The following tutorial is available on the Wallaroo Github Repository.

Wallaroo Inference Server: Computer Vision YoloV8n

This notebook is used in conjunction with the Wallaroo Inference Server Free Edition for Hugging Face Summarizer. This provides a free license for performing inferences through the Computer Vision YoloV8n model.

Tutorial Notes

To run this tutorial in the Wallaroo JupyterHub Service, import the tensorflow-cpu library by executing the following command in the terminal shell:

pip install tensorflow-cpu==2.13.1 --user

Then proceed with the tutorial. This only applies to running this tutorial in Wallaroo’s JupyterHub service, and does not affect model upload and packaging in Wallaroo.

Prerequisites

• A deployed Wallaroo Inference Server Free Edition with one of the following options:
  • Wallaroo.AI Yolov8 Inference Server- x64
  • Wallaroo.AI Yolov8 Inference Server- GPU
• Access via port 8080 to the Wallaroo Inference Server Free Edition.

Note that GPU inference server require a VM with Nvidia GPU CUDA support.

Computer Vision Yolo8 Model Schemas

Inputs

The Resnet Model takes the following inputs.

Outputs

Wallaroo Inference Server API Endpoints

The following HTTPS API endpoints are available for Wallaroo Inference Server.

Pipelines Endpoint

• Endpoint: HTTPS GET /pipelines
• Returns:
  • List of pipelines with the following fields.
    • id (String): The name of the pipeline.
    • status (String): The pipeline status. Running indicates the pipeline is available for inferences.

Pipeline Endpoint Example

The following demonstrates using curl to retrieve the Pipelines endpoint. Replace the HOSTNAME with the address of your Wallaroo Inference Server.

!curl HOSTNAME:8080/pipelines

{"pipelines":[{"id":"yolo-v8","status":"Running"}]}

Models Endpoint

• Endpoint: GET /models
• Returns:
  • List of models with the following fields.
    • name (String): The name of the model.
    • sha (String): The sha hash of the model.
    • status (String): The model status. Running indicates the models is available for inferences.
    • version (String): The model version in UUID format.

Models Endpoint Example

The following demonstrates using curl to retrieve the Models endpoint. Replace the HOSTNAME with the address of your Wallaroo Inference Server.

!curl HOSTNAME:8080/models

{"models":[{"name":"yolo-v8","sha":"3ed5cd199e0e6e419bd3d474cf74f2e378aacbf586e40f24d1f8c89c2c476a08","status":"Running","version":"af82c216-4590-41ad-8579-48b7eccc7144"}]}

Inference Endpoint

The following endpoints are available from the Wallaroo Server for Computer Vision Yolov8n deployment.

• Endpoint: HTTPS POST /pipelines/hf-summarizer-standard
• Headers:
  • Content-Type: application/vnd.apache.arrow.file: For Apache Arrow tables.
  • Content-Type: application/json; format=pandas-records: For pandas DataFrame in record format.
• Input Parameters: The images must be in 640x640 format converted to a float tensor.DataFrame in application/json; format=pandas-records OR Apache Arrow table in application/vnd.apache.arrow.file with the shape (n, 3, 640, 640) then flattened, with the tensor values in the field images.

The following code is used to create a DataFrame from a 640x640 image.

import cv2
import torch
import numpy as np
import pandas as pd

# load the image from disk, convert to BGR, resize to specified width, height, convert the image back to RGB
# convert the image to a float tensor and returns it.  Also return the original resized image for drawing bounding boxes in BGR
def imageResize(image, 640, 640):
    #self.print("Image Mode:"+image.mode)
    im_pillow = np.array(image)
    image = cv2.cvtColor(im_pillow, cv2.COLOR_BGR2RGB) #scott
    image = cv2.flip(im_pillow, 1)
    image = cv2.flip(image, 1)
    #image = cv2.imread(im_pillow)
    #image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
    #image = cv2.cvtColor(im_pillow, cv2.COLOR_GRAY2BGR)
    self.debug("Resizing to w:"+str(width) + " height:"+str(height))
    image = cv2.resize(image, (width, height))
    
    # convert the image from BGR to RGB channel ordering and change the
    # image from channels last to channels first ordering
    #image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    image = image.transpose((2, 0, 1))

    # add the batch dimension, scale the raw pixel intensities to the
    # range [0, 1], and convert the image to a floating point tensor
    image = np.expand_dims(image, axis=0)
    image = image / 255.0
    tensor = torch.FloatTensor(image)
    tensor.flatten()

    npArray = tensor.cpu().numpy()
    dictData = {"images":[npArray]}
    dataframedata = pd.DataFrame(dictData)

• Returns:
  • Headers
    • Content-Type: application/json; format=pandas-records: pandas DataFrame in record format.
  • Data

    • time (Integer): The time since UNIX epoch.

    • in: The original input.

      • images: The flattened tensor values for the original image.

    • out: The outputs of the inference result separated by data type.

      • output0: The float outputs for the inference. This list is flattened, and when reshaped into (1,84,8400) with each row correlating to a detected object. The elements break down as follows:

        • [0:3]: The bounding box with the positions left, top, width, height.
        • [4:]: The classes and scores of the detected object.

        For more details for breaking down the Yolo8n inference results into objects, see the CVDemoUtils.py module with the Computer Vision Yolov8n Deployment in Wallaroo

    • check_failures (List[Integer]): Whether any validation checks were triggered. For more information, see Wallaroo SDK Essentials Guide: Pipeline Management: Anomaly Testing.

    • metadata: Additional data for the inference.

      • last_model: The model used for the inference.
        • model_name (String): The name of the model used.
        • model_sha (String): The sha of the model used.
      • pipeline_version (String): The pipeline version in UUID format.
      • elapsed (List[Integer]): A list of time in nanoseconds for:
        • [0] The time to serialize the input.
        • [1…n] How long each step took.
      • dropped (List): Any dropped input tables.

Inference Endpoint Example

The Wallaroo Inference Server accepts pandas DataFrame or Apache Arrow tables as inference inputs. The sample file ./data/dogbike.df.json was converted from the file ./data/dogbike.png as an example using the helper module CVDemoUtils and WallarooUtils are used to transform a sample image into a pandas DataFrame. This DataFrame is then submitted to the Yolov8n model deployed in Wallaroo.

The following code segment demonstrates converting the image to a DataFrame.

from CVDemoUtils import CVDemo
from WallarooUtils import Util
cvDemo = CVDemo()
util = Util()

width, height = 640, 640
tensor1, resizedImage1 = cvDemo.loadImageAndResize('./data/dogbike.png', width, height)
tensor1.flatten()

# add the tensor to a DataFrame and save the DataFrame in pandas record format
df = util.convert_data(tensor1,'images')
df.to_json("dogbike.df.json", orient = 'records')

The following code segment demonstrates performing an inference through the Wallaroo Inference Server with the Yolov8n model deployed. Replace HOSTNAME with the hostname or IP address of your Wallaroo Inference Server instance.

!curl -X POST HOSTNAME:8080/pipelines/yolo-v8 \
    -H "Content-Type: application/json; format=pandas-records" \
    -d @./data/dogbike.df.json > edge-results.df.json

  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100 28.7M  100 13.6M  100 15.0M  12.7M  14.1M  0:00:03  0:00:01  0:00:02 18.9M01  0:00:01 --:--:-- 27.0M