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Low-Precision Deployment for Paddle Serving

(English|简体中文)

Intel CPU supports int8 and bfloat16 models, NVIDIA TensorRT supports int8 and float16 models.

Obtain the quantized model through PaddleSlim tool

Train the low-precision models please refer to PaddleSlim.

Deploy the quantized model from PaddleSlim using Paddle Serving with Nvidia TensorRT int8 mode

Firstly, download the Resnet50 int8 model and convert to Paddle Serving's saved model。

wget https://paddle-inference-dist.bj.bcebos.com/inference_demo/python/resnet50/ResNet50_quant.tar.gz
tar zxvf ResNet50_quant.tar.gz

python -m paddle_serving_client.convert --dirname ResNet50_quant

Start RPC service, specify the GPU id and precision mode

python -m paddle_serving_server.serve --model serving_server --port 9393 --gpu_ids 0 --use_trt --precision int8

Request the serving service with Client

from paddle_serving_client import Client
from paddle_serving_app.reader import Sequential, File2Image, Resize, CenterCrop
from paddle_serving_app.reader import RGB2BGR, Transpose, Div, Normalize

client = Client()
client.load_client_config(
    "serving_client/serving_client_conf.prototxt")
client.connect(["127.0.0.1:9393"])

seq = Sequential([
    File2Image(), Resize(256), CenterCrop(224), RGB2BGR(), Transpose((2, 0, 1)),
    Div(255), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True)
])

image_file = "daisy.jpg"
img = seq(image_file)
fetch_map = client.predict(feed={"image": img}, fetch=["save_infer_model/scale_0.tmp_0"])
print(fetch_map["save_infer_model/scale_0.tmp_0"].reshape(-1))

Reference