Welcome to your quick guide on leveraging the power of Torch-TensorRT! If you’re looking to accelerate the inference latency of your PyTorch models, you’re in the right place. This blog will walk you through the installation, quickstarts, and troubleshooting for maximizing performance using Torch-TensorRT on NVIDIA platforms.

What is Torch-TensorRT?

Torch-TensorRT is an ingenious tool that brings TensorRT’s power to the PyTorch ecosystem, allowing you to boost inference speeds by up to 5 times! Think of it as giving your PyTorch model a turbo boost. Whether you’re a machine learning engineer or just a passionate developer, it’s your call to take the leap into optimized performance.

Installation

Getting started is as simple as a line of code! Below are the ways to install Torch-TensorRT.

• For stable versions, simply use:

pip install torch-tensorrt

• For nightly versions, execute:

pip install --pre torch-tensorrt --index-url https://download.pytorch.org/whl/nightly/cu124

• Alternatively, check out the ready-to-run NVIDIA NGC PyTorch Container, which includes all dependencies, proper versions, and example notebooks.

For more advanced installation methods, please see here.

Quickstart Guide

Ready to dive in? Here are two quick options to get you up and running:

Option 1: Using torch.compile

This is the quick route! It allows you to utilize Torch-TensorRT directly within your model compilation:

import torch
import torch_tensorrt

model = MyModel().eval().cuda()  # define your model here
x = torch.randn((1, 3, 224, 224)).cuda()  # define inputs

optimized_model = torch.compile(model, backend='tensorrt')
optimized_model(x)  # compiled on the first run
optimized_model(x)  # this will be fast!

Option 2: Exporting Your Model

If you need to optimize your model ahead of time or deploy it in a C++ environment, follow this export-style workflow:

import torch
import torch_tensorrt

model = MyModel().eval().cuda()  # define your model here
inputs = [torch.randn((1, 3, 224, 224)).cuda()]  # representative inputs

rt_gm = torch_tensorrt.compile(model, ir='dynamo', inputs)
torch_tensorrt.save(rt_gm, 'trt.ep', inputs=inputs)  # For PyTorch
torch_tensorrt.save(rt_gm, 'trt.ts', output_format='torchscript', inputs=inputs)  # For C++

These lines set the stage for high-speed operations! You can easily switch to deployment based on your needs—either in PyTorch or directly in C++ by loading the TorchScript file.

Troubleshooting

Sometimes things don’t go as planned. If you encounter issues while setting up Torch-TensorRT, consider the following troubleshooting tips:

• Ensure that all required dependencies are installed, as outlined in the official documentation.
• Check CUDA and TensorRT versions to confirm compatibility, especially if you are upgrading from an older version.
• For model compilation issues, confirm that your model adheres to the TensorRT supported operators and data types.
• If you experience a performance drop or unexpected behavior, ensure you are using the latest version of Torch-TensorRT.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Further Resources

Unleash the full potential of your models with these useful resources:

• Up to 50% faster Stable Diffusion inference with one line of code
• Run your model in FP8 with Torch-TensorRT
• Tech Talk (GTC 23)

Harness the speed of Torch-TensorRT and let your AI models shine!