Sample Models

If ModelMesh Serving was deployed using --quickstart, a set of example models are shared via a MinIO instance to use when getting started with ModelMesh Serving and experimenting with the provided runtimes.

InferenceServices

The config/example-isvcs directory contains InferenceService manifests for many of the example models. Assuming that the entry specified below is added to the storage configuration secret, the InferenceServices can be deployed and used for experimentation.

Naming Conventions

Models are organized into virtual directories based on model type:

s3://modelmesh-example-models/
└── <model-type>/
    ├── <model-name-file>
    └── <model-name>/
        └── <model-data>

Available Models

Virtual Object Tree

s3://modelmesh-example-models/
├── keras
│   └── mnist.h5
├── lightgbm
│   └── mushroom.bst
│   └── mushroom-fil
│       ├── 1
│       │   └── model.txt
│       └── config.pbtxt
├── onnx
│   └── mnist.onnx
├── pytorch
│   └── cifar
│       ├── 1
│       │   └── model.pt
│       └── config.pbtxt
├── sklearn
│   └── mnist-svm.joblib
├── tensorflow
│   └── mnist.savedmodel
│       ├── saved_model.pb
│       └── variables
│           ├── variables.data-00000-of-00001
│           └── variables.index
└── xgboost
    └── mushroom.json
    └── mushroom-fil
        ├── 1
        │   └── xgboost.json
        └── config.pbtxt

Example Inference Requests

Below are example inference requests for interacting with the above sample models. To learn more about how to send inference requests, please see here.

Keras

This is a sample inference request to a Keras model trained on the MNIST dataset.

MODEL_NAME=example-keras-mnist
grpcurl \
  -plaintext \
  -proto fvt/proto/kfs_inference_v2.proto \
  -d '{ "model_name": "'"${MODEL_NAME}"'", "inputs": [{ "name": "conv2d_input", "shape": [1, 28, 28, 1], "datatype": "FP32", "contents": { "fp32_contents": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01176471, 0.07058824, 0.07058824, 0.07058824, 0.49411765, 0.53333336, 0.6862745, 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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] }}]}' \
  localhost:8033 \
  inference.GRPCInferenceService.ModelInfer

Response:

{
  "modelName": "example-keras-mnist__ksp-f3ccc51d50",
  "modelVersion": "1",
  "outputs": [
    {
      "name": "dense_1",
      "datatype": "FP32",
      "shape": ["1", "10"]
    }
  ],
  "rawOutputContents": [
    "W62FLsHAVi+KZrwtL+AdOw1jZygaYn8/9PDqMNc7wy9DOLYy94CYNA=="
  ]
}

LightGBM

This is a sample inference request to a LightGBM model trained on a mushroom dataset.

MODEL_NAME=example-lightgbm-mushroom
grpcurl \
  -plaintext \
  -proto fvt/proto/kfs_inference_v2.proto \
  -d '{ "model_name": "'"${MODEL_NAME}"'", "inputs": [{ "name": "predict", "shape": [1, 126], "datatype": "FP32", "contents": { "fp32_contents": [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0] }}]}' \
  localhost:8033 \
  inference.GRPCInferenceService.ModelInfer

Response:

{
  "modelName": "example-lightgbm-mushroom__ksp-297b372253",
  "outputs": [
    {
      "name": "predict",
      "datatype": "FP64",
      "shape": ["1"],
      "contents": {
        "fp64Contents": [2.1157326350598036e-5]
      }
    }
  ]
}

ONNX

This is a sample inference request to an ONNX model trained on the MNIST dataset.

MODEL_NAME=example-onnx-mnist
grpcurl \
  -plaintext \
  -proto fvt/proto/kfs_inference_v2.proto \
  -d '{ "model_name": "'"${MODEL_NAME}"'", "inputs": [{ "name": "Input3", "shape": [1, 1, 28, 28], "datatype": "FP32", "contents": { "fp32_contents": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01176471, 0.07058824, 0.07058824, 0.07058824, 0.49411765, 0.53333336, 0.6862745, 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  localhost:8033 \
  inference.GRPCInferenceService.ModelInfer

Response:

{
  "modelName": "example-onnx-mnist__ksp-0dbc77db1a",
  "modelVersion": "1",
  "outputs": [
    {
      "name": "Plus214_Output_0",
      "datatype": "FP32",
      "shape": ["1", "10"]
    }
  ],
  "rawOutputContents": [
    "mLoDwY/998CcHVvA9M5FQQREQcH7IYpBtyIpwUCMNj/5llRAAVquPw=="
  ]
}

PyTorch

This is a sample inference request to a PyTorch model trained on the CIFAR dataset.

MODEL_NAME=example-pytorch-cifar
grpcurl \
  -plaintext \
  -proto fvt/proto/kfs_inference_v2.proto \
  -d '{ "model_name": "'"${MODEL_NAME}"'", "inputs": [{ "name": "INPUT__0", "shape": [1, 3, 32, 32], "datatype": "FP32", "contents": { "fp32_contents": [0.84313726, 0.8117647, 0.81960785, 0.81960785, 0.81960785, 0.81960785, 0.81960785, 0.81960785, 0.81960785, 0.81960785, 0.827451, 0.827451, 0.827451, 0.827451, 0.827451, 0.827451, 0.827451, 0.81960785, 0.8117647, 0.8039216, 0.81960785, 0.81960785, 0.81960785, 0.827451, 0.81960785, 0.827451, 0.81960785, 0.81960785, 0.81960785, 0.827451, 0.827451, 0.81960785, 0.8666667, 0.84313726, 0.84313726, 0.84313726, 0.84313726, 0.84313726, 0.84313726, 0.84313726, 0.84313726, 0.84313726, 0.8509804, 0.8509804, 0.8509804, 0.8509804, 0.8509804, 0.8509804, 0.85882354, 0.8509804, 0.8509804, 0.8352941, 0.8352941, 0.8352941, 0.84313726, 0.8509804, 0.8509804, 0.8509804, 0.84313726, 0.84313726, 0.84313726, 0.8509804, 0.8509804, 0.84313726, 0.85882354, 0.8352941, 0.8352941, 0.8352941, 0.8352941, 0.8352941, 0.8352941, 0.8352941, 0.8352941, 0.8352941, 0.8352941, 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-0.94509804, -0.8666667, -0.8666667, -0.90588236, -0.94509804, -0.9764706, -0.9843137, -0.9764706, -0.9843137, -0.4980392, 0.14509809, 0.16078436, 0.12941182, 0.105882406, 0.07450986, 0.011764765, -0.043137252, -0.16862744, -0.3490196, -0.4588235, -0.45098037, -0.3098039, -0.11372548, -0.011764705, 0.058823586, 0.23921573, 0.3803922, 0.52156866, -0.4980392, -0.8352941, -0.99215686, -0.9843137, -1, -0.9607843, -0.92941177, -0.9607843, -0.9843137, -1, -1, -0.99215686, -0.99215686, -0.9137255, -0.5372549, -0.46666664, -0.47450978, -0.4588235, -0.47450978, -0.5372549, -0.6156863, -0.67058825, -0.6392157, -0.44313723, -0.19999999, -0.09019607, -0.058823526, 0.003921628, 0.15294123, 0.3411765, 0.43529415, 0.5372549, -0.35686272, -0.6627451, -0.99215686, -0.9843137, -1, -0.9843137, -0.9607843, -0.9843137, -1, -1, -1, -1, -1, -0.9843137, -0.7019608, -0.5764706, -0.6392157, -0.6627451, -0.7176471, -0.73333335, -0.69411767, -0.56078434, -0.35686272, -0.17647058, -0.09803921, -0.09803921, -0.0745098, 0.027451038, 0.17647064, 0.36470592, 0.4666667, 0.56078434, -0.27058822, -0.60784316, -0.9764706, -0.99215686, -1, -0.99215686, -0.9764706, -0.9843137, -0.99215686, -1, -1, -0.99215686, -0.9843137, -1, -0.78039217, -0.6313726, -0.81960785, -0.90588236, -0.827451, -0.5686275, -0.36470586, -0.2235294, -0.12941176, -0.12156862, -0.11372548, -0.06666666, -0.011764705, 0.07450986, 0.18431377, 0.3411765, 0.47450984, 0.5764706, -0.30196077, -0.70980394, -0.9137255, -0.96862745, -0.9843137, -0.96862745, -0.96862745, -0.9843137, -0.99215686, -0.99215686, -0.99215686, -0.9843137, -0.9529412, -0.9843137, -0.8980392, -0.8117647, -0.8039216, -0.6784314, -0.5137255, -0.35686272, -0.20784312, -0.11372548, -0.12156862, -0.12941176, -0.09803921, -0.011764705, 0.07450986, 0.13725495, 0.26274514, 0.4039216, 0.5137255, 0.5764706, -0.35686272, -0.7176471, -0.827451, -0.8666667, -0.88235295, -0.88235295, -0.8980392, -0.90588236, -0.90588236, -0.8980392, -0.8901961, -0.88235295, -0.84313726, -0.8352941, -0.7882353, -0.64705884, -0.47450978, -0.36470586, -0.3333333, -0.3098039, -0.17647058, -0.1372549, -0.1607843, -0.12156862, -0.06666666, -0.0039215684, 0.082352996, 0.18431377, 0.28627455, 0.43529415, 0.5372549, 0.5686275, -0.3490196, -0.62352943, -0.7019608, -0.70980394, -0.7254902, -0.7411765, -0.73333335, -0.7019608, -0.6862745, -0.6784314, -0.654902, -0.6392157, -0.64705884, -0.62352943, -0.5372549, -0.41960782, -0.3490196, -0.27843136, -0.23921567, -0.23921567, -0.1607843, -0.16862744, -0.15294117, -0.082352936, -0.035294116, 0.043137312, 0.105882406, 0.20000005, 0.27843142, 0.39607847, 0.49803925, 0.56078434] }}]}' \
  localhost:8033 \
  inference.GRPCInferenceService.ModelInfer

Response:

{
  "modelName": "example-pytorch-cifar__ksp-d2b754f2bf",
  "modelVersion": "1",
  "outputs": [
    {
      "name": "OUTPUT__0",
      "datatype": "FP32",
      "shape": ["1", "10"]
    }
  ],
  "rawOutputContents": [
    "SSh5QBsXiEDHyh/AzdkswNdZEcBmbn/ABbdmwDAcd8B//+pATZs7QA=="
  ]
}

SKLearn

This is a sample inference request to an SKLearn model trained on a version of the MNIST dataset.

MODEL_NAME=example-sklearn-mnist-svm
grpcurl \
  -plaintext \
  -proto fvt/proto/kfs_inference_v2.proto \
  -d '{ "model_name": "'"${MODEL_NAME}"'", "inputs": [{ "name": "predict", "shape": [1, 64], "datatype": "FP32", "contents": { "fp32_contents": [0.0, 0.0, 1.0, 11.0, 14.0, 15.0, 3.0, 0.0, 0.0, 1.0, 13.0, 16.0, 12.0, 16.0, 8.0, 0.0, 0.0, 8.0, 16.0, 4.0, 6.0, 16.0, 5.0, 0.0, 0.0, 5.0, 15.0, 11.0, 13.0, 14.0, 0.0, 0.0, 0.0, 0.0, 2.0, 12.0, 16.0, 13.0, 0.0, 0.0, 0.0, 0.0, 0.0, 13.0, 16.0, 16.0, 6.0, 0.0, 0.0, 0.0, 0.0, 16.0, 16.0, 16.0, 7.0, 0.0, 0.0, 0.0, 0.0, 11.0, 13.0, 12.0, 1.0, 0.0] }}]}' \
  localhost:8033 \
  inference.GRPCInferenceService.ModelInfer

Response:

{
  "modelName": "example-sklearn-mnist-svm__ksp-7702c1b55a",
  "outputs": [
    {
      "name": "predict",
      "datatype": "INT64",
      "shape": ["1"],
      "contents": {
        "int64Contents": ["8"]
      }
    }
  ]
}

TensorFlow

This is a sample inference request to a TensorFlow model trained on the MNIST dataset.

MODEL_NAME=example-tensorflow-mnist
grpcurl \
  -plaintext \
  -proto fvt/proto/kfs_inference_v2.proto \
  -d '{ "model_name": "'"${MODEL_NAME}"'", "inputs": [{ "name": "inputs", "shape": [1, 784], "datatype": "FP32", "contents": { "fp32_contents": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01176471, 0.07058824, 0.07058824, 0.07058824, 0.49411765, 0.53333336, 0.6862745, 0.10196079, 0.6509804, 1.0, 0.96862745, 0.49803922, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.11764706, 0.14117648, 0.36862746, 0.6039216, 0.6666667, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.88235295, 0.6745098, 0.99215686, 0.9490196, 0.7647059, 0.2509804, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.19215687, 0.93333334, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.9843137, 0.3647059, 0.32156864, 0.32156864, 0.21960784, 0.15294118, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.07058824, 0.85882354, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.7764706, 0.7137255, 0.96862745, 0.94509804, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.3137255, 0.6117647, 0.41960785, 0.99215686, 0.99215686, 0.8039216, 0.04313726, 0.0, 0.16862746, 0.6039216, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.05490196, 0.00392157, 0.6039216, 0.99215686, 0.3529412, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.54509807, 0.99215686, 0.74509805, 0.00784314, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04313726, 0.74509805, 0.99215686, 0.27450982, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.13725491, 0.94509804, 0.88235295, 0.627451, 0.42352942, 0.00392157, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.31764707, 0.9411765, 0.99215686, 0.99215686, 0.46666667, 0.09803922, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1764706, 0.7294118, 0.99215686, 0.99215686, 0.5882353, 0.10588235, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0627451, 0.3647059, 0.9882353, 0.99215686, 0.73333335, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9764706, 0.99215686, 0.9764706, 0.2509804, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.18039216, 0.50980395, 0.7176471, 0.99215686, 0.99215686, 0.8117647, 0.00784314, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.15294118, 0.5803922, 0.8980392, 0.99215686, 0.99215686, 0.99215686, 0.98039216, 0.7137255, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.09411765, 0.44705883, 0.8666667, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.7882353, 0.30588236, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.09019608, 0.25882354, 0.8352941, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.7764706, 0.31764707, 0.00784314, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.07058824, 0.67058825, 0.85882354, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.7647059, 0.3137255, 0.03529412, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.21568628, 0.6745098, 0.8862745, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.95686275, 0.52156866, 0.04313726, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.53333336, 0.99215686, 0.99215686, 0.99215686, 0.83137256, 0.5294118, 0.5176471, 0.0627451, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] }}]}' \
  localhost:8033 \
  inference.GRPCInferenceService.ModelInfer

Response:

{
  "modelName": "example-tensorflow-mnist__ksp-c3597b719f",
  "modelVersion": "1",
  "outputs": [
    {
      "name": "classes",
      "datatype": "INT64",
      "shape": ["1", "1"]
    }
  ],
  "rawOutputContents": ["BQAAAAAAAAA="]
}

XGBoost

This is a sample inference request to an XGBoost model trained on a mushroom dataset.

MODEL_NAME=example-xgboost-mushroom
grpcurl \
  -plaintext \
  -proto fvt/proto/kfs_inference_v2.proto \
  -d '{ "model_name": "'"${MODEL_NAME}"'", "inputs": [{ "name": "predict", "shape": [1, 126], "datatype": "FP32", "contents": { "fp32_contents": [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0] }}]}' \
  localhost:8033 \
  inference.GRPCInferenceService.ModelInfer

Response:

{
  "modelName": "example-xgboost-mushroom__ksp-f624b27e99",
  "outputs": [
    {
      "name": "predict",
      "datatype": "FP32",
      "shape": ["1"],
      "contents": {
        "fp32Contents": [0.012412078]
      }
    }
  ]
}

XGBoost (Triton FIL):

This is a sample inference request to an XGBoost model trained on a mushroom dataset and served using the FIL backend for Triton:

MODEL_NAME=example-xgboost-mushroom-fil
grpcurl \
  -plaintext \
  -proto fvt/proto/kfs_inference_v2.proto \
  -d '{ "model_name": "'"${MODEL_NAME}"'", "inputs": [{ "name": "input__0", "shape": [1, 126], "datatype": "FP32", "contents": { "fp32_contents": [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0] }}]}' \
  localhost:8033 \
  inference.GRPCInferenceService.ModelInfer

Response:

{
  "modelName": "example-xgboost-mushroom-fil__isvc-ffe6a3f20b",
  "modelVersion": "1",
  "outputs": [
    {
      "name": "output__0",
      "datatype": "FP32",
      "shape": ["1"]
    }
  ],
  "rawOutputContents": ["B1xLPA=="]
}

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example-models.md

example-models.md

Sample Models

InferenceServices

Naming Conventions

Available Models

Virtual Object Tree

Example Inference Requests

Keras

LightGBM

ONNX

PyTorch

SKLearn

TensorFlow

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XGBoost (Triton FIL):

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Sample Models

InferenceServices

Naming Conventions

Available Models

Virtual Object Tree

Example Inference Requests

Keras

LightGBM

ONNX

PyTorch

SKLearn

TensorFlow

XGBoost

XGBoost (Triton FIL):