CUDA conda packages

Last updated on 2025-06-15 | Edit this page

Overview

Questions

What is CUDA?
How can I use CUDA enabled conda packages?

Objectives

Understand how CUDA can be used with conda packages
Create a hardware accelerated environment

CUDA

CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). The CUDA ecosystem provides software developer software development kits (SDK) with APIs to CUDA that allow for software developers to write hardware accelerated programs with CUDA in various languages for NVIDIA GPUs. CUDA supports a number of languages including C, C++, Fortran, Python, and Julia. While there are other types of hardware acceleration development platforms, as of 2025 CUDA is the most abundant platform for scientific computing that uses GPUs and effectively the default choice for major machine learning libraries and applications.

CUDA is closed source and proprietary to NVIDIA, which means that NVIDIA has historically limited the download access of the CUDA toolkits and drivers to registered NVIDIA developers (while keeping the software free (monetarily) to use). CUDA then required a multi-step installation process with manual steps and decisions based on the target platform and particular CUDA version. This meant that when CUDA enabled environments were setup on a particular machine they were powerful and optimized, but brittle to change and could easily be broken if system wide updates (like for security fixes) occurred. CUDA software environments were bespoke and not many scientists understood how to construct and curate them.

CUDA on conda-forge

In late 2018 to better support the scientific developer community, NVIDIA started to release components of the CUDA toolkits on the nvidia conda channel. This provided the first access to start to create conda environments where the versions of different CUDA tools could be directly specified and downloaded. However, all of this work was being done internally in NVIDIA and as it was on a separate channel it was less visible and it still required additional knowledge to work with. In 2023, NVIDIA’s open source team began to move the release of CUDA conda packages from the nvidia channel to conda-forge, making it easier to discover and allowing for community support. With significant advancements in system driver specification support, CUDA 12 became the first version of CUDA to be released as conda packages through conda-forge and included all CUDA libraries from the CUDA compiler nvcc to the CUDA development libraries. They also released CUDA metapackages that allowed users to easily describe the version of CUDA they required (e.g. cuda-version=12.5) and the CUDA conda packages they wanted (e.g. cuda). This significantly improved the ability for researchers to easily create CUDA accelerated computing environments.

This is all possible via use of the __cuda virtual conda package, which is determined automatically by conda package managers from the hardware information associated with the machine the package manager is installed on.

With Pixi, a user can get this information with pixi info, which could have output that looks something like

BASH

pixi info

OUTPUT

System
------------
       Pixi version: 0.48.0
           Platform: linux-64
   Virtual packages: __unix=0=0
                   : __linux=6.8.0=0
                   : __glibc=2.35=0
                   : __cuda=12.4=0
                   : __archspec=1=skylake
          Cache dir: /home/<username>/.cache/rattler/cache
       Auth storage: /home/<username>/.rattler/credentials.json
   Config locations: No config files found

Global
------------
            Bin dir: /home/<username>/.pixi/bin
    Environment dir: /home/<username>/.pixi/envs
       Manifest dir: /home/<username>/.pixi/manifests/pixi-global.toml

CUDA use with Pixi

To be able to effectively use CUDA conda packages with Pixi, we make use of Pixi’s system requirement workspace table, which specifies the minimum system specifications needed to install and run a Pixi workspace’s environments.

To do this for CUDA, we just add the minimum supported CUDA version (based on the host machine’s NVIDIA driver API) we want to support to the table.

Example:

TOML

[system-requirements]
cuda = "12"  # Replace "12" with the specific CUDA version you intend to use

This ensures that packages depending on __cuda >= {version} are resolved correctly.

To demonstrate this a bit more explicitly, we can create a minimal project

BASH

pixi init ~/pixi-lesson/cuda-example
cd ~/pixi-lesson/cuda-example

OUTPUT

✔ Created /home/<username>/pixi-lesson/cuda-example/pixi.toml

where we specify a cuda system requirement

BASH

pixi workspace system-requirements add cuda 12

TOML

[workspace]
channels = ["conda-forge"]
name = "cuda-example"
platforms = ["linux-64"]
version = "0.1.0"

[system-requirements]
cuda = "12"

[tasks]

[dependencies]

`system-requirements` table can’t be target specific

As of Pixi v0.48.0, the system-requirements table can’t be target specific. To work around this, if you’re on a platform that doesn’t support the system-requirements it will ignore them without erroring unless they are required for the platform specific packages or actions you have. So, for example, you can have osx-arm64 as a platform and a system-requirements of cuda = "12" defined

TOML

[workspace]
...
platforms = ["linux-64"]
...

[system-requirements]
cuda = "12"

Pixi will ignore that requirement unless you try to use CUDA packages in osx-arm64 environments.

and then install the cuda-version metapacakge

BASH

pixi add "cuda-version 12.9.*"

OUTPUT

✔ Added cuda-version 12.9.*

TOML

[workspace]
channels = ["conda-forge"]
name = "cuda-example"
platforms = ["linux-64"]
version = "0.1.0"

[system-requirements]
cuda = "12"

[tasks]

[dependencies]
cuda-version = "12.9.*"

If we look at the metadata installed by the cuda-version package (the only thing it does)

BASH

$ cat .pixi/envs/default/conda-meta/cuda-version-*.json

JSON

{
  "build": "h4f385c5_3",
  "build_number": 3,
  "constrains": [
    "cudatoolkit 12.9|12.9.*",
    "__cuda >=12"
  ],
  "depends": [],
  "license": "LicenseRef-NVIDIA-End-User-License-Agreement",
  "md5": "b6d5d7f1c171cbd228ea06b556cfa859",
  "name": "cuda-version",
  "noarch": "generic",
  "sha256": "5f5f428031933f117ff9f7fcc650e6ea1b3fef5936cf84aa24af79167513b656",
  "size": 21578,
  "subdir": "noarch",
  "timestamp": 1746134436166,
  "version": "12.9",
  "fn": "cuda-version-12.9-h4f385c5_3.conda",
  "url": "https://conda.anaconda.org/conda-forge/noarch/cuda-version-12.9-h4f385c5_3.conda",
  "channel": "https://conda.anaconda.org/conda-forge/",
  "extracted_package_dir": "/home/<username>/.cache/rattler/cache/pkgs/cuda-version-12.9-h4f385c5_3",
  "files": [],
  "paths_data": {
    "paths_version": 1,
    "paths": []
  },
  "link": {
    "source": "/home/<username>/.cache/rattler/cache/pkgs/cuda-version-12.9-h4f385c5_3",
    "type": 1
  }
}

we see that it now enforces constraints on the versions of cudatoolkit that can be installed as well as the required __cuda virtual package provided by the system

JSON

{
  ...
  "constrains": [
    "cudatoolkit 12.9|12.9.*",
    "__cuda >=12"
  ],
  ...
}

Use the `feature` table to solve environment that your platform doesn’t support

CUDA is supported only by NVIDIA GPUs, which means that macOS operating system platforms (osx-64, osx-arm64) can’t support it. Similarly, if you machine doesn’t have an NVIDIA GPU, then the __cuda virtual package won’t exist and installs of CUDA packages will fail. However, there’s many situations in which you want to solve and environment for a platform that you don’t have and we can do this for CUDA as well.

If we make the Pixi workspace multiplatform

BASH

pixi workspace platform add linux-64 osx-arm64 win-64

OUTPUT

✔ Added linux-64
✔ Added osx-arm64
✔ Added win-64

TOML

[workspace]
channels = ["conda-forge"]
name = "cuda-example"
platforms = ["linux-64", "osx-arm64", "win-64"]
version = "0.1.0"

[tasks]

[dependencies]

We can then use Pixi’s platform specific target tables to add dependencies for an environment to only a specific platform. So, if we know that a dependency only exists for platform then we can have Pixi add it for only that platform with

BASH

pixi add --platform <platform> <dependency>

This now means that if we ask for any CUDA enbabled packages, we will get ones that are built to support cudatoolkit v12.9.*

BASH

pixi add --platform linux-64 cuda

OUTPUT

✔ Added cuda >=12.9.1,<13
Added these only for platform(s): linux-64

BASH

pixi list --platform linux-64 cuda

OUTPUT

Package                      Version  Build       Size       Kind   Source
cuda                         12.9.1   ha804496_0  26.7 KiB   conda  https://conda.anaconda.org/conda-forge/
cuda-cccl_linux-64           12.9.27  ha770c72_0  1.1 MiB    conda  https://conda.anaconda.org/conda-forge/
cuda-command-line-tools      12.9.1   ha770c72_0  20 KiB     conda  https://conda.anaconda.org/conda-forge/
cuda-compiler                12.9.1   hbad6d8a_0  20.2 KiB   conda  https://conda.anaconda.org/conda-forge/
cuda-crt-dev_linux-64        12.9.86  ha770c72_1  92.2 KiB   conda  https://conda.anaconda.org/conda-forge/
cuda-crt-tools               12.9.86  ha770c72_1  28.2 KiB   conda  https://conda.anaconda.org/conda-forge/
cuda-cudart                  12.9.79  h5888daf_0  22.7 KiB   conda  https://conda.anaconda.org/conda-forge/
cuda-cudart-dev              12.9.79  h5888daf_0  23.1 KiB   conda  https://conda.anaconda.org/conda-forge/
cuda-cudart-dev_linux-64     12.9.79  h3f2d84a_0  380 KiB    conda  https://conda.anaconda.org/conda-forge/
cuda-cudart-static           12.9.79  h5888daf_0  22.7 KiB   conda  https://conda.anaconda.org/conda-forge/
cuda-cudart-static_linux-64  12.9.79  h3f2d84a_0  1.1 MiB    conda  https://conda.anaconda.org/conda-forge/
cuda-cudart_linux-64         12.9.79  h3f2d84a_0  192.6 KiB  conda  https://conda.anaconda.org/conda-forge/
cuda-cuobjdump               12.9.82  hbd13f7d_0  237.5 KiB  conda  https://conda.anaconda.org/conda-forge/
cuda-cupti                   12.9.79  h9ab20c4_0  1.8 MiB    conda  https://conda.anaconda.org/conda-forge/
cuda-cupti-dev               12.9.79  h9ab20c4_0  4.4 MiB    conda  https://conda.anaconda.org/conda-forge/
cuda-cuxxfilt                12.9.82  hbd13f7d_0  211.4 KiB  conda  https://conda.anaconda.org/conda-forge/
cuda-driver-dev              12.9.79  h5888daf_0  22.5 KiB   conda  https://conda.anaconda.org/conda-forge/
cuda-driver-dev_linux-64     12.9.79  h3f2d84a_0  36.8 KiB   conda  https://conda.anaconda.org/conda-forge/
cuda-gdb                     12.9.79  ha677faa_0  378.2 KiB  conda  https://conda.anaconda.org/conda-forge/
cuda-libraries               12.9.1   ha770c72_0  20 KiB     conda  https://conda.anaconda.org/conda-forge/
cuda-libraries-dev           12.9.1   ha770c72_0  20.1 KiB   conda  https://conda.anaconda.org/conda-forge/
cuda-nsight                  12.9.79  h7938cbb_0  113.2 MiB  conda  https://conda.anaconda.org/conda-forge/
cuda-nvcc                    12.9.86  hcdd1206_1  24.3 KiB   conda  https://conda.anaconda.org/conda-forge/
cuda-nvcc-dev_linux-64       12.9.86  he91c749_1  13.8 MiB   conda  https://conda.anaconda.org/conda-forge/
cuda-nvcc-impl               12.9.86  h85509e4_1  26.6 KiB   conda  https://conda.anaconda.org/conda-forge/
cuda-nvcc-tools              12.9.86  he02047a_1  26.2 MiB   conda  https://conda.anaconda.org/conda-forge/
cuda-nvcc_linux-64           12.9.86  he0b4e1d_1  26.2 KiB   conda  https://conda.anaconda.org/conda-forge/
cuda-nvdisasm                12.9.88  hbd13f7d_0  5.3 MiB    conda  https://conda.anaconda.org/conda-forge/
cuda-nvml-dev                12.9.79  hbd13f7d_0  139.1 KiB  conda  https://conda.anaconda.org/conda-forge/
cuda-nvprof                  12.9.79  hcf8d014_0  2.5 MiB    conda  https://conda.anaconda.org/conda-forge/
cuda-nvprune                 12.9.82  hbd13f7d_0  69.3 KiB   conda  https://conda.anaconda.org/conda-forge/
cuda-nvrtc                   12.9.86  h5888daf_0  64.1 MiB   conda  https://conda.anaconda.org/conda-forge/
cuda-nvrtc-dev               12.9.86  h5888daf_0  35.7 KiB   conda  https://conda.anaconda.org/conda-forge/
cuda-nvtx                    12.9.79  h5888daf_0  28.6 KiB   conda  https://conda.anaconda.org/conda-forge/
cuda-nvvm-dev_linux-64       12.9.86  ha770c72_1  26.3 KiB   conda  https://conda.anaconda.org/conda-forge/
cuda-nvvm-impl               12.9.86  he02047a_1  20.4 MiB   conda  https://conda.anaconda.org/conda-forge/
cuda-nvvm-tools              12.9.86  he02047a_1  23.1 MiB   conda  https://conda.anaconda.org/conda-forge/
cuda-nvvp                    12.9.79  hbd13f7d_0  104.3 MiB  conda  https://conda.anaconda.org/conda-forge/
cuda-opencl                  12.9.19  h5888daf_0  30 KiB     conda  https://conda.anaconda.org/conda-forge/
cuda-opencl-dev              12.9.19  h5888daf_0  95.1 KiB   conda  https://conda.anaconda.org/conda-forge/
cuda-profiler-api            12.9.79  h7938cbb_0  23 KiB     conda  https://conda.anaconda.org/conda-forge/
cuda-runtime                 12.9.1   ha804496_0  19.9 KiB   conda  https://conda.anaconda.org/conda-forge/
cuda-sanitizer-api           12.9.79  hcf8d014_0  8.6 MiB    conda  https://conda.anaconda.org/conda-forge/
cuda-toolkit                 12.9.1   ha804496_0  20 KiB     conda  https://conda.anaconda.org/conda-forge/
cuda-tools                   12.9.1   ha770c72_0  19.9 KiB   conda  https://conda.anaconda.org/conda-forge/
cuda-version                 12.9     h4f385c5_3  21.1 KiB   conda  https://conda.anaconda.org/conda-forge/
cuda-visual-tools            12.9.1   ha770c72_0  19.9 KiB   conda  https://conda.anaconda.org/conda-forge/

To “prove” that this works, we can ask for the CUDA enabled version of PyTorch

BASH

pixi add --platform linux-64 pytorch-gpu

OUTPUT

✔ Added pytorch-gpu >=2.7.0,<3
Added these only for platform(s): linux-64

BASH

pixi list --platform linux-64 torch

OUTPUT

Package      Version  Build                           Size       Kind   Source
libtorch     2.7.0    cuda126_mkl_h99b69db_300        566.9 MiB  conda  https://conda.anaconda.org/conda-forge/
pytorch      2.7.0    cuda126_mkl_py312_h30b5a27_300  27.8 MiB   conda  https://conda.anaconda.org/conda-forge/
pytorch-gpu  2.7.0    cuda126_mkl_ha999a5f_300        46.1 KiB   conda  https://conda.anaconda.org/conda-forge/

TOML

[workspace]
channels = ["conda-forge"]
name = "cuda-example"
platforms = ["linux-64", "osx-arm64", "win-64"]
version = "0.1.0"

[system-requirements]
cuda = "12"

[tasks]

[dependencies]
cuda-version = "12.9.*"

[target.linux-64.dependencies]
cuda = ">=12.9.1,<13"
pytorch-gpu = ">=2.7.0,<3"

Redundancy in example

Note that we added the cuda package here for demonstraton purposes, but we didn’t need to as it would already be installed as a dependency of pytorch-gpu.

BASH

cat .pixi/envs/default/conda-meta/pytorch-gpu-*.json

JSON

{
  "build": "cuda126_mkl_ha999a5f_300",
  "build_number": 300,
  "depends": [
    "pytorch 2.7.0 cuda*_mkl*300"
  ],
  "license": "BSD-3-Clause",
  "license_family": "BSD",
  "md5": "84ecafc34c6f8933c2c9b00204832e38",
  "name": "pytorch-gpu",
  "sha256": "e1162a51e77491abae15f6b651ba8f064870181d57d40f9168747652d0f70cb0",
  "size": 47219,
  "subdir": "linux-64",
  "timestamp": 1746288556375,
  "version": "2.7.0",
  "fn": "pytorch-gpu-2.7.0-cuda126_mkl_ha999a5f_300.conda",
  "url": "https://conda.anaconda.org/conda-forge/linux-64/pytorch-gpu-2.7.0-cuda126_mkl_ha999a5f_300.conda",
  "channel": "https://conda.anaconda.org/conda-forge/",
  "extracted_package_dir": "/home/<username>/.cache/rattler/cache/pkgs/pytorch-gpu-2.7.0-cuda126_mkl_ha999a5f_300",
  "files": [],
  "paths_data": {
    "paths_version": 1,
    "paths": []
  },
  "link": {
    "source": "/home/<username>/.cache/rattler/cache/pkgs/pytorch-gpu-2.7.0-cuda126_mkl_ha999a5f_300",
    "type": 1
  }
}

and if on the supported linux-64 platform with a valid __cuda virtual pacakge check that it can see and find GPUs

PYTHON

# torch_detect_GPU.py
import torch
from torch import cuda

if __name__ == "__main__":
    if torch.backends.cuda.is_built():
        print(f"PyTorch build CUDA version: {torch.version.cuda}")
        print(f"PyTorch build cuDNN version: {torch.backends.cudnn.version()}")
        print(f"PyTorch build NCCL version: {torch.cuda.nccl.version()}")

        print(f"\nNumber of GPUs found on system: {cuda.device_count()}")

    if cuda.is_available():
        print(f"\nActive GPU index: {cuda.current_device()}")
        print(f"Active GPU name: {cuda.get_device_name(cuda.current_device())}")
    elif torch.backends.mps.is_available():
        mps_device = torch.device("mps")
        print(f"PyTorch has active GPU: {mps_device}")
    else:
        print(f"PyTorch has no active GPU")

BASH

pixi run python torch_detect_GPU.py

OUTPUT

PyTorch build CUDA version: 12.6
PyTorch build cuDNN version: 91001
PyTorch build NCCL version: (2, 26, 5)

Number of GPUs found on system: 1

Active GPU index: 0
Active GPU name: NVIDIA GeForce RTX 4060 Laptop GPU

Multi-environment Pixi workspaces

Create a new Pixi workspace that:

Contains an environment for linux-64, osx-arm64, and win-64 that supports the CPU version of PyTorch
Contains an environment for linux-64 that supports the GPU version of PyTorch
Supports CUDA v12.9

Show me the solution

Create a new workspace

BASH

pixi init ~/pixi-lesson/cuda-exercise
cd ~/pixi-lesson/cuda-exercise

OUTPUT

✔ Created /home/<username>/pixi-lesson/cuda-exercise/pixi.toml

Add support for all the target platforms

BASH

pixi workspace platform add linux-64 osx-arm64 win-64

OUTPUT

✔ Added linux-64
✔ Added osx-arm64
✔ Added win-64

TOML

[workspace]
channels = ["conda-forge"]
name = "cuda-exercise"
platforms = ["linux-64", "osx-arm64", "win-64"]
version = "0.1.0"

[tasks]

[dependencies]

Add pytorch-cpu to a cpu feature

BASH

pixi add --feature cpu pytorch-cpu

OUTPUT

✔ Added pytorch-cpu
Added these only for feature: cpu

and then create a cpu environment that contains the cpu feature

BASH

pixi workspace environment add --feature cpu cpu

OUTPUT

✔ Added environment cpu

and then instantiate the pytorch-cpu package with a particular version and solve

BASH

pixi add --feature cpu pytorch-cpu

OUTPUT

✔ Added pytorch-cpu >=1.1.0,<3
Added these only for feature: cpu

TOML

[workspace]
channels = ["conda-forge"]
name = "cuda-exercise"
platforms = ["linux-64", "osx-arm64", "win-64"]
version = "0.1.0"

[tasks]

[dependencies]

[feature.cpu.dependencies]
pytorch-cpu = ">=1.1.0,<3"

[environments]
cpu = ["cpu"]

Now, for the GPU environment, add CUDA system-requirements for linux-64 for the gpu feature

BASH

pixi workspace system-requirements add --feature gpu cuda 12

TOML

[workspace]
channels = ["conda-forge"]
name = "cuda-exercise"
platforms = ["linux-64", "osx-arm64", "win-64"]
version = "0.1.0"

[tasks]

[dependencies]

[feature.cpu.dependencies]
pytorch-cpu = ">=1.1.0,<3"

[feature.gpu.system-requirements]
cuda = "12"

[environments]
cpu = ["cpu"]

and create a gpu environment with the gpu feature

BASH

pixi workspace environment add --feature gpu gpu

OUTPUT

✔ Added environment gpu

TOML

[workspace]
channels = ["conda-forge"]
name = "cuda-exercise"
platforms = ["linux-64", "osx-arm64", "win-64"]
version = "0.1.0"

[tasks]

[dependencies]

[feature.cpu.dependencies]
pytorch-cpu = ">=1.1.0,<3"

[feature.gpu.system-requirements]
cuda = "12"

[environments]
cpu = ["cpu"]
gpu = ["gpu"]

then add the cuda-version metapackage and the pytorch-gpu pacakge for linux-64 to the gpu feature

BASH

pixi add --platform linux-64 --feature gpu 'cuda-version 12.9.*' pytorch-gpu

OUTPUT

✔ Added cuda-version 12.9.*
✔ Added pytorch-gpu >=2.7.0,<3
Added these only for platform(s): linux-64
Added these only for feature: gpu

TOML

[workspace]
channels = ["conda-forge"]
name = "cuda-exercise"
platforms = ["linux-64", "osx-arm64", "win-64"]
version = "0.1.0"

[tasks]

[dependencies]

[feature.cpu.dependencies]
pytorch-cpu = ">=1.1.0,<3"

[feature.gpu.system-requirements]
cuda = "12"

[feature.gpu.target.linux-64.dependencies]
cuda-version = "12.9.*"
pytorch-gpu = ">=2.7.0,<3"

[environments]
cpu = ["cpu"]
gpu = ["gpu"]

One can check the environment differences

BASH

pixi list --environment cpu
pixi list --environment gpu

and activate shells with different environments loaded

BASH

pixi shell --environment cpu

So in 23 lines of TOML

BASH

wc -l pixi.toml

OUTPUT

23 pixi.toml

we created separate CPU and GPU computational environments that are now fully reproducible with the associated pixi.lock!

Key Points

The cuda-version metapackage can be used to specify constrains on the versions of the __cuda virtual package and cudatoolkit.
Pixi can specify a minimum required CUDA version with the [system-requirements] table.
Pixi can solve environments for platforms that are not the system platform.
NVIDIA’s open source team and the conda-forge community support the CUDA conda packages on conda-forge.
The cuda metapackage is the primary place to go for user documetnation on the CUDA conda packages.

CUDA conda packages

Overview

Questions

Objectives

CUDA

CUDA on conda-forge

BASH

OUTPUT

CUDA use with Pixi

TOML

BASH

OUTPUT

BASH

TOML

system-requirements table can’t be target specific

TOML

BASH

OUTPUT

TOML

BASH

JSON

JSON

Use the feature table to solve environment that your platform doesn’t support

BASH

OUTPUT

TOML

BASH

BASH

OUTPUT

BASH

OUTPUT

BASH

OUTPUT

BASH

OUTPUT

TOML

Redundancy in example

BASH

JSON

PYTHON

BASH

OUTPUT

Multi-environment Pixi workspaces

Show me the solution

BASH

OUTPUT

BASH

OUTPUT

TOML

BASH

OUTPUT

BASH

OUTPUT

BASH

OUTPUT

TOML

BASH

TOML

BASH

OUTPUT

TOML

BASH

OUTPUT

TOML

BASH

BASH

BASH

OUTPUT

Key Points

`system-requirements` table can’t be target specific

Use the `feature` table to solve environment that your platform doesn’t support