Okay, so I wanted to mess around with TRT and ViT, see if I could get some speed boosts for my image classification stuff. Here’s how it went down.
Getting Started
First, I needed the right tools. I made sure I had these:
- TensorRT: Obviously, needed this installed.
- PyTorch: My go-to for building and training models.
- Transformers Library: From Hugging Face, makes it super easy to grab pre-trained ViT models.
- ONNX: For converting my PyTorch model to a format TensorRT likes.
I already had most of this stuff set up from other projects, so it was mostly just making sure everything was up to date.
The Model
I grabbed a pre-trained ViT model from Hugging Face. Nothing fancy, just a standard one for image classification. The Transformers library made this part a breeze. I literally just loaded it up with a few lines of code.
Conversion to ONNX
Now, TensorRT can’t directly use a PyTorch model, so I needed to convert it to ONNX. This was a bit tricky at first, but PyTorch has some built-in tools for this. I basically used `*`.
Building the TRT Engine
This is where the real TensorRT magic happens. I used the ONNX model I just created and the TensorRT API to build an optimized “engine”. This engine is what actually runs the inference super fast. I played around with different precision settings (like FP16) to see how it affected speed and accuracy. I found that FP16 gave me a good balance.
Running Inference
Finally, the fun part! I loaded up my TRT engine and fed it some images. It was noticeably faster than running the original PyTorch model. Seeing the speedup was pretty satisfying.
Tweaking and Optimizing
I spent some time tweaking things. I experimented with different batch sizes to see how that impacted performance. Larger batches usually mean more throughput, but there’s a limit. Also changing input size can affect performance, but I just chose the default size of ViT.
I tested different kinds of GPUs, after all, it turned out that TensorRT just can boost the speed.
The Results
Overall, I was happy with the results. I got a significant speedup using TensorRT with ViT, which is exactly what I was hoping for. It took a bit of work to get everything set up and optimized, but it was definitely worth it.
It’s not perfect, but I’m no expert. I’m sure there are more optimizations I could do. But for a quick experiment, it showed me the potential of combining TRT and ViT. I saved some of the engine files, for the next step test and play.
