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The post-training optimization tool can be used through
Command Line Interface
Python API
Deep Learning Workbench
All of the above
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1 Introduction to Model Optimizer and understand it’s significance
2 Converting and Preparing the Models with Model Optimizer
3 Quiz Question
4 Quiz Question
5 Model conversion overview with Model Optimizer
6 Converting a Model to Intermediate Representation (IR)
7 Understanding the General Conversion Parameters
8 Model Optimizer Demo
9 Lab: Convert a ONNX model and compare size of the model between FP32 and FP16
10 Framework specific optimization parameters
11 Lab: Convert a Tensorflow* model to Intermediate Representation
12 Quiz Question
13 Model optimization techniques and understanding how Model Optimizer works under the hood
14 Choosing the right Quantization option based on the Hardware Platform
15 Quiz Question
16 Understanding the advantages of INT8 quantization
17 Introduction to low precision optimization with Post-Training Optimization Tool(POT)
18 Optimizations - Advanced topics
19 Quiz Question
20 Quiz Question
21 Quiz Question
