Push Your Deployment
Pushing your Deployment is the process of sending your Deployment configuration to the runtime endpoint. This requires a running runtime enpoint. There are a number of approaches to follow, depending on the details of the plugins in your Deployment configuration:
- Using template pre and post scoring logic: There is no java compilation required so the basic push functionality can be used.
- Custom post scoring logic: Use the built in compilation functionality in the workbench or python before doing the push
- Additional custom classes: When using custom pre scoring or API logic or post scoring logic made up of multiple classes, you will need to implement a build pipeline to compile the java classes. These compiled java classes can then be copied to the plugins structure of a runtime before using the push functionality.
Template pre and post scoring logic
If you are using one of the template pre and post scoring logic options, as described in the documentation on the Deployment parameters documentation. Once your Deployment is configured either click the Push button in the workbench or use the python functionality. Note that when using python there are two options for pushing the deployment show in the code snippet below. The first shows how to push the deployment to a single runtime endpoint. The second shows how to push the deployment to an endpoint which may have multiple replicas being load balanced behind a single endpoint.
# Import the required packages
from prediction.apis import ecosystem_generation_engine as ge
from prediction import jwt_access
# Authenticate
ecosystem_password = getpass.getpass("Enter your ecosystem password")
auth = jwt_access.Authenticate("http://ecosystem-server:3001/api", "user@ecosystem.ai", ecosystem_password)
# Select the deployment to push
project_id = "Demo Project"
deployment_id = "demo_recommender"
version = "001"
deployment_step = get_deployment_step(auth, project_id, deployment_id, version)
# Push deployment and print properties file for single runtime endpoint
push_result = ge.process_push(auth,deployment_step)
if "ErrorMessage" in push_result:
print(push_result["ErrorMessage"])
else:
print(push_result["properties"])
# Push deployment and print properties file for multiple runtime endpoint in OpenShift
push_result = ge.process_push(auth,deployment_step)
if "ErrorMessage" in push_result:
print(push_result["ErrorMessage"])
else:
print(push_result["properties"])
# oc is used to manage the OpenShift environment so we need to configure the connection parameters.
# If oc is not available set use_oc to False and manual instructions will be printed
openshift_server = "https://api.crc.testing:6443"
oc_user = "developer"
oc_path = "/home/developer/.crc/oc"
dm.udate_properties_and_refresh(
name=deployment_id
,openshift_server=openshift_server
,oc_user=oc_user
,oc_path=oc_path
,properties=push_result["properties"]
)š„
Note
Custom pre and post scoring logic
When using custom pre and post scoring logic you will need to use the compile functionality in the Workbench to compile the java class before completing the push. In order to create and test your custom pre and post scoring logic we recommend that you follow the plugin development guide. Once you have pushed your custom logic back to the server you can use the Just In Time compilation functionality. To compile the class click the Compile button in the Plugins accordion of your Deployment Configuration on the workbench. This will use the /generateClass runtime endpoint to compile the java class, as part of this process and unique string will be appended to the name. Once the compile process is completed you will see that the name of the class on the workbench has changed to include this unique string, do not change this string as it is linked to the class that has now been compiled in the runtime. You can now follow the standard push process outlined in the previous section using either the workbench or python.
The compilation step can also be completed using the python package. The code snippet below shows how to compile and deploy custom post scoring logic.
# Import the required packages
from prediction.apis import ecosystem_generation_engine as ge
from prediction import jwt_access
# Authenticate
ecosystem_password = getpass.getpass("Enter your ecosystem password")
auth = jwt_access.Authenticate("http://ecosystem-server:3001/api", "user@ecosystem.ai", ecosystem_password)
# Select the deployment
project_id = "Demo Project"
deployment_id = "demo_recommender"
version = "001"
# Get the deployment step and extract the post scoring logic
deployment_step = dm.get_deployment_step(auth, project_id, deployment_id, version)
post_score_code = deployment_step["plugins"]["post_score_class_code"]
# Compile the post scoring logic file and add the new file name to the deployment step
compile_results = o.generate_class(auth_runtime, post_score_code)
deployment_step["plugins"]["post_score_class_text"] = compile_results["javaFileName"]
# Save the new deployment step
dm.update_deployment_step(auth, project_id, deployment_id, version, deployment_step)
#Push deployment and produce properties file
push_result = ge.process_push(auth,deployment_step)
if "ErrorMessage" in push_result:
print(push_result["ErrorMessage"])
else:
print(push_result["properties"])š„
Note
Additional custom classes
If you are adding additional custom classes to your Deployment configuration beyond the post scoring logic, you will need to implement a build pipeline to compile the java classes. In order to create and test your custom logic we recommend that you follow the plugin development guide. Here we will illustrate the process using a Tekton pipeline in OpenShift, other pipelining tools can also be used as long as the java classes are compiled and copied to the correct location in the runtime.
The below pipeline will configure a base runtime environment, clone a git repository containing the custom logic, compile the custom java classes, copy the compiled classes to the runtime and then push the Deployment from the server to the runtime.
apiVersion: tekton.dev/v1
kind: Task
metadata:
name: create-base-runtime
namespace: ecosystem
spec:
params:
- name: project
description: The namespace of the case to be deployed
type: string
- name: port
description: The port number of the service
type: string
- description: The name of the deployment
name: deployment-id
type: string
- description: The name of the project containing the deployment
name: ecosystem-project
type: string
- description: The ecosystem version of the deployment
name: ecosystem-version
type: string
- description: The version of the runtime container
name: runtime-version
type: string
- description: The name of the pvc used by the deployments
name: nfs-volume-claim
type: string
- description: The number of replicas to be created
name: replica-number
type: string
- description: The ecosystem license key
name: ecosystem-key
type: string
- description: The number of seconds between refreshes for the dynamic interaction configurations
name: monitoring-delay
type: string
- description: The mongo connection string
name: mongo-connect
type: string
steps:
- name: create-deployment-config
image: image-registry.openshift-image-registry.svc:5000/ecosystem/ecosystem-python:0.1.30
imagePullPolicy: IfNotPresent
workingDir: /workspace/source
script: |
#!/usr/bin/env python
from prediction.apis import deployment_management as dm
import yaml
ecosystem_key = "$(inputs.params.ecosystem-key)"
monitoring_delay = $(inputs.params.monitoring-delay)
port = $(inputs.params.port)
deployment_id = "$(inputs.params.deployment-id)"
mongo_connect = "$(inputs.params.mongo-connect)"
version = "$(inputs.params.runtime-version)"
namespace = "$(inputs.params.project)"
volume = "$(inputs.params.nfs-volume-claim)"
replicas = $(inputs.params.replica-number)
port = $(inputs.params.port)
print(mongo_connect)
environment_variables = [
f"MASTER_KEY={ecosystem_key}",
f"MONITORING_DELAY={monitoring_delay}",
"CASSANDRA_CONFIG=/config/cassandra.conf",
"TZ=Africa/Johannesburg",
f"PORT={port}",
f"MONGO_CONNECT={mongo_connect}"
]
deployment_config = dm.get_openshift_deployment_config(
name=deployment_id
, version=version
, environment_variables=environment_variables
, namespace=namespace
, volume=volume
, replicas=replicas
, port=port
)
deployment_config["spec"]["template"]["spec"]["containers"][0]["image"] = f"image-registry.openshift-image-registry.svc:5000/ecosystem/ecosystem-runtime:{version}"
print(deployment_config)
with open(f'deployment-{deployment_id}.yml', 'w+') as f:
yaml.dump(deployment_config, f)
- name: create-base-deployment
image: image-registry.openshift-image-registry.svc:5000/openshift/cli:latest
workingDir: /workspace/source
script: |
#!/bin/bash
oc patch deployment management --type='json' -p='[{"op": "add", "path": "/spec/template/spec/containers/0/volumeMounts/-", "value": {"mountPath": "/$(inputs.params.deployment-id)-data", "name": "tc-ecosystem-disk", "subPath": "$(inputs.params.deployment-id)-data"}}]'
sleep 15
managementpodname=$(oc get pods -l deployment=management -o jsonpath="{.items[0].metadata.name}")
echo $managementpodname
oc exec $managementpodname -- /bin/sh -c "cd /$(inputs.params.deployment-id)-data&&mkdir logs"
echo Create base deployment and test
oc project $(inputs.params.project)
oc apply -f deployment-$(inputs.params.deployment-id).yml
echo "Checking pod for running status"
sleep 5
pod_status=$(oc get pods -l deployment=$(inputs.params.deployment-id) -o jsonpath="{.items[0]['status.phase']}")
while :
do
if [[ $pod_status == "Running" ]]; then
echo "Pod is running"
break
fi
echo $pod_status
sleep 30
pod_status=$(oc get pods -l deployment=$(inputs.params.deployment-id) -o jsonpath="{.items[0]['status.phase']}")
done
podname=$(oc get pods -l deployment=$(inputs.params.deployment-id) -o jsonpath="{.items[0].metadata.name}")
echo "Pod Name: "
echo $podname
sleep 10
echo "Checking local container ping"
wget -qO- --header="accept: */*" http://localhost:8091/ping
local_ping=$(oc rsh $podname wget -qO- --header="accept: */*" http://localhost:$(inputs.params.port)/ping)
echo $local_ping
workspaces:
- name: source
apiVersion: tekton.dev/v1
kind: Task
metadata:
name: copy-maven-build-files
namespace: ecosystem
spec:
params:
- description: The name of the deployment
name: deployment-id
type: string
steps:
- name: copy-files
image: image-registry.openshift-image-registry.svc:5000/openshift/cli:latest
imagePullPolicy: IfNotPresent
workingDir: /workspace/source
script: |
#!/bin/bash
oc patch deployment management --type='json' -p='[{"op": "add", "path": "/spec/template/spec/containers/0/volumeMounts/-", "value": {"mountPath": "/$(inputs.params.deployment-id)-plugins", "name": "tc-ecosystem-disk", "subPath": "$(inputs.params.deployment-id)-plugins"}}]'
managementpodname=$(oc get pods -l deployment=management -o jsonpath="{.items[0].metadata.name}")
runtimepodname=$(oc get pods -l deployment=$(inputs.params.deployment-id) -o jsonpath="{.items[0].metadata.name}")
echo $managementpodname
echo $runtimepodname
cd /workspace/source/clone/
mkdir base
cd base
oc cp $runtimepodname:/app/com/ecosystem/. .
oc exec $managementpodname -- /bin/sh -c "cd /$(inputs.params.deployment-id)-plugins&&rm -r *"
oc cp . $managementpodname:/$(inputs.params.deployment-id)-plugins/
echo "Check for plugins folder and copy required files if present"
cd ..
if [ -d "target" ]; then
echo "Plugins folder found, copying to persistent volume claim"
cd target
oc cp /workspace/source/clone/target/classes/com/ecosystem/plugin/. $managementpodname:/$(inputs.params.deployment-id)-plugins/plugin/
oc cp /workspace/source/clone/target/classes/com/ecosystem/runtime/ProductMaster.class $managementpodname:/$(inputs.params.deployment-id)-plugins/runtime/ProductMaster.class
fi
workspaces:
- name: source
apiVersion: tekton.dev/v1
kind: Task
metadata:
name: create-runtime-and-endpoint
namespace: ecosystem
spec:
params:
- name: project
description: The namespace of the case to be deployed
type: string
- name: server-user
description: username for logging into the ecosystem server
type: string
- name: server-password
description: password for logging into the ecosystem server
type: string
- name: server-url
description: ecosystem server url
type: string
- name: port
description: The port number of the service
type: string
- description: The name of the deployment
name: deployment-id
type: string
- description: The name of the project containing the deployment
name: ecosystem-project
type: string
- description: The ecosystem version of the deployment
name: ecosystem-version
type: string
- description: The version of the runtime container
name: runtime-version
type: string
- description: The name of the pvc used by the deployments
name: nfs-volume-claim
type: string
- description: The number of replicas to be created
name: replica-number
type: string
- description: The ecosystem license key
name: ecosystem-key
type: string
- description: The number of seconds between refreshes for the dynamic interaction configurations
name: monitoring-delay
type: string
- description: The mongo connection string
name: mongo-connect
type: string
steps:
- name: create-deployment-config
image: image-registry.openshift-image-registry.svc:5000/ecosystem/ecosystem-python:0.1.30
imagePullPolicy: IfNotPresent
workingDir: /workspace/source
script: |
#!/usr/bin/env python
from prediction.apis import deployment_management as dm
import yaml
ecosystem_key = "$(inputs.params.ecosystem-key)"
monitoring_delay = $(inputs.params.monitoring-delay)
port = $(inputs.params.port)
deployment_id = "$(inputs.params.deployment-id)"
mongo_connect = "$(inputs.params.mongo-connect)"
version = "$(inputs.params.runtime-version)"
namespace = "$(inputs.params.project)"
volume = "$(inputs.params.nfs-volume-claim)"
replicas = $(inputs.params.replica-number)
port = $(inputs.params.port)
print(mongo_connect)
environment_variables = [
f"MASTER_KEY={ecosystem_key}",
f"MONITORING_DELAY={monitoring_delay}",
"CASSANDRA_CONFIG=/config/cassandra.conf",
"TZ=Africa/Johannesburg",
f"PORT={port}",
#f"MONGO_CONNECT={mongo_connect}"
]
deployment_config = dm.get_openshift_deployment_config(
name=deployment_id
, version=version
, environment_variables=environment_variables
, namespace=namespace
, volume=volume
, replicas=replicas
, port=port
)
deployment_config["spec"]["template"]["spec"]["containers"][0]["image"] = f"image-registry.openshift-image-registry.svc:5000/ecosystem/ecosystem-runtime:{version}"
deployment_config["spec"]["template"]["spec"]["containers"][0]["volumeMounts"].append(
{
"mountPath": "/app/com/ecosystem",
"name": "tc-ecosystem-disk",
"subPath": "$(inputs.params.deployment-id)-plugins"
}
)
print(deployment_config)
with open(f'deployment-{deployment_id}.yml', 'w+') as f:
yaml.dump(deployment_config, f)
- name: create-base-deployment
image: image-registry.openshift-image-registry.svc:5000/openshift/cli:latest
workingDir: /workspace/source
script: |
#!/bin/bash
echo Create base deployment and test
oc project $(inputs.params.project)
oc apply -f deployment-$(inputs.params.deployment-id).yml
echo "Checking pod for running status"
sleep 5
pod_status=$(oc get pods -l deployment=$(inputs.params.deployment-id) -o jsonpath="{.items[0]['status.phase']}")
while :
do
if [[ $pod_status == "Running" ]]; then
echo "Pod is running"
break
fi
echo $pod_status
sleep 30
pod_status=$(oc get pods -l deployment=$(inputs.params.deployment-id) -o jsonpath="{.items[0]['status.phase']}")
done
podname=$(oc get pods -l deployment=$(inputs.params.deployment-id) -o jsonpath="{.items[0].metadata.name}")
echo "Pod Name: "
echo $podname
sleep 10
echo "Checking local container ping"
local_ping=$(oc rsh $podname wget -qO- --header="accept: */*" http://localhost:$(inputs.params.port)/ping)
while :
do
if [[ $local_ping == *" successful"* ]]; then
echo "Local ping successful"
break
fi
echo $local_ping
sleep 10
local_ping=$(oc rsh $podname wget -qO- --header="accept: */*" http://localhost:$(inputs.params.port)/ping)
done
echo $local_ping
echo "Creating service"
oc expose deployment $(inputs.params.deployment-id) --port=$(inputs.params.port)
sleep 10
echo "Checking service ping"
service_ip=$(oc get service $(inputs.params.deployment-id) -o jsonpath='{.spec.clusterIP}')
service_ping=$(wget -qO- --header="accept: */*" http://${service_ip}:$(inputs.params.port)/ping)
if [[ $service_ping == *" successful"* ]]; then
echo "Service ping successful"
else
echo "Service ping failed, stopping setup"
exit 1
fi
echo "Creating route"
oc expose svc $(inputs.params.deployment-id) --port=$(inputs.params.port)
oc annotate route $(inputs.params.deployment-id) haproxy.router.openshift.io/balance=roundrobin
oc annotate route $(inputs.params.deployment-id) haproxy.router.openshift.io/disable_cookies='true'
sleep 15
echo "Checking route ping"
route_path=$(oc get route $(inputs.params.deployment-id) -o jsonpath='{.spec.host}')
route_ping=$(wget -qO- --header="accept: */*" http://${route_path}/ping)
if [[ $route_ping == *" successful"* ]]; then
echo "Route ping successful"
else
echo "Route ping failed, stopping setup"
exit 1
fi
echo "Route Path:"
echo http://${route_path}
echo http://${route_path} > route_name.txt
- name: update-deployment-and-refresh
image: image-registry.openshift-image-registry.svc:5000/ecosystem/ecosystem-python:0.1.30
imagePullPolicy: IfNotPresent
workingDir: /workspace/source
script: |
#!/usr/bin/env python
from prediction.apis import deployment_management as dm
from prediction.apis import ecosystem_generation_engine as ge
from prediction import jwt_access
auth = jwt_access.Authenticate("$(inputs.params.server-url)/api", "$(inputs.params.server-user)", "$(inputs.params.server-password)")
ecosystem_project = "$(inputs.params.ecosystem-project)"
ecosystem_version = "$(inputs.params.ecosystem-version)"
deployment_id = "$(inputs.params.deployment-id)"
deployment_step = dm.get_deployment_step(auth,ecosystem_project,deployment_id,ecosystem_version)
print(ecosystem_project,deployment_id,ecosystem_version)
print(deployment_step)
with open("route_name.txt", "r") as f:
for line in f:
route_name = line.strip()
print(route_name)
if deployment_step["project_status"] == "experiment":
deployment_step["paths"]["scoring_engine_path_dev"] = route_name
push_result = ge.process_push(auth,deployment_step)
if "ErrorMessage" in push_result:
print(push_result["ErrorMessage"])
else:
print(push_result["properties"])
workspaces:
- name: source
apiVersion: image.openshift.io/v1
kind: ImageStream
metadata:
name: ecosystem-python
namespace: ecosystem
spec:
lookupPolicy:
local: true
tags:
- name: "0.1.30"
from:
kind: DockerImage
name: docker.io/ecosystemai/ecosystem-algorithms:latest
referencePolicy:
type: Local
apiVersion: image.openshift.io/v1
kind: ImageStream
metadata:
name: maven
namespace: ecosystem
spec:
lookupPolicy:
local: true
tags:
- name: '3.9.9-amazoncorretto-17'
from:
kind: DockerImage
name: maven:3.9.9-amazoncorretto-17
referencePolicy:
type: Local
apiVersion: image.openshift.io/v1
kind: ImageStream
metadata:
name: ecosystem-runtime
namespace: ecosystem
spec:
lookupPolicy:
local: true
tags:
- name: "0.9.5.0"
from:
kind: DockerImage
name: docker.io/ecosystemai/ecosystem-runtime-solo:0.9.5.0
referencePolicy:
type: Local
apiVersion: tekton.dev/v1
kind: Pipeline
metadata:
name: ecosystem-deployment-pipeline
namespace: ecosystem
spec:
params:
- description: git repo url
name: git-url
type: string
- description: git repo branch
name: git-branch
type: string
default: main
- description: path of folder in git branch
name: git-branch-path
type: string
default: ""
- description: The namespace of the case to be deployed
name: project
type: string
default: bdp-rts-dev
- description: The name of the deployment
name: deployment-id
type: string
- description: The name of the project containing the deployment
name: ecosystem-project
type: string
- description: The ecosystem version of the deployment
name: ecosystem-version
type: string
- description: The ecosystem version of the deployment
name: runtime-version
type: string
- description: Prefix for the deployment name, should be oc-tc- or oc-fs-
name: prefix
type: string
default: "oc-fs-"
- description: Monitoring delayt environment variable
name: monitoring-delay
type: string
default: 600
- description: Mongo connection string
name: mongo-connect
type: string
- description: The storage class of the persistent volume claims
name: storage-class
type: string
default: thin
- name: nfs-volume-claim
type: string
default: tc-ecosystem-disk
- name: replica-number
type: string
default: 1
- name: environment-variables
description: Environment variables to be passed to the ecosystem.Ai runtime
type: array
- name: port
description: The port number of the service
type: string
default: 8091
- name: server-user
description: username for logging into the ecosystem server
type: string
- name: server-password
description: password for logging into the ecosystem server
type: string
- name: server-url
description: ecosystem server url
type: string
- name: ecosystem-key
description: The ecosystem license key
type: string
tasks:
- name: git-clone
params:
- name: url
value: $(params.git-url)
- name: revision
value: $(params.git-branch)
- name: refspec
value: ''
- name: submodules
value: 'false'
- name: depth
value: '1'
- name: sslVerify
value: 'false'
- name: crtFileName
value: ca-bundle.crt
- name: subdirectory
value: ''
- name: sparseCheckoutDirectories
value: $(params.git-branch-path)
- name: deleteExisting
value: 'true'
- name: httpProxy
value: ''
- name: httpsProxy
value: ''
- name: noProxy
value: ''
- name: verbose
value: 'true'
- name: gitInitImage
value: 'gcr.io/tekton-releases/github.com/tektoncd/pipeline/cmd/git-init:v0.40.2'
- name: userHome
value: /home/git
taskRef:
kind: Task
name: git-clone
workspaces:
- name: output
subPath: clone
workspace: source
- name: maven
params:
- name: MAVEN_IMAGE
value: 'image-registry.openshift-image-registry.svc:5000/ecosystem/maven:3.9.9-amazoncorretto-17'
- name: GOALS
value:
- clean
- compile
- name: MAVEN_MIRROR_URL
value: 'file:///workspace/maven-local-repo'
- name: SERVER_USER
value: ''
- name: SERVER_PASSWORD
value: ''
- name: PROXY_USER
value: ''
- name: PROXY_PASSWORD
value: ''
- name: PROXY_PORT
value: ''
- name: PROXY_HOST
value: ''
- name: PROXY_NON_PROXY_HOSTS
value: ''
- name: PROXY_PROTOCOL
value: http
- name: CONTEXT_DIR
value: .
runAfter:
- git-clone
taskRef:
kind: Task
name: maven
workspaces:
- name: source
subPath: clone
workspace: source
- name: maven-settings
workspace: custom-maven-settings
- name: maven-local-repo
subPath: m2-repo
workspace: maven-local-repo
- name: create-base-runtime
params:
- name: project
value: $(params.project)
- name: git-branch-path
value: $(params.git-branch-path)
- name: port
value: $(params.port)
- name: deployment-id
value: $(params.deployment-id)
- name: ecosystem-project
value: $(params.ecosystem-project)
- name: ecosystem-version
value: $(params.ecosystem-version)
- name: runtime-version
value: $(params.runtime-version)
- name: nfs-volume-claim
value: $(params.nfs-volume-claim)
- name: replica-number
value: $(params.replica-number)
- name: ecosystem-key
value: $(params.ecosystem-key)
- name: monitoring-delay
value: $(params.monitoring-delay)
- name: mongo-connect
value: $(params.mongo-connect)
runAfter:
- maven
taskRef:
kind: Task
name: create-base-runtime
workspaces:
- name: source
workspace: source
- name: copy-maven-build-files
params:
- name: deployment-id
value: $(params.deployment-id)
runAfter:
- create-base-runtime
taskRef:
kind: Task
name: copy-maven-build-files
workspaces:
- name: source
workspace: source
- name: create-runtime-and-endpoint
params:
- name: project
value: $(params.project)
- name: git-branch-path
value: $(params.git-branch-path)
- name: server-user
value: $(params.server-user)
- name: server-password
value: $(params.server-password)
- name: server-url
value: $(params.server-url)
- name: port
value: $(params.port)
- name: deployment-id
value: $(params.deployment-id)
- name: ecosystem-project
value: $(params.ecosystem-project)
- name: ecosystem-version
value: $(params.ecosystem-version)
- name: runtime-version
value: $(params.runtime-version)
- name: nfs-volume-claim
value: $(params.nfs-volume-claim)
- name: replica-number
value: $(params.replica-number)
- name: ecosystem-key
value: $(params.ecosystem-key)
- name: monitoring-delay
value: $(params.monitoring-delay)
- name: mongo-connect
value: $(params.mongo-connect)
runAfter:
- copy-maven-build-files
taskRef:
kind: Task
name: create-runtime-and-endpoint
workspaces:
- name: source
workspace: source
workspaces:
- name: source
- name: custom-maven-settings
- name: maven-local-repo
apiVersion: tekton.dev/v1
kind: PipelineRun
metadata:
name: maven-test-pipeline-run
spec:
pipelineRef:
name: ecosystem-deployment-pipeline
params:
- name: git-url
value: 'https://github.com/ecogenetic/example-dynamic-recommender.git'
- name: git-branch
value: ""
- name: git-branch-path
value: ""
- name: prefix
value: ""
- name: environment-variables
value:
- MASTER_KEY=<Key>
- MONITORING_DELAY=120
- FEATURE_DELAY=99999
- CASSANDRA_CONFIG=/config/cassandra.conf
- TZ=Africa/Johannesburg
- PORT=8999
- name: server-user
value: user@ecosystem.ai
- name: deployment-id
value: example-dynamic-recommender
- name: ecosystem-project
value: "Example Project"
- name: ecosystem-version
value: "001"
- name: runtime-version
value: "0.9.5.0"
- name: project
value: "ecosystem"
workspaces:
- name: custom-maven-settings
configMap:
name: custom-maven-settings
- name: source
volumeClaimTemplate:
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 1Gi
- name: maven-local-repo
persistentvolumeclaim:
claimName: tc-ecosystem-disk Last updated on