# Model Optimizer Developer Guide {#openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide} ## Introduction Model Optimizer is a cross-platform command-line tool that facilitates the transition between the training and deployment environment, performs static model analysis, and adjusts deep learning models for optimal execution on end-point target devices. Model Optimizer process assumes you have a network model trained using supported deep learning frameworks: Caffe*, TensorFlow*, Kaldi*, MXNet* or converted to the ONNX* format. Model Optimizer produces an Intermediate Representation (IR) of the network, which can be inferred with the [Inference Engine](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md). > **NOTE**: Model Optimizer does not infer models. Model Optimizer is an offline tool that runs before the inference takes place. The scheme below illustrates the typical workflow for deploying a trained deep learning model: ![](img/workflow_steps.png) The IR is a pair of files describing the model: * .xml - Describes the network topology * .bin - Contains the weights and biases binary data. Below is a simple command running Model Optimizer to generate an IR for the input model: ```sh python3 mo.py --input_model INPUT_MODEL ``` To learn about all Model Optimizer parameters and conversion technics, see the [Converting a Model to IR](prepare_model/convert_model/Converting_Model.md) page. > **TIP**: You can quick start with the Model Optimizer inside the OpenVINO™ [Deep Learning Workbench](@ref > openvino_docs_get_started_get_started_dl_workbench) (DL Workbench). > [DL Workbench](@ref workbench_docs_Workbench_DG_Introduction) is the OpenVINO™ toolkit UI that enables you to > import a model, analyze its performance and accuracy, visualize the outputs, optimize and prepare the model for > deployment on various Intel® platforms. ## Videos


Model Optimizer Concept. Duration: 3:56	Model Optimizer Basic Operation. Duration: 2:57.	Choosing the Right Precision. Duration: 4:18.