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Installation#

Pre-requisites#

This section describes the recommended dependencies to use nvImageCodec.

Supported operating systems and architectures:

Architecture

Distribution Info

Name

Version

x86_64

Amazon Linux

2023

Azure Linux

3

Debian

12, 13

Fedora

44

Kylin

11

OpenSUSE

15

RHEL

8, 9, 10

Rocky

8, 9, 10

SLES

15

SUSE

16

Ubuntu

22.04, 24.04, 26.04

Windows

10, 11

arm64-sbsa

Amazon Linux

2023

Azure Linux

3

Debian

12, 13

Kylin

11

RHEL

8, 9, 10

SLES

15

SUSE

16

Ubuntu

22.04, 24.04, 26.04

aarch64-jetson

Ubuntu

22.04

Additional requirements:

  • Linux

    • NVIDIA driver >= 530.30.02

    • aarch64-jetson requires CUDA Toolkit >= 12.1, < 13.0

  • Windows

  • CUDA Toolkit >= 12.1

  • Python >= 3.9 (3.9 is deprecated and will be removed in a future release)

  • GCC >= 14.0

  • cmake >= 3.18

Install nvImageCodec library#

You can download and install the appropriate built binary packages from the nvImageCodec Developer Page or install nvImageCodec Python from PyPI as it is described below.

CUDA version

Instructions

CUDA 12.x

pip install nvidia-nvimgcodec-cu12

CUDA 13.x

pip install nvidia-nvimgcodec-cu13

CUDA 12.x (Tegra platforms)

pip install nvidia-nvimgcodec-tegra-cu12

Install optional dependencies#

You can also install optional dependencies to run the nvjpeg, nvjpeg2k and nvtiff extensions. To install nvImageCodec with all the optional dependencies, you can do

CUDA version

Instructions

CUDA 12.x

pip install "nvidia-nvimgcodec-cu12[all]"

CUDA 13.x

pip install "nvidia-nvimgcodec-cu13[all]"

CUDA 12.x (Tegra platforms)

pip install "nvidia-nvimgcodec-tegra-cu12[all]"

Alternatively, you can specify a subset of the dependencies: nvjpeg, nvjpeg2k, nvtiff. The examples below use the CUDA 12 package; replace cu12 with cu13 on CUDA 13.

Example

Instructions

nvjpeg2k extension support

pip install "nvidia-nvimgcodec-cu12[nvjpeg2k]"

nvjpeg2k and nvtiff extension support

pip install "nvidia-nvimgcodec-cu12[nvjpeg2k,nvtiff]"

In the following subsections, you can see how to install those dependencies manually, instead of relying on nvimagecodec’s “extra” packages.

The “extra” packages declare the version ranges nvImageCodec is built and tested against:

Library

Supported versions

nvJPEG2000

>=0.12.0,<0.13

nvTIFF

>=0.9.0,<0.10

nvCOMP

>=6,<7

nvJPEG2000 and nvTIFF are pre-1.0 libraries whose APIs are still evolving, so they are pinned to the minor version. nvCOMP is past 1.0 and is pinned to the major version. If you install these libraries manually, make sure the installed version falls within the range above.

Manual installation of nvJPEG library#

If you do not have CUDA Toolkit installed, or you would like install nvJPEG library independently, you can install it manually as a Python package

CUDA version

Instructions

CUDA 12.x

pip install nvidia-nvjpeg-cu12

CUDA 13.x

pip install nvidia-nvjpeg

Manual installation of nvJPEG2000 library#

nvJPEG2000 library can be installed in the system, or installed as a Python package. For the latter, follow the instructions below. nvJPEG2000 is optional. The supported version is nvJPEG2000 0.12.x, which is the version nvImageCodec is tested with; we recommend using the newest 0.12 release. If nvJPEG2000 is already installed, use pip install --upgrade ... to move to it.

CUDA version

Instructions

CUDA 12.x

pip install nvidia-nvjpeg2k-cu12

CUDA 13.x

pip install nvidia-nvjpeg2k-cu13

CUDA 12.x (Tegra platforms)

pip install nvidia-nvjpeg2k-tegra-cu12

Please see also nvJPEG2000 installation documentation for more information.

Manual installation of nvTIFF library#

nvTIFF library can be installed in the system, or installed as a Python package. For the latter, follow the instructions below. nvTIFF is optional. nvImageCodec supports nvTIFF 0.9.x and is tested with it; we recommend using the newest 0.9 release. If nvTIFF is already installed, use pip install --upgrade ... to move to it.

CUDA version

Instructions

CUDA 12.x

pip install nvidia-nvtiff-cu12

CUDA 13.x

pip install nvidia-nvtiff-cu13

CUDA 12.x (Tegra platforms)

pip install nvidia-nvtiff-tegra-cu12

Please see also nvTIFF installation documentation for more information.

Manual installation of nvCOMP library#

nvCOMP library can be installed in the system, or installed as a Python package. For the latter, follow the instructions below. nvCOMP is required if you want to use nvTIFF with images that use Deflate compression.

CUDA version

Instructions

CUDA 12.x

pip install nvidia-libnvcomp-cu12

CUDA 13.x

pip install nvidia-libnvcomp-cu13

Please see also nvCOMP installation documentation for more information.