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Decoupled Model Examples

In this section we demonstrate an end-to-end examples for developing and serving decoupled models in Python backend.

repeat_model.py and square_model.py demonstrate how to write a decoupled model where each request can generate 0 to many responses. These files are heavily commented to describe each function call. These example models are designed to show the flexibility available to decoupled models and in no way should be used in production. These examples circumvents the restriction placed by the instance count and allows multiple requests to be in process even for single instance. In real deployment, the model should not allow the caller thread to return from execute until that instance is ready to handle another set of requests.

Deploying the Decoupled Models

  1. Create the model repository:
mkdir -p models/repeat_int32/1
mkdir -p models/square_int32/1

# Copy the Python models
cp examples/decoupled/repeat_model.py models/repeat_int32/1/model.py
cp examples/decoupled/repeat_config.pbtxt models/repeat_int32/config.pbtxt
cp examples/decoupled/square_model.py models/square_int32/1/model.py
cp examples/decoupled/square_config.pbtxt models/square_int32/config.pbtxt
  1. Start the tritonserver:
tritonserver --model-repository `pwd`/models

Running inference on Repeat model:

Send inference requests to repeat model using repeat_client.py.

python3 examples/decoupled/repeat_client.py

You should see an output similar to the output below:

stream started...
async_stream_infer
model_name: "repeat_int32"
id: "0"
inputs {
  name: "IN"
  datatype: "INT32"
  shape: 4
}
inputs {
  name: "DELAY"
  datatype: "UINT32"
  shape: 4
}
inputs {
  name: "WAIT"
  datatype: "UINT32"
  shape: 1
}
outputs {
  name: "OUT"
}
outputs {
  name: "IDX"
}
raw_input_contents: "\004\000\000\000\002\000\000\000\000\000\000\000\001\000\000\000"
raw_input_contents: "\001\000\000\000\002\000\000\000\003\000\000\000\004\000\000\000"
raw_input_contents: "\005\000\000\000"

enqueued request 0 to stream...
infer_response {
  model_name: "repeat_int32"
  model_version: "1"
  id: "0"
  outputs {
    name: "IDX"
    datatype: "UINT32"
    shape: 1
  }
  outputs {
    name: "OUT"
    datatype: "INT32"
    shape: 1
  }
  raw_output_contents: "\000\000\000\000"
  raw_output_contents: "\004\000\000\000"
}

infer_response {
  model_name: "repeat_int32"
  model_version: "1"
  id: "0"
  outputs {
    name: "IDX"
    datatype: "UINT32"
    shape: 1
  }
  outputs {
    name: "OUT"
    datatype: "INT32"
    shape: 1
  }
  raw_output_contents: "\001\000\000\000"
  raw_output_contents: "\002\000\000\000"
}

infer_response {
  model_name: "repeat_int32"
  model_version: "1"
  id: "0"
  outputs {
    name: "IDX"
    datatype: "UINT32"
    shape: 1
  }
  outputs {
    name: "OUT"
    datatype: "INT32"
    shape: 1
  }
  raw_output_contents: "\002\000\000\000"
  raw_output_contents: "\000\000\000\000"
}

infer_response {
  model_name: "repeat_int32"
  model_version: "1"
  id: "0"
  outputs {
    name: "IDX"
    datatype: "UINT32"
    shape: 1
  }
  outputs {
    name: "OUT"
    datatype: "INT32"
    shape: 1
  }
  raw_output_contents: "\003\000\000\000"
  raw_output_contents: "\001\000\000\000"
}

PASS: repeat_int32
stream stopped...

Look how a single request generated 4 responses.

Running inference on Square model:

Send inference requests to square model using square_client.py.

python3 examples/decoupled/square_client.py

You should see an output similar to the output below:

stream started...
async_stream_infer
model_name: "square_int32"
id: "0"
inputs {
  name: "IN"
  datatype: "INT32"
  shape: 1
}
outputs {
  name: "OUT"
}
raw_input_contents: "\004\000\000\000"

enqueued request 0 to stream...
async_stream_infer
model_name: "square_int32"
id: "1"
inputs {
  name: "IN"
  datatype: "INT32"
  shape: 1
}
outputs {
  name: "OUT"
}
raw_input_contents: "\002\000\000\000"

enqueued request 1 to stream...
async_stream_infer
model_name: "square_int32"
id: "2"
inputs {
  name: "IN"
  datatype: "INT32"
  shape: 1
}
outputs {
  name: "OUT"
}
raw_input_contents: "\000\000\000\000"

enqueued request 2 to stream...
async_stream_infer
model_name: "square_int32"
id: "3"
inputs {
  name: "IN"
  datatype: "INT32"
  shape: 1
}
outputs {
  name: "OUT"
}
raw_input_contents: "\001\000\000\000"

enqueued request 3 to stream...
infer_response {
  model_name: "square_int32"
  model_version: "1"
  id: "0"
  outputs {
    name: "OUT"
    datatype: "INT32"
    shape: 1
  }
  raw_output_contents: "\004\000\000\000"
}

infer_response {
  model_name: "square_int32"
  model_version: "1"
  id: "1"
  outputs {
    name: "OUT"
    datatype: "INT32"
    shape: 1
  }
  raw_output_contents: "\002\000\000\000"
}

infer_response {
  model_name: "square_int32"
  model_version: "1"
  id: "0"
  outputs {
    name: "OUT"
    datatype: "INT32"
    shape: 1
  }
  raw_output_contents: "\004\000\000\000"
}

infer_response {
  model_name: "square_int32"
  model_version: "1"
  id: "3"
  outputs {
    name: "OUT"
    datatype: "INT32"
    shape: 1
  }
  raw_output_contents: "\001\000\000\000"
}

infer_response {
  model_name: "square_int32"
  model_version: "1"
  id: "1"
  outputs {
    name: "OUT"
    datatype: "INT32"
    shape: 1
  }
  raw_output_contents: "\002\000\000\000"
}

infer_response {
  model_name: "square_int32"
  model_version: "1"
  id: "0"
  outputs {
    name: "OUT"
    datatype: "INT32"
    shape: 1
  }
  raw_output_contents: "\004\000\000\000"
}

infer_response {
  model_name: "square_int32"
  model_version: "1"
  id: "0"
  outputs {
    name: "OUT"
    datatype: "INT32"
    shape: 1
  }
  raw_output_contents: "\004\000\000\000"
}

PASS: square_int32
stream stopped...

Look how responses were delivered out-of-order of requests. The generated responses can be tracked to their request using the id field.