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This change adds a test for locating libcuda as a driver library. This includes a failing test on a system where libcuda.so.1 is in the ldcache, but not at one of the predefined library search paths. A testdata folder with sample root filesystems is included to test various combinations. Signed-off-by: Evan Lezar <elezar@nvidia.com> |
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.github | ||
cmd | ||
deployments | ||
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hack | ||
internal | ||
packaging | ||
pkg | ||
scripts | ||
test | ||
testdata | ||
third_party | ||
tools/container | ||
vendor | ||
.common-ci.yml | ||
.dockerignore | ||
.gitignore | ||
.gitlab-ci.yml | ||
.gitmodules | ||
.golangci.yml | ||
.nvidia-ci.yml | ||
CHANGELOG.md | ||
CONTRIBUTING.md | ||
DEVELOPMENT.md | ||
go.mod | ||
go.sum | ||
LICENSE | ||
Makefile | ||
README.md | ||
RELEASE.md | ||
versions.mk |
NVIDIA Container Toolkit
Introduction
The NVIDIA Container Toolkit allows users to build and run GPU accelerated containers. The toolkit includes a container runtime library and utilities to automatically configure containers to leverage NVIDIA GPUs.
Product documentation including an architecture overview, platform support, and installation and usage guides can be found in the documentation repository.
Getting Started
Make sure you have installed the NVIDIA driver for your Linux Distribution Note that you do not need to install the CUDA Toolkit on the host system, but the NVIDIA driver needs to be installed
For instructions on getting started with the NVIDIA Container Toolkit, refer to the installation guide.
Usage
The user guide provides information on the configuration and command line options available when running GPU containers with Docker.
Issues and Contributing
Checkout the Contributing document!
- Please let us know by filing a new issue
- You can contribute by creating a merge request to our public GitLab repository