Nvidia Rtx A4000 Driver

[Violet Mcdow]

Whileit is possible the latest nvidia drivers do support the 4050 cards, since they are slated for release soon I would really doubt the drivers are available yet to support them in fedora (or linux in general)

Even nvidia is saying the drivers for the 4000 series were only to be released 2 days ago. No matter what else intervenes, it will take some time for the distributors (rpmfusion, etc.) to download those drivers, package them, and put them through testing before releasing them.

You could go directly to nvidia and download + install their version. That will leave you stuck with manually reinstalling with every new kernel update if you do not have the patience to wait for the normal rpmfusion release process for the updated nvidia drivers.

The NVIDIA CUDA Toolkit enables developers to build NVIDIA GPU accelerated compute applications for desktop computers, enterprise, and data centers to hyperscalers. It consists of the CUDA compiler toolchain including the CUDA runtime (cudart) and various CUDA libraries and tools. To build an application, a developer has to install only the CUDA Toolkit and necessary libraries required for linking.

In order to run a CUDA application, the system should have a CUDA enabled GPU and an NVIDIA display driver that is compatible with the CUDA Toolkit that was used to build the application itself. If the application relies on dynamic linking for libraries, then the system should have the right version of such libraries as well.

Every CUDA toolkit also ships with an NVIDIA display driver package for convenience. This driver supports all the features introduced in that version of the CUDA Toolkit. Please check the toolkit and driver version mapping in the release notes. The driver package includes both the user mode CUDA driver (libcuda.so) and kernel mode components necessary to run the application.

But this is not always required. CUDA Compatibility guarantees allow for upgrading only certain components and that will be the focus of the rest of this document. We will see how the upgrade to a new CUDA Toolkit can be simplified to not always require a full system upgrade.

From CUDA 11 onwards, applications compiled with a CUDA Toolkit release from within a CUDA major release family can run, with limited feature-set, on systems having at least the minimum required driver version as indicated below. This minimum required driver can be different from the driver packaged with the CUDA Toolkit but should belong to the same major release.

While applications built against any of the older CUDA Toolkits always continued to function on newer drivers due to binary backward compatibility, before CUDA 11, applications built against newer CUDA Toolkit releases were not supported on older drivers without forward compatibility package.

Minimum required driver version guidance can be found in the CUDA Toolkit Release Notes. Note that if the minimum required driver version is not installed in the system, applications will return an error as shown below.

Developers and system admins should note two important caveats when relying on minor version compatibility. If either of these caveats are limiting, then a new CUDA driver from the same minor version of the toolkit that the application was built with or later is required.

Sometimes features introduced in a CUDA Toolkit version may actually span both the toolkit and the driver. In such cases an application that relies on features introduced in a newer version of the toolkit and driver may return the following error on older drivers: cudaErrorCallRequiresNewerDriver. As mentioned earlier, admins should then upgrade the installed driver also.

Application developers can avoid running into this problem by having the application explicitly check for the availability of features. Refer to the CUDA Compatibility Developers Guide for more details.

Minor version compatibility has another benefit that offers flexibility in the use and deployment of libraries. Applications that use libraries that support minor version compatibility can be deployed on systems with a different version of the toolkit and libraries without recompiling the application for the difference in the library version. This holds true for both older and newer versions of the libraries provided they are all from the same major release family. Note that libraries themselves have interdependencies that should be considered. For example, each cuDNN version requires a certain version of cuBLAS.

However, if an application is unable to leverage the minor version compatibility due to any of the aforementioned reasons, then the Forward Compatibility model can be used as an alternative even though Forward Compatibility is mainly intended for compatibility across major toolkit versions.

Increasingly, data centers and enterprises may not want to update the NVIDIA GPU Driver across major release versions due to the rigorous testing and validation that happens before any system level driver installations are done.

CUDA Compatibility is installed and the application can now run successfully as shown below. In this example, the user sets LD_LIBRARY_PATH to include the files installed by the cuda-compat-12-1 package.

The CUDA compat package is named after the highest toolkit that it can support. If you are on the R470 driver but require 12.5 application support, please install the cuda-compat package for 12.5. But when performing a full system upgrade, when choosing to install both the toolkit and the driver, remove any forward compatible packages present in the system.

For example, if you are upgrading the driver to 525.60.13 which is the minimum required driver version for the 12.x toolkits, then the cuda-compat package is not required in most cases. 11.x and 12.x applications will be supported due to backward compatibility and future 12.x applications will be supported due to minor-version compatibility.

But there are feature restrictions that may make this option less desirable for your scenario - for example: Applications requiring PTX JIT compilation support. Unlike the minor-version compatibility that is defined between CUDA runtime and CUDA driver, forward compatibility is defined between the kernel driver and the CUDA driver, and hence such restrictions do not apply. In order to circumvent the limitation, a forward compatibility package may be used in such scenarios as well.

There are specific features in the CUDA driver that require kernel-mode support and will only work with a newer kernel mode driver. A few features depend on other user-mode components and are therefore also unsupported.

[1] This relies on CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_POSIX_FILE_DESCRIPTOR_SUPPORTED and CU_DEVICE_ATTRIBUTE_VIRTUAL_ADDRESS_MANAGEMENT_SUPPORTED, which should be queried if you intend to use the full range of this functionality.

In addition to the CUDA driver and certain compiler components, there are other drivers in the system installation stack (for example, OpenCL) that remain on the old version. The forward-compatible upgrade path is for CUDA only.

A well-written application should use following error codes to determine if CUDA Forward Compatible Upgrade is supported. System administrators should be aware of these error codes to determine if there are errors in the deployment.

CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE = 804. This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

Shared deployment: Allows sharing the same compat package across installed toolkits in the system. Download and extract the latest forward compatibility package with the highest toolkit version in its name. Using RPATH, or through LD_LIBRARY_PATH or through an automatic loader (for example, ld.so.conf), point to that package. This is the most recommended choice.

The CUDA driver maintains backward compatibility to continue support of applications built on older toolkits. Using a compatible minor driver version, applications build on CUDA Toolkit 11 and newer are supported on any driver from within the corresponding major release. Using the CUDA Forward Compatibility package, system administrators can run applications built using a newer toolkit even when an older driver that does not satisfy the minimum required driver version is installed on the system. This forward compatibility allows the CUDA deployments in data centers and enterprises to benefit from the faster release cadence and the latest features and performance of CUDA Toolkit.

Faster upgrades to the latest CUDA releases: Enterprises or data centers with NVIDIA GPUs have complex workflows and require advance planning for NVIDIA driver rollouts. Not having to update the driver for newer CUDA releases can mean that new versions of the software can be made available faster to users without any delays.

Faster upgrades of the CUDA libraries: Users can upgrade to the latest software libraries and applications built on top of CUDA (for example, math libraries or deep learning frameworks) without an upgrade to the entire CUDA Toolkit or driver. This is possible as these libraries and frameworks do not have a direct dependency on the CUDA runtime, compiler or driver.

11.4 UMD (User Mode Driver) and later will extend forward compatibility supportto select NGC Ready NVIDIA RTX boards. Prior to that forward compatibility will be supported only on NVIDIA Data Center cards.

Some features such as (CUDA-GL interop, Power 9 ATS, cuMemMap APIs) are not supported.These features depend on a new kernel mode driver and thus are not supported.These are explicitly called out in the documentation.

All existing CUDA features (from older minor releases) work. Users may have to incorporate checks intheir application when using new features in the minor release (that require a new driver) to ensure graceful errors.

All CUDA releases supported through the lifetime of the datacenter driver branch. For example,R418 (CUDA 10.1) EOLs in March 2022 - so all CUDA versions released (including major releases) during this timeframe are supported.

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