Forecast Retail Deployment in Wallaroo

This tutorial demonstrates how to:

• Deploy a Forecast Python trained model into a Wallaroo Ops server and perform inferences on it.
• Publish the pipeline to the OCI registry configured in the Wallaroo Ops server.
• Add an edge location to the Wallaroo pipeline publish.
• Deploy the pipeline as a Wallaroo Server on an edge device through Docker, and display the inference logs submitted to the Wallaroo Ops server.

Wallaroo Ops Center provides the ability to publish Wallaroo pipelines to an Open Continer Initative (OCI) compliant registry, then deploy those pipelines on edge devices as Docker container or Kubernetes pods. See Wallaroo SDK Essentials Guide: Pipeline Edge Publication for full details.

This demonstration will focus on deployment to the edge.

References

• Wallaroo Workspaces: Workspaces are environments were users upload models, create pipelines and other artifacts. The workspace should be considered the fundamental area where work is done. Workspaces are shared with other users to give them access to the same models, pipelines, etc.
• Wallaroo Model Upload and Registration: ML Models are uploaded to Wallaroo through the SDK or the MLOps API to a workspace. ML models include default runtimes (ONNX, Python Step, and TensorFlow) that are run directly through the Wallaroo engine, and containerized runtimes (Hugging Face, PyTorch, etc) that are run through in a container through the Wallaroo engine.
• Wallaroo Pipelines: Pipelines are used to deploy models for inferencing. Each model is a pipeline step in a pipelines, where the inputs of the previous step are fed into the next. Pipeline steps can be ML models, Python scripts, or Custom Model (these contain necessary models and artifacts for running a model).
• Wallaroo SDK Essentials Guide: Pipeline Edge Publication: Details on publishing a Wallaroo pipeline to an OCI Registry and deploying it as a Wallaroo Server instance.

Data Scientist Steps

The following details the steps a Data Scientist performs in uploading and verifying the model in a Wallaroo Ops server.

Load Libraries

The first step is loading the required libraries including the Wallaroo Python module.

# Import Wallaroo Python SDK
import wallaroo
from wallaroo.object import EntityNotFoundError
from wallaroo.framework import Framework

# used to display DataFrame information without truncating
from IPython.display import display
import pandas as pd
pd.set_option('display.max_colwidth', None)

import pyarrow as pa

Connect to the Wallaroo Instance through the User Interface

The next 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.

Connect to the Wallaroo Instance Exercise

Connect to the Wallaroo instance. If connecting through the JupyterHub service, then only the wallaroo.Client() is required. If connecting externally through the Wallaroo SDK, use the wallaroo.client(api_endpoint, auth_endpoint) method.

Sample code:

wl = wallaroo.Client()

# connect to Wallaroo here

wl = wallaroo.Client()

Create a New Workspace

We’ll use the SDK below to create our workspace , assign as our current workspace, then display all of the workspaces we have at the moment. We’ll also set up variables for our models and pipelines down the road, so we have one spot to change names to whatever fits your organization’s standards best.

To allow this tutorial to be run by multiple users in the same Wallaroo instance, update suffix with your first and last name. For example:

suffix = 'lazel-geth'

Create a New Workspace Exercise

Set the model name, file name, pipeline name, and workspace name.

Sample code:

suffix = ''

model_name = 'retail-forecast'
model_filename = './models/forecast/forecast_standard.py'
pipeline_name = 'retail-forecast'
workspace_name = f'retail-forecast-edge-demo{suffix}'

# set variables

suffix = ''

model_name = 'retail-forecast'
model_filename = './models/forecast/forecast_standard.py'
pipeline_name = 'retail-forecast'
workspace_name = f'retail-forecast-edge-demo{suffix}'

Set the Current Workspace

Set the current workspace where the models are uploaded to and pipelines created.

• References
  • Wallaroo SDK Essentials Guide: Workspace Management

Set the Current Workspace Exercise

Setting the workspace is performed with the wallaroo.client.set_current_workspace(workspace) method.

Sample code:

workspace = get_workspace(workspace_name, client)
wl.set_current_workspace(workspace)

def get_workspace(name):
    workspace = None
    for ws in wl.list_workspaces():
        if ws.name() == name:
            workspace= ws
    if(workspace == None):
        workspace = wl.create_workspace(name)
    return workspace

workspace = get_workspace(workspace_name)
wl.set_current_workspace(workspace)

{'name': 'retail-forecast-edge-demo', 'id': 9, 'archived': False, 'created_by': 'a6e82da8-817d-4cca-bb62-5dbacd38ca22', 'created_at': '2023-12-05T23:12:54.354351+00:00', 'models': [{'name': 'forecast-control-model', 'versions': 1, 'owner_id': '""', 'last_update_time': datetime.datetime(2023, 12, 5, 23, 12, 57, 994250, tzinfo=tzutc()), 'created_at': datetime.datetime(2023, 12, 5, 23, 12, 57, 994250, tzinfo=tzutc())}], 'pipelines': [{'name': 'retail-forecast', 'create_time': datetime.datetime(2023, 12, 5, 23, 13, 1, 779624, tzinfo=tzutc()), 'definition': '[]'}]}

Upload the Model

When a model is uploaded to a Wallaroo cluster, it is optimized and packaged to make it ready to run as part of a pipeline. In many times, the Wallaroo Server can natively run a model without any Python overhead. In other cases, such as a Python script, a custom Python environment will be automatically generated. This is comparable to the process of “containerizing” a model by adding a small HTTP server and other wrapping around it.

Our pretrained model is a Python script, which is specified in the framework parameter. To properly receive and return inference results, we specify the input and output schemas in Apache Arrow format.

• References
  • Wallaroo SDK Essentials Guide: Model Uploads and Registrations: Python Models

Upload the Model Exercise

The model name and file name were set in the variables above. Use them to upload the model.

Sample code:

# set the input and output schemas

input_schema = pa.schema([
    pa.field('count', pa.list_(pa.int64()))
])

output_schema = pa.schema([
    pa.field('forecast', pa.list_(pa.int64())),
    pa.field('weekly_average', pa.list_(pa.float64()))
])

# upload the models

model_version = wl.upload_model('forecast-control-model', 
                './models/forecast/forecast_standard.py', 
                framework=Framework.PYTHON).configure(
                "python", 
                input_schema=input_schema, 
                output_schema=output_schema
                )

# Upload forecasting model

# set the input and output schemas

input_schema = pa.schema([
    pa.field('count', pa.list_(pa.int64()))
])

output_schema = pa.schema([
    pa.field('forecast', pa.list_(pa.int64())),
    pa.field('weekly_average', pa.list_(pa.float64()))
])

# upload the models

model_version = wl.upload_model('forecast-control-model', 
                './models/forecast/forecast_standard.py', 
                framework=Framework.PYTHON).configure(
                "python", 
                input_schema=input_schema, 
                output_schema=output_schema
                )

Pipeline Deployment Configuration

For our pipeline we set the deployment configuration to set the resources the pipeline will be allocated from the Kubernetes cluster hosting the Wallaroo Ops instance. The Hugging Face model is deployed as a Containerized Runtime in Wallaroo, so the configuration specified the sidekick cpu and memory options.

• References
  • Wallaroo SDK Essentials Guide: Pipeline Deployment Configuration

Pipeline Deployment Configuration Exercise

Use the deployment configuration below.

deploy_config = wallaroo.DeploymentConfigBuilder().replica_count(1).cpus(0.5).memory("1Gi").build()

Build and Deploy the Pipeline

Now we build our pipeline and set our Yolo8 model as a pipeline step, then deploy the pipeline using the deployment configuration above.

• References
  • Wallaroo SDK Essentials Guide: Pipeline Management

Build and Deploy the Pipeline

We’ll do both commands in one step:

• Build the pipeline with wallaroo.client.build_pipeline.
• Set the model as a pipeline step with wallaroo.pipeline.add_model_step(model) method.

Sample code:

pipeline = wl.build_pipeline(pipeline_name) \
            .add_model_step(model_version)        

# build pipeline and set pipeline step

pipeline = wl.build_pipeline(pipeline_name) \
            .add_model_step(model_version)        

Deploy the Pipeline

We deploy the pipeline with the wallaroo.pipeline.deploy(deployment_config) command, using the deployment configuration set up in previous steps.

• References
  • Wallaroo SDK Essentials Guide: Pipeline Management

Deploy the Pipeline Exercise

Deploy the pipeline.

Sample code:

pipeline.deploy(deployment_config=deployment_config)

pipeline.deploy(deployment_config=deploy_config)

Inference Request

We submit the DataFrame to the pipeline using wallaroo.pipeline.infer_from_file and display the results. We’ll use both the Wallaroo SDK and the MLOps API.

Inference Request Exercise

Perform an inference request. We’ll generate our sample dataframe, then use it for the inference.

Sample Code:

single_result = pipeline.infer_from_file('./data/forecast/testdata-standard.df.json')
display(single_result)

We’ll then do the same through the Pipeline Inference URL through an API call.

Sample Code:

!curl {deploy_url} \
    -H "Content-Type: application/json; format=pandas-records" \
    -H "Authorization: {wl.auth.auth_header()['Authorization']}" \
    -H "Accept:{headers['Accept']}" \
     --data @./data/forecast/testdata-standard.df.json

single_result = pipeline.infer_from_file('./data/forecast/testdata-standard.df.json')
display(single_result)

# API inference here

!curl {deploy_url} \
    -H "Content-Type: application/json; format=pandas-records" \
    -H "Authorization: {wl.auth.auth_header()['Authorization']}" \
    -H "Accept:{headers['Accept']}" \
     --data @./data/forecast/testdata-standard.df.json

[{"time":1701818005459,"in":{"count":[1526,1550,1708,1005,1623,1712,1530,1605,1538,1746,1472,1589,1913,1815,2115,2475,2927,1635,1812,1107,1450,1917,1807,1461,1969,2402,1446,1851]},"out":{"forecast":[1764,1749,1743,1741,1740,1740,1740],"weekly_average":[1745.2857142857142]},"check_failures":[],"metadata":{"last_model":"{\"model_name\":\"forecast-control-model\",\"model_sha\":\"3cd2acdd1f513f46615be7aa5beac16f09903be851e91f20f6dcdead4a48faa0\"}","pipeline_version":"","elapsed":[52701,33466756],"dropped":[],"partition":"engine-6464f7f889-tzwvp"}}]

Undeploy the Pipeline

With the testing complete, we undeploy the pipeline and return the resources back to the cluster.

# undeploy the pipeline

pipeline.undeploy()

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.

Publish a Pipeline Returns

Publish Exercise

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.

Save the publish to a variable for later use.

Sample code:

pub = pipeline.publish(deployment_config)
pub

# create publish here

pub = pipeline.publish(deploy_config)
pub

Waiting for pipeline publish... It may take up to 600 sec.
Pipeline is Publishing...Published.

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).

List Published Pipeline Exercise

List the pipelines and verify which ones are published or not.

Sample code:

wl.list_pipelines()

# list pipelines

wl.list_pipelines()

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:

List Publishes from a Pipeline Exercise

List the publishes from a pipeline.

Sample code:

pipeline.publishes()

pipeline.publishes()

Add Edge Location

With the pipeline publish created, we can add an Edge Location. This allows the edge deployment to upload its inference results back to the Wallaroo Ops location, which are then added to the pipeline the publish originated from. These are added to the pipeline logs partition metadata.

First we’ll retrieve the pipeline logs for our current pipeline, and show the current pipeline logs metadata.

Add Edge Location Exercise

Display the log information with the metadata.partition, then add the edge location to the publish. Note that edge names must be unique, so add your first and last name to the list.

Sample code:

logs = pipeline.logs(dataset=['time', 'out.output0', 'metadata'])
display(logs.loc[:, ['time', 'metadata.partition']])

first_last_name = '-Gale-Karlach'

edge_name = f'edge-forecast-retail-demo{first_last_name}'

edge_publish = pub.add_edge(edge_name)
display(edge_publish)

# get the log metadata

logs = pipeline.logs(dataset=['time', 'out.weekly_average', 'metadata'])
display(logs.loc[:, ['time', 'out.weekly_average', 'metadata.partition']])

Now we’ll add the edge location.

For the edge name, set it to firstname-lastname-edge-llm-summarization.

pub = pipeline.publishes()[0]
pub

# create the location

edge_name = 'edge-forecast-retail-demo'

edge_publish = pub.add_edge(edge_name)
display(edge_publish)

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

Note the use of the -v ./data:/persist option. This will store the one time authentication token stored in the EDGE_BUNDLE

mkdir ./data

docker run -p 8080:8080 \
    -v ./data:/persist \
    -e DEBUG=true -e OCI_REGISTRY={your registry server} \
    -e EDGE_BUNDLE={edge_publish.docker_run_variables['EDGE_BUNDLE']} \
    -e CONFIG_CPUS=4 \
    -e OCI_USERNAME=oauth2accesstoken \
    -e OCI_PASSWORD={registry token here} \
    -e PIPELINE_URL={your registry server}/pipelines/yolo8demonstration: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 volumes settings allows for persistent volumes to store the session information. Without it, the one-time authentication token included in the EDGE_BUNDLE settings would have to be regenerated.

services:
  engine:
    image: {Your Engine URL}
    volumes:
      - ./data:/persist
    ports:
      - 8080:8080
    environment:
      EDGE_BUNDLE: abcdefg
      PIPELINE_URL: {Your Pipeline URL}
      OCI_REGISTRY: {Your Edge Registry URL}
      OCI_USERNAME:  {Your Registry Username}
      OCI_PASSWORD: {Your Token or Password}
      CONFIG_CPUS: 4

For example:

services:
  engine:
    image: sample-registry.com/engine:v2023.3.0-main-3707
    ports:
      - 8080:8080
    environment:
      PIPELINE_URL: sample-registry.com/pipelines/yolo8demonstration:bf70eaf7-8c11-4b46-b751-916a43b1a555
      OCI_REGISTRY: sample-registry.com
      OCI_USERNAME:  _json_key_base64
      OCI_PASSWORD: abc123
      CONFIG_CPUS: 4

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 yolo8demonstration-engine-1  Recreated                                                                                                                                                                 0.5s
Attaching to yolo8demonstration-engine-1
yolo8demonstration-engine-1  | Wallaroo Engine - Standalone mode
yolo8demonstration-engine-1  | Login Succeeded
yolo8demonstration-engine-1  | Fetching manifest and config for pipeline: sample-registry.com/pipelines/yolo8demonstration:bf70eaf7-8c11-4b46-b751-916a43b1a555
yolo8demonstration-engine-1  | Fetching model layers
yolo8demonstration-engine-1  | digest: sha256:c6c8869645962e7711132a7e17aced2ac0f60dcdc2c7faa79b2de73847a87984
yolo8demonstration-engine-1  |   filename: c6c8869645962e7711132a7e17aced2ac0f60dcdc2c7faa79b2de73847a87984
yolo8demonstration-engine-1  |   name: yolov8n
yolo8demonstration-engine-1  |   type: model
yolo8demonstration-engine-1  |   runtime: onnx
yolo8demonstration-engine-1  |   version: 693e19b5-0dc7-4afb-9922-e3f7feefe66d
yolo8demonstration-engine-1  |
yolo8demonstration-engine-1  | Fetched
yolo8demonstration-engine-1  | Starting engine
yolo8demonstration-engine-1  | Looking for preexisting `yaml` files in //modelconfigs
yolo8demonstration-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.

Kubernetes provides persistent volume support, so no settings are required.

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`
   

Docker Deployment Code Generation Exercise

The following code segment generates a docker run template based on the previously published pipeline. Replace the $REGISTRYURL, $REGISTRYUSERNAME, and $REGISTRYPASSWORD to match the OCI Registry being used.

docker_deploy = f'''
mkdir data
docker run -p 8080:8080 \\
    -v ./data:/persist \\
    -e DEBUG=true \\
    -e OCI_REGISTRY=$REGISTRYURL \\
    -e EDGE_BUNDLE={edge_publish.docker_run_variables['EDGE_BUNDLE']} \\
    -e CONFIG_CPUS=1 \\
    -e OCI_USERNAME=$REGISTRYUSERNAME \\
    -e OCI_PASSWORD=$REGISTRYPASSWORD \\
    -e PIPELINE_URL={edge_publish.pipeline_url} \\
    {edge_publish.engine_url}
'''

print(docker_deploy)

mkdir data
docker run -p 8080:8080 \
    -v ./data:/persist \
    -e DEBUG=true \
    -e OCI_REGISTRY=$REGISTRYURL \
    -e EDGE_BUNDLE=ZXhwb3J0IEJVTkRMRV9WRVJTSU9OPTEKZXhwb3J0IEVER0VfTkFNRT1lZGdlLWZvcmVjYXN0LXJldGFpbC1kZW1vCmV4cG9ydCBKT0lOX1RPS0VOPTFjNTVjZWJiLTMxNzUtNDk1MC04NDBmLTc5NjIxMzJmYjM5MgpleHBvcnQgT1BTQ0VOVEVSX0hPU1Q9ZG9jLXRlc3QuZWRnZS53YWxsYXJvb2NvbW11bml0eS5uaW5qYQpleHBvcnQgUElQRUxJTkVfVVJMPWdoY3IuaW8vd2FsbGFyb29sYWJzL2RvYy1zYW1wbGVzL3BpcGVsaW5lcy9yZXRhaWwtZm9yZWNhc3Q6NGRmYzgzMzctYTRmMi00MmNlLWI1Y2EtYzQwMWYyOWRkZGViCmV4cG9ydCBXT1JLU1BBQ0VfSUQ9OQ== \
    -e CONFIG_CPUS=1 \
    -e OCI_USERNAME=$REGISTRYUSERNAME \
    -e OCI_PASSWORD=$REGISTRYPASSWORD \
    -e PIPELINE_URL=ghcr.io/wallaroolabs/doc-samples/pipelines/retail-forecast:4dfc8337-a4f2-42ce-b5ca-c401f29dddeb \
    ghcr.io/wallaroolabs/doc-samples/engines/proxy/wallaroo/ghcr.io/wallaroolabs/standalone-mini:v2023.4.0-4103

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.

curl localhost:8080/pipelines
{"pipelines":[{"id":"yolo8demonstration","status":"Running"}]}

The following example uses the host localhost. Replace with your own host name of your Edge deployed pipeline.

!curl localhost:8080/pipelines

{"pipelines":[{"id":"retail-forecast","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.

{"models":[{"name":"yolov8n","sha":"3ed5cd199e0e6e419bd3d474cf74f2e378aacbf586e40f24d1f8c89c2c476a08","status":"Running","version":"7af40d06-d18f-4b3f-9dd3-0a15248f01c8"}]}

The following example uses the host localhost. Replace with your own host name of your Edge deployed pipeline.

!curl localhost:8080/models

{"models":[{"name":"forecast-control-model","version":"3baf8cf9-f638-4b94-b3cb-163a82da959e","sha":"3cd2acdd1f513f46615be7aa5beac16f09903be851e91f20f6dcdead4a48faa0","status":"Running"}]}

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.

Once deployed, we can perform an inference through the deployment URL. We’ll assume we’re running the inference request through the localhost and submitting the local file ./data/dogbike.df.json. Note that our inference endpoint is pipelines/yolo8demonstration - the same as our pipeline name.

The following example demonstrates sending an inference request to the edge deployed pipeline and storing the results in a pandas DataFrame in record format. The results can then be exported to other processes to render the detected images or other use cases.

!curl HOSTNAME:8080/pipelines/retail-forecast \
    -H "Content-Type: application/json; format=pandas-records" \
    --data @./data/forecast/testdata-standard.df.json

[{"time":1701962296374,"in":{"count":[1526,1550,1708,1005,1623,1712,1530,1605,1538,1746,1472,1589,1913,1815,2115,2475,2927,1635,1812,1107,1450,1917,1807,1461,1969,2402,1446,1851]},"out":{"forecast":[1764,1749,1743,1741,1740,1740,1740],"weekly_average":[1745.2857142857142]},"check_failures":[],"metadata":{"last_model":"{\"model_name\":\"forecast-control-model\",\"model_sha\":\"3cd2acdd1f513f46615be7aa5beac16f09903be851e91f20f6dcdead4a48faa0\"}","pipeline_version":"","elapsed":[251572,1052979425],"dropped":[],"partition":"edge-forecast-retail-demo"}}]

Display Partition Logs

To view the edge deployed pipeline logs, we can use wallaroo.pipeline.export_logs method to retrieve all of the recent logs from this pipeline, and show the edge inference results were sent with the edge name in the partition metadata.

Sample code:

# display log information here with partition

pipeline.export_logs(directory='./logs/partition-edge-observability-forecasting',
                     file_prefix='edge-logs',
                     dataset=['time', 'metadata'])

# display the partition only results

df_logs = pd.read_json('./logs/partition-edge-observability-forecasting/edge-logs-1.json', 
                       orient="records", 
                       lines=True)

# display just the entries with out edge location
display(df_logs[df_logs['metadata.partition']==edge_name].loc[:, ['time', 'metadata.partition']])

# display log information here with partition

pipeline.export_logs(directory='./logs/partition-edge-observability-forecasting',
                     file_prefix='edge-logs',
                     dataset=['time', 'metadata'])

# display the partition only results

df_logs = pd.read_json('./logs/partition-edge-observability-forecasting/edge-logs-1.json', 
                       orient="records", 
                       lines=True)

# display just the entries with out edge location
display(df_logs[df_logs['metadata.partition']==edge_name].loc[:, ['time', 'metadata.partition']])