Wallaroo pipelines can be published to a Edge Open Container Initiative (OCI) Registry Service, known here as the Edge Registry Service, as a container images. This allows the Wallaroo pipelines to be deployed in other environments, such as Docker or Kubernetes with all of the pipeline model. When deployed, these pipelines can perform inferences from the ML models exactly as if they were deployed as part of a Wallaroo instance.
When a pipeline is updated with new model steps or deployment configurations, the updated pipeline is republished to the Edge Registry as a new repo and version. This allows DevOps engineers to update an Wallaroo pipeline in any container supporting environment with the new versions of the pipeline.
Pipeline Publishing Flow
A typical ML Model and Pipeline deployment to Wallaroo Ops and to remote locations as a Wallaroo Inference server is as follows:
- Components:
- Wallaroo Ops: The Wallaroo Ops provides the backbone services for ML Model deployment. This is where ML models are uploaded, pipelines created and deployed for inferencing, pipelines published to OCI compliant registries, and other functions.
- Wallaroo Inference Server: A remote deployment of a published Wallaroo pipeline with the Wallaroo Inference Engine outside the Wallaroo Ops instance. When the edge name is added to a Wallaroo publish, the Wallaroo Inference Server’s inference logs are submitted to the Wallaroo Ops instance. These inference logs are stored as part of the Wallaroo pipeline the remote deployment is published from.
- DevOps:
- Add Edge Publishing and Edge Observability to the Wallaroo Ops center. See Edge Deployment Registry Guide for details on updating the Wallaroo instance with Edge Publishing and Edge Observability.
- Data Scientists:
- Develop and train models.
- Test their deployments in Wallaroo Ops Center as Pipelines with:
- Pipeline Steps: The models part of the inference flow.
- Pipeline Deployment Configurations: CPUs, RAM, GPU, and Architecture settings to run the pipeline.
- Publish the Pipeline from the Wallaroo Ops to an OCI Registry: Store a image version of the Pipeline with models and pipeline configuration into the OCI Registry set by the DevOps engineers as the Wallaroo Edge Registry Service.
- DevOps:
- Retrieve the new or updated Wallaroo published pipeline from the Wallaroo Edge Registry Service.
- (Optional): Add an edge to the Wallaroo publish. This provides the
EDGE_BUNDLE
with the credentials for the Wallaroo Inference Server to transmit its inference result logs back to the Wallaroo Ops instance. These inference logs are added to the originating Wallaroo pipeline, labeled with themetadata.partition
being the name of the edge deployed Wallaroo Inference server. For more details, see Wallaroo SDK Essentials Guide: Pipeline Edge Publication: Edge Observability - Deploy the Pipeline as a Wallaroo Inference Server as a Docker or Kubernetes container, updating the resource allocations as part of the Helm chart, Docker Compose file, etc.
Enable Wallaroo Edge Registry
Set Edge Registry Service
Wallaroo Pipeline Publishes aka Wallaroo Servers are automatically routed to the Edge Open Container Initiative (OCI) Registry Service registered in the Wallaroo instance. This is enabled through either the Wallaroo Administrative Dashboard through kots
, or by enabling it through a helm
chart setting. From here on out, we will refer to it as the Edge Registry Service.
Set Edge Registry Service through Kots
To set the Edge Registry Settings through the Wallaroo Administrative Dashboard:
Launch the Wallaroo Administrative Dashboard using the following command, replacing the
--namespace
parameter with the Kubernetes namespace for the Wallaroo instance:kubectl kots admin-console --namespace wallaroo
Open a browser at the URL detailed in the step above and authenticate using the console password set as described in the as detailed in the Wallaroo Install Guides.
From the top menu, select Config then scroll to Edge Deployment.
Enable Provide OCI registry credentials for pipelines.
Enter the following:
- Registry URL: The address of the registry service. For example:
us-west1-docker.pkg.dev
. - email: The email address of the user account used to authenticate to the service.
- username: The account used to authenticate to the registry service.
- password: The password or token used to authenticate to the registry service.
- Registry URL: The address of the registry service. For example:
Save the updated configuration, then deploy it. Once complete, the edge registry settings will be available.
Set Edge Registry Service through Helm
The helm
settings for adding the Edge Server configuration details are set through the ociRegistry
element, with the following settings.
- ociRegistry: Sets the Edge Server registry information.
- enabled:
true
enables the Edge Server registry information,false
disables it. - registry: The registry url. For example:
reg.big.corp:3579
. - repository: The repository within the registry. This may include the cloud account, or the full path where the Wallaroo published pipelines should be kept. For example:
account123/wallaroo/pipelines
. - email: Optional field to track the email address of the registry credential.
- username: The username to the registry. This may vary based on the provider. For example, GCP Artifact Registry with service accounts uses the username
_json_key_base64
with the password as a base64 processed token of the credential information. - password: The password or token for the registry service.
- enabled:
Set Edge Observability Service
Edge Observability allows published Wallaroo Servers to community with the Wallaroo Ops center to update their associated Wallaroo Pipeline with inference results, visible in the Pipeline logs.
This process will create a new Kubernetes service edge-lb
. Based on the configuration options below, the service will require an additional IP address separate from the Wallaroo service api-lb
. The edge-lb
will require a DNS hostname.
Set Edge Observability Service through Kots
To enable Edge Observability using the Wallaroo Administrative Dashboard for kots
installed instances of Wallaroo Ops:
Launch the Wallaroo Administrative Dashboard using the following command, replacing the
--namespace
parameter with the Kubernetes namespace for the Wallaroo instance:kubectl kots admin-console --namespace wallaroo
Open a browser at the URL detailed in the step above and authenticate using the console password set as described in the as detailed in the Wallaroo Install Guides.
Access Config and scroll to Edge Deployment and enable Enable pipelines deployed on the edge to send data back to the OpsCenter.
Set the following:
Specify the OpsCenter hostname or IP address, as reachable from edge sites.: Set the DNS address in the format
https://service.{suffix domain}
. For example, if the domain suffix iswallaroo.example.com
and the Wallaroo Edge Observabilty Service is set to the hostnameedge
, then the URL to access theedge
service is:edge.wallaroo.example.com
Edge ingress mode: Set one of the following.
- None - Services are cluster local and kubernetes port forwarding must be used for access.
- Internal - Private network users can connect directly and do not need to port forward anything.
- External - Internet facing users can connect directly to interactive Wallaroo services. Exercise caution.
Save the updated configuration, then deploy it. Once complete, the edge observability service is available.
Set the DNS Hostname as described in the steps Set Edge Observability Service DNS Hostname.
Set Edge Observability Service through Helm
To enable the Edge Observability Service for Wallaroo Ops Helm based installation, include the following variables for the helm settings. For these instructions they are stored in local-values.yaml
:
edgelb:
serviceType: LoadBalancer
enabled: true
opscenterHost: mitch4.edge.wallaroocommunity.ninja
pipelines:
enabled: true
Update the Wallaroo Helm installation with the same version as the Wallaroo ops and the channel. For example, if updating Wallaroo Enterprise server, use the following:
helm upgrade wallaroo oci://registry.replicated.com/wallaroo/ee/wallaroo --version 2023.4.0-4092 --values local-values.yaml
This process will take 5-15 minutes depending on other configuration options. Once complete, set the DNS address as described in Set Edge Observability Service DNS Hostname.
Set Edge Observability Service DNS Hostname
Once enabled, the Wallaroo Edge Observability Service requires a DNS address. The following instructions are specified for Edge ingress mode:External.
Obtain the external IP address of the the Wallaroo Edge Observability Service with the following command, replacing the
-n wallaroo
namespace option with the one the Wallaroo Ops instance is installed into.EDGE_LOADBALANCER=$(kubectl get svc edge-lb -n wallaroo -o jsonpath='{.status.loadBalancer.ingress[0].ip}') && echo $EDGE_LOADBALANCER
Set the DNS address to the hostname set in the step Set Edge Observability Service through Kots if using
kots
to install, or Set Edge Observability Service through Helm if usinghelm
.
Registry Setup Guides
The following are short guides for setting up the credentials for different registry services. Refer to the registry documentation for full details.
The following process is used with a GitHub Container Registry to create the authentication tokens for use with a Wallaroo instance’s Private Model Registry configuration.
See the GitHub Working with the Container registry for full details.
The following process is used register a GitHub Container Registry with Wallaroo.
Create a new token as per the instructions from the Creating a personal access token (classic) guide. Note that a classic token is recommended for this process. Store this token in a secure location as it will not be able to be retrieved later from GitHub. Verify the following permissions are set:
Select the
write:packages
scope to download and upload container images and read and write their metadata.Select the
read:packages
scope to download container images and read their metadata (selected whenwrite:packages
is selected by default).Select the
delete:packages
scope to delete container images.
Store the token in a secure location.
This can be tested with docker
by logging into the specified registry. For example:
docker login -u {Github Username} --password {Your Token} ghcr.io/{Your Github Username or Organization}
The following process is an example of setting up an Artifact Registry Service with Google Cloud Platform (GCP) that is used to store containerized model images and retrieve them for use with Wallaroo.
Uploading and downloading containerized models to a Google Cloud Platform Registry follows these general steps.
Create the GCP registry.
Create a Service Account that will manage the registry service requests.
Assign appropriate Artifact Registry role to the Service Account
Retrieve the Service Account credentials.
Using either a specific user, or the Service Account credentials, upload the containerized model to the registry service.
Add the service account credentials to the Wallaroo instance’s containerized model private registry configuration.
Prerequisites
The commands below use the Google gcloud
command line tool, and expect that a Google Cloud Platform account is created and the gcloud
application is associated with the GCP Project for the organization.
For full details on the process and other methods, see the Google GCP documentation.
- Create the Registry
The following is based on the Create a repository using the Google Cloud CLI.
The following information is needed up front:
- $REPOSITORY_NAME: What to call the registry.
- $LOCATION: Where the repository will be located. GCP locations are derived through the
gcloud artifacts locations list
command. - $DESCRIPTION: Any details to be displayed. Sensitive data should not be included.
The follow example script will create a GCP registry with the minimum requirements.
REPOSITORY_NAME="YOUR NAME"
LOCATION="us-west1"
DESCRIPTION="My amazing registry."
gcloud artifacts repositories create REPOSITORY \
--repository-format=docker \
--location=LOCATION \
--description="$DESCRIPTION" \
--async
- Create a GCP Registry Service Account
The GCP Registry Service Account is used to manage the GCP registry service. The steps are details from the Google Create a service account guide.
The gcloud
process for these steps are:
Connect the
gcloud
application to the organization’s project.$PROJECT_ID="YOUR PROJECT ID" gcloud config set project $PROJECT_ID
Create the service account with the following:
- The name of the service account.
- A description of its purpose.
- The name to show when displayed.
SA_NAME="YOUR SERVICE ACCOUNT NAME" DESCRIPTION="Wallaroo container registry SA" DISPLAY_NAME="Wallaroo the Roo" gcloud iam service-accounts create $SA_NAME \ --description=$DESCRIPTION \ --display-name=$DISPLAY_NAME
- Assign Artifact Registry Role
Assign one or more of the following accounts to the new registry role based on the following criteria, as detailed in the Google GCP Repository Roles and Permissions Guide.
- For
pkg.dev
domains.
Role | Description |
---|---|
Artifact Registry Reader (roles/artifactregistry.reader) | View and get artifacts, view repository metadata. |
Artifact Registry Writer (roles/artifactregistry.writer) | Read and write artifacts. |
Artifact Registry Repository Administrator (roles/artifactregistry.repoAdmin) | Read, write, and delete artifacts. |
Artifact Registry Administrator (roles/artifactregistry.admin) | Create and manage repositories and artifacts. |
- For
gcr.io
repositories.
Role | Description |
---|---|
Artifact Registry Create-on-push Writer (roles/artifactregistry.createOnPushWriter) | Read and write artifacts. Create gcr.io repositories. |
Artifact Registry Create-on-push Repository Administrator (roles/artifactregistry.createOnPushRepoAdmin) | Read, write, and delete artifacts. Create gcr.io repositories. |
For this example, we will add the Artifact Registry Create-on-push Writer
to the created Service Account from the previous step.
Add the role to the service account, specifying the
member
as the new service account, and the role as the selected role. For this example, apkg.dev
is assumed for the Artifact Registry type.# for pkg.dev ROLE="roles/artifactregistry.writer" # for gcr.io #ROLE="roles/artifactregistry.createOnPushWriter gcloud projects add-iam-policy-binding \ $PROJECT_ID \ --member="serviceAccount:$SA_NAME@$PROJECT_ID.iam.gserviceaccount.com" \ --role=$ROLE
- Authenticate to Repository
To push and pull image from the new registry, we’ll use our new service account and authenticate through the local Docker application. See the GCP Push and pull images for details on using Docker and other methods to add artifacts to the GCP artifact registry.
- Set up Service Account Key
To set up the Service Account key, we’ll use the Google Console IAM & ADMIN dashboard based on the Set up authentication for Docker, using the JSON key approach.
From GCP console, search for
IAM & Admin
.Select Service Accounts.
Select the service account to generate keys for.
Select the Email address listed and store this for later steps with the key generated through this process.
Select Keys, then Add Key, then Create new key.
Select JSON, then Create.
Store the key in a safe location.
- Convert SA Key to Base64
The key file downloaded in Set up Service Account Key needs to be converted to base64 with the following command, replacing the locations of KEY_FILE
and KEYFILEBASE64
:
KEY_FILE = ~/.gcp-sa-registry-keyfile.json
KEYFILEBASE64 = ~/.gcp-sa-registry-keyfile-b64.json
base64 -i $KEY_FILE -o $KEYFILEBASE64
This base64 key is then used as the authentication token, with the username _json_key_base64
.
This can be tested with docker
by logging into the specified registry. For example:
token=$(cat $KEYFILEBASE64)
cat $tok | docker login -u _json_key_base64 --password-stdin https://{GCP artifact registry region}.pkg.dev
Publish a Pipeline to the Edge Registry Service
See also the reference documentation: wallaroo.pipeline.publish.
Publish a Pipeline
Pipelines are published as images to the edge registry set in the Enable Wallaroo Edge Registry with the wallaroo.pipeline.publish
method.
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.
Parameter | Type | Description |
---|---|---|
deployment_config | wallaroo.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
Field | Type | Description |
---|---|---|
id | integer | Numerical Wallaroo id of the published pipeline. |
pipeline version id | integer | Numerical Wallaroo id of the pipeline version published. |
status | string | The status of the pipeline publication. Values include:
|
Engine URL | string | The URL of the published pipeline engine in the edge registry. |
Pipeline URL | string | The URL of the published pipeline in the edge registry. |
Helm Chart URL | string | The URL of the helm chart for the published pipeline in the edge registry. |
Helm Chart Reference | string | The help chart reference. |
Helm Chart Version | string | The version of the Helm Chart of the published pipeline. This is also used as the Docker tag. |
Engine Config | wallaroo.deployment_config.DeploymentConfig | The pipeline configuration included with the published pipeline. |
Created At | DateTime | When the published pipeline was created. |
Updated At | DateTime | When the published pipeline was updated. |
Publish a Pipeline Example
The following example shows how to publish a pipeline to the edge registry service associated with the Wallaroo instance.
# set the configuration
deployment_config = wallaroo.DeploymentConfigBuilder().replica_count(1).cpus(0.5).memory("900Mi").build()
# build the pipeline
pipeline = wl.build_pipeline("publish-example")
# add a model as a model step
pipeline.add_model_step(m2)
publish = pipeline.publish(deployment_config)
display(publish)
ID | 4 |
Pipeline Version ID | 10 |
Status | Published |
Engine URL | sample-registry.example.com/engine:main |
Pipeline URL | sample-registry.example.com/pipelines/p1:6c3d9899-1335-456b-aaa0-52d03a017cc4 |
Helm Chart URL | sample-registry.example.com/charts/p1 |
Helm Chart Reference | sample-registry.example.com/charts@sha256:5523891f66fde830a9fc08603b3536dc2c4c1b16b51931ad2fdf9839e6eba129 |
Helm Chart Version | 0.0.1-6c3d9899-1335-456b-aaa0-52d03a017cc4 |
Engine Config | {’engine’: {‘resources’: {’limits’: {‘cpu’: 0.5, ‘memory’: ‘900Mi’}, ‘requests’: {‘cpu’: 0.5, ‘memory’: ‘900Mi’}}}, ’engineAux’: {‘images’: {}}, ’enginelb’: {}} |
Created By | db13ee60-4162-4a42-a571-61c32e225e3e |
Created At | 2023-08-17 14:04:44.939862+00:00 |
Updated At | 2023-08-17 14:04:44.939862+00:00 |
List Publishes
All publishes created from a pipeline are displayed with the wallaroo.pipeline.publishes
method.
List Publishes Parameters
N/A
List Publishes Returns
A List of the following fields:
Field | Type | Description |
---|---|---|
id | integer | Numerical Wallaroo id of the published pipeline. |
pipeline_version_id | integer | Numerical Wallaroo id of the pipeline version published. |
engine_url | string | The URL of the published pipeline engine in the edge registry. |
pipeline_url | string | The URL of the published pipeline in the edge registry. |
created_by | string | The email address of the user that published the pipeline. |
Created At | DateTime | When the published pipeline was created. |
Updated At | DateTime | When the published pipeline was updated. |
List Publishes Example
The following shows a list of publishes from a pipeline.
pipeline.publishes()
id | pipeline_version_id | engine_url | pipeline_url | created_by | created_at | updated_at |
---|---|---|---|---|---|---|
1 | 4 | sample-registry.example.com/engine:main | sample-registry.example.com/pipelines/p1:01ab76f3-d007-48c4-870d-3d3c5a46902d | db13ee60-4162-4a42-a571-61c32e225e3e | 2023-16-Aug 20:22:31 | 2023-16-Aug 20:22:31 |
2 | 6 | sample-registry.example.com/engine:main | sample-registry.example.com/pipelines/p1:d3fb1b50-fe9a-4f54-bc32-fb9ff4ba49ee | db13ee60-4162-4a42-a571-61c32e225e3e | 2023-17-Aug 12:43:46 | 2023-17-Aug 12:43:46 |
3 | 8 | sample-registry.example.com/engine:main | sample-registry.example.com/pipelines/p1:97548f13-a791-41f5-bd54-c75649b72856 | db13ee60-4162-4a42-a571-61c32e225e3e | 2023-17-Aug 13:52:38 | 2023-17-Aug 13:52:38 |
4 | 10 | sample-registry.example.com/engine:main | sample-registry.example.com/pipelines/p1:6c3d9899-1335-456b-aaa0-52d03a017cc4 | db13ee60-4162-4a42-a571-61c32e225e3e | 2023-17-Aug 14:04:44 | 2023-17-Aug 14:04:44 |
Edge Observability
Edge Observability allows edge deployments of Wallaroo Server to transmit inference results back to the Wallaroo Ops center and become part of the pipeline’s logs. This is valuable for data scientists and MLOps engineers to retrieve edge deployment logs for use in model observability, drift, and other use cases.
Before starting, the Edge Observability Service must be enabled in the Wallaroo Ops center. See the Edge Deployment Registry Guide for details on enabling the Wallaroo Edge Deployment service.
Wallaroo Server edge observability is enabled when a new edge location is added to the pipeline publish. Each location has its own EDGE_BUNDLE
settings, a Base64 encoded set of instructions informing the edge deployed Wallaroo Server on how to communicate with Wallaroo Ops center.
Add Edge
Wallaroo Servers edge deployments are added to a Wallaroo pipeline’s publish with the wallaroo.pipeline_publish.add_edge(name: string, tags: List[string])
method. The name
is the unique primary key for each edge added to the pipeline publish and must be unique.
Add Edge Parameters
wallaroo.pipeline_publish.add_edge(name: string, tags: List[string])
has the following parameters.
Field | Type | Description |
---|---|---|
name | String (Required) | The name of the edge location. This must be a unique value across all edges in the Wallaroo instance. |
tags | List[String] (Optional) | A list of optional tags. |
Add Edge Returns
This returns a Publish Edge with the following fields:
Field | Type | Description |
---|---|---|
id | Integer | The integer ID of the pipeline publish. |
created_at | DateTime | The DateTime of the pipeline publish. |
docker_run_variables | String | The Docker variables in JSON entry with the key EDGE_BUNDLE as a base64 encoded value that include the following: The BUNDLE_VERSION , EDGE_NAME , JOIN_TOKEN_ , OPSCENTER_HOST , PIPELINE_URL , and WORKSPACE_ID . For example: {'EDGE_BUNDLE': 'abcde'} |
engine_config | String | The Wallaroo wallaroo.deployment_config.DeploymentConfig for the pipeline. |
pipeline_version_id | Integer | The integer identifier of the pipeline version published. |
status | String | The status of the publish. Published is a successful publish. |
updated_at | DateTime | The DateTime when the pipeline publish was updated. |
user_images | List[String] | User images used in the pipeline publish. |
created_by | String | The UUID of the Wallaroo user that created the pipeline publish. |
engine_url | String | The URL for the published pipeline’s Wallaroo engine in the OCI registry. |
error | String | Any errors logged. |
helm | String | The helm chart, helm reference and helm version. |
pipeline_url | String | The URL for the published pipeline’s container in the OCI registry. |
pipeline_version_name | String | The UUID identifier of the pipeline version published. |
additional_properties | String | Any other identities. |
Add Edge Example
The following example demonstrates creating a publish from a pipeline, then adding a new edge to the publish.
# create publish
xgb_pub=xgboost_pipeline_version.publish(deploy_config)
display(xgb_pub)
Waiting for pipeline publish... It may take up to 600 sec.
Pipeline is Publishing...Published.
ID | 1 |
Pipeline Version | f388c109-8d57-4ed2-9806-aa13f854576b |
Status | Published |
Engine URL | ghcr.io/wallaroolabs/doc-samples/engines/proxy/wallaroo/ghcr.io/wallaroolabs/standalone-mini:v2023.4.0-main-4079 |
Pipeline URL | ghcr.io/wallaroolabs/doc-samples/pipelines/edge-pipeline:f388c109-8d57-4ed2-9806-aa13f854576b |
Helm Chart URL | oci://ghcr.io/wallaroolabs/doc-samples/charts/edge-pipeline |
Helm Chart Reference | ghcr.io/wallaroolabs/doc-samples/charts@sha256:429aae187be641c22de5a333c737219a5ffaf908ac3673781cdf83f4ebbf7abc |
Helm Chart Version | 0.0.1-f388c109-8d57-4ed2-9806-aa13f854576b |
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 By | john.hummel@wallaroo.ai |
Created At | 2023-10-29 23:35:03.508703+00:00 |
Updated At | 2023-10-29 23:35:03.508703+00:00 |
Docker Run Variables | {} |
xgb_edge = xgb_pub.add_edge("xgb-ccfraud-edge-publish")
print(xgb_edge)
ID | 2 |
Pipeline Version | 60fb5c6e-db3e-497d-afc8-ccc149beba4a |
Status | Published |
Engine URL | ghcr.io/wallaroolabs/doc-samples/engines/proxy/wallaroo/ghcr.io/wallaroolabs/standalone-mini:v2023.4.0-main-4079 |
Pipeline URL | ghcr.io/wallaroolabs/doc-samples/pipelines/edge-pipeline:60fb5c6e-db3e-497d-afc8-ccc149beba4a |
Helm Chart URL | oci://ghcr.io/wallaroolabs/doc-samples/charts/edge-pipeline |
Helm Chart Reference | ghcr.io/wallaroolabs/doc-samples/charts@sha256:2de830d875ac8e60984c391091e5fdc981ad74e56925545c99b5e5b222c612bc |
Helm Chart Version | 0.0.1-60fb5c6e-db3e-497d-afc8-ccc149beba4a |
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 By | john.hummel@wallaroo.ai |
Created At | 2023-10-29 23:35:21.956532+00:00 |
Updated At | 2023-10-29 23:35:21.956532+00:00 |
Docker Run Variables | {'EDGE_BUNDLE': 'abcde'} |
Remove Edge
Edges are removed with the wallaroo.pipeline_publish.remove_edge(name: string)
Remove Edge Parameters
wallaroo.pipeline_publish.remove_edge(name: string)
has the following parameters.
Field | Type | Description |
---|---|---|
name | String (Required) | The name of the edge location being removed. |
Remove Edge Returns
Null
Remove Edge Example
This example will add two edges to a pipeline publish, list the edges for the pipeline, then remove one of the edges.
edge_01_name = f'edge-ccfraud-observability{random_suffix}'
edge01 = pub.add_edge(edge_01_name)
edge_02_name = f'edge-ccfraud-observability-02{random_suffix}'
edge02 = pub.add_edge(edge_02_name)
pipeline.list_edges()
ID | Name | Tags | Pipeline Version | SPIFFE ID |
---|---|---|---|---|
898bb58c-77c2-4164-b6cc-f004dc39e125 | edge-ccfraud-observabilityymgy | [] | 6 | wallaroo.ai/ns/deployments/edge/898bb58c-77c2-4164-b6cc-f004dc39e125 |
1f35731a-f4f6-4cd0-a23a-c4a326b73277 | edge-ccfraud-observability-02ymgy | [] | 6 | wallaroo.ai/ns/deployments/edge/1f35731a-f4f6-4cd0-a23a-c4a326b73277 |
sample = pub.remove_edge(edge_02_name)
display(sample)
ID | Name | Tags | Pipeline Version | SPIFFE ID |
---|---|---|---|---|
898bb58c-77c2-4164-b6cc-f004dc39e125 | edge-ccfraud-observabilityymgy | [] | 6 | wallaroo.ai/ns/deployments/edge/898bb58c-77c2-4164-b6cc-f004dc39e125 |
List Edges
The method wallaroo.pipeline.list_edges()
displays any edges added to a pipeline’s publishes.
List Edges Parameters
None
List Edges Returns
The following fields are returned from a List of edges.
Parameter | Type | Description |
---|---|---|
cpus | Float | The number of cpus assigned as part of the pipeline configuration. |
id | String | The identifier of the edge in UUID format. |
memory | String | The memory assigned as part of the pipeline configuration in Kubernetes memory format. |
name | String | The assigned name for the edge. Edge names are used as the primary key. |
tags | List[String] | A list of tags assigned to the edge. |
pipeline_version_id | Integer | The pipeline version numerical idenfier. |
spiffe_id | String | The deployment edge identifier used to for edge communications. |
additional_properties | Dict | Any additional properties. |
List Edges Example
edge_01_name = f'edge-ccfraud-observability{random_suffix}'
edge01 = pub.add_edge(edge_01_name)
edge_02_name = f'edge-ccfraud-observability-02{random_suffix}'
edge02 = pub.add_edge(edge_02_name)
pipeline.list_edges()
ID | Name | Tags | Pipeline Version | SPIFFE ID |
---|---|---|---|---|
898bb58c-77c2-4164-b6cc-f004dc39e125 | edge-ccfraud-observabilityymgy | [] | 6 | wallaroo.ai/ns/deployments/edge/898bb58c-77c2-4164-b6cc-f004dc39e125 |
1f35731a-f4f6-4cd0-a23a-c4a326b73277 | edge-ccfraud-observability-02ymgy | [] | 6 | wallaroo.ai/ns/deployments/edge/1f35731a-f4f6-4cd0-a23a-c4a326b73277 |
Edge Bundle Token TTL
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
WORKSPACE_ID
.
For example:
{'edgeBundle': 'ZXhwb3J0IEJVTkRMRV9WRVJTSU9OPTEKZXhwb3J0IEVER0VfTkFNRT14Z2ItY2NmcmF1ZC1lZGdlLXRlc3QKZXhwb3J0IEpPSU5fVE9LRU49MzE0OGFkYTUtMjg1YS00ZmNhLWIzYjgtYjUwYTQ4ZDc1MTFiCmV4cG9ydCBPUFNDRU5URVJfSE9TVD1kb2MtdGVzdC5lZGdlLndhbGxhcm9vY29tbXVuaXR5Lm5pbmphCmV4cG9ydCBQSVBFTElORV9VUkw9Z2hjci5pby93YWxsYXJvb2xhYnMvZG9jLXNhbXBsZXMvcGlwZWxpbmVzL2VkZ2UtcGlwZWxpbmU6ZjM4OGMxMDktOGQ1Ny00ZWQyLTk4MDYtYWExM2Y4NTQ1NzZiCmV4cG9ydCBXT1JLU1BBQ0VfSUQ9NQ=='}
base64 -D
ZXhwb3J0IEJVTkRMRV9WRVJTSU9OPTEKZXhwb3J0IEVER0VfTkFNRT14Z2ItY2NmcmF1ZC1lZGdlLXRlc3QKZXhwb3J0IEpPSU5fVE9LRU49MzE0OGFkYTUtMjg1YS00ZmNhLWIzYjgtYjUwYTQ4ZDc1MTFiCmV4cG9ydCBPUFNDRU5URVJfSE9TVD1kb2MtdGVzdC5lZGdlLndhbGxhcm9vY29tbXVuaXR5Lm5pbmphCmV4cG9ydCBQSVBFTElORV9VUkw9Z2hjci5pby93YWxsYXJvb2xhYnMvZG9jLXNhbXBsZXMvcGlwZWxpbmVzL2VkZ2UtcGlwZWxpbmU6ZjM4OGMxMDktOGQ1Ny00ZWQyLTk4MDYtYWExM2Y4NTQ1NzZiCmV4cG9ydCBXT1JLU1BBQ0VfSUQ9NQ==^D
export BUNDLE_VERSION=1
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
export WORKSPACE_ID=5
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
.
- If this period is exceeded, the authentication credentials will expire and a new edge bundle must be created with a new and valid
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 newJOIN_TOKEN
.
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.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 forEDGE_BUNDLE
is valid for one deployment. For subsequent deployments, generate a new edge location with its ownEDGE_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.
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 variabletok
:cat $tok | docker login -u _json_key_base64 --password-stdin https://sample-registry.com
Then deploy the Wallaroo published pipeline with an edge added to the pipeline publish through
docker run
.IMPORTANT NOTE: Edge deployments with Edge Observability enabled with the
EDGE_BUNDLE
option include an authentication token that only authenticates once. To store the token long term, include the persistent volume flag-v {path to storage}
setting.Deployment with
EDGE_BUNDLE
for observability.docker run -p 8080:8080 \ -v ./data:/persist \ -e DEBUG=true \ -e OCI_REGISTRY=$REGISTRYURL \ -e EDGE_BUNDLE=ZXhwb3J0IEJVTkRMRV9WRVJTSU9OPTEKZXhwb3J0IEVER0VfTkFNRT1lZGdlLWNjZnJhdWQtb2JzZXJ2YWJpbGl0eXlhaWcKZXhwb3J0IEpPSU5fVE9LRU49MjZmYzFjYjgtMjUxMi00YmU3LTk0ZGUtNjQ2NGI1MGQ2MzhiCmV4cG9ydCBPUFNDRU5URVJfSE9TVD1kb2MtdGVzdC5lZGdlLndhbGxhcm9vY29tbXVuaXR5Lm5pbmphCmV4cG9ydCBQSVBFTElORV9VUkw9Z2hjci5pby93YWxsYXJvb2xhYnMvZG9jLXNhbXBsZXMvcGlwZWxpbmVzL2VkZ2Utb2JzZXJ2YWJpbGl0eS1waXBlbGluZTozYjQ5ZmJhOC05NGQ4LTRmY2EtYWVjYy1jNzUyNTdmZDE2YzYKZXhwb3J0IFdPUktTUEFDRV9JRD03 \ -e CONFIG_CPUS=1 \ -e OCI_USERNAME=$REGISTRYUSERNAME \ -e OCI_PASSWORD=$REGISTRYPASSWORD \ -e PIPELINE_URL=ghcr.io/wallaroolabs/doc-samples/pipelines/edge-observability-pipeline:3b49fba8-94d8-4fca-aecc-c75257fd16c6 \ ghcr.io/wallaroolabs/doc-samples/engines/proxy/wallaroo/ghcr.io/wallaroolabs/standalone-mini:v2023.4.0-main-4079
Connection to the Wallaroo Ops instance from edge deployment with
EDGE_BUNDLE
is verified with the long entryNode attestation was successful
.Deployment without observability.
docker run -p 8080:8080 \ -e DEBUG=true \ -e OCI_REGISTRY=$REGISTRYURL \ -e CONFIG_CPUS=1 \ -e OCI_USERNAME=$REGISTRYUSERNAME \ -e OCI_PASSWORD=$REGISTRYPASSWORD \ -e PIPELINE_URL=ghcr.io/wallaroolabs/doc-samples/pipelines/edge-observability-pipeline:3b49fba8-94d8-4fca-aecc-c75257fd16c6 \ ghcr.io/wallaroolabs/doc-samples/engines/proxy/wallaroo/ghcr.io/wallaroolabs/standalo
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.
services:
engine:
image: {Your Engine URL}
ports:
- 8080:8080
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: 4
The procedure is:
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 variabletok
to the registryus-west1-docker.pkg.dev
:cat $tok | docker login -u _json_key_base64 --password-stdin https://sample-registry.com
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 thevolumes:
tag.services: engine: image: sample-registry.com/engine:v2023.3.0-main-3707 ports: - 8080:8080 volumes: - ./data:/persist environment: PIPELINE_URL: sample-registry.com/pipelines/edge-cv-retail:bf70eaf7-8c11-4b46-b751-916a43b1a555 EDGE_BUNDLE: ZXhwb3J0IEJVTkRMRV9WRVJTSU9OPTEKZXhwb3J0IEVER0VfTkFNRT1lZGdlLWNjZnJhdWQtb2JzZXJ2YWJpbGl0eXlhaWcKZXhwb3J0IEpPSU5fVE9LRU49MjZmYzFjYjgtMjUxMi00YmU3LTk0ZGUtNjQ2NGI1MGQ2MzhiCmV4cG9ydCBPUFNDRU5URVJfSE9TVD1kb2MtdGVzdC5lZGdlLndhbGxhcm9vY29tbXVuaXR5Lm5pbmphCmV4cG9ydCBQSVBFTElORV9VUkw9Z2hjci5pby93YWxsYXJvb2xhYnMvZG9jLXNhbXBsZXMvcGlwZWxpbmVzL2VkZ2Utb2JzZXJ2YWJpbGl0eS1waXBlbGluZTozYjQ5ZmJhOC05NGQ4LTRmY2EtYWVjYy1jNzUyNTdmZDE2YzYKZXhwb3J0IFdPUktTUEFDRV9JRD03 OCI_REGISTRY: sample-registry.com OCI_USERNAME: _json_key_base64 OCI_PASSWORD: abc123 CONFIG_CPUS: 4
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.
Login to the registry service with
helm registry login
. For example, if the token is stored in the variabletok
:helm registry login sample-registry.com --username _json_key_base64 --password $tok
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}
Extract the
tgz
file and copy thevalues.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}
For Wallaroo Server deployments with edge location set, the values include
edgeBundle
as generated when the edge was added to the pipeline publish.ociRegistry: registry: {your registry service} username: {registry username here} password: {registry token here} edgeBundle: abcdefg
Store this into another file, suc as local-values.yaml
.
Create the namespace to deploy the pipeline to. For example, the namespace
wallaroo-edge-pipeline
would be:kubectl create -n wallaroo-edge-pipeline
Deploy the
helm
installation withhelm install
through one of the following options: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}
Specify the expended directory from the downloaded
tgz
file.helm install --namespace {namespace} --values {local values file} {helm install name} {helm directory path}
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}
Once deployed, the DevOps engineer will have to forward the appropriate ports to the
svc/engine-svc
service in the specific pipeline. For example, usingkubectl port-forward
to the namespaceccfraud
that would be:kubectl port-forward svc/engine-svc -n ccfraud01 8080 --address 0.0.0.0`
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
, orError
if there are any issues.
List Pipelines Example
curl localhost:8080/pipelines
{"pipelines":[{"id":"edge-cv-retail","status":"Running"}]}
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
{"models":[{"name":"resnet-50","sha":"c6c8869645962e7711132a7e17aced2ac0f60dcdc2c7faa79b2de73847a87984","status":"Running","version":"693e19b5-0dc7-4afb-9922-e3f7feefe66d"}]}
Edge Inference Endpoint
The inference endpoint takes the following pattern:
/pipelines/{pipeline-name}
: Thepipeline-name
is the same as returned from the/pipelines
endpoint asid
.
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
Returns:
[{"check_failures":[],"elapsed":[1067541,21209776],"model_name":"resnet-50","model_version":"2e05e1d0-fcb3-4213-bba8-4bac13f53e8d","original_data":null,"outputs":[{"Int64":{"data":[535],"dim":[1],"v":1}},{"Float":{"data":[0.00009498586587142199,0.00009141524787992239,0.0004606838047038764,0.00007667174941161647,0.00008047101437114179,...],"dim":[1,1001],"v":1}}],"pipeline_name":"edge-cv-demo","shadow_data":{},"time":1694205578428}]
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:
{'edgeBundle': 'ZXhwb3J0IEJVTkRMRV9WRVJTSU9OPTEKZXhwb3J0IEVER0VfTkFNRT14Z2ItY2NmcmF1ZC1lZGdlLXRlc3QKZXhwb3J0IEpPSU5fVE9LRU49MzE0OGFkYTUtMjg1YS00ZmNhLWIzYjgtYjUwYTQ4ZDc1MTFiCmV4cG9ydCBPUFNDRU5URVJfSE9TVD1kb2MtdGVzdC5lZGdlLndhbGxhcm9vY29tbXVuaXR5Lm5pbmphCmV4cG9ydCBQSVBFTElORV9VUkw9Z2hjci5pby93YWxsYXJvb2xhYnMvZG9jLXNhbXBsZXMvcGlwZWxpbmVzL2VkZ2UtcGlwZWxpbmU6ZjM4OGMxMDktOGQ1Ny00ZWQyLTk4MDYtYWExM2Y4NTQ1NzZiCmV4cG9ydCBXT1JLU1BBQ0VfSUQ9NQ=='}
base64 -D
ZXhwb3J0IEJVTkRMRV9WRVJTSU9OPTEKZXhwb3J0IEVER0VfTkFNRT14Z2ItY2NmcmF1ZC1lZGdlLXRlc3QKZXhwb3J0IEpPSU5fVE9LRU49MzE0OGFkYTUtMjg1YS00ZmNhLWIzYjgtYjUwYTQ4ZDc1MTFiCmV4cG9ydCBPUFNDRU5URVJfSE9TVD1kb2MtdGVzdC5lZGdlLndhbGxhcm9vY29tbXVuaXR5Lm5pbmphCmV4cG9ydCBQSVBFTElORV9VUkw9Z2hjci5pby93YWxsYXJvb2xhYnMvZG9jLXNhbXBsZXMvcGlwZWxpbmVzL2VkZ2UtcGlwZWxpbmU6ZjM4OGMxMDktOGQ1Ny00ZWQyLTk4MDYtYWExM2Y4NTQ1NzZiCmV4cG9ydCBXT1JLU1BBQ0VfSUQ9NQ==^D
export BUNDLE_VERSION=1
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
export WORKSPACE_ID=5
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
.
- If this period is exceeded, the authentication credentials will expire and a new edge bundle must be created with a new and valid
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 newJOIN_TOKEN
.