arachne-runtime 0.1.1

A thin library for executing different types of DNN models from a common API

Arachne Runtime

Arachne Runtime is a thin Python library for executing different types of DNN models from a common Python API. It wraps original DNN library runtime and absorbs the differences among DNN libraries. Now, we support three types of DNN models as its inputs (e.g., tflite, onnx, and tvm). It also supports RPC feature to help testing DNN models on remote edge devices such as Jetson devices.

Installation

pip install arachne-runtime

In addition to the above command, you need to install the DNN library runtimes.

TFLite

pip install tensorflow

ONNX Runtime

pip install onnxruntime

TVM

TVM requires you to build its library. Please follow the official document

Usage

Local Execution

To execute a DNN model via Arachne Runtime, first init a runtime module by arachne_runtime.init. Then, you can set numpy.ndarray as inputs by a set_input method. After setting all inputs, a run method executes the inference. The outputs of inference results can be retrieved by a get_output method.

import arachne_runtime

# TFLite
tflite_interpreter_opts = {"num_threads": 4}
runtime_module = arachne_runtime.init(
    runtime="tflite", model_file="/path/to/model.tflite", **tflite_interpreter_opts
)
runtime_module.set_input(0, input_data)
runtime_module.run()
out = runtime_module.get_output(0)

# ONNX Runtime

ort_opts = {"providers": ["CPUExecutionProvider"]}
runtime_module = arachne_runtime.init(
    runtime="onnx", model_file="/path/to/model.onnx", **ort_opts
)
runtime_module.set_input(0, input_data)
runtime_module.run()
out = runtime_module.get_output(0)

# TVM Graph Executor

runtime_module = arachne_runtime.init(
    runtime="tvm", model_file="/path/to/tvm_model.tar", env_file="/path/to/env.yaml"
)
runtime_module.set_input(0, input_data)
runtime_module.run()
aout = runtime_module.get_output(0)

Note that, in the case of TVM, users have to pass an additional YAML file (env.yaml) to the API. This is because models compiled by TVM does not contains the model signature which is required by Arachne Runtime. The type of tvm.runtime.device which is needed by the TVM Graph Executor has to be specified by users as well. Typically, the YAML file looks like below.

model_spec:
  inputs:
  - dtype: float32
    name: input_1
    shape:
    - 1
    - 224
    - 224
    - 3
  outputs:
  - dtype: float32
    name: predictions/Softmax:0
    shape:
    - 1
    - 1000
tvm_device: cpu

Remote Execution

With RPC, you can train and build a DNN model on your local machine then run it on the remote device. It is useful when the remote device resource are limited.

To try the RPC feature, first you have to follow the installation step and start a RPC server on the remote device.

# Remote device
python -m arachne_runtime.rpc.server --port 5051

Then, you can init a RPC runtime module by arachne_runtime.rpc.init on the local machine. The rest of APIs is similar to the local execution.

import arachne_runtime

# TFLite
tflite_interpreter_opts = {"num_threads": 4}
runtime_module = arachne_runtime.init(
    runtime="tflite", model_file="/path/to/model.tflite", rpc_info={"host": "hostname", "port": 5051}, **tflite_interpreter_opts
)

# To close rpc connection, call done()
runtime_module.done()

Runtime Plugin

Please refer the plugin_examples for more details.

License

Arachne Runtime is licensed under the MIT license.