CUDA Toolkit and Corresponding Driver Versions ĬUDA 10.1 (10.1.105 general release, and updates)įor convenience, the NVIDIA driver is installed as part of the CUDA Toolkit installation. The version of the development NVIDIA GPU Driver packaged in each CUDA Toolkit release is shown below. ** CUDA 11.0 was released with an earlier driver version, but by upgrading to Tesla Recommended Drivers 450.80.02 (Linux) / 452.39 (Windows), minor version compatibility is possible across the CUDA 11.x family of toolkits. * Using a Minimum Required Version that is different from Toolkit Driver Version could be allowed in compatibility mode – please read the CUDA Compatibility Guide for details. Minimum Required Driver Version for CUDA Minor Version Compatibility* CUDA Toolkit and Minimum Required Driver Version for CUDA Minor Version Compatibility CUDA minor version compatibility is described in detail in Table 2. The minimum required driver version for CUDA minor version compatibility is shown below. Note: Starting with CUDA 11.0, the toolkit components are individually versioned, and the toolkit itself is versioned as shown in the table below. More information on compatibility can be found at. The CUDA driver is backward compatible, meaning that applications compiled against a particular version of the CUDA will continue to work on subsequent (later) driver releases. Įach release of the CUDA Toolkit requires a minimum version of the CUDA driver. For more information various GPU products that are CUDA capable, visit. Running a CUDA application requires the system with at least one CUDA capable GPU and a driver that is compatible with the CUDA Toolkit. Starting with CUDA 11, the various components in the toolkit are versioned independently.įor CUDA 12.1 Update 1, the table below indicates the versions: Table 1. CUDA Toolkit Major Component Versions CUDA Components I have navigated to the correct folder “~\Fast_AI\Github material\fastai\pytorch”, when I list the files I can see setup.py, but it does not allow to run.The release notes have been reorganized into two major sections: the general CUDA release notes, and the CUDA libraries release notes including historical information for 12.x releases. When I try to run the setup.py I receive an error:Įrror: The system cannot find the file specified Is it possible to set up pyTorch with this GPU? RuntimeError: cuda runtime error (48) : no kernel image is available for execution on the device at /opt/conda/conda-bld/pytorch_1518244421288/work/torch/lib/THC/generic/THCTensorMath.cu:15 Warnings.warn(old_gpu_warn % (d, name, major, capability)) PyTorch no longer supports this GPU because it is too old. Learn = ConvLearner.pretrained(arch, data, precompute=True)įound GPU0 GeForce GTX 760 which is of cuda capability 3.0. Nevertheless, when I execute the first main block of code in lesson 1 that is: arch=resnet34ĭata = om_paths(PATH, tfms=tfms_from_model(arch, sz)) I have also ensured that both CUDA and CuDNN are installed properly as both of these commands return “True”: _available() I made sure that the GPU supports CUDA as it actually has over 1000 CUDA cores as listed here. I am trying to set up the tutorials locally.
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