Example Helm Deployments

predictor with canary

Setup Seldon Core

Use the setup notebook to Setup Cluster with Ambassador Ingress and Install Seldon Core.

!kubectl create namespace seldon

Error from server (AlreadyExists): namespaces "seldon" already exists

VERSION = !cat ../version.txt
VERSION = VERSION[0]
VERSION

'1.19.0-dev'

Serve Single Model

!helm upgrade -i mymodel ../helm-charts/seldon-single-model --set model.image=seldonio/mock_classifier:$VERSION --namespace seldon

Release "mymodel" has been upgraded. Happy Helming!
NAME: mymodel
LAST DEPLOYED: Thu Dec  4 12:11:31 2025
NAMESPACE: seldon
STATUS: deployed
REVISION: 3
TEST SUITE: None

!helm template mymodel ../helm-charts/seldon-single-model --set model.image=seldonio/mock_classifier:$VERSION | pygmentize -l json

---
# Source: seldon-single-model/templates/seldondeployment.json
{
  "kind": "SeldonDeployment",
  "apiVersion": "machinelearning.seldon.io/v1",
  "metadata": {
    "name": "mymodel",
    "namespace": "seldon",
    "labels": {}
  },
  "spec": {
      "name": "mymodel",
      "protocol": "seldon",
    "annotations": {},
    "predictors": [
      {
        "name": "default",
        "graph": {
          "name": "model",
          "type": "MODEL",
        },
        "componentSpecs": [
          {
            "spec": {
              "containers": [
                {
                  "name": "model",
                  "image": "seldonio/mock_classifier:1.19.0-dev",
                  "env": [
                      {
                        "name": "LOG_LEVEL",
                        "value": "INFO"
                      },
                    ],
                  "resources": {"requests":{"memory":"1Mi"}},
                }
              ]
            },
          }
        ],
        "replicas": 1
      }
    ]
  }
}

!kubectl wait sdep/mymodel \
  --for=condition=ready \
  --timeout=120s \
  -n seldon

seldondeployment.machinelearning.seldon.io/mymodel condition met

Get predictions

from seldon_core.seldon_client import SeldonClient

sc = SeldonClient(
    deployment_name="mymodel",
    namespace="seldon",
    gateway_endpoint="localhost:8003",
    gateway="ambassador",
)

REST Request

from tenacity import retry, stop_after_delay, wait_exponential

@retry(stop=stop_after_delay(300), wait=wait_exponential(multiplier=1, min=0.5, max=5))
def predict():
    r = sc.predict(transport="rest")
    assert r.success == True
    return r

predict()

Success:True message:
Request:
meta {
}
data {
  tensor {
    shape: 1
    shape: 1
    values: 0.71543328677795837
  }
}

Response:
{'data': {'names': ['proba'], 'tensor': {'shape': [1, 1], 'values': [0.09963978586361734]}}, 'meta': {'requestPath': {'model': 'seldonio/mock_classifier:1.19.0-dev'}}}

Response: {'data': {'names': ['proba'], 'tensor': {'shape': [1, 1], 'values': [0.05335370865277927]}}, 'meta': {}}

Response:
{'data': {'names': ['proba'], 'tensor': {'shape': [1, 1], 'values': [0.05335370865277927]}}, 'meta': {}}

GRPC Request

@retry(stop=stop_after_delay(300), wait=wait_exponential(multiplier=1, min=0.5, max=5))
def predict():
    r = sc.predict(transport="grpc")
    assert r.success == True
    return r

predict()

Success:True message:
Request:
{'meta': {}, 'data': {'tensor': {'shape': [1, 1], 'values': [0.555819139294561]}}}
Response:
{'meta': {'requestPath': {'model': 'seldonio/mock_classifier:1.19.0-dev'}}, 'data': {'names': ['proba'], 'tensor': {'shape': [1, 1], 'values': [0.08620740652415673]}}}

!helm delete mymodel --namespace seldon

release "mymodel" uninstalled

Serve REST AB Test

!helm upgrade -i myabtest ../helm-charts/seldon-abtest --namespace seldon

Release "myabtest" does not exist. Installing it now.
NAME: myabtest
LAST DEPLOYED: Thu Dec  4 12:12:21 2025
NAMESPACE: seldon
STATUS: deployed
REVISION: 1
TEST SUITE: None

!helm template ../helm-charts/seldon-abtest | pygmentize -l json

---
# Source: seldon-abtest/templates/ab_test_2pods.json
{
    "apiVersion": "machinelearning.seldon.io/v1alpha2",
    "kind": "SeldonDeployment",
    "metadata": {
	"labels": {
	    "app": "seldon"
	},
	"name": "release-name"
    },
    "spec": {
	"name": "release-name",
	"predictors": [
	    {
		"name": "default",
		"replicas": 1,
		"componentSpecs": [{
		    "spec": {
			"containers": [
			    {
                                "image": "seldonio/mock_classifier:1.19.0-dev",
				"imagePullPolicy": "IfNotPresent",
				"name": "classifier-1",
				"resources": {
				    "requests": {
					"memory": "1Mi"
				    }
				}
			    }],
			"terminationGracePeriodSeconds": 20
		    }},
	        {
		    "metadata":{
			"labels":{
			    "version":"v2"
			}
		    },    
			"spec":{
			    "containers":[
				{
                                "image": "seldonio/mock_classifier:1.19.0-dev",
				"imagePullPolicy": "IfNotPresent",
				"name": "classifier-2",
				"resources": {
				    "requests": {
					"memory": "1Mi"
				    }
				}
			    }
			],
			"terminationGracePeriodSeconds": 20
				   }
				   }],
		"graph": {
		    "name": "release-name",
		    "implementation":"RANDOM_ABTEST",
		    "parameters": [
			{
			    "name":"ratioA",
			    "value":"0.5",
			    "type":"FLOAT"
			}
		    ],
		    "children": [
			{
			    "name": "classifier-1",
			    "type":"MODEL",
			    "children":[]
			},
			{
			    "name": "classifier-2",
			    "type":"MODEL",
			    "children":[]
			}   
		    ]
		}
	    }
	]
    }
}

!kubectl wait sdep/myabtest \
  --for=condition=ready \
  --timeout=120s \
  -n seldon

seldondeployment.machinelearning.seldon.io/myabtest condition met

Get predictions

from seldon_core.seldon_client import SeldonClient

sc = SeldonClient(
    deployment_name="myabtest",
    namespace="seldon",
    gateway_endpoint="localhost:8003",
    gateway="ambassador",
)

REST Request

@retry(stop=stop_after_delay(300), wait=wait_exponential(multiplier=1, min=0.5, max=5))
def predict():
    r = sc.predict(transport="rest")
    assert r.success == True
    return r

predict()

Success:True message:
Request:
meta {
}
data {
  tensor {
    shape: 1
    shape: 1
    values: 0.26095295840328658
  }
}

Response:
{'data': {'names': ['proba'], 'tensor': {'shape': [1, 1], 'values': [0.0656377202541611]}}, 'meta': {'requestPath': {'classifier-2': 'seldonio/mock_classifier:1.19.0-dev'}}}

Response: {'data': {'names': ['proba'], 'tensor': {'shape': [1, 1], 'values': [0.11299965170860979]}}, 'meta': {}}

Response:
{'data': {'names': ['proba'], 'tensor': {'shape': [1, 1], 'values': [0.11299965170860979]}}, 'meta': {}}

gRPC Request

@retry(stop=stop_after_delay(300), wait=wait_exponential(multiplier=1, min=0.5, max=5))
def predict():
    r = sc.predict(transport="grpc")
    assert r.success == True
    return r

predict()

Success:True message:
Request:
{'meta': {}, 'data': {'tensor': {'shape': [1, 1], 'values': [0.163692647815478]}}}
Response:
{'meta': {'requestPath': {'classifier-2': 'seldonio/mock_classifier:1.19.0-dev'}}, 'data': {'names': ['proba'], 'tensor': {'shape': [1, 1], 'values': [0.05991890833984506]}}}

!helm delete myabtest --namespace seldon

release "myabtest" uninstalled

Serve REST Multi-Armed Bandit

!helm upgrade -i mymab ../helm-charts/seldon-mab --namespace seldon

Release "mymab" does not exist. Installing it now.
NAME: mymab
LAST DEPLOYED: Thu Dec  4 12:13:04 2025
NAMESPACE: seldon
STATUS: deployed
REVISION: 1
TEST SUITE: None

!helm template ../helm-charts/seldon-mab | pygmentize -l json

---
# Source: seldon-mab/templates/mab.json
{
    "apiVersion": "machinelearning.seldon.io/v1alpha2",
    "kind": "SeldonDeployment",
    "metadata": {
		"labels": {"app":"seldon"},
		"name": "release-name"
    },
    "spec": {
	"name": "release-name",
	"predictors": [
	    {
		"name": "default",
		"replicas": 1,
		"componentSpecs": [{
		    "spec": {
			"containers": [
			    {
                                "image": "seldonio/mock_classifier:1.19.0-dev",				
				"imagePullPolicy": "IfNotPresent",
				"name": "classifier-1",
				"resources": {
				    "requests": {
					"memory": "1Mi"
				    }
				}
			    }],
			"terminationGracePeriodSeconds": 20
		    }},
	        {
			"spec":{
			    "containers":[
				{
                                "image": "seldonio/mock_classifier:1.19.0-dev",								    
				"imagePullPolicy": "IfNotPresent",
				"name": "classifier-2",
				"resources": {
				    "requests": {
					"memory": "1Mi"
				    }
				}
			    }
			],
			"terminationGracePeriodSeconds": 20
			}
		},
	        {
		    "spec":{
			"containers": [{
                            "image": "seldonio/mab_epsilon_greedy:1.19.0-dev",								    			    
			    "name": "eg-router"
			}],
			"terminationGracePeriodSeconds": 20
		    }}
	        ],
		"graph": {
		    "name": "eg-router",
		    "type":"ROUTER",
		    "parameters": [
			{
			    "name": "n_branches",
			    "value": "2",
			    "type": "INT"
			},
			{
			    "name": "epsilon",
			    "value": "0.2",
			    "type": "FLOAT"
			},
			{
			    "name": "verbose",
			    "value": "1",
			    "type": "BOOL"
			}
		    ],
		    "children": [
			{
			    "name": "classifier-1",
			    "type":"MODEL",
			    "children":[]
			},
			{
			    "name": "classifier-2",
			    "type":"MODEL",
			    "children":[]
			}   
		    ]
		},
		"svcOrchSpec": {
		"resources": {"requests":{"cpu":"0.1"}},
"env": [
{
"name": "SELDON_LOG_MESSAGES_EXTERNALLY",
"value": "false"
},
{
"name": "SELDON_LOG_MESSAGE_TYPE",
"value": "seldon.message.pair"
},
{
"name": "SELDON_LOG_REQUESTS",
"value": "false"
},
{
"name": "SELDON_LOG_RESPONSES",
"value": "false"
},
]
},
		"labels": {"fluentd":"true","version":"1.19.0-dev"}
	    }
	]
    }
}

!kubectl wait sdep/mymab \
  --for=condition=ready \
  --timeout=120s \
  -n seldon

seldondeployment.machinelearning.seldon.io/mymab condition met

Get predictions

from seldon_core.seldon_client import SeldonClient

sc = SeldonClient(
    deployment_name="mymab",
    namespace="seldon",
    gateway_endpoint="localhost:8003",
    gateway="ambassador",
)

REST Request

@retry(stop=stop_after_delay(300), wait=wait_exponential(multiplier=1, min=0.5, max=5))
def predict():
    r = sc.predict(transport="rest")
    assert r.success == True
    return r

predict()

Success:True message:
Request:
meta {
}
data {
  tensor {
    shape: 1
    shape: 1
    values: 0.13298918130078008
  }
}

Response:
{'data': {'names': ['proba'], 'tensor': {'shape': [1, 1], 'values': [0.058212613572549546]}}, 'meta': {'requestPath': {'classifier-1': 'seldonio/mock_classifier:1.19.0-dev'}}}

Response: {'data': {'names': ['proba'], 'tensor': {'shape': [1, 1], 'values': [0.05643175042558145]}}, 'meta': {}}

Response:
{'data': {'names': ['proba'], 'tensor': {'shape': [1, 1], 'values': [0.05643175042558145]}}, 'meta': {}}

gRPC Request

@retry(stop=stop_after_delay(300), wait=wait_exponential(multiplier=1, min=0.5, max=5))
def predict():
    r = sc.predict(transport="grpc")
    assert r.success == True
    return r

predict()

Success:True message:
Request:
{'meta': {}, 'data': {'tensor': {'shape': [1, 1], 'values': [0.1279822023238696]}}}
Response:
{'meta': {'requestPath': {'classifier-1': 'seldonio/mock_classifier:1.19.0-dev'}}, 'data': {'names': ['proba'], 'tensor': {'shape': [1, 1], 'values': [0.05793871786684459]}}}

!helm delete mymab --namespace seldon

release "mymab" uninstalled