Batch

Requires mlserver to be installed.

Deprecated: The MLServer CLI infer feature is experimental and will be removed in future work.

pip install mlserver

Note: The Seldon CLI allows you to view information about underlying Seldon resources and make changes to them through the scheduler in non-Kubernetes environments. However, it cannot modify underlying manifests within a Kubernetes cluster. Therefore, using the Seldon CLI for control plane operations in a Kubernetes environment is not recommended. For more details, see Seldon CLI.

Seldon V2 Batch Examples

import os
os.environ["NAMESPACE"] = "seldon-mesh"

MESH_IP=!kubectl get svc seldon-mesh -n ${NAMESPACE} -o jsonpath='{.status.loadBalancer.ingress[0].ip}'
MESH_IP=MESH_IP[0]
import os
os.environ['MESH_IP'] = MESH_IP
MESH_IP

'172.18.255.2'

Deploy Models and Pipelines

cat models/sklearn-iris-gs.yaml

apiVersion: mlops.seldon.io/v1alpha1
kind: Model
metadata:
  name: iris
spec:
  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.3.5/iris-sklearn"
  requirements:
  - sklearn
  memory: 100Ki

cat pipelines/iris.yaml

apiVersion: mlops.seldon.io/v1alpha1
kind: Pipeline
metadata:
  name: iris-pipeline
spec:
  steps:
    - name: iris
  output:
    steps:
    - iris

cat models/tfsimple1.yaml

apiVersion: mlops.seldon.io/v1alpha1
kind: Model
metadata:
  name: tfsimple1
spec:
  storageUri: "gs://seldon-models/triton/simple"
  requirements:
  - tensorflow
  memory: 100Ki

cat pipelines/tfsimple.yaml

apiVersion: mlops.seldon.io/v1alpha1
kind: Pipeline
metadata:
  name: tfsimple
spec:
  steps:
    - name: tfsimple1
  output:
    steps:
    - tfsimple1

kubectl apply -f models/sklearn-iris-gs.yaml -n ${NAMESPACE}
kubectl apply -f pipelines/iris.yaml -n ${NAMESPACE}

kubectl apply -f models/tfsimple1.yaml -n ${NAMESPACE}
kubectl apply -f pipelines/tfsimple.yaml -n ${NAMESPACE}

model.mlops.seldon.io/iris created
pipeline.mlops.seldon.io/iris-pipeline created
model.mlops.seldon.io/tfsimple1 created
pipeline.mlops.seldon.io/tfsimple created

kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}
kubectl wait --for condition=ready --timeout=300s pipelines --all -n ${NAMESPACE}

model.mlops.seldon.io/iris condition met
model.mlops.seldon.io/tfsimple1 condition met
pipeline.mlops.seldon.io/iris-pipeline condition met
pipeline.mlops.seldon.io/tfsimple condition met

Test Predictions

seldon model infer iris --inference-host ${MESH_IP}:80 \
  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}' | jq -M .

{
  "model_name": "iris_1",
  "model_version": "1",
  "id": "25e1c1b9-a20f-456d-bdff-c75d5ba83b1f",
  "parameters": {},
  "outputs": [
    {
      "name": "predict",
      "shape": [
        1,
        1
      ],
      "datatype": "INT64",
      "parameters": {
        "content_type": "np"
      },
      "data": [
        2
      ]
    }
  ]
}

seldon pipeline infer iris-pipeline --inference-host ${MESH_IP}:80 \
  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}' |  jq -M .

{
  "model_name": "",
  "outputs": [
    {
      "data": [
        2
      ],
      "name": "predict",
      "shape": [
        1,
        1
      ],
      "datatype": "INT64",
      "parameters": {
        "content_type": "np"
      }
    }
  ]
}

seldon model infer tfsimple1 --inference-host ${MESH_IP}:80 \
  '{"outputs":[{"name":"OUTPUT0"}], "inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}' | jq -M .

{
  "model_name": "tfsimple1_1",
  "model_version": "1",
  "outputs": [
    {
      "name": "OUTPUT0",
      "datatype": "INT32",
      "shape": [
        1,
        16
      ],
      "data": [
        2,
        4,
        6,
        8,
        10,
        12,
        14,
        16,
        18,
        20,
        22,
        24,
        26,
        28,
        30,
        32
      ]
    }
  ]
}

seldon pipeline infer tfsimple --inference-host ${MESH_IP}:80 \
  '{"outputs":[{"name":"OUTPUT0"}], "inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}' | jq -M .

{
  "model_name": "",
  "outputs": [
    {
      "data": [
        2,
        4,
        6,
        8,
        10,
        12,
        14,
        16,
        18,
        20,
        22,
        24,
        26,
        28,
        30,
        32
      ],
      "name": "OUTPUT0",
      "shape": [
        1,
        16
      ],
      "datatype": "INT32"
    },
    {
      "data": [
        0,
        0,
        0,
        0,
        0,
        0,
        0,
        0,
        0,
        0,
        0,
        0,
        0,
        0,
        0,
        0
      ],
      "name": "OUTPUT1",
      "shape": [
        1,
        16
      ],
      "datatype": "INT32"
    }
  ]
}

MLServer Iris Batch Job

cat batch-inputs/iris-input.txt | head -n 1 | jq -M .

{
  "inputs": [
    {
      "name": "predict",
      "data": [
        0.38606369295833043,
        0.006894049558299753,
        0.6104082981607108,
        0.3958954239450676
      ],
      "datatype": "FP64",
      "shape": [
        1,
        4
      ]
    }
  ]
}

%%bash
mlserver infer -u ${MESH_IP} -m iris -i batch-inputs/iris-input.txt -o /tmp/iris-output.txt --workers 5

2023-06-30 11:05:32,389 [mlserver] INFO - server url: 172.18.255.2
2023-06-30 11:05:32,389 [mlserver] INFO - model name: iris
2023-06-30 11:05:32,389 [mlserver] INFO - request headers: {}
2023-06-30 11:05:32,389 [mlserver] INFO - input file path: batch-inputs/iris-input.txt
2023-06-30 11:05:32,389 [mlserver] INFO - output file path: /tmp/iris-output.txt
2023-06-30 11:05:32,389 [mlserver] INFO - workers: 5
2023-06-30 11:05:32,389 [mlserver] INFO - retries: 3
2023-06-30 11:05:32,389 [mlserver] INFO - batch interval: 0.0
2023-06-30 11:05:32,389 [mlserver] INFO - batch jitter: 0.0
2023-06-30 11:05:32,389 [mlserver] INFO - connection timeout: 60
2023-06-30 11:05:32,389 [mlserver] INFO - micro-batch size: 1
2023-06-30 11:05:32,503 [mlserver] INFO - Finalizer: processed instances: 100
2023-06-30 11:05:32,503 [mlserver] INFO - Total processed instances: 100
2023-06-30 11:05:32,503 [mlserver] INFO - Time taken: 0.11 seconds

%%bash
mlserver infer -u ${MESH_IP} -m iris-pipeline.pipeline -i batch-inputs/iris-input.txt -o /tmp/iris-pipeline-output.txt --workers 5

2023-06-30 11:05:35,857 [mlserver] INFO - server url: 172.18.255.2
2023-06-30 11:05:35,858 [mlserver] INFO - model name: iris-pipeline.pipeline
2023-06-30 11:05:35,858 [mlserver] INFO - request headers: {}
2023-06-30 11:05:35,858 [mlserver] INFO - input file path: batch-inputs/iris-input.txt
2023-06-30 11:05:35,858 [mlserver] INFO - output file path: /tmp/iris-pipeline-output.txt
2023-06-30 11:05:35,858 [mlserver] INFO - workers: 5
2023-06-30 11:05:35,858 [mlserver] INFO - retries: 3
2023-06-30 11:05:35,858 [mlserver] INFO - batch interval: 0.0
2023-06-30 11:05:35,858 [mlserver] INFO - batch jitter: 0.0
2023-06-30 11:05:35,858 [mlserver] INFO - connection timeout: 60
2023-06-30 11:05:35,858 [mlserver] INFO - micro-batch size: 1
2023-06-30 11:05:36,145 [mlserver] INFO - Finalizer: processed instances: 100
2023-06-30 11:05:36,146 [mlserver] INFO - Total processed instances: 100
2023-06-30 11:05:36,146 [mlserver] INFO - Time taken: 0.29 seconds

cat /tmp/iris-output.txt | head -n 1 | jq -M .

{
  "model_name": "iris_1",
  "model_version": "1",
  "id": "46bdfca2-8805-4a72-b1ce-95e4f38c1a19",
  "parameters": {
    "inference_id": "46bdfca2-8805-4a72-b1ce-95e4f38c1a19",
    "batch_index": 0
  },
  "outputs": [
    {
      "name": "predict",
      "shape": [
        1,
        1
      ],
      "datatype": "INT64",
      "parameters": {
        "content_type": "np"
      },
      "data": [
        1
      ]
    }
  ]
}

cat /tmp/iris-pipeline-output.txt | head -n 1 | jq .

{
  "model_name": "",
  "id": "37e8c013-b348-41e8-89b9-fea86a4f9632",
  "parameters": {
    "batch_index": 1
  },
  "outputs": [
    {
      "name": "predict",
      "shape": [
        1,
        1
      ],
      "datatype": "INT64",
      "parameters": {
        "content_type": "np"
      },
      "data": [
        1
      ]
    }
  ]
}

Triton TFSimple Batch Job

cat batch-inputs/tfsimple-input.txt | head -n 1 | jq -M .

{
  "inputs": [
    {
      "name": "INPUT0",
      "data": [
        75,
        39,
        9,
        44,
        32,
        97,
        99,
        40,
        13,
        27,
        25,
        36,
        18,
        77,
        62,
        60
      ],
      "datatype": "INT32",
      "shape": [
        1,
        16
      ]
    },
    {
      "name": "INPUT1",
      "data": [
        39,
        7,
        14,
        58,
        13,
        88,
        98,
        66,
        97,
        57,
        49,
        3,
        49,
        63,
        37,
        12
      ],
      "datatype": "INT32",
      "shape": [
        1,
        16
      ]
    }
  ]
}

%%bash
mlserver infer -u ${MESH_IP} -m tfsimple1 -i batch-inputs/tfsimple-input.txt -o /tmp/tfsimple-output.txt --workers 5 -b

2023-06-30 11:22:52,662 [mlserver] INFO - server url: 172.18.255.2
2023-06-30 11:22:52,662 [mlserver] INFO - model name: tfsimple1
2023-06-30 11:22:52,662 [mlserver] INFO - request headers: {}
2023-06-30 11:22:52,662 [mlserver] INFO - input file path: batch-inputs/tfsimple-input.txt
2023-06-30 11:22:52,662 [mlserver] INFO - output file path: /tmp/tfsimple-output.txt
2023-06-30 11:22:52,662 [mlserver] INFO - workers: 5
2023-06-30 11:22:52,662 [mlserver] INFO - retries: 3
2023-06-30 11:22:52,662 [mlserver] INFO - batch interval: 0.0
2023-06-30 11:22:52,662 [mlserver] INFO - batch jitter: 0.0
2023-06-30 11:22:52,662 [mlserver] INFO - connection timeout: 60
2023-06-30 11:22:52,662 [mlserver] INFO - micro-batch size: 1
2023-06-30 11:22:52,755 [mlserver] INFO - Finalizer: processed instances: 100
2023-06-30 11:22:52,755 [mlserver] INFO - Total processed instances: 100
2023-06-30 11:22:52,756 [mlserver] INFO - Time taken: 0.09 seconds

%%bash
mlserver infer -u ${MESH_IP} -m tfsimple.pipeline -i batch-inputs/tfsimple-input.txt -o /tmp/tfsimple-pipeline-output.txt --workers 5

2023-06-30 11:22:54,065 [mlserver] INFO - server url: 172.18.255.2
2023-06-30 11:22:54,065 [mlserver] INFO - model name: tfsimple.pipeline
2023-06-30 11:22:54,065 [mlserver] INFO - request headers: {}
2023-06-30 11:22:54,065 [mlserver] INFO - input file path: batch-inputs/tfsimple-input.txt
2023-06-30 11:22:54,065 [mlserver] INFO - output file path: /tmp/tfsimple-pipeline-output.txt
2023-06-30 11:22:54,065 [mlserver] INFO - workers: 5
2023-06-30 11:22:54,065 [mlserver] INFO - retries: 3
2023-06-30 11:22:54,065 [mlserver] INFO - batch interval: 0.0
2023-06-30 11:22:54,065 [mlserver] INFO - batch jitter: 0.0
2023-06-30 11:22:54,065 [mlserver] INFO - connection timeout: 60
2023-06-30 11:22:54,065 [mlserver] INFO - micro-batch size: 1
2023-06-30 11:22:54,302 [mlserver] INFO - Finalizer: processed instances: 100
2023-06-30 11:22:54,302 [mlserver] INFO - Total processed instances: 100
2023-06-30 11:22:54,303 [mlserver] INFO - Time taken: 0.24 seconds

cat /tmp/tfsimple-output.txt | head -n 1 | jq -M .

{
  "model_name": "tfsimple1_1",
  "model_version": "1",
  "id": "19952272-b023-4079-aa08-f1880ded05e5",
  "parameters": {
    "inference_id": "19952272-b023-4079-aa08-f1880ded05e5",
    "batch_index": 1
  },
  "outputs": [
    {
      "name": "OUTPUT0",
      "shape": [
        1,
        16
      ],
      "datatype": "INT32",
      "parameters": {},
      "data": [
        115,
        69,
        97,
        112,
        73,
        106,
        58,
        182,
        114,
        66,
        64,
        110,
        100,
        24,
        22,
        77
      ]
    },
    {
      "name": "OUTPUT1",
      "shape": [
        1,
        16
      ],
      "datatype": "INT32",
      "parameters": {},
      "data": [
        -77,
        33,
        25,
        -52,
        -49,
        -88,
        -48,
        0,
        -50,
        26,
        -44,
        46,
        -2,
        18,
        -6,
        -47
      ]
    }
  ]
}

cat /tmp/tfsimple-pipeline-output.txt | head -n 1 | jq -M .

{
  "model_name": "",
  "id": "46b05aab-07d9-414d-be96-c03d1863552a",
  "parameters": {
    "batch_index": 3
  },
  "outputs": [
    {
      "name": "OUTPUT0",
      "shape": [
        1,
        16
      ],
      "datatype": "INT32",
      "data": [
        140,
        164,
        85,
        58,
        152,
        76,
        70,
        56,
        100,
        141,
        98,
        181,
        115,
        177,
        106,
        193
      ]
    },
    {
      "name": "OUTPUT1",
      "shape": [
        1,
        16
      ],
      "datatype": "INT32",
      "data": [
        -10,
        0,
        -11,
        -38,
        2,
        -36,
        -52,
        -8,
        -18,
        57,
        94,
        -5,
        -27,
        17,
        58,
        -1
      ]
    }
  ]
}

Cleanup

kubectl delete -f models/sklearn-iris-gs.yaml -n ${NAMESPACE}
kubectl delete -f pipelines/iris.yaml -n ${NAMESPACE}

model.mlops.seldon.io "iris" deleted
pipeline.mlops.seldon.io "iris-pipeline" deleted

kubectl delete -f models/tfsimple1.yaml -n ${NAMESPACE}
kubectl delete -f pipelines/tfsimple.yaml -n ${NAMESPACE}

model.mlops.seldon.io "tfsimple1" deleted
pipeline.mlops.seldon.io "tfsimple" deleted