![]() All of the TensorFlow versions work with DeepLabCut.Install CUDA (versions up to CUDA11 are supported, together with TF2.5).Note, DeepLabCut is up to date with the latest CUDA and tensorflow versions!.Here are some of the relevant texts from the install page (which I find confusing/inconsistent): In short, what are the current recommendations for the GPU install? I’ve carefully read the installation instructions and tips and followed links to external (stackoverflow) threads, but I feel the install docs are not consistent. I recall from earlier installations that this is tricky business, and want to get it right and avoid a mess. NVIDIA Performance Primitives (NPP) libraryĬUDA Toolkit for RedHat Enterprise Linux 5.3ĬUDA Toolkit for RedHat Enterprise Linux 4.Hi, I’m trying to get up and running with the latest DLC (use case is single subject) and am stuck on the GPU-related aspects. Notebook Developer Drivers for WinVista & Win7 More recent production driver packages for developers and end users may be available at For additional tools and solutions for Windows, Linux and MAC OS, such as CUDA Fortran, CULA, CUDA-dgb, please visit our Tools and Ecosystem Pageĭownload Quick Links Windows XP, Windows VISTA, Windows 7 Description of Downloadĭeveloper Drivers for WinVista & Win7 (197.13) Note: The developer driver packages below provide baseline support for the widest number of NVIDIA products in the smallest number of installers. Support for the latest OpenCL spec revision 1.0.48 and latest official Khronos OpenCL headers as of. ![]() ![]() Byte Addressable Stores, for faster video/image processing and compression algorithms.32-bit global and local atomics for fast, convenient data manipulation.OpenCL Images support, for better/faster image filtering.Ability to control compiler optimization settings via support for pragma unroll in OpenCL kernels and an extension that allows programmers to set compiler flags.Query for Compute Capability, so you can target optimizations for GPU architectures (cl_nv_device_attribute_query).Graphics Interoperability with OpenCL, Direc3D9, Direct3D10, and Direct3D11 for high performance visualization.Support for all the OpenCL features in the latest R195 production driver package:.On Linux, use cuda-gdb and cuda-memcheck, and check out the solutions from Allinea and TotalView that will be available soon.On Windows, use the new Parallel Nsight development environment for Visual Studio, with integrated GPU debugging and profiling tools (was code-named "Nexus").Now that more sophisticated hardware debugging tools are available and more are on the way, NVIDIA will be focusing on supporting these tools instead of the legacy device emulation functionality. Support for device emulation mode has been packaged in a separate version of the CUDA C Runtime (CUDART), and is deprecated in this release.CUDA C/C++ kernels are now compiled to standard ELF format.CUDA Toolkit libraries are now versioned, enabling applications to require a specific version, support multiple versions explicitly, etc. ![]()
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