Deep Learning Model Package Management Tool

This section offers a guide on using the deep learning model package management tool and some notes worth your attention.

Introduction

Deep learning model package management tool is designed to manage all deep learning model packages in Mech-Vision. It can be used to optimize single model packages or cascaded model packages exported by Mech-DLK 2.2.0 or above and manage and monitor the operation mode, hardware type, model efficiency, and model package status. Besides, this tool can be used to monitor the GPU memory usage of the IPC.

If deep learning Steps are used in the project, you can import the model package to the deep learning model package management tool first and then use the models in the relative Steps. Importing the model package to the tool facilitates optimizing the model package in advance.

From Mech-DLK 2.4.1, model packages can be divided into single model packages and cascaded model packages.

  • Single model package: There is only one deep learning model in the model package, such as an “Instance Segmentation” model.

  • Cascaded model package: Multiple models are cascaded in the model package, and the output result of the previous model is input to the next model. For example, there are two models, “Object Detection” and “Instance Segmentation” in the model package and the inference sequence is menu:Object Detection [ Instance Segmentation]. The output of “Object Detection” is input to the “Instance Segmentation” model.

Interface Introduction

You can open the tool in either of the following two ways:

  • After creating or opening a project, select Deep Learning  Deep Learning Model Package Management Tool in the menu bar.

  • In an opened project, select the “Deep Learning Model Package Inference” Step, and click the Model Package Management Tool button in the Step Parameters panel.

The options in the deep learning model package management tool are shown in the table below.

Option Description

Available model package

The names of imported model packages

Project name

The Mech-Vision project in which the corresponding model package is used

Model package type

The type of the model package, such as “Object Detection” (single model package) and “Object Detection + Defect Segmentation” (cascaded model package)

Operation mode

The operation mode of the model package, including “Sharing mode” and “Performance mode”

Hardware type

The hardware type used for model package inference. If you are using a GPU model, you can modify the hardware type, i.e., GPU (default), GPU (optimization), and CPU

Model efficiency

The inference efficiency of the model package

Model package status

The status of the model package, such as “Loading and optimizing”, “Loading completed”, and “Optimization failed”

Operation mode

  • Sharing mode: Once this option is selected, when multiple Steps use the same model package, the inferences will be done one by one in sequence and less memory will be used.

  • Performance mode: Once this option is selected, when multiple Steps use the same model package, the inferences will be done simultaneously, and the inference speed will be relatively fast. However, more memory will be used in this mode.

Hardware type

  • CPU: Use CPU for model package inference. Compared to GPU, the inference time is longer and the recognition accuracy is lower.

  • GPU (default): Once this option is selected, the model package will not be optimized according to the hardware type and the deep learning inference will not be speeded up.

  • GPU (optimization): Once this option is selected, the model package will be optimized according to the hardware type and the one-time optimization process takes about 5 to 15 minutes. When an optimized model package is used, the inference time will be reduced.

The deep learning model package management tool determines the Hardware Type option by detecting the computer hardware type. The display rules for each Hardware Type option are as follows.

  • CPU: This option is shown when a computer with an Intel CPU is detected.

  • GPU (default), GPU (optimization): These options are shown when a computer with an NVIDIA discrete graphics card is detected, and the graphics card driver version is 472.50 or higher.

Usage

Follow the steps below to learn about common procedures for using the deep learning model package management tool.

Import the Deep Learning Model Package

  1. Open the deep learning model package management tool, and click Import in the upper left corner.

  2. Select the model package you want to import in the pop-up Select File window and click Open. Then the deep learning model package will appear in the list in the window, which suggests that the model package is imported successfully.

To import a model package successfully, the minimum version requirement for the graphics driver is 472.50, and the minimum requirement for the CPU is a 6th-generation Intel Core processor. It is not recommended to use a graphics driver above version 500, which may cause fluctuations in the execution time of deep learning Steps. If the hardware cannot meet the requirement, the deep learning model package cannot be imported successfully.

Select the Deep Learning Model Package in the Step

After importing the model package into the tool, if you want to use the model package in the Deep Learning Model Package Inference Step, you can select the model package in the drop-down list of the Model Package Name parameter in the Step Parameters panel.

deep learning model management step select model

Remove the Imported Deep Learning Model Package

If you want to remove an imported deep learning model package, select the model package first, and click the Remove button in the upper right corner.

deep learning model management log out model

When the deep learning model package is Loading and optimizing or the project using the deep learning model package is running, the model package cannot be removed.

Switch the Operation Mode

If you want to switch the Operation mode for deep learning model package inference, you can click deep learning model management icon 1 in the Operation mode column in the deep learning model package management tool, and select Sharing mode or Performance mode.

deep learning model management select operating mode

  • When the deep learning model package is Loading and optimizing or In use(i.e., the project using the model package is running), the Operation mode cannot be changed.

  • When the operation mode of the model package is Sharing mode, the GPU ID in the Deep Learning Model Package Inference Step cannot be changed.

Switch the Hardware Type

You can change the hardware type for deep learning model package inference to GPU (default), GPU (optimization), or CPU.

Click deep learning model management icon 1 in the Hardware type column in the deep learning model package management tool, and select GPU (default), GPU (optimization), or CPU.

deep learning model management select hardware type

  • Only model packages exported by Mech-DLK 2.4.1 or above support switching between CPU and GPU.

  • You cannot use the GPU (default) for either of the following types of model packages:

    • Instance segmentation model package and instance segmentation super model package exported by Mech-DLK 2.2.0.

    • Model package that only contains the DLKMT file exported by Mech-DLK.

  • When the deep learning model package is Loading and optimizing or In use(i.e., the project using the model package is running), the Hardware type cannot be changed.

Configure the Model Efficiency

When a model package exported by Mech-DLK 2.4.1 or above is used, the model efficiency can be configured. The procedures for configuring the model efficiency are as follows.

  1. Import the deep learning model package.

  2. Click the Configure button in the Model efficiency column, a Model Efficiency configuration window will pop up, and you can configure the “Batch size” and “Precision”.

    The “Batch size” and “Precision” parameters can affect the model efficiency configuration.

    • Batch size: the number of images that will be passed through the neural network at once during inference, ranging from 1 to 128. Increasing the value will increase the model’s inference speed, but more video memory will be used. If the value is not set properly, the inference speed will be slowed down. The instance segmentation models do not support configuring “Batch size”, and the default “Batch size” must be set to 1.

    • Precision (Only available when the “Hardware type” is “GPU optimization”): FP32: high-precision model with lower inference speed. FP16: low-precision model with higher inference speed. For example, if “FP32” is switched to “FP16,” the inference result will change due to a reduced model precision.

      It is recommended to set the “Batch size” the same as the actual number of images that are passed through the neural network.

      If the set “Batch size” is much larger than the actual number of images that are passed through the neural network, part of the resources will be wasted, resulting in slower inference.

      For example, if the number of images is 26, and the “Batch size” is set to 20, two separate inferences will be performed. In the first inference, 20 images will be fed to the neural network, while in the second inference, 6 images will be fed to the neural network. For the second inference, the set “Batch size” is much larger than the actual number of images that are passed through the neural network, part of the resources will be wasted, resulting in slower inference. Therefore, please set the “Batch size” to a proper value to ensure the efficient use of the resources.

Notes

Fail to Import a Deep Learning Model Package

  • When there is already an imported deep learning model package, and you want to import another one with the same name, please modify the name of the new model package or remove the model package with the same name, and then start to import.

deep learning model management model name 1

  • When you have imported a model package, and start to import another one with a different name but the same content, an error message “Failed to import the deep learning model package.” will pop up.

deep learning model management model name 2

  • When the software and hardware cannot meet the minimum requirement, the deep learning model package cannot be imported successfully. The minimum required version for the graphics driver is 472.50, and the minimum required CPU version is 6th generation Intel Core.

Fail to Optimize a Deep Learning Model Package

When there is not enough memory, the deep learning model package may not be able to be optimized successfully. A pop-up window as shown below will appear. You can fix the problem according to the solutions in the window.

deep learning model management load model fail 1

Notes for Compatibilities

  • Deep learning model packages exported by Mech-DLK 2.4.1 can be used in Mech-Vision 1.7.1. However, there may be some compatibility issues. It is recommended to use deep learning model packages exported by Mech-DLK 2.4.1 or above with Mech-Vision 1.7.2 or above.

    • Cascaded model packages cannot be used in Mech-Vision.

    • The model efficiency cannot be configured.

    • The performance of image classification may be diminished.

    • Model packages cannot be used on CPU devices.

  • If model packages optimized with Mech-Vision 1.7.1 are used in Mech-Vision 1.7.2, the execution time may be longer when the optimized model package is used in the “Deep Learning Model Package Inference” Step for the first time.

  • Please pay attention to the following issue when the Hardware type for model package inference is set to GPU (optimization).

    If the model package is not optimized in Mech-Vision 1.7.X, and it is optimized in Mech-Vision 1.8.0 or later versions, the model package cannot function properly in Mech-Vision 1.7.X. You should click Open folder in the Deep Learning Model Package Management Tool, delete the cache folder corresponding to the model package, and optimize the model package again.

    You can check the cache folder corresponding to the model package in the model_config.json file.

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