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Deploying an ONNX Model

This README showcases how to deploy a simple ResNet model on Triton Inference Server.

Step 1: Set Up Triton Inference Server

To use Triton, we need to build a model repository. The structure of the repository as follows:

model_repository
|
+-- resnet
    |
    +-- config.pbtxt
    +-- 1
        |
        +-- model.onnx

The config.pbtxt configuration file is optional. The configuration file is autogenerated by Triton Inference Server if the user doesn't provide it. If you are new to Triton, it is highly recommended to review Part 1 of the conceptual guide.

mkdir -p model_repository/densenet_onnx/1
wget -O model_repository/densenet_onnx/1/model.onnx \
     https://contentmamluswest001.blob.core.windows.net/content/14b2744cf8d6418c87ffddc3f3127242/9502630827244d60a1214f250e3bbca7/08aed7327d694b8dbaee2c97b8d0fcba/densenet121-1.2.onnx

docker run --gpus all --rm -p 8000:8000 -p 8001:8001 -p 8002:8002 -v ${PWD}/model_repository:/models nvcr.io/nvidia/tritonserver:<xx.yy>-py3 tritonserver --model-repository=/models

Step 3: Using a Triton Client to Query the Server

Install dependencies & download an example image to test inference.

docker run -it --rm --net=host -v ${PWD}:/workspace/ nvcr.io/nvidia/tritonserver:<yy.mm>-py3-sdk bash
pip install torchvision

wget  -O img1.jpg "https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg"

Building a client requires three basic points. Firstly, we setup a connection with the Triton Inference Server.

client = httpclient.InferenceServerClient(url="localhost:8000")

Secondly, we specify the names of the input and output layer(s) of our model as well as describe the shape and datatype of the expected input.

inputs = httpclient.InferInput("data_0", transformed_img.shape, datatype="FP32")
inputs.set_data_from_numpy(transformed_img, binary_data=True)

outputs = httpclient.InferRequestedOutput("fc6_1", binary_data=True, class_count=1000)

Lastly, we send an inference request to the Triton Inference Server.

# Querying the server
results = client.infer(model_name="densenet_onnx", inputs=[inputs], outputs=[outputs])
inference_output = results.as_numpy('fc6_1').astype(str)

print(np.squeeze(inference_output)[:5])

The output of the same should look like below:

['11.549026:92' '11.232335:14' '7.528014:95' '6.923391:17' '6.576575:88']

The output format here is <confidence_score>:<classification_index>. To learn how to map these to the label names and more, refer to our documentation. The client code above is available in client.py.