sync_client.py

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import sys

import numpy as np
import tritonclient.http as httpclient
from tritonclient.utils import *

model_name = "bls_sync"
shape = [4]

with httpclient.InferenceServerClient("localhost:8000") as client:
    input0_data = np.random.rand(*shape).astype(np.float32)
    input1_data = np.random.rand(*shape).astype(np.float32)
    inputs = [
        httpclient.InferInput(
            "INPUT0", input0_data.shape, np_to_triton_dtype(input0_data.dtype)
        ),
        httpclient.InferInput(
            "INPUT1", input1_data.shape, np_to_triton_dtype(input1_data.dtype)
        ),
        httpclient.InferInput("MODEL_NAME", [1], np_to_triton_dtype(np.object_)),
    ]
    inputs[0].set_data_from_numpy(input0_data)
    inputs[1].set_data_from_numpy(input1_data)

    # Will perform the inference request on the 'add_sub' model.
    inputs[2].set_data_from_numpy(np.array(["add_sub"], dtype=np.object_))

    outputs = [
        httpclient.InferRequestedOutput("OUTPUT0"),
        httpclient.InferRequestedOutput("OUTPUT1"),
    ]

    response = client.infer(model_name, inputs, request_id=str(1), outputs=outputs)

    result = response.get_response()
    output0_data = response.as_numpy("OUTPUT0")
    output1_data = response.as_numpy("OUTPUT1")
    print("=========='add_sub' model result==========")
    print(
        "INPUT0 ({}) + INPUT1 ({}) = OUTPUT0 ({})".format(
            input0_data, input1_data, output0_data
        )
    )
    print(
        "INPUT0 ({}) - INPUT1 ({}) = OUTPUT1 ({})".format(
            input0_data, input1_data, output1_data
        )
    )
    if not np.allclose(input0_data + input1_data, output0_data):
        print("BLS sync example error: incorrect sum")
        sys.exit(1)

    if not np.allclose(input0_data - input1_data, output1_data):
        print("BLS sync example error: incorrect difference")
        sys.exit(1)

    # Will perform the inference request on the pytorch model:
    inputs[2].set_data_from_numpy(np.array(["pytorch"], dtype=np.object_))
    response = client.infer(model_name, inputs, request_id=str(1), outputs=outputs)

    result = response.get_response()
    output0_data = response.as_numpy("OUTPUT0")
    output1_data = response.as_numpy("OUTPUT1")
    print("\n")
    print("=========='pytorch' model result==========")
    print(
        "INPUT0 ({}) + INPUT1 ({}) = OUTPUT0 ({})".format(
            input0_data, input1_data, output0_data
        )
    )
    print(
        "INPUT0 ({}) - INPUT1 ({}) = OUTPUT1 ({})".format(
            input0_data, input1_data, output1_data
        )
    )
    if not np.allclose(input0_data + input1_data, output0_data):
        print("BLS sync example error: incorrect sum")
        sys.exit(1)

    if not np.allclose(input0_data - input1_data, output1_data):
        print("BLS sync example error: incorrect difference")
        sys.exit(1)

    # Will perform the same inference request on an undefined model. This leads
    # to an exception:
    print("\n")
    print("=========='undefined' model result==========")
    try:
        inputs[2].set_data_from_numpy(np.array(["undefined_model"], dtype=np.object_))
        _ = client.infer(model_name, inputs, request_id=str(1), outputs=outputs)
    except InferenceServerException as e:
        print(e.message())

    print("PASS: BLS Sync")
    sys.exit(0)