Wallaroo Pipeline Edge Publication Management

How to manage pipeline publications

Wallaroo Pipelines are deployed to edge devices by publishing them to Open Container Initiative (OCI) registries. This is managed through the Wallaroo MLOps API, the Wallaroo SDK, and through the Wallaroo Dashboard.

The following describes how to use the Wallaroo Dashboard to:

  • View published pipeline information
  • Publish a pipeline to an OCI Registry
  • Deploy a pipeline to an edge device from an OCI Registry

Wallaroo Dashboard Pipeline Publish Management

Wallaroo pipeline publications are managed through the Wallaroo Dashboard Pipeline pages. This requires that Edge Deployment Registry is enabled.

Wallaroo pipelines are published as containers to OCI registries, and are referred to as publishes.

Access Wallaroo Pipeline Publishes

To view the publishes for a specific pipeline through the Wallaroo Dashboard:

  1. Login to the Wallaroo Dashboard through your browser.
  2. From the Workspace select menu on the upper left, select the workspace the pipeline is associated in.
  3. Select the pipeline to view the Pipeline Versions, which contain the Pipeline Publishes for each Pipeline Versions.
  4. The list of pipeline versions are available in the Version History section.
    1. Unpublished versions are indicated with a black box (A) to the right of the pipeline version. Published pipelines are indicated with a gray box. (B). Publish details are visible by selecting Check Info (C).

      Wallaroo Published Pipeline Versions
    2. Select Check Info to view pipeline details.

      PIpeline Publish Details
      • Pipeline location (A): The URL for the containerized pipeline.
      • PIpeline Chart (B): The URL for the Helm chart of the published pipeline and engine.
      • Engine url (B): The URL for the Wallaroo Engine required to deploy the pipeline and perform inference requests.

Publish a Wallaroo Pipeline Version

To publish a version of the Wallaroo pipeline:

From the Pipeline Versions view:

  1. Select the black box to the right of a Pipeline Version identifier. Grey boxes indicate that the pipeline version is already published.
  2. Wait for the publish to complete. Depending on the number and size of the pipeline steps in the pipeline version, this may take anywhere from 1 to 10 minutes.

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.
  • EDGE_BUNDLE (Optional): The base64 encoded edge token and other values to connect to the Wallaroo Ops instance. This is used for edge management and transmitting inference results for observability. IMPORTANT NOTE: The token for EDGE_BUNDLE is valid for one deployment. For subsequent deployments, generate a new edge location with its own EDGE_BUNDLE.

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.

  1. Login through docker to confirm access to the registry service. First, docker login. For example, logging into the artifact registry with the token stored in the variable tok:

    cat $tok | docker login -u _json_key_base64 --password-stdin https://sample-registry.com
  2. Then deploy the Wallaroo published pipeline with an edge added to the pipeline publish through docker run.

    IMPORTANT NOTE: Edge deployments with Edge Observability enabled with the EDGE_BUNDLE option include an authentication token that only authenticates once. To store the token long term, include the persistent volume flag -v {path to storage} setting.

    Deployment with EDGE_BUNDLE for observability.

    docker run -p 8080:8080 \
    -v ./data:/persist \
    -e DEBUG=true \
    -e CONFIG_CPUS=1 \
    -e PIPELINE_URL=ghcr.io/wallaroolabs/doc-samples/pipelines/edge-observability-pipeline:3b49fba8-94d8-4fca-aecc-c75257fd16c6 \

    Connection to the Wallaroo Ops instance from edge deployment with EDGE_BUNDLE is verified with the long entry Node attestation was successful.

    Deployment without observability.

    docker run -p 8080:8080 \
    -e DEBUG=true \
    -e CONFIG_CPUS=1 \
    -e PIPELINE_URL=ghcr.io/wallaroolabs/doc-samples/pipelines/edge-observability-pipeline:3b49fba8-94d8-4fca-aecc-c75257fd16c6 \

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 tag is used to preserve the login session from the one-time token generated as part of the EDGE_BUNDLE.

EDGE_BUNDLE is only required when adding an edge to a Wallaroo publish for observability. The following is deployed without observability.

    image: {Your Engine URL}
      - 8080:8080
      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

The procedure is:

  1. Login through docker to confirm access to the registry service. First, docker login. For example, logging into the artifact registry with the token stored in the variable tok to the registry us-west1-docker.pkg.dev:

    cat $tok | docker login -u _json_key_base64 --password-stdin https://sample-registry.com
  2. Set up the compose.yaml file.

    IMPORTANT NOTE: Edge deployments with Edge Observability enabled with the EDGE_BUNDLE option include an authentication token that only authenticates once. To store the token long term, include the persistent volume with the volumes: tag.

        image: sample-registry.com/engine:v2023.3.0-main-3707
            - 8080:8080
            - ./data:/persist
        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: 4
  3. Then deploy with docker compose up.

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.

IMPORTANT NOTE: Edge deployments with Edge Observability enabled with the EDGE_BUNDLE option include an authentication token that only authenticates once. Helm chart installations automatically add a persistent volume during deployment to store the authentication session data for future deployments.

  1. Login to the registry service with helm registry login. For example, if the token is stored in the variable tok:

    helm registry login sample-registry.com --username _json_key_base64 --password $tok
  2. 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}
  3. 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:

        registry: {your registry service}
        username:  {registry username here}
        password: {registry token here}

    For Wallaroo Server deployments with edge location set, the values include edgeBundle as generated when the edge was added to the pipeline publish.

        registry: {your registry service}
        username:  {registry username here}
        password: {registry token here}
    edgeBundle: abcdefg

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
  2. 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}
  3. 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`

Edge Deployment Endpoints

The following endpoints are available for API calls to the edge deployed pipeline.

List Pipelines

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.

List Pipelines Example

curl localhost:8080/pipelines

List Models

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.

List Models Example

curl localhost:8080/models

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.

Edge Inference Endpoint Example

The following example demonstrates sending an Apache Arrow table to the Edge deployed pipeline, requesting the inference results back in a pandas DataFrame records format.

curl -X POST localhost:8080/pipelines/edge-cv-retail -H "Content-Type: application/vnd.apache.arrow.file" -H 'Accept: application/json; format=pandas-records'  --data-binary @./data/image_224x224.arrow



Edge Bundle Token Time To Live

When an edge is added to a pipeline publish, the field docker_run_variables contains a JSON value for edge devices to connect to the Wallaroo Ops instance.

The settings are stored in the key EDGE_BUNDLE as a base64 encoded value that include the following:

  • BUNDLE_VERSION: The current version of the bundled Wallaroo pipeline.
  • EDGE_NAME: The edge name as defined when created and added to the pipeline publish.
  • JOIN_TOKEN_: The one time authentication token for authenticating to the Wallaroo Ops instance.
  • OPSCENTER_HOST: The hostname of the Wallaroo Ops edge service. See Edge Deployment Registry Guide for full details on enabling pipeline publishing and edge observability to Wallaroo.
  • PIPELINE_URL: The OCI registry URL to the containerized pipeline.
  • WORKSPACE_ID: The numerical ID of the workspace.

For example:

base64 -D
export EDGE_NAME=xgb-ccfraud-edge-test
export JOIN_TOKEN=3148ada5-285a-4fca-b3b8-b50a48d7511b
export OPSCENTER_HOST=doc-test.edge.wallaroocommunity.ninja
export PIPELINE_URL=ghcr.io/wallaroolabs/doc-samples/pipelines/edge-pipeline:f388c109-8d57-4ed2-9806-aa13f854576b

The JOIN_TOKEN is a one time access token. Once used, a JOIN_TOKEN expires. The authentication session data is stored in persistent volumes. Persistent volumes must be specified for docker and docker compose based deployments of Wallaroo pipelines; helm based deployments automatically provide persistent volumes to store authentication credentials.

The JOIN_TOKEN has the following time to live (TTL) parameters.

  • Once created, the JOIN_TOKEN is valid for 24 hours. After it expires the edge will not be allowed to contact the OpsCenter the first time and a new edge bundle will have to be created.
  • After an Edge joins to Wallaroo Ops for the first time with persistent storage, the edge must contact the Wallaroo Ops instance at least once every 7 days.
    • If this period is exceeded, the authentication credentials will expire and a new edge bundle must be created with a new and valid JOIN_TOKEN.

Wallaroo edges require unique names. To create a new edge bundle with the same name:

  • Use the Remove Edge to remove the edge by name.
  • Use Add Edge to add the edge with the same name. A new EDGE_BUNDLE is generated with a new JOIN_TOKEN.