faster execution, so those weights are more likely to be selected in the resulting engine. However, you can enable TensorRT to cast weights to the respective precision and evaluate the inference cost. When using trtexec with an ONNX file, there is currently no option to use the precision specified inside the ONNX file.

TensorRT performance is heavily correlated to the respective operation precision INT8 or FP16 and FP32. In case the operations in the graph are not supported by TensorRT, you can write plugins to support custom operations in an engine.īefore going through the effort of implementing a custom operator, evaluate whether the operation can be realized through a combination of other ONNX operators. Most of the command-line options can be easily translated to TensorRT C++/Python API functions.įor investigating TensorRT performance, precision, or compatibility, useful tools include polygraph or trex. In this post, I briefly discuss what is needed for acquiring a performant engine from an ONNX file using trtexec. For more information about TensorRT through ONNX Runtime, see TensorRT Execution Provider. When you are using TensorRT through ONNX Runtime, only some of the TensorRT native build features provided by trtexec or the TensorRT API are exposed. Run the following command to create an engine: trtexec –onnx='model.onnx' –saveEngine='t' This tool can also be used to generate an engine file that can be deployed with the Python or C++ API later through the -saveEngine option. Building the graph manually provides the most granular access to TensorRT but also requires the most effort.Ī quick way to evaluate TensorRT on a given ONNX file is to use trtexec. Generating a performant and correct engineĪ network graph definition and its weights can be either parsed from an ONNX file or by building a network manually using the NetworkDefinition API. The file is also specific to a TensorRT version but will be compatible with future versions after 8.6. The inclusion of compiled kernels and the serialization to a file make this engine compatible only with a GPU of the same compute capability. Nothing else apart from this engine is needed during deployment to execute the network. The evaluated strategy is serialized within a TensorRT engine, which is shipped with the application to achieve the best inference performance in production.

#STYLIZER 6 ON MULTIPLE COMPUTERS FULL#

To apply these optimizations, TensorRT must have the full network definition and its weights.

TensorRT optimizations include reordering operations in a graph, optimizing the memory layout of weights, and fusing ops to a single kernel to reduce memory traffic to VRAM. Provided with an AI model architecture, TensorRT can be used pre-deployment to run an excessive search for the most efficient execution strategy. NVIDIA TensorRT is a solution for speed-of-light inference deployment on NVIDIA hardware.

#STYLIZER 6 ON MULTIPLE COMPUTERS SERIES#

This post is the fifth in a series about optimizing end-to-end AI.

Stylizer 6 on multiple computers