# Known Issues

The following issues have been identified in this release:

[GPU]

- Internal issues on supporting NVIDIA driver versions.
  Due to internal issues related to NVIDIA drivers, all users of VisionPro Deep Learning 1.0.1 MUST use NVIDIA driver that is of
  - 457.09 or higher version for NVIDIA GeForce® series.
  - R460 U2 (461.09) or higher version for NVIDIA RTX / Quadro® series.
- C API malfunctioning depending on NVIDIA drivers.
  Due to internal issues related to runtime, C API may not work as intended. To prevent this issue, all users of C API MUST follow the steps below.
  - Remove the currently installed NVIDIA driver from your PC.
  - Reboot your PC and install the DCH NVIDIA driver of which version is compatible with VisionPro Deep Learning 1.0.1 (457.09 or higher, or R460 U2 (461.09) or higher for NVIDIA RTX / Quadro®)
- Processing Time could be increased compared to 1.0.0 under old GPUs.
  Processing Time could be increased compared to 1.0.0 when using NVIDIA GeForce® RTX™ 20 series or older series due to NVIDIA internal issues.

[OS]

- OS compatibility issues on Windows Server 2016
  Due to OS compatibility issues caused from NVIDIA drivers, Windows Server 2016 is not supported on VisionPro Deep Learning 1.0.1. For the users with Windows Server 2016 environment, upgrading from Windows Server 2016 to Windows Server 2019 is required for the utilization of VisionPro Deep Learning 1.0.1 servers. Windows Server 2019 is only supported for 'server's configured to use the Deep Learning Client/Server functionality.

[Deep Learning Tools]

- Multiple GPUs are not supported efficiently for High Detail mode.
  You cannot get an advantage of using multiple GPUs when you proceed training or processing the other tool specifically with Green Classify Tool in High Detail mode at the same time. For example, when training or processing job of Focused mode and High Detail mode are simultaneously being processed, each job could be slower than just training or processing one job at a time.
- High Detail mode uses different resize algorithm to SuaKIT. (Green Classify Tool in High Detail mode only)
  To use imported SuaKIT model with VPDL runtime API, please use SuaKIT resize API before processing.
- Once you are done with training High Detail mode of Green Classify Tool, you cannot change the name of the class.
- Processing results are different between GPU and CPU.
  The difference is in the second decimal place of the result. For example, When you process with CPU and GPU respectively, different results can be obtained, such as 0.80 and 0.81.
- Defect regions do not extend to the left side of the view in Red Analyze Tool.
  To avoid this problem, when the defect area touch the left side of the image, please set 4 pixel more wider ROI than original image size. For example, if you have (x=0,y=0,w=100,h=100) image and it has detected area touch to left side of the image, please set (x=-4, y=0, w=104, h=100) ROI size.
- When you set the smaller feature size (especially smaller than 10) and train the Red Analyze tool (both in Supervised and Unsupervised mode), the workspace is getting bigger.
vvb file which is for saving heat map within the workspace is the main reason. : for example,

- feature size=10, sampling density=3, 8192x819 image: 1 vvb file size=94MB
- feature size=10, sampling density=10, 8192x8192 image: 1 vvb file size=1GB
- feature size=40, sampling density=3, 8192x8192 image: 1 vvb file size=23KB

[Workspace]

- If the tool is not saved well because of unspecific reasons, you cannot export or clone the workspace/tool.
  You can avoid this problem with processing the tools again and save the workspace.
- Boost::filesystem::rename error
  This error occurs when the user or system repetitively save the workspace in a short time. This is just an alert that there was an issue while saving. You can solve this problem only with saving the workspace again.
- Group is shown in the workspace imported from ViDi Suite.

  - If you import workspaces including "Group" feature which are made 4.1 and before, "Group" is still displayed in Cognex Deep Learning Studio 1.0.1, but you cannot modify this.

  - See the FAQ for the workaround for this issue.

  - It is recommended to delete "Group" in the previous workspaces and import to Cognex Deep Learning Studio 1.0.1.

[Big Workspace]

- Especially with bigger than 10GB workspaces,

- Cloning work does not end when you clone tools in different big workspaces at the same time.
- Some images are not included in the created report.

[Report]

- During creating a report when you log off your PC, some of images are shown black.
  To avoid this issue, please do not log off your PC (turn off remote environment or lock screen) while you are creating a report.

[Plugins]

- You can add same multiple plugins using Manage Plugins.

[VisionPro Deep Learning Service]

- MultipleDevicesPerTool is no longer supported for VisionPro Deep Learning. Therefore you will have “One or more error occurred” error, if you select “--gpu-mode =MultipleDevicesPerTool” for service.
  Please choose other option to run VisionPro Deep Learning Service.