From fab0a96060c29e4b8c308f4ad1088ef7eb2b7752 Mon Sep 17 00:00:00 2001 From: Daniel Ching Date: Sun, 28 Dec 2025 14:02:18 -0600 Subject: [PATCH 1/8] DOC: Move CUDA user guide from cuda-feedstock to conda-forge docs --- docs/maintainer/knowledge_base.md | 216 +++++++++++++++++++++++++++++- 1 file changed, 215 insertions(+), 1 deletion(-) diff --git a/docs/maintainer/knowledge_base.md b/docs/maintainer/knowledge_base.md index e25c114e8bb..4e19a9bb0f7 100644 --- a/docs/maintainer/knowledge_base.md +++ b/docs/maintainer/knowledge_base.md @@ -1891,7 +1891,197 @@ if you're using a `c_stdlib_version` of `2.28`, set it to `alma8`. -## CUDA builds +## Support for NVIDIA CUDA + +### CUDA for End-Users + +This section is for people who wish to use conda environments to run CUDA-enabled software. + +#### Prerequisites + +You must have an NVIDIA GPU on your machine and you must install the CUDA drivers. The CUDA +driver _cannot_ be installed with conda and must be installed on your system using an +appropriate installation method. See the [CUDA documentation](https://docs.nvidia.com/cuda/) +for instructions on how to install the CUDA driver +([Linux](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html), +[Windows](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html)). + +:::tip + +Conda CUDA packages are installable even if no CUDA driver or NVIDIA GPU is available, but +they will not run properly. See [Common problems and known +issues](#common-problems-and-known-issues) for why this is the case. + +::: + +#### Installing CUDA + +##### Basic Installation + +The easiest one-step solution to install the full CUDA Toolkit is to install the [`cuda` +metapackage](https://github.com/conda-forge/cuda-feedstock/) with this command: + +``` +conda install -c conda-forge cuda cuda-version=12.5 +``` + +Let's break down this command. We are requesting the installation of two metapackages: +`cuda` and `cuda-version`. + +The `cuda` metapackage pulls in all the components of the CUDA Toolkit (CTK) and is roughly +equivalent to installing the CUDA Toolkit with traditional system package managers like +`apt` or `yum` on Linux. Similarly to such package managers, the individual components may +also be installed independently. + +The [`cuda-version` +metapackage](https://github.com/conda-forge/cuda-version-feedstock/blob/main/recipe/README.md) +is used to select the version of CUDA to install. This metapackage is important because +individual components of the CTK are typically versioned independently (the current versions +may be found in the [release +notes](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html)). The +`cuda-version` metapackage provides a standard way to install the version of a specific CUDA +component corresponding to a given version of the CTK. This way, you never have to specify a +particular version of any CTK package; you just specify the `cuda-version` that you want, +then list packages you want installed and conda will take care of finding the right versions +for you. The above command will install all components of CUDA from the latest patch release +of CUDA 12.5. + +:::warning + +The following CUDA components are not available as conda packages: + +- Driver libraries, such as (but not limited to) + - `libcuda` + - `libnvidia-ml` library +- Documentation and Samples +- Fabric Manager +- GPUDirect Storage (GDS) +- IMEX +- NVSwitch Configuration and Query (NSCQ) +- Nsight Systems + +::: + +##### Installing individual components of the CUDA Toolkit + +Rather than installing all of CUDA at once, users may instead install just the packages that +they need. For example, to install just `libcublas` and `libcusparse` one may run: +``` +conda install -c conda-forge libcublas libcusparse cuda-version= +``` +The best way to get a current listing is to run: +``` +conda install --dry-run -c conda-forge cuda cuda-version= +``` +For a complete listing of the packages that were originally created, see [this +issue](https://github.com/conda-forge/staged-recipes/issues/21382). + +##### Metapackages + +For convenience, a number of additional metapackages are available: +- `cuda-runtime`: All CUDA runtime libraries needed to run a CUDA application +- `cuda-libraries`: All libraries required to run a CUDA application requiring libraries beyond the CUDA runtime (such as the CUDA math libraries) as well as packages needed to perform JIT compilation +- `cuda-visual-tools`: GUIs for visualizing and profiling such as Nsight Compute +- `cuda-command-line-tools`: Command line tools for analyzing and profiling such as cupti, cuda-gdb, and Compute Sanitizer +- `cuda-tools`: All tools for analyzing and profiling, both GUI (includes cuda-visual-tools) and CLI (includes cuda-command-line-tools) + +##### Selecting ARM Variant + +Many of NVIDIA's CUDA packages are built for both SBSA and Tegra devices. For CUDA 12.9, +Tegra devices are a separate ARM variant. To install packages built for CUDA 12.9 Tegra, add +the `arm-variant=*=tegra` package to your environment. For CUDA 13.0 and later, the +arm-variant package is no longer required. Tegra devices are no longer a separate +architecture from SBSA. + +:::info + +Support for Tegra builds on conda-forge is only available for CUDA 12.9 and later. This +means that only Orin (sm_87) and later devices are supported. + +::: + +#### CUDA C++ Core Libraries (CCCL) + +CCCL is a special case among CUDA packages. Due to 1) being header-only, 2) fast-moving, and +3) independently-evolving, consumers may want a different (newer) version of CCCL than the +one corresponding to their CTK version. Instructions on how to install a suitable CCCL +package from conda can be found +[in the CCCL README](https://github.com/NVIDIA/cccl/?tab=readme-ov-file#conda). +See [this issue](https://github.com/conda-forge/cuda-cccl-impl-feedstock/issues/2) +for more information on the history of these packages. + +#### Package Naming Conventions + +CUDA libraries are split into separate packages with standard naming: + +- The base package (e.g., `libcublas`) provides runtime shared libraries. +- The `*-dev` package includes headers, symlinks, and CMake files for compiling code that dynamically links the library. +- The `*-static` package includes the static library for static linking, and usually requires `*-dev` to be present for build files, though this isn't enforced. + +For example, [libcublas](https://github.com/conda-forge/libcublas-feedstock) is available as `libcublas`, `libcublas-dev`, and `libcublas-static`. + +#### Development Metapackages + +The above discussion of naming also applies to metapackages. For instance, the +`cuda-libraries` package contains all the runtime libraries, while `cuda-libraries-dev` also +includes dependencies on the corresponding `*-dev` packages. In addition, for the purposes +of development there are a few additional key metapackages: +- `cuda-compiler`: All packages required to compile a minimal CUDA program (one that does + not require e.g. extra math libraries like cuBLAS or cuSPARSE). + + +#### Compatability between the `conda-forge` vs `nvidia` channels + +Understanding the difference between the CUDA packages on the `conda-forge` and `nvidia` +channels requires a bit of history because of how the relationship has evolved over time. In +particular, how these channels may or may not coexist will depend on the versions of CUDA +that you need support for. + +##### Pre-CUDA 12: + +Prior to CUDA 12, the only package available on `conda-forge` was the `cudatoolkit` package, +a community-maintained, monolithic package containing the entire repackaged CTK. During the +CUDA 11 release cycle, NVIDIA began maintaining a set of CUDA Toolkit packages in the +`nvidia` channel. Unlike the monolithic `conda-forge` package, the `nvidia` channel +distributed the CTK split into components such that each library was given its own package. +This package organization made it possible to install separate components independently and +better aligned the conda packaging ecosystem with other package managers, such as those for +Linux distributions. However, this organization introduced a number of changes that were at +times confusing -- such as the introduction of a `cuda-toolkit` (note the hyphen) +metapackage that installs a partially overlapping set of components to the original +`cudatoolkit` -- and at other times breaking, particularly in conda environments configured +to pull packages from both `conda-forge` and the `nvidia` channel. Therefore, in a CUDA 11 +world the `conda-forge` and `nvidia` channels were difficult to use in the same environment +without some care. + +##### CUDA 12.0-12.4 + +With the CUDA 12 release, NVIDIA contributed the new packaging structure to `conda-forge`, +introducing the same set of packages that existed on the `nvidia` channel as a replacement +for the old `cudatoolkit` package on `conda-forge`. This was done starting with CUDA 12.0 to +indicate the breaking nature of these changes compared to the prior CUDA 11.x packaging in +`conda-forge`. These packages became the standard mechanism for delivering CUDA conda +packages. Due to the scale of the reorganization, the CUDA 12.0, 12.1, and 12.2 releases +also involved numerous additional fixes to the packaging structure to better integrate them +in the Conda ecosystem. Due to the number of such changes that were required and the focus +on improving the quality of these installations, during this time period no corresponding +updates were provided for packages on the `nvidia` channel. While the `conda-forge` and +`nvidia` channel package lists were the same (i.e. the same packages existed in both places +with the same core contents like libraries and headers), the `nvidia` channel did not +include many of the incremental fixes made on `conda-forge` to improve things like symlinks, +static library handling, proper package constraints, etc. As a result, `nvidia` and +`conda-forge` CUDA packages remained incompatible from CUDA 12.0-12.4. + +##### CUDA 12.5+ + +With CUDA 12.5, the `nvidia` channel was fully aligned with `conda-forge`. Packages on both +channels are identical, ensuring safe coexistence of the two channels within the same conda +environment. + +Going forward, CUDA packages on the `conda-forge` and `nvidia` channels should be expected +to remain compatible. + +### CUDA for Maintainers Although the provisioned CI machines do not feature a GPU, conda-forge does provide mechanisms to build CUDA-enabled packages. @@ -1904,6 +2094,30 @@ If a feedstock does need access to additional resources (like GPUs), please see ### Common problems and known issues +#### What if I see an error saying `__cuda` is too old? + +The `__cuda` virtual package is used by `conda` to represent the maximum CUDA version fully +supported by the available CUDA driver. See the [conda docs on virtual +packages](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-virtual.html) +for more information. + +To update the `__cuda` virtual package, you must install a newer driver: +- [Linux instructions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#driver-installation) +- [Windows instructions](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html#installing-cuda-development-tools) + +If conda has incorrectly identified the CUDA driver, you can override by setting the +`CONDA_OVERRIDE_CUDA` environment variable to a version number like `"12.5"` or `""` to +indicate that no CUDA driver is detected. + +#### Can I install CUDA conda packages in a CPU-only environment (such as free-tier CIs)? + +Yes! All of the CUDA packages can be installed in an environment without the presence of a +physical GPU or CUDA driver. The inter-package dependency is established properly so that +this use case is covered. If you want to test package installation assuming a certain driver +version is installed, use the `CONDA_OVERRIDE_CUDA` environment variable mentioned above. +Even if the package requires CUDA to run, this allows the packaging and dependency +resolution to be tested in a CPU-only environment. + #### `nvcuda.dll` cannot be found on Windows From b92b5a10c0191fffcab708b6f9423a54b2ab8298 Mon Sep 17 00:00:00 2001 From: Daniel Ching Date: Tue, 13 Jan 2026 17:22:06 -0600 Subject: [PATCH 2/8] DOC: Apply some suggestions --- docs/maintainer/knowledge_base.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/maintainer/knowledge_base.md b/docs/maintainer/knowledge_base.md index 4e19a9bb0f7..82b21867eb9 100644 --- a/docs/maintainer/knowledge_base.md +++ b/docs/maintainer/knowledge_base.md @@ -1922,7 +1922,7 @@ The easiest one-step solution to install the full CUDA Toolkit is to install the metapackage](https://github.com/conda-forge/cuda-feedstock/) with this command: ``` -conda install -c conda-forge cuda cuda-version=12.5 +conda install -c conda-forge cuda cuda-version=12 ``` Let's break down this command. We are requesting the installation of two metapackages: @@ -2030,7 +2030,7 @@ of development there are a few additional key metapackages: not require e.g. extra math libraries like cuBLAS or cuSPARSE). -#### Compatability between the `conda-forge` vs `nvidia` channels +#### Compatibility between the `conda-forge` vs `nvidia` channels Understanding the difference between the CUDA packages on the `conda-forge` and `nvidia` channels requires a bit of history because of how the relationship has evolved over time. In @@ -2063,7 +2063,7 @@ indicate the breaking nature of these changes compared to the prior CUDA 11.x pa `conda-forge`. These packages became the standard mechanism for delivering CUDA conda packages. Due to the scale of the reorganization, the CUDA 12.0, 12.1, and 12.2 releases also involved numerous additional fixes to the packaging structure to better integrate them -in the Conda ecosystem. Due to the number of such changes that were required and the focus +in the conda ecosystem. Due to the number of such changes that were required and the focus on improving the quality of these installations, during this time period no corresponding updates were provided for packages on the `nvidia` channel. While the `conda-forge` and `nvidia` channel package lists were the same (i.e. the same packages existed in both places From ff7e261576afb3639fbcb4ee5b7978bfae22c1c8 Mon Sep 17 00:00:00 2001 From: Daniel Ching Date: Tue, 13 Jan 2026 17:23:26 -0600 Subject: [PATCH 3/8] DOC: List GDS as a Driver library --- docs/maintainer/knowledge_base.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/maintainer/knowledge_base.md b/docs/maintainer/knowledge_base.md index 82b21867eb9..0f69fbbb154 100644 --- a/docs/maintainer/knowledge_base.md +++ b/docs/maintainer/knowledge_base.md @@ -1953,9 +1953,9 @@ The following CUDA components are not available as conda packages: - Driver libraries, such as (but not limited to) - `libcuda` - `libnvidia-ml` library + - GPUDirect Storage (GDS) - Documentation and Samples - Fabric Manager -- GPUDirect Storage (GDS) - IMEX - NVSwitch Configuration and Query (NSCQ) - Nsight Systems From 5bb2f0711e2ef7474f285031b6d3b4d12687d56d Mon Sep 17 00:00:00 2001 From: Daniel Ching Date: Tue, 13 Jan 2026 17:46:41 -0600 Subject: [PATCH 4/8] DOC: Hide history of nvidia channel behind
--- docs/maintainer/knowledge_base.md | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/docs/maintainer/knowledge_base.md b/docs/maintainer/knowledge_base.md index 0f69fbbb154..47ca4042456 100644 --- a/docs/maintainer/knowledge_base.md +++ b/docs/maintainer/knowledge_base.md @@ -2037,7 +2037,8 @@ channels requires a bit of history because of how the relationship has evolved o particular, how these channels may or may not coexist will depend on the versions of CUDA that you need support for. -##### Pre-CUDA 12: +
+Pre-CUDA 12 Prior to CUDA 12, the only package available on `conda-forge` was the `cudatoolkit` package, a community-maintained, monolithic package containing the entire repackaged CTK. During the @@ -2053,8 +2054,9 @@ metapackage that installs a partially overlapping set of components to the origi to pull packages from both `conda-forge` and the `nvidia` channel. Therefore, in a CUDA 11 world the `conda-forge` and `nvidia` channels were difficult to use in the same environment without some care. - -##### CUDA 12.0-12.4 +
+
+CUDA 12.0-12.4 With the CUDA 12 release, NVIDIA contributed the new packaging structure to `conda-forge`, introducing the same set of packages that existed on the `nvidia` channel as a replacement @@ -2071,8 +2073,9 @@ with the same core contents like libraries and headers), the `nvidia` channel di include many of the incremental fixes made on `conda-forge` to improve things like symlinks, static library handling, proper package constraints, etc. As a result, `nvidia` and `conda-forge` CUDA packages remained incompatible from CUDA 12.0-12.4. - -##### CUDA 12.5+ +
+
+CUDA 12.5+ With CUDA 12.5, the `nvidia` channel was fully aligned with `conda-forge`. Packages on both channels are identical, ensuring safe coexistence of the two channels within the same conda @@ -2080,6 +2083,7 @@ environment. Going forward, CUDA packages on the `conda-forge` and `nvidia` channels should be expected to remain compatible. +
### CUDA for Maintainers From 13e465ee89685601b2a97e579c976ae0cca9b1d0 Mon Sep 17 00:00:00 2001 From: Daniel Ching Date: Tue, 13 Jan 2026 17:57:32 -0600 Subject: [PATCH 5/8] STY: Apply pre-commit changes --- docs/maintainer/knowledge_base.md | 20 ++++++++++++++------ 1 file changed, 14 insertions(+), 6 deletions(-) diff --git a/docs/maintainer/knowledge_base.md b/docs/maintainer/knowledge_base.md index 47ca4042456..92d9e86e756 100644 --- a/docs/maintainer/knowledge_base.md +++ b/docs/maintainer/knowledge_base.md @@ -1951,9 +1951,9 @@ of CUDA 12.5. The following CUDA components are not available as conda packages: - Driver libraries, such as (but not limited to) - - `libcuda` - - `libnvidia-ml` library - - GPUDirect Storage (GDS) + - `libcuda` + - `libnvidia-ml` library + - GPUDirect Storage (GDS) - Documentation and Samples - Fabric Manager - IMEX @@ -1966,19 +1966,24 @@ The following CUDA components are not available as conda packages: Rather than installing all of CUDA at once, users may instead install just the packages that they need. For example, to install just `libcublas` and `libcusparse` one may run: + ``` conda install -c conda-forge libcublas libcusparse cuda-version= ``` + The best way to get a current listing is to run: + ``` conda install --dry-run -c conda-forge cuda cuda-version= ``` + For a complete listing of the packages that were originally created, see [this issue](https://github.com/conda-forge/staged-recipes/issues/21382). ##### Metapackages For convenience, a number of additional metapackages are available: + - `cuda-runtime`: All CUDA runtime libraries needed to run a CUDA application - `cuda-libraries`: All libraries required to run a CUDA application requiring libraries beyond the CUDA runtime (such as the CUDA math libraries) as well as packages needed to perform JIT compilation - `cuda-visual-tools`: GUIs for visualizing and profiling such as Nsight Compute @@ -2002,8 +2007,7 @@ means that only Orin (sm_87) and later devices are supported. #### CUDA C++ Core Libraries (CCCL) -CCCL is a special case among CUDA packages. Due to 1) being header-only, 2) fast-moving, and -3) independently-evolving, consumers may want a different (newer) version of CCCL than the +CCCL is a special case among CUDA packages. Due to 1) being header-only, 2) fast-moving, and 3) independently-evolving, consumers may want a different (newer) version of CCCL than the one corresponding to their CTK version. Instructions on how to install a suitable CCCL package from conda can be found [in the CCCL README](https://github.com/NVIDIA/cccl/?tab=readme-ov-file#conda). @@ -2026,10 +2030,10 @@ The above discussion of naming also applies to metapackages. For instance, the `cuda-libraries` package contains all the runtime libraries, while `cuda-libraries-dev` also includes dependencies on the corresponding `*-dev` packages. In addition, for the purposes of development there are a few additional key metapackages: + - `cuda-compiler`: All packages required to compile a minimal CUDA program (one that does not require e.g. extra math libraries like cuBLAS or cuSPARSE). - #### Compatibility between the `conda-forge` vs `nvidia` channels Understanding the difference between the CUDA packages on the `conda-forge` and `nvidia` @@ -2054,6 +2058,7 @@ metapackage that installs a partially overlapping set of components to the origi to pull packages from both `conda-forge` and the `nvidia` channel. Therefore, in a CUDA 11 world the `conda-forge` and `nvidia` channels were difficult to use in the same environment without some care. +
CUDA 12.0-12.4 @@ -2073,6 +2078,7 @@ with the same core contents like libraries and headers), the `nvidia` channel di include many of the incremental fixes made on `conda-forge` to improve things like symlinks, static library handling, proper package constraints, etc. As a result, `nvidia` and `conda-forge` CUDA packages remained incompatible from CUDA 12.0-12.4. +
CUDA 12.5+ @@ -2083,6 +2089,7 @@ environment. Going forward, CUDA packages on the `conda-forge` and `nvidia` channels should be expected to remain compatible. +
### CUDA for Maintainers @@ -2106,6 +2113,7 @@ packages](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-virt for more information. To update the `__cuda` virtual package, you must install a newer driver: + - [Linux instructions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#driver-installation) - [Windows instructions](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html#installing-cuda-development-tools) From 0d5ab04d98f12906d578ff21a378e184e95a909a Mon Sep 17 00:00:00 2001 From: Daniel Ching <9604511+carterbox@users.noreply.github.com> Date: Wed, 14 Jan 2026 13:23:44 -0600 Subject: [PATCH 6/8] Update docs/maintainer/knowledge_base.md Co-authored-by: h-vetinari --- docs/maintainer/knowledge_base.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/maintainer/knowledge_base.md b/docs/maintainer/knowledge_base.md index 20dff82b214..a092a47cb87 100644 --- a/docs/maintainer/knowledge_base.md +++ b/docs/maintainer/knowledge_base.md @@ -1913,7 +1913,7 @@ component corresponding to a given version of the CTK. This way, you never have particular version of any CTK package; you just specify the `cuda-version` that you want, then list packages you want installed and conda will take care of finding the right versions for you. The above command will install all components of CUDA from the latest patch release -of CUDA 12.5. +of the CUDA 12.x series. :::warning From 0daebbe4b34c5ccd4cec6d0872ccf5c6730c025e Mon Sep 17 00:00:00 2001 From: Daniel Ching <9604511+carterbox@users.noreply.github.com> Date: Wed, 14 Jan 2026 16:01:46 -0600 Subject: [PATCH 7/8] Apply suggestions from code review Co-authored-by: jakirkham --- docs/maintainer/knowledge_base.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/docs/maintainer/knowledge_base.md b/docs/maintainer/knowledge_base.md index a092a47cb87..ca9754d737a 100644 --- a/docs/maintainer/knowledge_base.md +++ b/docs/maintainer/knowledge_base.md @@ -2015,7 +2015,7 @@ that you need support for. Prior to CUDA 12, the only package available on `conda-forge` was the `cudatoolkit` package, a community-maintained, monolithic package containing the entire repackaged CTK. During the -CUDA 11 release cycle, NVIDIA began maintaining a set of CUDA Toolkit packages in the +CUDA 11 release cycle, NVIDIA began developing a set of CUDA Toolkit packages in the `nvidia` channel. Unlike the monolithic `conda-forge` package, the `nvidia` channel distributed the CTK split into components such that each library was given its own package. This package organization made it possible to install separate components independently and @@ -2033,7 +2033,7 @@ without some care. CUDA 12.0-12.4 With the CUDA 12 release, NVIDIA contributed the new packaging structure to `conda-forge`, -introducing the same set of packages that existed on the `nvidia` channel as a replacement +introducing the CUDA Toolkit packages that existed on the `nvidia` channel as a replacement for the old `cudatoolkit` package on `conda-forge`. This was done starting with CUDA 12.0 to indicate the breaking nature of these changes compared to the prior CUDA 11.x packaging in `conda-forge`. These packages became the standard mechanism for delivering CUDA conda @@ -2056,6 +2056,8 @@ With CUDA 12.5, the `nvidia` channel was fully aligned with `conda-forge`. Packa channels are identical, ensuring safe coexistence of the two channels within the same conda environment. +Additionally [Anaconda has incorporated compatible CUDA 12 packages in its `defaults` channel]( https://www.anaconda.com/blog/anaconda-accelerates-ai-development-and-deployment-with-nvidia-cuda-toolkit). + Going forward, CUDA packages on the `conda-forge` and `nvidia` channels should be expected to remain compatible. From 5ee7e51d321395f7f9093acbc034629fbdab0b7a Mon Sep 17 00:00:00 2001 From: Daniel Ching Date: Mon, 6 Apr 2026 19:07:45 -0500 Subject: [PATCH 8/8] STY: Reorganize sentence --- docs/maintainer/knowledge_base.mdx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/maintainer/knowledge_base.mdx b/docs/maintainer/knowledge_base.mdx index 04b6fd37f32..e262d1f76f2 100644 --- a/docs/maintainer/knowledge_base.mdx +++ b/docs/maintainer/knowledge_base.mdx @@ -2416,8 +2416,8 @@ indicate the breaking nature of these changes compared to the prior CUDA 11.x pa packages. Due to the scale of the reorganization, the CUDA 12.0, 12.1, and 12.2 releases also involved numerous additional fixes to the packaging structure to better integrate them in the conda ecosystem. Due to the number of such changes that were required and the focus -on improving the quality of these installations, during this time period no corresponding -updates were provided for packages on the `nvidia` channel. While the `conda-forge` and +on improving the quality of these installations, no corresponding updates were provided for +packages on the `nvidia` channel during this time period. While the `conda-forge` and `nvidia` channel package lists were the same (i.e. the same packages existed in both places with the same core contents like libraries and headers), the `nvidia` channel did not include many of the incremental fixes made on `conda-forge` to improve things like symlinks,