opkth.blogg.se - Nvidia quadro p2000 driver

If you've installed the latest driver version then your graphics driver probably supports every CUDA version compatible with your graphics card (see section 1). You can acquire the newest driver for your system from NVIDIA's website. Since CUDA relies on low-level communication with the graphics card you need to have an up-to-date driver in order use the latest versions of CUDA.įirst, make sure you have an NVIDIA graphics driver installed on your system. The graphics driver is the software that allows your operating system to communicate with your graphics card. How to check if your GPU/graphics driver supports a particular CUDA version Newer versions of the CUDA library rely on newer hardware features, which is why we need to determine the compute capability in order to determine the supported versions of CUDA.Ģ.

Note: Compute capability refers to the computational features supported by your graphics card. If your card doesn't support the required CUDA version then see the options in section 4 of this answer. For example, CUDA 9.2 is not supported for compute compatibility 2.1.

In the bullet list preceding the table check to see if the required CUDA version is supported by the compute capability of your graphics card.

For example, the GeForce 820M compute capability is 2.1.

Locate your graphics card model in the big table and take note of the compute capability version.

To determine which versions of CUDA are supported The best resource is probably this section on the CUDA Wikipedia page. NVIDIA doesn't do a great job of providing CUDA compatibility information in a single location. AMD and Intel graphics cards do not support CUDA. How to check if your GPU/graphics card supports a particular CUDA versionįirst, identify the model of your graphics card.īefore moving forward ensure that you've got an NVIDIA graphics card. This is because PyTorch, unless compiled from source, is always delivered with a copy of the CUDA library.ġ. Note: If you install pre-built binaries (using either pip or conda) then you do not need to install the CUDA toolkit or runtime on your system before installing PyTorch with CUDA support.

The PyTorch binaries must be built with support for the compute capability of your graphics card.

Your graphics card driver must support the required version of CUDA.Your graphics card must support the required version of CUDA.The system requirements to use PyTorch with CUDA are as follows: Various circumstance-dependent options for resolving issues are described in the last section of this answer. Since I've seen a lot of questions that refer to issues like this I'm writing a broad answer on how to check if your system is compatible with CUDA, specifically targeted at using PyTorch with CUDA support. Your graphics card does not support CUDA 9.0.