# VisionPro Deep Learning Service Command Line Arguments

There are several command line arguments that can be used with the VisionPro Deep Learning Service application. The following commands can be used to specify the location of workspaces, port numbers for training Training is the process that your tool, which is a neural network, is learning about the features (pixels) based on the labels you made. For example, a tool will learn the defect/normal pixels in each image based on the defect/normal labels you drew. The goal of the tool Training is learning enough to give the correct inspection results of whether an unseen image is defective or not. The key to training is to ensure that you include all possible variations within your training set, and that your images are accurately labeled. Training times vary by the application, tool setup and the GPU in the PC being used to train the network. and runtime, and GPU usage:

Command | Description
--- | ---
--http-address=http://address:port | Specifies the HTTP address that the server will listen to for training requests. The default is http://localhost:8080.
--workspace-path=file path | Specifies the directory location where the workspace will be saved.
--training-image-cache-path=path | Specifies the path where training images are stored for use in multiple training passes, improving training performance and minimizing I/O incurred when opening images. Designating a specific path is especially helpful in these cases: If an image conversion is needed (for example, when JPG or PNG formats are used or color-to-greyscale conversion is needed). If the workspace directory is on a slow device or remote file system and the specified path is on a fast, local device, such as a solid state drive. Note: Ensure that the path you specify is located on a device with enough free storage to completely cache all of the training images. If a large number of PNG or JPG images are used, the needed storage space can be several times the size of the workspace directory.
--training-workspace-path=file path | Specifies the directory location in which to load and save workspace files.
--runtime-workspace-path=file path | Specifies the directory location in which to load and save runtime workspace A runtime workspace is a configuration file that does not contain images or databases, containing only streams and tools, which makes it a smaller version of a full Workspace. This configuration file can be loaded in the library in order to perform some analysis. files.
--enable-runtime=[0 or 1] | Specifies whether or not to start the runtime service and listen for runtime requests. This is disabled by default.
--runtime-only | Starts a runtime-only service that only listens for connections to the runtime API. Note: This option conflicts with the --workspace-path and --training-workspace-path commands. The auto-save feature A feature is a visually distinguishable area in an image. Features typically represent something of interest for the application (a defect, an object, a particular component of an object). does not apply to runtime workspaces. Therefore, if the --runtime-only command is activated while the --auto-save-internal command has been set, the auto-save feature will be ignored.
--optimized-gpu-memory=memory size, in MB | Specifies the size of the pre-allocated optimized memory buffer. This setting is activated by default, with the default size of 2GB. To deactivate the feature, first issue the --optimized-gpu-memory-override=1 command, and then --optimized-gpu-memory=0 command. To use a memory buffer size other than the default setting, first issue the --optimized-gpu-memory-override=1 command, and then the --optimized-gpu-memory=<memory size in MB> command. Note: The GPU Memory Optimization setting is enabled by default. For more information, see Optimized GPU Memory.
--optimized-gpu-memory-override=[0 or 1] | If using the --optimized-gpu-memory setting, set to 1. If set to 0, the default amount of memory will be allocated.
--gpu-devices=comma separated indexed list of GPUs | Specifies the GPUs that will be used on initialization, via an indexed list. For example: --gpu-devices=0,1
--gpu-mode=NoSupport or SingleDevicePerTool or MultipleDevicesPerTool | Specifies the GPU mode that will be used by the VisionPro Deep Learning Service application.
SingleDevicePerTool | The application uses a single GPU for training and processing an image.
MultipleDevicesPerTool | This option is not supported in the current release.
NoSupport | The application does not use a GPU. Note: This option conflicts with --gpu-devices and/or --optimized-gpu-memory.
Note: See Configuring Multiple GPUs for more information about the GPU modes.
--auto-save-interval=duration in minutes | Specifies an amount of time between auto-saves of workspaces. The default is 5 minutes. Set to -1 to never auto-save. Note: The auto-save feature does not apply to runtime workspaces. Therefore, if the --runtime-only command is activated while the --auto-save-internal command has been set, the auto-save feature will be ignored.
--activate-debug-logs=[0 or 1] | Activates logging for debugging purposes.
--help | Used to return a list of command line options.
--version | Requests version information.