Accelerate AI Inference with Pruna and Triton Inference Server

Efficiently deploying AI models can be challenging as they grow and complexity. By combining Pruna, our powerful optimization tool, with NVIDIA's Triton Inference Server, you can achieve scalable, high-performance AI deployments with reduced latency and memory usage.

This blog demonstrates how to integrate Pruna's model optimization techniques with Triton Server. While it uses the Stable Diffusion model as an example, this blog also applies to other AI models. By the end of this blog, you’ll know how to achieve faster, leaner, and more efficient AI model serving.

Why Pruna and Triton Server?

Pruna is a powerful tool designed to optimize deep learning models by applying techniques like pruning, quantization, compilation, and more, tailored for high-performance inference. This enables higher speedup of your model, thus providing a better user experience or serving more users for the same number of resources.

Triton Inference Server, on the other hand, provides a scalable, production-ready platform for deploying models. Its GPU support, batching, and extensible backend make it ideal for real-time AI applications. Be careful. Do not confuse Triton Inference Server which helps to serve models with Triton Kernels which help to support different model precisions.

Combining these two tools allows you to:

• Optimize model performance using Pruna's advanced compression techniques.

• Scale and deploy optimized models seamlessly with Triton.

• Leverage Triton’s efficient request handling and multimodel support.

Pruna + Triton Workflow Overview

Here’s a high-level view of the process:

1. Prepare the Model: Use Pruna to optimize your machine learning model.

2. Integrate with Triton: Deploy the optimized model in Triton using its flexible Python backend.

3. Deploy and Test: Run Triton with your optimized model and validate performance with a client script.

4. Example: Deploying Stable Diffusion with Pruna and Triton

   Let’s break down how to deploy an optimized Stable Diffusion model using Pruna and Triton, which is based on the following example GitHub repository.

Step 1: Preparing the Environment

Before getting started, ensure you have the following installed:

• Docker: It is needed to run the Triton Inference Server.

• Python with version 3.8 or higher: It is needed to work with Pruna and the Triton Client Library.

• Triton Client Library: Install it with pip install tritonclient[grpc].

Step 2: Build the Triton + Pruna Docker Image

Create a Dockerfile to build an image that includes Triton Server, Pruna, and all required dependencies. You can find a full example of a Dockerfile here. In the Dockerfile, we achieve the following steps:

1. Start with NVIDIA's Triton Server base image.

2. Install Pruna with GPU support (pruna[gpu]).

3. Add any necessary Python libraries for your model (e.g. PyTorch, diffusers, transformers…).

Build the image:

docker build -t

Note: You can check out the full Dockerfile example here.

Step 3: Configure the Model for Triton

Triton uses a model repository to manage models. In this tutorial, we serve on Stable Diffusion model as shown in the directory structure. Note that you can adapt this structure to add other models if you need to serve more models.

model_repository/
└── stable_diffusion/
    ├── config.pbtxt
    └── 1/
        └── model.py

Model Configuration (config.pbtxt)

The config.pbtxt file defines the input-output interface and GPU settings for the model. We provide a full example of the config.pbtxt here. For Stable Diffusion, the configuration might look like this:

• Inputs: A single string (text prompt).

• Outputs: A 512x512 image with 3 color channels.

• Batch Size: Supports up to 4 simultaneous requests.

If you have different input and output types for your model, you can easily adapt the config.pbtxt with the tritonserve-torch docs here.

Python Backend Implementation (model.py)

The model.py file handles the model's loading and inference logic. With Pruna, you can integrate optimizations like step caching to reduce computation time. The key steps are:

1. Load the Stable Diffusion pipeline.

2. Apply Pruna’s step caching compiler with your token.

3. Define the Triton inference workflow.

You can refer to the config.pbtxt and model.py in the repository for a complete example.

Step 4: Run the Triton Server

Once the model repository is ready, run Triton with your Docker container:

docker run --rm --gpus=all -p 8000:8000 -p 8001:8001 -p 8002:8002 \\
   -v "path/to/your/model_repository:/models" \\

Here are some details on the meaning of the parameters of the command line:

• --rm: Removes the container once it stops.

• --gpus=all: Enables GPU acceleration, using all available GPUs.

• -p 8000:8000 -p 8001:8001 -p 8002:8002: Exposes port 8000 for HTTP and gRPC inference requests and or model repository control API.

• -v "/absolute/path/to/your/model_repository:/models": Mounts the model repository directory to /models inside the container. Make sure to replace path/to/your/model_repository with the actual path to your model repository.

• tritonserver_pruna: The name of the Docker image being used.

• tritonserver --model-repository=/models: Runs Triton and specifies the directory where models are stored.

Step 5: Run the Client Script

With the server running, use the tritonclient Python library to send a request. The following example script sends text prompts to the stable_diffusion model appearing in the directory structure in and retrieves the generated images.

from tritonclient.grpc import InferenceServerClient, InferInput

# Connect to Triton Server
client = InferenceServerClient(url="localhost:8001")

# Prepare the input
input_text = np.array(["a serene mountain view"], dtype=object).reshape(-1, 1)
input_tensor = InferInput("INPUT_TEXT", input_text.shape, "BYTES")
input_tensor.set_data_from_numpy(input_text)

# Perform inference
response = client.infer(model_name="stable_diffusion", inputs=[input_tensor])
output_data = response.as_numpy("OUTPUT")
print(f"Generated image: {output_data}")

🎉That's it. You now have a working example of using Pruna AI with Triton server!🎉

Final Thoughts

Pruna and Triton Inference Server together represent a powerful toolkit for optimizing and deploying machine learning models. By leveraging Pruna's cutting-edge optimization techniques and Triton's robust serving capabilities, you can deliver high-performance AI solutions with minimal overhead.

Whether you're building real-time applications or serving complex generative models like Stable Diffusion, this combination ensures you get the most out of your hardware.

Ready to supercharge your AI deployments? Start integrating Pruna with Triton today! 🚀

For more details, check out Pruna’s Triton example repository, Pruna's documentation or Triton's PyTorch example and join our AI efficiency discord community!