Wallaroo Edge Arbitrary Python Deployment Demonstration

A demonstration on publishing an Arbitrary Python model for edge deployment via the Wallaroo SDK.

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

Arbitrary Python Edge Deploy

This tutorial demonstrates how to use arbitrary python as a ML Model in Wallaroo and deploy the pipeline, then deploy that same pipeline to edge devices through an Open Container Initiative Registry, registered in the Wallaroo instance as the Edge Deployment Registry.

Tutorial Goals

  • In Wallaroo Ops:
    • Arbitrary Python Tutorial Deploy Model in Wallaroo Upload and Deploy: Deploys the KMeans model in an arbitrary Python package in Wallaroo Ops, and perform sample inferences. The file models/model-auto-conversion-BYOP-vgg16-clustering.zip is provided so users can go right to testing deployment.
    • Publish pipeline to Open Container Initiative Registry, registered in the Wallaroo Ops instance as the Edge Deployment Registry.: This will containerize the pipeline and copy the engine, python steps, model, and deployment configuration to the registry service.
  • In a remote aka edge device:
    • Deploy the Pipeline to an Edge Device: The pipeline will be deployed to an Edge device as a remote Wallaroo Inference Server through Docker.

Arbitrary Python Script Requirements

The entry point of the arbitrary python model is any python script that must include the following.

  • class ImageClustering(Inference): The default inference class. This is used to perform the actual inferences. Wallaroo uses the _predict method to receive the inference data and call the appropriate functions for the inference.
    • def __init__: Used to initialize this class and load in any other classes or other required settings.
    • def expected_model_types: Used by Wallaroo to anticipate what model types are used by the script.
    • def model(self, model): Defines the model used for the inference. Accepts the model instance used in the inference.
      • self._raise_error_if_model_is_wrong_type(model): Returns the error if the wrong model type is used. This verifies that only the anticipated model type is used for the inference.
      • self._model = model: Sets the submitted model as the model for this class, provided _raise_error_if_model_is_wrong_type is not raised.
    • def _predict(self, input_data: InferenceData): This is the entry point for Wallaroo to perform the inference. This will receive the inference data, then perform whatever steps and return a dictionary of numpy arrays.
  • class ImageClusteringBuilder(InferenceBuilder): Loads the model and prepares it for inferencing.
    • def inference(self) -> ImageClustering: Sets the inference class being used for the inferences.
    • def create(self, config: CustomInferenceConfig) -> ImageClustering: Creates an inference subclass, assigning the model and any attributes required for it to function.

All other methods used for the functioning of these classes are optional, as long as they meet the requirements listed above.

References

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.

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

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

import requests

Open a Connection to Wallaroo

The next step is 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(). If logging in externally, update the wallarooPrefix and wallarooSuffix variables with the proper DNS information. For more information on Wallaroo DNS settings, see the Wallaroo DNS Integration Guide.

wl = wallaroo.Client()

Set Variables

We’ll set the name of our workspace, pipeline, models and files. Workspace names must be unique across the Wallaroo workspace. For this, we’ll add in a randomly generated 4 characters to the workspace name to prevent collisions with other users’ workspaces. If running this tutorial, we recommend hard coding the workspace name so it will function in the same workspace each time it’s run.

workspace_name = f'vgg16-clustering-workspace'
pipeline_name = f'vgg16-clustering-pipeline'

model_name = 'vgg16-clustering'
model_file_name = './models/model-auto-conversion-BYOP-vgg16-clustering.zip'

Create Workspace and Pipeline

We will now create the Wallaroo workspace to store our model and set it as the current workspace. Future commands will default to this workspace for pipeline creation, model uploads, etc. We’ll create our Wallaroo pipeline that is used to deploy our arbitrary Python model.

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

pipeline = wl.build_pipeline(pipeline_name)

Upload Arbitrary Python Model

Arbitrary Python models are uploaded to Wallaroo through the Wallaroo Client upload_model method.

Upload Arbitrary Python Model Parameters

The following parameters are required for Arbitrary Python models. Note that while some fields are considered as optional for the upload_model method, they are required for proper uploading of a Arbitrary Python model to Wallaroo.

ParameterTypeDescription
namestring (Required)The name of the model. Model names are unique per workspace. Models that are uploaded with the same name are assigned as a new version of the model.
pathstring (Required)The path to the model file being uploaded.
frameworkstring (Upload Method Optional, Arbitrary Python model Required)Set as Framework.CUSTOM.
input_schemapyarrow.lib.Schema (Upload Method Optional, Arbitrary Python model Required)The input schema in Apache Arrow schema format.
output_schemapyarrow.lib.Schema (Upload Method Optional, Arbitrary Python model Required)The output schema in Apache Arrow schema format.
convert_waitbool (Upload Method Optional, Arbitrary Python model Optional) (Default: True)
  • True: Waits in the script for the model conversion completion.
  • False: Proceeds with the script without waiting for the model conversion process to display complete.

Once the upload process starts, the model is containerized by the Wallaroo instance. This process may take up to 10 minutes.

Upload Arbitrary Python Model Return

The following is returned with a successful model upload and conversion.

FieldTypeDescription
namestringThe name of the model.
versionstringThe model version as a unique UUID.
file_namestringThe file name of the model as stored in Wallaroo.
image_pathstringThe image used to deploy the model in the Wallaroo engine.
last_update_timeDateTimeWhen the model was last updated.

For our example, we’ll start with setting the input_schema and output_schema that is expected by our ImageClustering._predict() method.

input_schema = pa.schema([
    pa.field('images', pa.list_(
        pa.list_(
            pa.list_(
                pa.int64(),
                list_size=3
            ),
            list_size=32
        ),
        list_size=32
    )),
])

output_schema = pa.schema([
    pa.field('predictions', pa.int64()),
])

Upload Model

Now we’ll upload our model. The framework is Framework.CUSTOM for arbitrary Python models, and we’ll specify the input and output schemas for the upload.

model = wl.upload_model(model_name, 
                        model_file_name, 
                        framework=Framework.CUSTOM, 
                        input_schema=input_schema, 
                        output_schema=output_schema, 
                        convert_wait=True)
model
Waiting for model loading - this will take up to 10.0min.
Model is pending loading to a container runtime...
Model is attempting loading to a container runtime.....................successful

Ready
Namevgg16-clustering
Version220e1d9f-0f7f-4647-8aff-6968755b5f3c
File Namemodel-auto-conversion-BYOP-vgg16-clustering.zip
SHA7bb3362b1768c92ea7e593451b2b8913d3b7616c19fd8d25b73fb6990f9283e0
Statusready
Image Pathproxy.replicated.com/proxy/wallaroo/ghcr.io/wallaroolabs/mlflow-deploy:v2023.3.0-3798
Updated At2023-19-Sep 19:18:56

Deploy Pipeline

The model is uploaded and ready for use. We’ll add it as a step in our pipeline, then deploy the pipeline. For this example we’re allocated 0.25 cpu and 4 Gi RAM to the pipeline through the pipeline’s deployment configuration.

pipeline.add_model_step(model)
namevgg16-clustering-pipeline
created2023-09-08 14:52:44.325178+00:00
last_updated2023-09-08 14:56:09.249050+00:00
deployedFalse
tags
versions50d6586a-0661-4f26-802d-c71da2ceea2e, d94e44b3-7ff6-4138-8b76-be1795cb6690, 8d2a8143-2255-408a-bd09-e3008a5bde0b
stepsvgg16-clustering
publishedTrue
deployment_config = DeploymentConfigBuilder() \
    .cpus(1).memory('4Gi') \
    .build()

pipeline.deploy(deployment_config=deployment_config)
pipeline.status()
{'status': 'Running',
 'details': [],
 'engines': [{'ip': '10.244.3.202',
   'name': 'engine-76955445fb-bbff5',
   'status': 'Running',
   'reason': None,
   'details': [],
   'pipeline_statuses': {'pipelines': [{'id': 'vgg16-clustering-pipeline',
      'status': 'Running'}]},
   'model_statuses': {'models': [{'name': 'vgg16-clustering',
      'version': '220e1d9f-0f7f-4647-8aff-6968755b5f3c',
      'sha': '7bb3362b1768c92ea7e593451b2b8913d3b7616c19fd8d25b73fb6990f9283e0',
      'status': 'Running'}]}}],
 'engine_lbs': [{'ip': '10.244.4.236',
   'name': 'engine-lb-584f54c899-cfvmf',
   'status': 'Running',
   'reason': None,
   'details': []}],
 'sidekicks': [{'ip': '10.244.3.203',
   'name': 'engine-sidekick-vgg16-clustering-53-86b66d46d8-r9qq8',
   'status': 'Running',
   'reason': None,
   'details': [],
   'statuses': '\n'}]}

Run inference

Everything is in place - we’ll now run a sample inference with some toy data. In this case we’re randomly generating some values in the data shape the model expects, then submitting an inference request through our deployed pipeline.

deploy_url = pipeline._deployment._url()

headers = wl.auth.auth_header()

headers['Content-Type']='application/json; format=pandas-records'
headers['Accept']='application/json; format=pandas-records'

dataFile = './data/vgg16_test.df.json'
# inference through Pipeline Deploy URL

!curl -X POST {deploy_url} \
     -H "Authorization:{headers['Authorization']}" \
     -H "Content-Type:{headers['Content-Type']}" \
     -H "Accept:{headers['Accept']}" \
     --data @{dataFile}
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Undeploy Pipelines

The inference is successful, so we will undeploy the pipeline and return the resources back to the cluster.

pipeline.undeploy()
namevgg16-clustering-pipeline
created2023-09-08 14:52:44.325178+00:00
last_updated2023-09-19 19:19:03.122448+00:00
deployedFalse
tags
versions7f6d902c-c264-401f-879c-c4a59997de70, 50d6586a-0661-4f26-802d-c71da2ceea2e, d94e44b3-7ff6-4138-8b76-be1795cb6690, 8d2a8143-2255-408a-bd09-e3008a5bde0b
stepsvgg16-clustering
publishedTrue

Publish the Pipeline for Edge Deployment

It worked! For a demo, we’ll take working once as “tested”. So now that we’ve tested our pipeline, we are ready to publish it for edge deployment.

Publishing it means assembling all of the configuration files and model assets and pushing them to an Open Container Initiative (OCI) repository set in the Wallaroo instance as the Edge Registry service. DevOps engineers then retrieve that image and deploy it through Docker, Kubernetes, or similar deployments.

See Edge Deployment Registry Guide for details on adding an OCI Registry Service to Wallaroo as the Edge Deployment Registry.

This is done through the SDK command wallaroo.pipeline.publish(deployment_config) which has the following parameters and returns.

Publish a Pipeline Parameters

The publish method takes the following parameters. The containerized pipeline will be pushed to the Edge registry service with the model, pipeline configurations, and other artifacts needed to deploy the pipeline.

ParameterTypeDescription
deployment_configwallaroo.deployment_config.DeploymentConfig (Optional)Sets the pipeline deployment configuration. For example: For more information on pipeline deployment configuration, see the Wallaroo SDK Essentials Guide: Pipeline Deployment Configuration.

Publish a Pipeline Returns

FieldTypeDescription
idintegerNumerical Wallaroo id of the published pipeline.
pipeline version idintegerNumerical Wallaroo id of the pipeline version published.
statusstringThe status of the pipeline publication. Values include:
  • PendingPublish: The pipeline publication is about to be uploaded or is in the process of being uploaded.
  • Published: The pipeline is published and ready for use.
Engine URLstringThe URL of the published pipeline engine in the edge registry.
Pipeline URLstringThe URL of the published pipeline in the edge registry.
Helm Chart URLstringThe URL of the helm chart for the published pipeline in the edge registry.
Helm Chart ReferencestringThe help chart reference.
Helm Chart VersionstringThe version of the Helm Chart of the published pipeline. This is also used as the Docker tag.
Engine Configwallaroo.deployment_config.DeploymentConfigThe pipeline configuration included with the published pipeline.
Created AtDateTimeWhen the published pipeline was created.
Updated AtDateTimeWhen the published pipeline was updated.

Publish Example

We will now publish the pipeline to our Edge Deployment Registry with the pipeline.publish(deployment_config) command. deployment_config is an optional field that specifies the pipeline deployment. This can be overridden by the DevOps engineer during deployment.

pub = pipeline.publish(deployment_config)
pub
Waiting for pipeline publish... It may take up to 600 sec.
Pipeline is Publishing........Published.
ID1
Pipeline Version50d6586a-0661-4f26-802d-c71da2ceea2e
StatusPublished
Engine URLghcr.io/wallaroolabs/doc-samples/engines/proxy/wallaroo/ghcr.io/wallaroolabs/standalone-mini:v2023.3.0-3798
Pipeline URLghcr.io/wallaroolabs/doc-samples/pipelines/vgg16-clustering-pipeline:50d6586a-0661-4f26-802d-c71da2ceea2e
Helm Chart URLghcr.io/wallaroolabs/doc-samples/charts/vgg16-clustering-pipeline
Helm Chart Referenceghcr.io/wallaroolabs/doc-samples/charts@sha256:95d3e467608bb389d00491cef157442ba757cb33d74f73ebffef6479241b1156
Helm Chart Version0.0.1-50d6586a-0661-4f26-802d-c71da2ceea2e
Engine Config{'engine': {'resources': {'limits': {'cpu': 1.0, 'memory': '512Mi'}, 'requests': {'cpu': 1.0, 'memory': '512Mi'}}}, 'engineAux': {'images': {}}, 'enginelb': {'resources': {'limits': {'cpu': 1.0, 'memory': '512Mi'}, 'requests': {'cpu': 1.0, 'memory': '512Mi'}}}}
User Images[]
Created Byjohn.hummel@wallaroo.ai
Created At2023-09-08 14:56:09.365449+00:00
Updated At2023-09-08 14:56:09.365449+00:00

List Published Pipeline

The method wallaroo.client.list_pipelines() shows a list of all pipelines in the Wallaroo instance, and includes the published field that indicates whether the pipeline was published to the registry (True), or has not yet been published (False).

wl.list_pipelines()
namecreatedlast_updateddeployedtagsversionsstepspublished
vgg16-clustering-pipeline2023-08-Sep 14:52:442023-08-Sep 14:56:09False50d6586a-0661-4f26-802d-c71da2ceea2e, d94e44b3-7ff6-4138-8b76-be1795cb6690, 8d2a8143-2255-408a-bd09-e3008a5bde0bvgg16-clusteringTrue

List Publishes from a Pipeline

All publishes created from a pipeline are displayed with the wallaroo.pipeline.publishes method. The pipeline_version_id is used to know what version of the pipeline was used in that specific publish. This allows for pipelines to be updated over time, and newer versions to be sent and tracked to the Edge Deployment Registry service.

List Publishes Parameters

N/A

List Publishes Returns

A List of the following fields:

FieldTypeDescription
idintegerNumerical Wallaroo id of the published pipeline.
pipeline_version_idintegerNumerical Wallaroo id of the pipeline version published.
engine_urlstringThe URL of the published pipeline engine in the edge registry.
pipeline_urlstringThe URL of the published pipeline in the edge registry.
created_bystringThe email address of the user that published the pipeline.
Created AtDateTimeWhen the published pipeline was created.
Updated AtDateTimeWhen the published pipeline was updated.
pipeline.publishes()
idpipeline_version_nameengine_urlpipeline_urlcreated_bycreated_atupdated_at
150d6586a-0661-4f26-802d-c71da2ceea2eghcr.io/wallaroolabs/doc-samples/engines/proxy/wallaroo/ghcr.io/wallaroolabs/standalone-mini:v2023.3.0-3798ghcr.io/wallaroolabs/doc-samples/pipelines/vgg16-clustering-pipeline:50d6586a-0661-4f26-802d-c71da2ceea2ejohn.hummel@wallaroo.ai2023-08-Sep 14:56:092023-08-Sep 14:56:09

DevOps Deployment

We now have our pipeline published to our Edge Registry service. We can deploy this in a x86 environment running Docker that is logged into the same registry service that we deployed to.

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

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:

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 with a computer vision ML model, the same used in the Wallaroo Use Case Tutorials Computer Vision: Retail tutorials.

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

docker run -p 8080:8080 \
    -v ./data:/persist \
    -e DEBUG=true -e OCI_REGISTRY={your registry server} \
    -e CONFIG_CPUS=4 \
    -e OCI_USERNAME=oauth2accesstoken \
    -e OCI_PASSWORD={registry token here} \
    -e PIPELINE_URL={your registry server}/pipelines/edge-cv-retail:bf70eaf7-8c11-4b46-b751-916a43b1a555 \
    {your registry server}/engine:v2023.3.0-main-3707

Docker Compose Deployment

For users who prefer to use docker compose, the following sample compose.yaml file is used to launch the Wallaroo Edge pipeline. This is the same used in the Wallaroo Use Case Tutorials Computer Vision: Retail tutorials. The session and other data is stored with the volumes entry to add a persistent volume.

services:
  engine:
    image: {Your Engine URL}
    ports:
      - 8080:8080
    volumes:
      - ./data:/persist
    environment:
      PIPELINE_URL: {Your Pipeline URL}
      OCI_REGISTRY: {Your Edge Registry URL}
      OCI_USERNAME:  {Your Registry Username}
      OCI_PASSWORD: {Your Token or Password}
      CONFIG_CPUS: 1

For example:

services:
  engine:
    image: sample-registry.com/engine:v2023.3.0-main-3707
    ports:
      - 8080:8080
    environment:
      PIPELINE_URL: sample-registry.com/pipelines/edge-cv-retail:bf70eaf7-8c11-4b46-b751-916a43b1a555
      OCI_REGISTRY: sample-registry.com
      OCI_USERNAME:  _json_key_base64
      OCI_PASSWORD: abc123
      CONFIG_CPUS: 1

Docker Compose Deployment Example

The deployment and undeployment is then just a simple docker compose up and docker compose down. The following shows an example of deploying the Wallaroo edge pipeline using docker compose.

docker compose up
[+] Running 1/1
 ✔ Container cv_data-engine-1  Recreated                                                                                                                                                                 0.5s
Attaching to cv_data-engine-1
cv_data-engine-1  | Wallaroo Engine - Standalone mode
cv_data-engine-1  | Login Succeeded
cv_data-engine-1  | Fetching manifest and config for pipeline: sample-registry.com/pipelines/edge-cv-retail:bf70eaf7-8c11-4b46-b751-916a43b1a555
cv_data-engine-1  | Fetching model layers
cv_data-engine-1  | digest: sha256:c6c8869645962e7711132a7e17aced2ac0f60dcdc2c7faa79b2de73847a87984
cv_data-engine-1  |   filename: c6c8869645962e7711132a7e17aced2ac0f60dcdc2c7faa79b2de73847a87984
cv_data-engine-1  |   name: resnet-50
cv_data-engine-1  |   type: model
cv_data-engine-1  |   runtime: onnx
cv_data-engine-1  |   version: 693e19b5-0dc7-4afb-9922-e3f7feefe66d
cv_data-engine-1  |
cv_data-engine-1  | Fetched
cv_data-engine-1  | Starting engine
cv_data-engine-1  | Looking for preexisting `yaml` files in //modelconfigs
cv_data-engine-1  | Looking for preexisting `yaml` files in //pipelines

Helm Deployment

Published pipelines can be deployed through the use of helm charts.

Helm deployments take up to two steps - the first step is in retrieving the required values.yaml and making updates to override.

  1. Pull the helm charts from the published pipeline. The two fields are the Helm Chart URL and the Helm Chart version to specify the OCI . This typically takes the format of:
helm pull oci://{published.helm_chart_url} --version {published.helm_chart_version}
  1. Extract the tgz file and copy the values.yaml and copy the values used to edit engine allocations, etc. The following are required for the deployment to run:
ociRegistry:
  registry: {your registry service}
  username:  {registry username here}
  password: {registry token here}

Store this into another file, suc as local-values.yaml.

  1. Create the namespace to deploy the pipeline to. For example, the namespace wallaroo-edge-pipeline would be:
kubectl create -n wallaroo-edge-pipeline
  1. Deploy the helm installation with helm install through one of the following options:

    1. Specify the tgz file that was downloaded and the local values file. For example:

      helm install --namespace {namespace} --values {local values file} {helm install name} {tgz path}
      
    2. Specify the expended directory from the downloaded tgz file.

      helm install --namespace {namespace} --values {local values file} {helm install name} {helm directory path}
      
    3. Specify the Helm Pipeline Helm Chart and the Pipeline Helm Version.

      helm install --namespace {namespace} --values {local values file} {helm install name} oci://{published.helm_chart_url} --version {published.helm_chart_version}
      
  2. Once deployed, the DevOps engineer will have to forward the appropriate ports to the svc/engine-svc service in the specific pipeline. For example, using kubectl port-forward to the namespace ccfraud that would be:

    kubectl port-forward svc/engine-svc -n ccfraud01 8080 --address 0.0.0.0`
    

The following code segment generates a docker compose template based on the previously published pipeline.

docker_compose = f'''
services:
  engine:
    image: {pub.engine_url}
    ports:
      - 8080:8080
    volumes:
      - ./data:/persist
    environment:
      PIPELINE_URL: {pub.pipeline_url}
      OCI_USERNAME: YOUR USERNAME 
      OCI_PASSWORD: YOUR PASSWORD OR TOKEN
      OCI_REGISTRY: {YOUR OCI EDGE REGISTRY}
      CONFIG_CPUS: 1
'''

print(docker_compose)
services:
  engine:
    image: ghcr.io/wallaroolabs/doc-samples/engines/proxy/wallaroo/ghcr.io/wallaroolabs/standalone-mini:v2023.3.0-3798
    ports:
      - 8080:8080
    environment:
      PIPELINE_URL: ghcr.io/wallaroolabs/doc-samples/pipelines/vgg16-clustering-pipeline:50d6586a-0661-4f26-802d-c71da2ceea2e
      OCI_USERNAME: YOUR USERNAME 
      OCI_PASSWORD: YOUR PASSWORD OR TOKEN
      OCI_REGISTRY: ghcr.io
      CONFIG_CPUS: 4

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 HOSTNAME. Replace this URL with the URL of you edge deployment.

!curl HOSTNAME:8080/pipelines
{"pipelines":[{"id":"vgg16-clustering-pipeline","status":"Running"}]}

The endpoint /models returns a List of models with the following fields:

  • name (String): The model name.
  • sha (String): The sha hash value of the ML model.
  • status (String): The status of either Running or Error if there are any issues.
  • version (String): The model version. This matches the version designation used by Wallaroo to track model versions in UUID format.
!curl HOSTNAME:8080/models
{"models":[{"name":"vgg16-clustering","sha":"7bb3362b1768c92ea7e593451b2b8913d3b7616c19fd8d25b73fb6990f9283e0","status":"Running","version":"15f93d20-495d-4e73-bd76-b4ab614e796a"}]}

Edge Inference Endpoint

The inference endpoint takes the following pattern:

  • /pipelines/{pipeline-name}: The pipeline-name is the same as returned from the /pipelines endpoint as id.

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:

  • check_failures (List[Integer]): Whether any validation checks were triggered. For more information, see Wallaroo SDK Essentials Guide: Pipeline Management: Anomaly Testing.
  • elapsed (List[Integer]): A list of time in nanoseconds for:
    • [0] The time to serialize the input.
    • [1…n] How long each step took.
  • model_name (String): The name of the model used.
  • model_version (String): The version of the model in UUID format.
  • original_data: The original input data. Returns null if the input may be too long for a proper return.
  • outputs (List): The outputs of the inference result separated by data type, where each data type includes:
    • data: The returned values.
    • dim (List[Integer]): The dimension shape returned.
    • v (Integer): The vector shape of the data.
  • pipeline_name (String): The name of the pipeline.
  • shadow_data: Any shadow deployed data inferences in the same format as outputs.
  • time (Integer): The time since UNIX epoch.
numpy = [np.random.randint(0, 256, (32, 32, 3), dtype=np.uint8)] * 2

input_data = {
        "images": numpy,
}
dataframe = pd.DataFrame(input_data)
dataframe

# set the content type and accept headers
headers = {
    'Content-Type': 'application/json; format=pandas-records'
}

data = dataframe.to_json(orient="records")

host = 'http://HOSTNAME:8080'

deployurl = f'{host}/pipelines/vgg16-clustering-pipeline'

response = requests.post(
                deployurl, 
                headers=headers, 
                data=data, 
                verify=True
            )

display(pd.DataFrame(response.json()))
check_failureselapsedmodel_namemodel_versionoriginal_dataoutputspipeline_nameshadow_datatime
0[][3814506, 41446817]vgg16-clustering15f93d20-495d-4e73-bd76-b4ab614e796aNone[{'Int64': {'data': [1, 1], 'dim': [2, 1], 'v'...vgg16-clustering-pipeline{}1694187135169