# Launch VisionPro Deep Learning

In the launcher, you can choose to run VisionPro Deep Learning locally or by connecting to a server. By selecting Options, you can reach several options that control the GPU Mode, which GPU device to use, the allocation of GPU memory, in addition to project settings.

Command | Description
--- | ---
GPU Mode | Specifies the GPU mode to be used by the application. The following options are available:
SingleDevicePerTool | The application uses a single GPU 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 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.
Optimized GPU Memory | Specifies the size of the preallocated optimized memory buffer. Note: You can also reach the Optimized GPU Memory option from the GUI by selecting the Compute Devices option of the Help menu. For more information, see Optimized GPU Memory.
Auto Save Interval | Specifies how often a workspace is saved automatically. The default is every 5 minutes.
Training Image Cache Path | Specifies a cache location for training images. This is useful if the images in a stream are in non-raster format (for example JPG, PNG, or GIF), since they must be converted to a raster (BMP) format for training. By default, this conversion happens multiple times for each image. By enabling the Training Image Cache Path option, the application only converts the images once then stores the converted images in a local cache directory. Use a local cache directory, preferably on a Solid-State Drive (SSD). This option can also speed up training in cases where the workspace is stored on a slower drive or a remote storage device.
Locale | Specifies the language of the VisionPro Deep Learning GUI.
Debug Logging | Specifies whether or not debug logs are enabled for the project.