IEI Technology Mustang-V100-MX4, User Manual

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Mustang-V100-MX4
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variables manually.
The environment variables are set. Continue to the next section to configure the Model
Optimizer.
5.3.4 Configure the Model Optimizer
Important: These steps are required. You must configure the Model Optimizer for at least
one framework. The Model Optimizer will fail if you do not complete the steps in this
section.
If you see an error indicating Python is not installed when you know you installed it, your
computer might not be able to find the program. For instructions to add Python to your
system environment variables, see
Update Your Windows Environment Variables .
The Model Optimizer is a key component of the Intel® Distribution of OpenVINO™ toolkit.
You cannot do inference on your trained model without running the model through the
Model Optimizer. When you run a pre-trained model through the Model Optimizer, your
output is an Intermediate Representation (IR) of the network. The IR is a pair of files that
describe the whole model:
•
.xml: Describes the network topology
•
.bin: Contains the weights and biases binary data
The Inference Engine reads, loads, and infers the IR files, using a common API across the
CPU, GPU, or VPU hardware.
The Model Optimizer is a Python*-based command line tool (mo.py), which is located in
C:\Intel\computer_vision_sdk_<version>\deployment_tools\model_optimizer , where
<version> is the version of the Intel® Distribution of OpenVINO™ toolkit that you installed.
Use this tool on models trained with popular deep learning frameworks such as Caffe,
TensorFlow, MXNet, and ONNX to convert them to an optimized IR format that the
Inference Engine can use.
This section explains how to use scripts to configure the Model Optimizer either for all of
the supported frameworks at the same time or for individual frameworks. If you want to