Embarking on the journey to deploy deep learning models can feel a lot like assembling a complicated piece of furniture from a popular Swedish store. Instead of planks and screws, we’re working with models and configurations, and sometimes the instructions can be a bit tricky. But fear not! This guide will walk you through deploying models using the Triton Inference Server step-by-step, making it user-friendly so you can set up your inference pipeline with confidence.

Overview of the Deployment Process

Deploying models on the Triton Inference Server involves several crucial steps:

• Pre-processing Raw Images: Adjusting your inputs to meet model requirements.
• Performing Text Detection: Identifying text within images using a detection model.
• Cropping Images: Isolating areas of interest where text is present.
• Text Recognition: Converting detected text into readable characters.
• Outputting Final Text: Displaying the recognized text for user consumption.

Step-by-Step Guide to Deploy Models

Step 1: Preparing Your Environment

Before we begin deploying models, ensure you have the necessary libraries and a working environment set up. We recommend using Docker containers optimized for your framework (TensorFlow or PyTorch) to simplify the process.

Step 2: Cloning the Repository

Start by cloning the necessary repository for your Triton models:

bash
cd Conceptual_Guide/Part_1-model_deployment

Step 3: Downloading Model Files

Now, let’s download and prepare the models:

bash
# Download and unzip the EAST model for text detection
wget https://www.dropbox.com/s/r2ingd0l3zt8hxsfrozen_east_text_detection.tar.gz
tar -xvf frozen_east_text_detection.tar.gz

# Convert the model to ONNX format
export - ONNX Model Conversion Steps Here

Step 4: Setting Up Model Repository

Once the models are ready, create a model repository structure that Triton can read. It’ll look something like this:

bash
mkdir -p model_repository/text_detection/1
mv detection.onnx model_repository/text_detection/1/model.onnx
mkdir -p model_repository/text_recognition/1
mv str.onnx model_repository/text_recognition/1/model.onnx

Step 5: Configuring Models

Create configuration files for each model within the respective model directories. These files tell Triton how to utilize each model:

text_detection {
    backend: "onnxruntime"
    max_batch_size: 256
    input [
        {
            name: "input_images:0"
            data_type: TYPE_FP32
            dims: [-1, -1, -1, 3]
        }
    ]
    output [
        {
            name: "feature_fusionConv_7Sigmoid:0"
            data_type: TYPE_FP32
            dims: [-1, -1, -1, 1]
        }, 
        {
            name: "feature_fusionconcat_3:0"
            data_type: TYPE_FP32
            dims: [-1, -1, -1, 5]
        }
    ]
}

Step 6: Launching the Triton Server

With everything set, it’s time to launch the Triton server. Use the following command to get your server up and running:

bash
docker run --gpus=all -it --shm-size=256m --rm -p8000:8000 -p8001:8001 -p8002:8002 -v $(pwd)/model_repository:/models nvcr.io/nvidia/tritonserver:xx.xx-py3

Step 7: Building a Client Application

Now that your server is live, you need a client to send requests. A simple Python script can accomplish this:

python
import tritonclient.http as httpclient
client = httpclient.InferenceServerClient(url="localhost:8000")

# Process input and send inference requests
preprocessed_image = ... # Load and preprocess the image
detection_input = httpclient.InferInput("input_images:0", preprocessed_image.shape, datatype="FP32")
detection_input.set_data_from_numpy(preprocessed_image, binary_data=True)

detection_response = client.infer(model_name="text_detection", inputs=[detection_input])

Troubleshooting Common Issues

If you encounter issues during deployment, here are a few troubleshooting tips:

• Ensure your models are correctly formatted as ONNX files.
• Check that the model repository follows the required structure.
• Verify server logs to identify any potential errors in configuration files.
• If models are not loading, confirm the specified backend matches the model type.

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

Final Thoughts

By following these steps, you should be well on your way to deploying models using Triton Inference Server. Remember, deploying AI models is much like assembling that complex piece of furniture – take your time, ensure you follow each step, and soon you will have a functional inference pipeline ready to tackle real-world challenges.

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.