This tutorial and the assets can be downloaded as part of the Wallaroo Tutorials repository.

U-Net for Brain Segmentation Deployment in Wallaroo

The following example uses the U-Net for brain segmentation model trained to detect lower-grade gliomas to demonstrate how to:

• Deploy the published pipeline to an edge device as a Wallaroo Inference Server, and perform the same inference calls.

This tutorial requires the tutorial U-Net for Brain Segmentation Deployment in Wallaroo is run first to create the publish in the user’s edge registry.

Prerequisites

• A Wallaroo Community Edition or Enterprise Edition server with Edge Deployment enabled.
  • For a free license of Wallaroo Community Edition, go to https://portal.wallaroo.community/.
• A x64 edge device capable of running Docker

References

• Edge Deployment Registry Guide
• Brain MRI segmentation: The original evaluation and training images. These can be used with this inference server.

Tutorial Steps

Import Libraries

The first step is to import the libraries we’ll be using. These are included by default in the Wallaroo instance’s JupyterHub service.

Verify that the following Python libraries are installed. The wallaroo library is included with the Wallaroo Ops JupyterHub instance.

• pillow

• torchvision

• pandas

• pyarrow

• wallaroo

• References

  • Wallaroo SDK Install Guides

import wallaroo
from wallaroo.pipeline   import Pipeline
from wallaroo.deployment_config import DeploymentConfigBuilder
from wallaroo.framework import Framework

# used to convert the Image into a numpy array
from PIL import Image
from torchvision import transforms

import pyarrow as pa
import numpy as np
import pandas as pd

import requests

# used to display dataframe information without truncating
from IPython.display import display
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)

Connect to the Wallaroo Instance

The first step is to connect to Wallaroo through the Wallaroo client. The Python library is included in the Wallaroo install and available through the Jupyter Hub interface provided with your Wallaroo environment.

This is accomplished using the wallaroo.Client() command, which provides a URL to grant the SDK permission to your specific Wallaroo environment. When displayed, enter the URL into a browser and confirm permissions. Store the connection into a variable that can be referenced later.

If logging into the Wallaroo instance through the internal JupyterHub service, use wl = wallaroo.Client(). For more information on Wallaroo Client settings, see the Client Connection guide.

The option request_timeout provides additional time for the Wallaroo model upload process to complete.

wl = wallaroo.Client()

Create Retrieve WOrkspace and Pipeline

We retrieve the workspace and pipeline used to create the pipeline publish.

The workspace will then be set as the Current Workspace. Model uploads and pipeline creation through the SDK are set in the current workspace.

• References
  • Wallaroo SDK Essentials Guide: Workspace Management

workspace_name = "unet-detection-run-anywhere-demonstration"
model_name = "pt-unet"
model_file_name = './models/unet.pt'
pipeline_name = "pt-unet"

workspace = wl.get_workspace(name=workspace_name, create_if_not_exist=True)
wl.set_current_workspace(workspace)

pipeline = wl.get_pipeline(pipeline_name)

Retrieve Pipeline Publish

We retrieve the pipeline publish details via the pipeline.publishes() method. We assume that this pipeline only has the one publish created from the previous tutorial run, and display it’s details that include the Docker Run command we will use for the deployment step.

pipeline.publishes()

pipeline.publishes()[0]

docker run \
    -p $EDGE_PORT:8080 \
    -e OCI_USERNAME=$OCI_USERNAME \
    -e OCI_PASSWORD=$OCI_PASSWORD \
    -e CONFIG_CPUS=4 --cpus=4.0 --memory=3g \
    ghcr.io/wallaroolabs/doc-samples/engines/proxy/wallaroo/ghcr.io/wallaroolabs/fitzroy-mini:v2024.4.0-6077

helm install --atomic $HELM_INSTALL_NAME \
    oci://ghcr.io/wallaroolabs/doc-samples/charts/pt-unet \
    --namespace $HELM_INSTALL_NAMESPACE \
    --version 0.0.1-18a2e65e-d600-471f-80fe-6b60030f9dda \
    --set ociRegistry.username=$OCI_USERNAME \
    --set ociRegistry.password=$OCI_PASSWORD

pipeline.publishes()[0].pipeline_url

'ghcr.io/wallaroolabs/doc-samples/pipelines/pt-unet:18a2e65e-d600-471f-80fe-6b60030f9dda'

DevOps - Pipeline Edge Deployment

Once a pipeline is deployed to the Edge Registry service, it can be deployed in environments such as Docker, Kubernetes, or similar container running services by a DevOps engineer.

Docker Deployment

First, the DevOps engineer must authenticate to the same OCI Registry service used for the Wallaroo Edge Deployment registry.

For more details, check with the documentation on your artifact service. The following are provided for the three major cloud services:

• Set up authentication for Docker
• Authenticate with an Azure container registry
• Authenticating Amazon ECR Repositories for Docker CLI with Credential Helper

For the deployment, the engine URL is specified with the following environmental variables:

• DEBUG (true|false): Whether to include debug output.
• OCI_REGISTRY: The URL of the registry service.
• CONFIG_CPUS: The number of CPUs to use.
• OCI_USERNAME: The edge registry username.
• OCI_PASSWORD: The edge registry password or token.
• PIPELINE_URL: The published pipeline URL.

Docker Deployment Example

Using our sample environment, here’s sample deployment using Docker.

For docker run commands, the persistent volume for storing session data is stored with -v ./data:/persist. Updated as required for your deployments.

# create docker run 

docker_command = f'''
docker run \
    -p $EDGE_PORT:8080 \\
    -e OCI_USERNAME=$OCI_USERNAME \\
    -e OCI_PASSWORD=$OCI_PASSWORD \\
    -e CONFIG_CPUS=4 --cpus=4.0 --memory=3g \\
    -e PIPELINE_URL={pipeline.publishes()[0].pipeline_url} \\
    ghcr.io/wallaroolabs/doc-samples/engines/proxy/wallaroo/ghcr.io/wallaroolabs/fitzroy-mini:v2024.4.0-6077

'''

print(docker_command)

docker run     -p $EDGE_PORT:8080 \
    -e OCI_USERNAME=$OCI_USERNAME \
    -e OCI_PASSWORD=$OCI_PASSWORD \
    -e CONFIG_CPUS=4 --cpus=4.0 --memory=3g \
    -e PIPELINE_URL=ghcr.io/wallaroolabs/doc-samples/pipelines/pt-unet:18a2e65e-d600-471f-80fe-6b60030f9dda \
    ghcr.io/wallaroolabs/doc-samples/engines/proxy/wallaroo/ghcr.io/wallaroolabs/fitzroy-mini:v2024.4.0-6077

Edge Deployed Pipeline API Endpoints

Once deployed, we can check the pipelines and models available. We’ll use a curl command, but any HTTP based request will work the same way.

The endpoint /pipelines returns:

• id (String): The name of the pipeline.
• status (String): The status as either Running, or Error if there are any issues.

For this example, the deployment is made on a machine called testboy.local. Replace this URL with the URL of your edge deployment.

deploy_url = 'http://testboy.local:8081/pipelines'

response = requests.get(
                    deploy_url
                )

display(response.json())

{'pipelines': [{'id': 'pt-unet',
   'version': '18a2e65e-d600-471f-80fe-6b60030f9dda',
   'status': 'Running'}]}

Edge Inference Endpoint

The inference endpoint takes the following pattern:

• /infer

Wallaroo inference endpoint URLs accept the following data inputs through the Content-Type header:

• Content-Type: application/vnd.apache.arrow.file: For Apache Arrow tables.
• Content-Type: application/json; format=pandas-records: For pandas DataFrame in record format.

Once deployed, we can perform an inference through the deployment URL.

The endpoint returns Content-Type: application/json; format=pandas-records by default with the following fields:

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

• in: The original input data. Returns null if the input may be too long for a proper return.

• out (List): The outputs of the inference result separated by the model’s output fields.

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

• metadata (String): The metadata including the model name, etc.

For this example, we will use the same DataFrame with the image data and perform the same API inference request, this time through the edge device located at hostname testboy.local. Adjust the URL according to your edge deployment.

input_image = Image.open("./data/TCGA_CS_4944.png")
display(input_image)

# preprocess
m, s = np.mean(input_image, axis=(0, 1)), np.std(input_image, axis=(0, 1))
preprocess = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=m, std=s),
])
input_tensor = preprocess(input_image)
input_batch = input_tensor.unsqueeze(0)

nimage = input_batch.detach().numpy()
nimage.shape

(1, 3, 256, 256)

nimage = input_tensor.detach().numpy()

input_data = {
        "input": [nimage]
}
dataframe = pd.DataFrame(input_data)

headers = {
        'Content-Type': 'application/json; format=pandas-records'
    }
# 

deploy_url = 'http://testboy.local:8081/infer'

response = requests.post(
                    deploy_url, 
                    headers=headers, 
                    data=dataframe.to_json(orient="records")
                )

display(pd.DataFrame(response.json()).loc[0, 'out']['output'][0:5])

[1.475215e-05, 1.463294e-05, 1.3977289e-05, 1.3887882e-05, 1.4543533e-05]

Undeploy Edge Pipeline

To undeploy the edge pipeline, either use Control-C from the terminal. Or, use:

docker ps

CONTAINER ID   IMAGE                                                                                                      COMMAND                  CREATED         STATUS                 PORTS                                                                                                                                                                         NAMES
690f24d4d9b1   ghcr.io/wallaroolabs/doc-samples/engines/proxy/wallaroo/ghcr.io/wallaroolabs/fitzroy-mini:v2024.4.0-6077   "/usr/bin/tini -- /s…"   2 seconds ago   Up 1 second            0.0.0.0:8081->8080/tcp, :::8081->8080/tcp                                                                                                                                     nifty_spence

Find the Docker container running.  This example we can see the. `wallaroolabs` image running, and give it the kill command from the container id.  When the image is no longer available with `docker ps`, the edge deployment of Wallaroo Inference Server has stopped.

```bash
docker stop 690f24d4d9b1

docker ps
CONTAINER ID   IMAGE                  COMMAND                  CREATED        STATUS       PORTS                       NAMES