def test_http_out_of_shared_memory(self):
triton_client = tritonhttpclient.InferenceServerClient("localhost:8000")
inputs = []
inputs.append(tritonhttpclient.InferInput('INPUT', [1], "UINT8"))
inputs[0].set_data_from_numpy(np.arange(1, dtype=np.uint8))
# Set up too small CUDA shared memory for outputs, expect query
# returns default value
triton_client.unregister_system_shared_memory()
triton_client.unregister_cuda_shared_memory()
shm_op0_handle = cudashm.create_shared_memory_region(
"output0_data", 1, 0)
shm_op1_handle = cudashm.create_shared_memory_region(
"output1_data", 1, 0)
triton_client.register_cuda_shared_memory(
"output0_data", cudashm.get_raw_handle(shm_op0_handle), 0, 1)
triton_client.register_cuda_shared_memory(
"output1_data", cudashm.get_raw_handle(shm_op1_handle), 0, 1)
outputs = []
outputs.append(
tritonhttpclient.InferRequestedOutput('OUTPUT0', binary_data=True))
outputs[-1].set_shared_memory("output0_data", 1)
outputs.append(
tritonhttpclient.InferRequestedOutput('OUTPUT1', binary_data=True))
outputs[-1].set_shared_memory("output1_data", 1)
try:
triton_client.infer(model_name="query",
inputs=inputs,
outputs=outputs)
self.assertTrue(False, "expect error with query information")
except InferenceServerException as ex:
self.assertTrue("OUTPUT0 CPU 0" in ex.message())
self.assertTrue("OUTPUT1 CPU 0" in ex.message())
cudashm.destroy_shared_memory_region(shm_op0_handle)
cudashm.destroy_shared_memory_region(shm_op1_handle)
triton_client.unregister_system_shared_memory()
triton_client.unregister_cuda_shared_memory()
def _request_generator(cls, batched_image_data):
""" Set the input data """
inputs = [
httpclient.InferInput(cls.INPUT_NAME, batched_image_data.shape,
cls.DTYPE)
]
inputs[0].set_data_from_numpy(batched_image_data, binary_data=True)
outputs = [
httpclient.InferRequestedOutput(output_name, binary_data=True)
for output_name in cls.OUTPUT_NAMES
]
yield inputs, outputs
def run_inference(X, X_shape=(1, 3, 224, 224), X_dtype='FP32', model_name='cub200_resnet34', input_name=['INPUT__0'], output_name='OUTPUT__0',
url='ecm-clearml-compute-gpu-002.westeurope.cloudapp.azure.com', model_version='1', port=8000, VERBOSE=False):
url = url+':'+str(port)
triton_client = http.InferenceServerClient(url=url, verbose=VERBOSE)
input0 = http.InferInput(input_name[0], X_shape, X_dtype)
input0.set_data_from_numpy(X, binary_data=False)
output = http.InferRequestedOutput(output_name, binary_data=False)
response = triton_client.infer(model_name, model_version=model_version, inputs=[input0], outputs=[output])
y_pred_proba = response.as_numpy(output_name)
y_pred = y_pred_proba.argmax(1)
return y_pred_proba, y_pred
